System and method for pressure detection in medical device environments

Abstract

This invention provides a system, method, and program for predicting and managing operational pressures using artificial intelligence, structured scoring logic, and embedded prescriptive guidance. [Solution] In the pressure management system, the software components include a data acquisition component 112 that receives operational data associated with multiple medical entities; a pressure modeling component 114 that calculates real-time pressure scores for medical entities; a prediction component 116 that applies an artificial intelligence model to predict pressure scores for medical entities; a score scoring component 118 that assigns pressure levels to medical entities; an action recommendation component 120 that identifies one or more prescriptive actions to reduce or prevent predicted pressure; and a user interface component 122 that displays a graphic interface including pressure prediction visualizations, a high-incidence site overview, and one or more selectable actions.

Description

Technical Field

[0001] The disclosure of the subject matter generally relates to artificial intelligence-based prediction and decision-making support systems in healthcare operations management, and more specifically to systems and methods for predicting resource pressure across multiple hospital departments and recommending prescriptive actions to reduce operational bottlenecks.

Background Art

[0002] Modern healthcare systems are facing increasing operational stress as the number of patients grows and the complexity of treatment increases, along with increasingly constrained resources for urgent care. Efficient patient flow from reception through treatment and discharge depends on the coordinated use of shared resources, including beds, nurses, imaging modalities, treatment recovery units, transport teams, and ancillary services. Even a single disruption in one area can cause cascading delays. For example, a patient may be scheduled for imaging but unable to proceed due to a shortage of transport staff. Other patients may be medically ready for immediate discharge but remain in place due to delays in bed cleaning or a shortage of placement staff.

[0003] These local inefficiencies often accumulate, leading to broader system-level effects, including long wait times, treatment delays, underutilization of resources in some areas, and saturation of resources in others. Clinical and administrative department heads often rely on manual workflows and fragmented information sources to identify and manage these pressures. Operational adjustments are often made through repeated team meetings, calls, or shift reports, diverting clinical staff from relaying basic status updates such as immediate discharge potential, transport status, or treatment delays, and potentially preventing full patient-facing responsibilities from being fulfilled.

[0004] Several healthcare organizations employ digital dashboards or alert tools, typically within their electronic health record systems, to monitor resource utilization or operational status. These tools often display snapshots of current conditions, such as bed occupancy rates, staffing levels, patient throughput, or pending tasks. While such tools are helpful for visualization, their ability to foresee emerging problems or guide coordinated responses is generally limited. These tools often function as passive monitoring mechanisms, relying on users to interpret multi-departmental data and take mitigation measures themselves.

[0005] In large, multi-departmental or multi-site hospital systems, interdependence between departments such as emergency medicine, bed management, imaging, various therapies, and treatment and recovery adds further complexity. Disruptions in one area can propagate to others, making it difficult to identify the root causes of operational pressures and prioritize interventions. Static representations of data may not provide sufficient context or prospects for effective action. As a result, care becomes reactive and fragmented, and preventable delays are left unprevented.

[0006] These issues highlight the current limitations in awareness and resource allocation approaches within the healthcare environment. It would be desirable to have improved methods for anticipating, clarifying, and responding to systemic pressures. [Overview of the Initiative]

[0007] The following outline provides a basic understanding of one or more embodiments. This outline does not identify central or important elements or define any scope of any particular embodiment or claim. The sole purpose of this outline is to present concepts in a simplified form as a prelude to the more detailed descriptions described later. In one or more embodiments described herein, systems, methods, and computer program products facilitate the prediction and management of operational pressures in a healthcare setting using artificial intelligence, pressure modeling, and prescriptive decision support. These embodiments support proactive operational coordination across departments, units, modalities, or other healthcare entities within a hospital system.

[0008] According to one or more embodiments, a system is provided. This system may include a non-transient, computer-readable memory that stores computer-executable components. The system may further include a processor that runs the computer-executable components. The computer-executable components may include a data ingestion component that receives operational data associated with multiple healthcare entities within a hospital system. The computer-executable components may further include a pressure modeling component that calculates real-time pressure scores for healthcare entities based on department-specific pressure factors and weightings. The computer-executable components may also include a prediction component that applies an artificial intelligence model to predict pressure scores for healthcare entities over a future time window. The computer-executable components may also include a scoring component that assigns pressure levels to healthcare entities using proportional logic or override logic based on critical factors or critical entities. The computer-executable components may further include an action recommendation component that identifies one or more prescriptive actions that reduce or prevent predicted pressure, each action being associated with an influence score, a cost score, and trigger conditions. In addition, computer-executable components may include user interface components that display a graphic interface including pressure prediction visualizations, hotspot overviews, and one or more selectable actions.

[0009] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the method may include the steps of receiving operational data from multiple medical entities within a hospital system, calculating a pressure score based on a weighted sum of normalized pressure factor values, and predicting future pressure scores using an artificial intelligence model trained on historical pressure data. The method may further include the steps of assigning pressure levels using override logic for critical factors and proportional logic for collective entities, identifying prescriptive actions to address predicted pressure conditions, and displaying pressure prediction visualizations, a high-risk area overview, and recommended actions via a graphical user interface.

[0010] According to one or more embodiments, a computer program product is provided. In various embodiments, this computer program product may include non-transient, computer-readable memory embodying program instructions. Program instructions may be executable by a processor to cause the processor to receive operational data from multiple medical entities. Program instructions may be executable by a processor to cause the processor to generate real-time pressure scores based on department-specific pressure factor scores and weightings. Program instructions may be executable by a processor to predict future pressure scores using an artificial intelligence model trained on historical data. Program instructions may be executable by a processor to cause the processor to assign pressure levels using override logic and proportional logic. Program instructions may be executable by a processor to identify prescriptive actions by relevant trigger conditions, influence scores, and cost scores. Program instructions may be executable by a processor to display pressure predictions, high-incidence locations, and selectable action recommendations within a graphical user interface. [Brief explanation of the drawing]

[0011] [Figure 1]This is a block diagram of one example of a non-limiting system that facilitates the prediction and management of operational pressures in a medical environment according to one or more embodiments described in this book. [Figure 2] This is a flowchart illustrating one example of a non-limiting computer-implemented method that facilitates the prediction and management of operational pressures in a medical setting, according to one or more embodiments described in this book. [Figure 3] This is a flowchart illustrating one example of a non-limiting computer-implemented method that facilitates the prediction and management of operational pressures in a medical setting, according to one or more embodiments described in this book. [Figure 4] This is a non-limiting user interface diagram illustrating an example of a visualization of pressure forecasts, current operational pressure levels, and prescriptive actions across multiple healthcare facilities within a regional healthcare system, according to one or more embodiments described in this book. [Figure 5A] Figure 5(B) shows, in conjunction with other figures, a non-limiting set of examples of department-specific pressure layers and corresponding predicted pressure factors according to one or more embodiments described in this book, which can be used by a pressure prediction system to generate department-level pressure scores across a hospital or healthcare system. [Figure 5B] Figure 5(A) shows, in conjunction with other figures, a non-limiting set of examples of department-specific pressure layers and corresponding predicted pressure factors according to one or more embodiments described in this book, which can be used by a pressure prediction system to generate department-level pressure scores across a hospital or healthcare system. [Figure 6] This is a non-limiting flowchart illustrating one example of how operational pressure can be processed and converted into prescriptive behavioral recommendations using both real-time and predictive inputs, as described in one or more embodiments of this book. [Figure 7] This figure shows a hierarchical pressure aggregation model according to one or more embodiments described in this book, illustrating how operational pressure scores can be derived and how to aggregate high-granularity pressure factors down to higher-level hospital system pressure indices. [Figure 8]This figure shows an example of a non-restrictive hierarchical framework for calculating, aggregating, and interpreting pressure within a healthcare network according to one or more embodiments described in this book. [Figure 9] This is a block diagram of an example non-limiting operating environment that may facilitate one or more embodiments described in this book. [Figure 10] This figure shows an example of a network environment that can be operated to implement the various forms of implementation described in this book. [Modes for carrying out the invention]

[0012] The following detailed descriptions are for illustrative purposes only and do not limit the embodiments or applications / uses of each embodiment. Furthermore, they are not intended to be limited by any explicit or implicit information presented in the "Background Art" or "Summary of the Invention" sections above, or in this "Modes for Carrying Out the Invention" section.

[0013] The following describes one or more embodiments with reference to the drawings. Similar reference numerals are used in the drawings to consistently refer to similar elements. For illustrative purposes, many specific details are described below to provide a more comprehensive understanding of one or more embodiments. However, it is evident that in various cases one or more embodiments may be carried out without these specific details.

[0014] Modern hospital systems face increasing operational complexity as they attempt to deliver timely, high-quality medical care under increasingly stringent constraints. These constraints can include limited available beds, fluctuating staffing levels, bottlenecks in diagnostic imaging and treatment / recovery areas, and delays in patient transport. As a result, hospitals may experience unpredictable periods of operational pressure where demand exceeds available resources. These conditions can lead to delays, inefficiencies, and potential risks to patient outcomes.

[0015] The concept of pressure in a hospital setting can manifest in various forms. For example, the emergency department may experience excessive waiting times due to a lack of available beds, even if several patients could be discharged immediately. The imaging department may face prolonged turnaround times due to staff shortages or modality-specific downtime. Treatment areas such as the PACU (Post-Anesthesia Recovery Unit) or invasive radiology may become saturated when scheduled surgeries are delayed or patients require more time to recover. These localized problems can propagate throughout the hospital, affecting lower-level departments that are not directly involved in the constraints.

[0016] Traditional tools used to manage hospital operations may include passive dashboards, retrospective reports, and manual operational coordination routines such as meetings or shift calls. While such tools can highlight useful metrics such as occupancy, patient throughput, or staffing allocation, they typically rely on manual interpretation and retrospective decision-making. These tools may lack predictive capabilities and often fail to provide guided recommendations for solutions.

[0017] The systems and methods described in this book can address these limitations by providing a framework for predicting and managing operational pressures using artificial intelligence, structured scoring logic, and embedded prescriptive guidance. At a high level, the disclosed systems can process operational data from each healthcare entity within a hospital system, calculate real-time pressure scores, predict future pressure levels, and recommend one or more actions to reduce or prevent predicted bottlenecks. The functionality of this system can be achieved through a unified interface that enables visualization, interaction, and decision support.

[0018] In some embodiments, the system may include a plurality of computer-executable software components stored in a non-transitory memory and executed by a processor. A data intake component can receive operational data from departments, units, modalities, or service lines. The data may include information related to patient arrivals, transport requests, imaging instruction queues, staffing levels, treatment recovery room occupancy, and other operational metrics. These data inputs can correspond to department-specific stress factors and can serve as inputs into a scoring model.

[0019] A pressure modeling component can calculate a real-time pressure score by evaluating relevant stress factors for each healthcare entity and combining them as a normalized weighted score. The normalization can be based on statistical transformations such as percentile ranks or standard deviations that reveal the context of stress factor values relative to a performance history. Each stress factor can be assigned a configurable weight, and the model can output a continuous pressure score, such as a value from zero to 100, for each entity.

[0020] A prediction component can apply a trained artificial intelligence model, such as a deep neural network, to predict future pressure scores over a given time horizon. The model can be trained on historical hospital data and can account for temporal trends, staffing schedules, day-of-week effects, and seasonal variations. Predictions can be generated hourly or at other configurable intervals. The system can either maintain separate prediction models for each department or use a shared model with department-specific feature mappings. In some embodiments, an ensemble prediction approach that combines multiple machine learning models can be used to improve the robustness and accuracy of the predictions.

[0021] The scoring component can assign separate pressure levels to each entity based on the calculated or predicted pressure scores. The pressure levels can include "normal", "medium", "rising", or "high". In some embodiments, the scoring component can apply proportional logic such that the pressure level of a parent entity is derived from the relative ratio of child entities whose threshold score has been exceeded. Additionally, the scoring component can apply override logic such that a critical factor or critical entity automatically raises the entity's pressure level when the score exceeds a set threshold. An override verification component can be used to track how often overrides are triggered and, when the threshold is frequently exceeded, alert the user or system administrator of the need to readjust the set values or recalibrate the model.

[0022] The action recommendation component can analyze the predicted pressure score or the current pressure score to identify one or more prescriptive actions designed to reduce or avoid pressure conditions. Each action can be associated with activation conditions, an influence score, and a cost score. The system can include a configurable mapping between pressure driving factors and types of actions. For example, if the ICU capacity is constrained, the system can recommend cleaning available beds, initiating patient level downgrades, or activating surge protocols. The influence score can reflect the estimated operational benefit of the action, and the cost score can indicate the realized effort, disruption, or resource utilization. The action recommendation component can generate a ranked list of actions based on a priority level, an influence score, a cost score, or a synthetic influence-to-cost ratio. In some cases, the effectiveness of the actions can be tracked over time to determine if the recommended interventions correlate with downstream pressure improvement, enabling feedback-based adjustment and performance optimization.

[0023] The user interface component can render a graphic interface that displays real-time and predicted pressure information, a high-pressure site overview, and recommended actions. The interface can support filtering by department, unit, or modality and may include visualizations such as bar graphs, time-series graphs, and interactive overlays. Predicted pressure may be displayed using gradients color-coded, with green for low pressure and red for high pressure. The high-pressure site overview can display a limited number of entities currently experiencing or expected to experience the highest pressure, each accompanied by a generated overview showing the dominant pressure-driving factor and expected duration. For example, the system may display that CT pressure is rising and is expected to last for 6 hours due to a large number of emergency (STAT) indications and limited scanner availability.

[0024] In some embodiments, the pressure explanation component can provide context-based explanations for predicted pressure levels. This component may generate an overview of contributing factors using rule-based logic or model interpretation methods. For example, a PACU pressure score of 93 may be explained by a high volume of surgical procedures, a slow discharge rate, and staff absenteeism. These explanations can enhance end-user confidence and interpretability, as well as help operations managers prioritize intervention points.

[0025] A graphic interface may include selectable elements that allow users to delve into specific actions or contributing factors. For example, selecting a recommendation to expedite transfer might open a list of patients eligible for transfer or relocation based on the patient's medical progress and destination availability. A recommendation to clean beds might link to a list of wards marked as uncleaned and unoccupied. These features can highlight actionable data in the context, helping to reduce manual inquiries or interdepartmental coordination.

[0026] In some embodiments, the user interface may include selectable icons or control elements (controls) that launch a chat-type interface. The chat agent can respond to natural language queries to assist in scenario exploration. For example, a user might ask, "What will happen to the treatment recovery room if the volume of surgeries increases on Tuesday?" or "Which department is most likely to experience high pressure tomorrow morning?" The chat agent can refer to predictive models and underlying operational data to provide reasoned responses, including estimated pressure curves, summaries, or suggested actions. The interface may further support a What-if simulation tool that allows the user to modify key parameters, such as staffing availability or treatment volume, to view estimated impacts on future pressure.

[0027] The system can also support data refresh and historical persistence. Predicted pressure scores can be updated at predetermined intervals based on new data ingestion, model retraining triggers, or periodic update cycles. Historical pressure data can be stored in persistent data stores such as time-series databases or structured operational logs. This historical data may include both raw operational metrics and derived pressure scores at each level of the hierarchy. The stored data can be used for auditing, analysis, trend assessment, and current model retraining. In some embodiments, an active learning component can be used to identify unreliable or anomalous predictions, triggering internal review, model validation, or targeted retraining to improve model accuracy over time.

[0028] To support the optimization of the entire system across multiple facilities, a multi-center coordination engine can be included. This component can analyze inter-hospital capacity, staffing levels, and transfer protocols to recommend load balancing strategies, such as transferring less urgent patients or elective cases to facilities under lower pressure. In regional healthcare systems, this functionality can help a central operations team proactively manage throughput across the entire system.

[0029] The escalation workflow integration component can be used to send high-priority alerts to clinical personnel, nursing supervisors, or other stakeholders through existing hospital communication channels such as secure wireless paging systems, EMR-integrated message delivery, or mobile alert platforms. For example, when pressure in the emergency department is predicted to reach critical levels within the next four hours, the system can send a structured alert, including recommended actions and linked supporting data, to the appropriate escalation channel.

[0030] A highly customizable pressure setting interface allows administrators to define, adjust, and test pressure factor models, weights, thresholds, and critical entities. These settings can be stored per department or per facility, enabling the system to adapt to local operational policies and substructures.

[0031] To improve accessibility, the system can provide a mobile-optimized dashboard that allows designated users to monitor predictive pressures, receive alerts, and initiate actionable workflows from secure mobile devices. The mobile interface can include compressed visualizations, real-time summaries, and one-tap links to core decision support features, enabling responsive adjustments even when personnel are not stationed at fixed terminals.

[0032] By combining operational data ingestion, real-time scoring, predictive forecasting, override-responsive escalation, action prioritization, simulation, explanation, cross-hospital coordination, and interactive visualization, the system can support proactive operational management across hospitals or integrated healthcare networks. The disclosed approach can help healthcare teams and operations managers anticipate new constraints, prioritize interventions, and coordinate decision-making across complex, interdependent departments to maintain patient flow and resource balance.

[0033] The various embodiments described in this book can be used to realize concrete improvements in healthcare operational technology. The disclosed systems and methods can provide practical applications for managing hospital resource pressures through artificial intelligence, real-time data processing, hierarchical scoring, and prescriptive decision support. These technological capabilities can be used to address specific problems in healthcare operations, namely, the problem of predicting and mitigating dynamic bottlenecks in staffing, throughput, and resource allocation across multiple departments. Such problems cannot be adequately solved using rhetoric, "pen and paper" methods, or traditional manual adjustment workflows.

[0034] The disclosed invention relates not to an abstract concept, but to a specific technical embodiment capable of transforming input operational data into structured pressure scores, future pressure predictions, and actionable interventions. The system output can be configured to influence real-world actions within a hospital, such as patient transfers, cleaning schedules, staffing, and escalating alerts. These operations can be achieved through specialized computational components that process data in ways not possible with conventional monitoring tools.

[0035] In various embodiments, the system may include computer-executable components that cannot be performed by the human mind. For example, the system may calculate a pressure score by taking in a large number of time-varying inputs, applying configurable weights, and normalizing the values ​​using a statistical baseline or historical percentiles. This pressure modeling method can take into account both real-time conditions and historical trends, allowing the score to be dynamically adjusted based on the context. These calculations require computational resources and can operate with data volumes and update frequencies that are unmanageable in manual workflows.

[0036] The system may further include a predictive component that applies artificial intelligence models to estimate pressure levels over future time intervals. These models may include neural networks trained on large amounts of historical operational data. The predictions may account for complex non-linear dependencies between staffing, patient flow, treatment delays, and resource availability. Training and running these models requires high-speed hardware, and these models may be updated or retrained based on current operational feedback. These predictive actions may only produce human-interpretable outputs after being processed and visualized through dedicated components.

[0037] Pressure levels can be assigned using a scoring component that applies hierarchical logic. The system can propagate scores from lower entities to higher entities using proportional rules, or it can override scores based on critical thresholds. These rules can be configured to reflect hospital-specific operating conditions and can change dynamically based on system state. An override verification component can be included to monitor threshold crossover frequency and prompt reconfiguration or alerts based on operating patterns.

[0038] In addition to identifying areas where pressure is building, the system can generate prescriptive actions to alleviate anticipated tension. Using the action recommendation component, it can select interventions based on expected impact and cost, generating a ranked list to help decision-makers prioritize limited resources. These actions may include cleaning beds, initiating patient transfers, reassigning personnel, clearing overcrowded areas, or shifting treatment volumes. Each action can be associated with operational criteria, and the system can link these recommendations with real-time data to help users implement them efficiently.

[0039] The user interface component can visualize pressure forecasts and recommendations using color-coded charts, filterable dashboards, and selectable action summaries. The interface may also allow users to explore contributing factors, simulate what-if scenarios, or dig down to unit-level data. In some embodiments, the system may include a chat interface capable of responding to natural language queries, allowing users to ask questions about predicted trends or suggested actions. The chat interface can serve as a lightweight decision support layer and may be accessible via desktop or mobile devices.

[0040] Furthermore, the technical functionality of the system can be extended using other components. A multi-site coordination engine can be included to support load balancing across hospitals within a single network. This engine can assess capacity, case mix, and resource distribution across each site and recommend inter-site transfers or case deferrals. The escalation workflow integration component can be configured to send alerts to a secure messaging system, enabling time-sensitive recommendations to be delivered to the appropriate personnel without requiring manual periodic checks (polling).

[0041] The system may also include an active learning component that can identify unreliable predictions or anomalous situations in operational behavior and flag them for manual review or retraining. Historical data, including prediction outputs and actual results, can be stored in a structured log to assist with auditing, reporting, and performance optimization. Pressure explanations can be generated using explainable models or heuristic logic to help users understand why pressure is expected to occur and what is driving up the score.

[0042] The disclosed inventions can be applied in real-world clinical settings and can influence physical processes. For example, a hospital command center may receive a warning that a treatment and recovery room is expected to exceed its capacity, along with a ranked list of patients who are eligible for discharge or transfer. A head nurse may monitor a pressure dashboard on a mobile device and take immediate action, such as initiating transport or reallocating personnel. These scenarios demonstrate that the inventions are not merely directed towards intellectual processes or abstract concepts, but can operate in tangible and transformative ways.

[0043] By improving how hospital systems interpret, predict, and respond to operational pressures, the disclosed embodiments can provide technical improvements to system performance, throughput efficiency, and healthcare coordination. These improvements go beyond automating known manual steps and can substantially enhance how resource constraints are managed at actual scale.

[0044] In various embodiments, a computer program product may be configured to predict and manage operational pressures across a medical system. This computer program product may include a non-transient, computer-readable medium storing program instructions that, when executed by one or more processors, facilitate a wide range of data processing, predictive, and visualization operations related to medical resource utilization, capacity, and system tensions.

[0045] Program instructions may include logic for receiving and processing operational data from multiple healthcare entities, such as different departments, service lines, care units, or transport teams within a hospital. This operational data can be taken from real-time data sources or periodically updated feeds (supply data) and may include metrics such as staffing levels, bed availability, treatment times, wait times, number of patients waiting for treatment, transport status, and imaging throughput. In some implementations, data acquisition may involve parsing HL7 messages, reading structured database fields, or consuming API endpoints from hospital information systems.

[0046] Upon receiving operational data, the computer program product can generate real-time pressure scores for each entity within the healthcare system. The pressure scores may be calculated based on normalized values ​​of department-specific pressure factors such as net vacant beds, staffing delta, imaging scan preparation time, or patient transport delays. These pressure factor scores can be weighted and combined using configurable coefficients to generate a unified pressure score for each department, unit, or facility. For example, a telemetry unit might have pressure scores derived from the following indicators: 40% staffing delta, 30% vacant beds, and 30% patient throughput.

[0047] To enable proactive operational situation assessment, the computer program product may include the ability to predict future pressure conditions using an artificial intelligence model trained on historical operational data. The artificial intelligence model may include a deep neural network or other machine learning architecture capable of taking in time-series data, learning characteristic interactions, and generating pressure score predictions over various assumed time periods. For example, the model may generate pressure predictions for the future from 1 hour to 72 hours, depending on the historical window and update frequency. Historical inputs may include admission trends, staffing shifts, discharge rates, time from instruction to completion, or other pressure-driven variables.

[0048] Once real-time and predicted pressure scores are calculated, the computer program product can assign categorical pressure levels to each entity. These pressure levels can be derived using override logic for critical factors and proportional logic for collective entities. For example, if the number of people waiting for treatment in a department exceeds a set critical threshold, that department may be automatically classified as "high pressure," regardless of the collective score. In contrast, the overall pressure level of a hospital can be calculated proportionally based on the distribution of pressure scores across each department and unit of the hospital. This approach can ensure that both local risks and organizational pressure factors are appropriately reflected in the pressure designation.

[0049] In response to pressure levels or forecasts, a computer program product can identify prescriptive actions to alleviate current or future operational tensions. Each prescriptive action may be defined by relevant trigger conditions, an impact score that estimates its effect on pressure reduction, and a cost score that reflects operational burden, staffing effort, or resource utilization. For example, an action might include opening up an emergency treatment unit when bed occupancy exceeds 95%, or reallocating treatment personnel to the emergency reception area during peak treatment waiting periods. Further prescriptive actions might include reprioritizing imaging queues, activating rapid discharge protocols, postponing non-urgent surgical procedures, or initiating an ICU grading downgrade workflow to reallocate high-emergency beds.

[0050] The computer program product may include a graphical user interface (GUI) that allows users to interactively explore current and predicted pressure conditions across healthcare systems. The GUI can present pressure information by assumed period, department, hospital, or service line, and can visualize pressure trends using color-coded bar graphs, time-series graphs, high-occurrence maps, or hierarchical structures. Users can interact with the GUI, filtering by entity type, severity level, or prediction interval to delve into specific departments or units to understand the contributing factors behind assigned pressure scores. For example, a user might select a treatment recovery unit to view a ranked breakdown of contributing factors such as understaffing or delayed treatment.

[0051] In some embodiments, a graphical user interface may support the modeling of interactive scenarios, allowing users to simulate virtual interventions using an integrated AI agent. Users can input operational changes, such as increasing ED staffing by 3 FTEs (full-time equivalents) or postponing a scheduled CT scan by 2 hours, and the system can simulate the estimated impact on pressure over time. The AI ​​agent can then return updated predictions or recommended optimized alternatives. This simulation capability can be used for daily planning, rush preparation, or resource allocation strategies.

[0052] The disclosed system architecture enables computer program products to scale across multiple facilities and support a wide range of operational scenarios, allowing hospital administrators, clinical leaders, and command center personnel to proactively manage treatment and reduce the risk of operational bottlenecks. The system can operate in real time or near real time and integrate with hospital dashboards, mobile devices, or alerting platforms to disseminate pressure forecasts and mitigation strategies across teams.

[0053] A hospital system may comprise one or more individual hospitals, each containing numerous departments. Departments may be organized based on functional roles such as warding, emergency care, imaging, various therapies, treatment and recovery, or patient transfer. Each department can be structured as a hierarchy of entities, with one department containing one or more sub-departments, each sub-department containing a set of operational units known as points of care. For example, a warding department may include sub-departments defined by a level of care or service line (e.g., adult internal medicine services or gynecology services), with unit-level entities such as telemedicine units or step-down units defined below each sub-department. A hospital may, at its discretion, support multiple warding departments that are independently scored and managed, such as separate adult and pediatric categories.

[0054] Each clinical setting, when subjected to operational pressure, can contribute to overall pressure at a higher level of aggregation. In the emergency department, clinical settings may include ED bays, pods, or waiting rooms. In the imaging department, clinical settings may include entities related to specific modalities such as MRI, CT, ultrasound, and echocardiography. Various therapy departments may include entities such as physical therapy (PT), occupational therapy (OT), and speech-language pathology (SLP). Treatment and recovery departments may include PACU units, catheterization labs, endoscopy areas, or invasive radiology rooms. Patient transfers, while not constituting a traditional clinical setting, can be represented as a separate entity because they play an operational role at the system or hospital level.

[0055] The operational pressure for any given entity can be defined as a numerical score calculated using pressure factor values. Pressure factors are discretely defined variables that represent measurable indicators of operational burden. Each department may be associated with a set of department-specific pressure factors. For example, a ward department might use pressure factors such as net vacant beds and staffing delta, while an emergency department might use pressure factors including the number of patients waiting for treatment, treatment waiting time, the percentage of patients who left without consultation (LWBS), and the average time from arrival to treatment. An imaging department might rely on scan preparation time and preliminary image interpretation preparation time, and a recovery department might use treatment waiting time, operating room waiting delays, and estimated patients waiting for treatment across multiple days. These factors can be normalized and aggregated to calculate real-time pressure scores and predicted pressure scores.

[0056] Two main methods can be used to determine the pressure score for a given entity: a calculation method and a proportional method. In the calculation method, a weighted sum of pressure factor values ​​is calculated based on department-specific weights to obtain a normalized pressure score, typically scaled between 1 and 100. Each pressure factor can be individually weighted, with the weights set to reflect operational priorities. For example, the staffing delta may be assigned a smaller impact than the net beds in a ward setting, or vice versa. Additionally, the entity may be configured to include critical factors. If a critical factor exceeds a predetermined threshold, the weighted sum can be overridden so that the entity's pressure score is directly set as the score for that factor.

[0057] In proportional methods, most commonly used for higher-level entities such as departments, hospitals, or entire systems, a pressure score for a given entity can be determined by aggregating the pressure scores of its immediate child entities. For example, a hospital's pressure score may be derived from a proportional distribution of pressure levels across its departments. Similarly, a department's pressure score may reflect the weighted pressure state of its constituent units or sub-departments. This method allows for system-level visualization of high-risk locations without requiring direct factor assessment at each individual level.

[0058] To support the modeling of historical and predicted pressure, pressure scores can be calculated across three time windows: historical (past 8 hours), present (now), and predicted (up to 72 hours ahead). Artificial intelligence models can use historical pressure factors to predict future values ​​and derive future pressure scores. All pressure scores, whether historical, present, or future, can be generated in 1-hour increments, enabling high-resolution tracking. Entities at each level, including units, departments, hospitals, and systems, can receive pressure scores either through direct calculation or proportional inheritance, depending on their role in the model hierarchy.

[0059] Pressure levels can be categorized into four bands: "normal," "medium," "rising," or "high." These categorization labels can be derived from fixed thresholds applied to numerical pressure scores. For example, pressure scores between 0 and 25 may be categorized as "normal," while scores above 75 may be categorized as "high." These categorizations enable operators to quickly interpret pressure conditions and prioritize responses.

[0060] Each pressure score can be linked to one or more prescriptive action recommendations, each of which may include trigger conditions, an influence score representing estimated benefits, and a cost score representing resource utilization. Actions may be recommended to address predicted or real-time pressure conditions and may include interventions such as opening emergency treatment units, reassigning personnel, adjusting imaging schedules, or accelerating discharge. Users can interact with these recommendations through a graphical user interface that displays pressure scores, a high-risk area overview, and interactive visualizations. The interface may support click-and-navigate capabilities to explore specific pressure factors, browse relevant patient lists, or simulate operational scenarios with alternative configurations or assumed durations. An integrated AI agent can help users perform scenario analysis by modifying key assumptions to predict the effect of actions on predicted pressure.

[0061] Overall, the disclosed systems and methods can support the planning of dynamic pressure monitoring and interventions on a large scale across diverse hospital environments, enabling proactive operational management based on intelligent pressure modeling.

[0062] Please note that the drawings and descriptions in this document are non-limiting examples of various embodiments and are not necessarily drawn to scale.

[0063] Figure 1 shows a block diagram of an example of a non-limiting system 100 that may facilitate real-time prediction, scoring, and mitigation of operational pressures across multiple healthcare entities within a hospital system. In various embodiments, the pressure management system 102 may include a processor 108 (e.g., a computer processing unit, a microprocessor) and a non-transient, computer-readable memory 110 that is operable or related to operation or communication with the processor 108. The non-transient, computer-readable memory 110 may store computer-executable instructions that, when executed by the processor 108, cause the processor 108 or other components of the pressure management system 102 (e.g., a data acquisition component 112, a pressure modeling component 114, a prediction component 116, a scoring component 118, a behavior recommendation component 120, a user interface component 122) to perform one or more operations related to pressure modeling, prediction, scoring, and action recommendations. In various embodiments, non-transient, computer-readable memory 110 can store computer-executable components (e.g., data acquisition component 112, pressure modeling component 114, prediction component 116, score scoring component 118, action recommendation component 120, user interface component 122), and the processor 108 can execute these computer-executable components.

[0064] In various embodiments, the pressure management system 102 may include a data acquisition component 112. The data acquisition component 112 can receive operational data from multiple medical entities. These entities may include, for example, ward units, emergency departments, imaging departments, operating rooms, intensive care units, treatment and recovery units, or auxiliary services such as transport services or environmental services. The data acquisition component 112 can acquire and process structured or semi-structured operational data relating to clinical throughput and resource availability, such as patient numbers, bed status, immediate discharge possibility, personnel responsibilities, transport activities, or imaging wait times. The data acquisition component 112 can receive data through interfaces such as HL7 feeds, FHIR APIs, EHR integration points, batch uploads, or secure direct database connections.

[0065] The data acquisition component 112 can normalize timestamps across different systems, resolve unit names to standard identifiers, and handle noisy or incomplete data using smoothing or substitution logic. For example, if a data feed lacks a transport completion timestamp, the data acquisition component 112 can infer the timing based on changes in patient position. In addition, the data acquisition component 112 can maintain data accuracy and timeliness by implementing deduplication logic, differential updates, or push-pull synchronization protocols. The data processed by the data acquisition component 112 can then be passed to the pressure modeling component 114 in real time.

[0066] In various embodiments, the pressure management system 102 may include a pressure modeling component 114. The pressure modeling component 114 can calculate a real-time pressure score for each healthcare entity using department-specific pressure factors and configurable weightings. The pressure modeling component 114 may be configured to handle each entity according to operational metrics such as staffed bed occupancy, patient throughput, treatment delays, or resource bottlenecks. The pressure modeling component 114 can normalize raw data to percentile scores based on historical distributions for each unit or department, enabling comparisons across time and contextual situations.

[0067] For example, the pressure modeling component 114 can assign higher scores to treatment units that reflect an increase in pressure in the treatment flow, such as an unusually high number of same-day withdrawals and prolonged PACU stays. The pressure modeling component 114 can also apply exponentially decaying weights to recent events to give a higher relevance to immediate interruptions while still capturing underlying trends. The output of the pressure modeling component 114 may be a departmental-level pressure score, which can then be sent to the prediction component 116.

[0068] In various embodiments, the pressure management system 102 may include a predictive component 116. The predictive component 116 can apply an artificial intelligence model to estimate future pressure scores for one or more medical entities over a user-defined time window. The predictive component 116 can use deep learning, gradient boosted trees, or temporal convolution models trained on multi-source hospital data, including historical pressure factor trends, staffing patterns, admission / discharge data, and ancillary service measures. The predictive component 116 can take into account seasonal trends (e.g., influenza season), scheduled events (e.g., surgical schedules), and delay effects between upstream and downstream departments.

[0069] For example, the prediction component 116 can identify that a 20% reduction in staffing for environmental services historically leads to bed turnover delays and increased pressure scores in medical and surgical units within two hours. The prediction component 116 can then generate hourly predictions for up to 48 hours to mark periods where high pressure is expected. The predictions from the prediction component 116 are passed to the score-scoring component 118, which can convert these predictions into actionable levels.

[0070] In various embodiments, the pressure management system 102 may include a scoring component 118. The scoring component 118 can assign pressure levels to medical entities using both override logic and proportional logic. The scoring component 118 can implement predefined escalation rules, such as automatically assigning a "high" pressure level when a critical resource (e.g., ICU nurse staffing) falls below a safety threshold. Simultaneously, the scoring component 118 can calculate a collective service line level or facility level pressure by combining weighted scores from sub-departments using proportional logic. For example, the scoring component 118 can generate a service level pressure score for cardiology by aggregating values ​​from catheterization labs, cardiology labs, and telemetry units.

[0071] The scoring component 118 can maintain temporal stability using a hysteresis threshold or moving median to prevent fluctuations in alerts due to transient anomaly situations. The output of the scoring component 118 can be used by the user interface component 122 to display color-coded levels and can also be sent to the action recommendation component 120 for mitigation planning.

[0072] In various embodiments, the pressure management system 102 may include a behavior recommendation component 120. The behavior recommendation component 120 can identify prescriptive actions that reduce or prevent predicted pressure. Each action may be associated with trigger conditions, a cost score (e.g., difficulty or resource utilization), and an impact score (e.g., reduction of predicted pressure). The behavior recommendation component 120 may include a rule engine, a reinforcement learning agent, or a hybrid model that selects actions from a curated playbook of hospital interventions. For example, if predicted pressure is high in a treatment recovery unit, the behavior recommendation component 120 may suggest reallocating the priority of selective treatments, reallocating nursing resources, or returning float pool personnel.

[0073] The action recommendation component 120 can also assess feasibility by querying current capacity. If all transport personnel are currently allocated, the action recommendation component 120 can suggest alternatives, such as suppressing transport-related actions and completing discharge orders in advance. The action recommendation component 120 can output a ranked list of actions, along with their rationale, estimated time to effect, and downstream effects.

[0074] In various embodiments, the pressure management system 102 may include a user interface component 112. The user interface component 122 may display an interactive dashboard showing real-time and predicted pressure, high-risk alerts, and recommended actions. The user interface component 122 may utilize heatmaps, graphs, score sheets, and drill-down tables to visualize information at the unit, department, or system level. The user interface component 122 may allow filtering by role (e.g., bed manager, transport supervisor) and enable users to view relevant metrics and recommendations.

[0075] The user interface component 122 can integrate a scenario simulation tool that allows users to select actions and see their virtual effects on pressure predictions. The user interface component 122 may also include a natural language assistant that can answer questions such as, "Why is the pressure high in Ward 3 West?" or "What actions should be taken in the PACU this afternoon?" All perspectives presented by the user interface component 122 may reflect information from the output of the scoring component 118, the prediction component 116, and the action recommendation component 120.

[0076] The processor 108 may connect to a display interface 124, a device interface 126, and a network interface controller 128. The display interface 124 can communicate with a monitor 134, and the device interface 126 may support a touchscreen, tablet, or microphone 136 for user input. The network interface controller 128 can connect the pressure management system 102 to a network 130, including a secure hospital network and cloud data services.

[0077] The pressure management system 102 can operate as a modular decision support environment. For example, the data acquisition component 112 can supply updated telemetry data to the pressure modeling component 114, which can then feed the scores from component 114 to the prediction component 116 for prediction. The predicted scores can then be categorized by the score scoring component 118 and supplied to the action recommendation component 120, which can then review and deploy the recommendations of the action recommendation component 120 via the user interface component 122. Each component operates in close synchronization with the others, ensuring responsive, context-based, and predictive operational management.

[0078] The pressure management system 102 may further include a pressure explanation component capable of generating interpretable data and justifications in easily understandable language for assigned pressure scores. The pressure explanation component can analyze the input data captured by the data acquisition component 112 and the resulting pressure scores generated by the pressure modeling component 114 to determine which pressure factors contributed most significantly to the current or predicted pressure state of the medical entity. These contributions are quantified and visualized as ranked driving elements such as "understaffed beds," "delayed transport," or "treatment recovery bottlenecks," accompanied by their respective contribution percentages. For example, the pressure explanation component may display that "understaffed beds" account for 45% of the pressure score of the telemetry unit and "delayed discharge" contributes 30%, allowing users to understand the relative impact of each driving element on the operation.

[0079] The pressure explanation component can be tightly integrated with the prediction component 116 and the score scoring component 118 to highlight which historical trends or predictive features had the greatest influence on increasing the predicted pressure. For example, if the artificial intelligence model used by the prediction component 116 identifies a sharp drop in imaging preparation as a leading indicator of pressure in the emergency department, the pressure explanation component can explicitly highlight this relationship to show the user not only what is happening but also why it is expected to happen. The pressure explanation component can also work in conjunction with the user interface component 122 to present this data in an interactive format, such as an extended display tooltip or side panel, alongside the predictive visualization.

[0080] In some embodiments, the pressure explanation component can further communicate with the action recommendation component 120 to reveal the background circumstances that led to the suggestion of a particular prescriptive action. For example, if the system recommends bed cleaning as a priority over transport reassignment, the pressure explanation component can explain that the dominant pressure-driven factor was room availability rather than throughput. This collaborative operation can enhance user trust in the system by increasing transparency and supporting information-driven decision-making. Thus, the pressure explanation component acts as a bridge between complex AI-driven calculations and human operational intuition, ensuring that users are not only alerted to pressure risks but also empowered to effectively understand and respond to them.

[0081] The pressure management system 102 may further include an override verification component that tracks, records, and audits all manual overrides made by users to pressure scores, escalation levels, recommended actions, or predicted outputs. Manual overrides may occur when a user with appropriate authority adjusts the system's output based on situational awareness, policy exceptions, or urgency factors not captured by real-time data. For example, a nursing supervisor might lower a unit's pressure level after reassigning staff, even if the current staffing scale has not yet been updated in the issuing system. The override verification component can capture such events and maintain a detailed record of each override action.

[0082] The override verification component can store structured metadata for each override, including a unique user identifier, role or qualification level, override timestamp, specific fields or values ​​changed, original system-generated values, manually entered values, selected justification codes, or free-form reasons, and downstream effects such as changes to a ranked list of recommended actions. This metadata may be stored in a secure, queryable log that allows authorized administrators to review override history and assess frequency, appropriateness, and impact on operational decision-making.

[0083] The override verification component connects with the user interface component 122 to provide real-time alerts or visual indicators when an override is active for a particular entity. It also works in conjunction with the scoring component 118 and the action recommendation component 120 to ensure that manual changes are consistently and reliably reflected in all dependent outputs. For example, if a user overrides the pressure level for a treatment unit from "high" to "normal," the override verification component can trigger a recalculation of downstream recommendations to ensure consistency with the new pressure level. In some embodiments, the override verification component can also generate reports for compliance, audit, or governance workflows, supporting traceability across system updates and serving as a foundational tool for ensuring transparency, accountability, and trust in semi-automated decision support environments.

[0084] The pressure management system 102 may further include an inter-site coordination component that enables synchronous pressure modeling, prediction, and mitigation across multiple hospitals, on-site buildings, or interconnected locations within a healthcare system. The inter-site coordination component can collect and aggregate operational data from each participating site using a secure network connection, a standardized data interface, or a cloud-based integration framework. This aggregation allows the system to calculate and compare pressure scores at the site level, detect bottlenecks across the entire system, and provide a linked regional operational dashboard.

[0085] The inter-facility coordination component can generate cross-facility visualizations that highlight where pressure is accumulating or easing, enabling upper management at the enterprise level to identify which locations are at risk and which have available capacity. For example, the inter-facility coordination component could show that Hospital A is approaching full occupancy in its telemetry unit, while Hospital B has 6 available staffed beds, prompting transfer planning. This component can generate comparative pressure scores for each service line (e.g., cardiology, surgery, imaging) across multiple facilities, enabling central decision-makers to direct resource rebalancing strategies.

[0086] The inter-facility coordination component connects with the score scoring component 118 to propagate unit-level pressure data upwards for a facility-wide rollup, and then aggregates this data for multi-site analysis. The inter-facility coordination component can also connect with the action recommendation component 120 to support enterprise-level interventions such as reallocating float personnel across multiple locations or activating a system-wide rush protocol. In addition, the inter-facility coordination component can support policy-based controls such as prioritizing patient transfers based on urgency, expertise alignment, or transport availability.

[0087] In some embodiments, the inter-facility coordination component may further include an alert mechanism that notifies a regional command center when certain thresholds are exceeded across multiple facilities. This mechanism, working in conjunction with the simulation and scenario modeling components, can support scenario planning across multiple locations, enabling upper management to explore how to redirect treatment volumes from one hospital to another to relieve pressure while maintaining medical continuity. By providing a centralized operational perspective and enabling data-driven reallocation of resources, the inter-facility coordination component can enhance cross-facility coordination and increase resilience across complex healthcare systems.

[0088] The pressure management system 102 may further include an escalation workflow component that can generate, initiate, and track multi-stage operational response plans based on detected or predicted high-pressure conditions. The escalation workflow component may consist of rule-based triggers or threshold-based logic that automatically activate a predetermined escalation sequence when the pressure score or pressure level exceeds a specific value. For example, if the emergency department is predicted to reach a "high" pressure level within the next two hours, the escalation workflow component can initiate a coordinated chain of operations such as notifying the bed management team, returning float pool personnel, reprioritizing discharge workflows, or accelerating environmental services to clean and prepare additional rooms.

[0089] The escalation workflow component can be connected to the scoring component 118 and the prediction component 116 to continuously monitor the situation for qualitative understanding, and can also work in conjunction with the action recommendation component 120 to determine which interventions should be grouped within a structured plan. Each step of the escalation plan can be associated with a designated role, an expected completion time, and a communication protocol (e.g., small wireless terminal, email, task queue). For example, upon activation, the escalation workflow component can automatically send a call message to the head nurse instructing them to assess admission bottlenecks, reallocate personnel through staffing coordinators, and initiate alerts to transport services to expedite patient transfers.

[0090] To support accountability and transparency, the escalation workflow component can also track task status, user approvals, timestamps, and downstream outcomes. Each step can be marked as pending, in progress, or completed, and exceptions or delays can trigger alerts or secondary workflows. In some embodiments, the escalation workflow component can visualize the active workflow in the user interface component 122, allowing the user to view the current escalation plan, monitor its progress, and modify each step as the status evolves.

[0091] The escalation workflow component can also support reusable templates and scenario-specific configuration adjustments. Hospitals can define hierarchical response plans (e.g., ED congestion, PACU overflow, or ICU saturation) that automatically adjust based on data representing the context. By providing structure, automation, and traceability to operational responses, the escalation workflow component can help hospital systems respond to pressure conditions with speed, coordination, and clarity.

[0092] The pressure management system 102 may further include simulation and scenario modeling components that enable interactive what-if analysis to support proactive decision-making. These simulation and scenario modeling components may allow users to explore the operational impact of hypothetical interventions or changes in resource availability before implementing these interventions or changes in a real-world environment. For example, a user could use the simulation and scenario modeling components to test whether initiating the early discharge of two patients from a high-pressure post-anesthetic care unit (PACU) would result in a meaningful reduction in estimated pressure over the next four hours. Similarly, a user could simulate the effect of reassigning transport personnel from a general ward to the emergency department to evaluate whether such action mitigates anticipated delays in patient transfer.

[0093] The simulation and scenario modeling components are integrated with the prediction component 116 and the score scoring component 118 to recalculate future pressure scores and pressure levels based on user-defined adjustments. By injecting simulated changes, such as artificial discharges, staffing increases, or bed reallocations, into the modeled data pipeline, the simulation and scenario modeling components can generate revised forecasts that reflect the changed operational conditions. These revised estimates can help users evaluate the effectiveness, urgency, and trade-offs of proposed actions under changing assumptions or constraints.

[0094] The simulation and scenario modeling components can further interact with the user interface component 122 to present results in a visually intuitive format. Simulated scenarios can be represented using color-coded graphs, comparison dashboards, or interactive overlays that highlight the difference between baseline predictions and simulated outcomes. Users can run multiple scenarios in parallel, save frequently used configurations, and export forecasts to operational planning tools. This capability empowers stakeholders to make informed decisions under uncertainty, reduce trial-and-error interventions, and prioritize actions that offer the highest estimated impact in mitigating operational pressures.

[0095] The pressure management system 102 may further include a mobile interface component that can deliver real-time operational status awareness and decision support to clinicians, administrators, and personnel via mobile devices such as smartphones and tablets. The mobile interface component can directly push notifications to authorized users of live pressure alerts, high-occurrence site summaries, and recommended prescriptive actions, enabling these users to monitor evolving conditions and respond to emergencies in progress. The mobile interface component may be a streamlined version of the graphic user interface provided by the user interface component 122, presenting an interface optimized for smaller screens and touch-based interactions.

[0096] The mobile interface component allows users to easily view departmental-level pressure scores, escalation status, and active mitigation workflows, while also supporting content modifications tailored to specific roles. For example, a head nurse could receive a push notification indicating that their unit has entered a high-pressure state due to delayed discharge, prompting them to reconsider and approve suggested interventions, such as contacting environmental services. The mobile interface component can shorten the time between recommendation and action by allowing users to approve, accept, escalate, or postpone recommended actions with a single tap.

[0097] The mobile interface component may also include secure communication features such as role-based access control, encrypted message transmission, and integration with hospital wireless paging systems or electronic health record (EHR) mobile apps. By enabling clinicians and operations managers to interact with the pressure management system 102 from any location within the hospital or across multiple locations, the mobile interface component can enhance responsiveness, reduce delays in operational coordination, and ensure that critical pressure relief workflows are not hindered by restrictions on desktop access.

[0098] The pressure management system 102 may further include an ensemble modeling component that can enhance the robustness, reliability, and accuracy of pressure forecasts by aggregating outputs from multiple forecasting models. The ensemble modeling component can receive intermediate pressure forecasts from different model architectures such as regressive neural networks, gradient boosted trees, or attention-based time encoders, and combine these forecasts using a dynamic weighting policy to generate a consensus forecast. These weights may be based on factors such as the type of department, the historical model accuracy of a given unit, the temporal forecast window (e.g., short-term vs. long-term), or the operational context.

[0099] For example, the ensemble modeling component might prioritize high-frequency, short-duration models for emergency department predictions due to rapid variability, while prioritizing seasonally adjusted trend models for treatment areas with predictable daily volumes. The ensemble modeling component can maintain an execution registry that tracks which model has historically performed best under similar conditions and use this registry to inform real-time weighting logic. In this way, the ensemble modeling component can adaptively shift emphasis across constituent models to respond to evolving data patterns, anomalous situations, or special events such as hospital system downtime or spikes in holiday activity.

[0100] In addition, the ensemble modeling component can also serve as a safety measure against model deviation or outlier sensitivity. By distributing prediction responsibility across diverse models, the ensemble modeling component can mitigate the effects of data quality issues or localized training bias. This can be particularly valuable in complex hospital systems where operational dynamics differ significantly between departments or facilities. The output of the ensemble modeling component can be provided to the prediction component 116 to assign future pressure levels, or it can be used directly by the scoring component 118. When integrated with a broader pressure management system 102, the ensemble modeling component can support continuous adaptive learning to maintain high-fidelity predictions that drive timely and appropriate interventions.

[0101] The pressure management system 102 may further include an active learning component that can enhance the overall intelligence and adaptive capabilities of the predictive model by incorporating human feedback into the model's lifecycle. The active learning component can analyze confidence scores associated with predicted pressure outputs from the predictive component 116 to identify instances where predictions fall below a configurable confidence threshold. These low-confidence predictions can then be marked for human review, such as by a clinical operations expert who can provide input for validation or correction. This process can ensure that borderline cases, anomalous situations, or novel situations are handled appropriately without requiring retraining with a complete dataset.

[0102] The active learning component can operate in a continuous loop, where newly labeled, expert-verified or corrected examples are periodically batched and used to progressively retrain lower-level AI models. The retraining process focuses only on the most uncertain or insightful examples, thereby minimizing the data labeling burden and improving model performance. For example, if the system is struggling to anticipate pressure in a newly established treatment unit with an atypical workflow, the active learning component can prioritize data from that department for reconsideration and targeted model improvement.

[0103] In some embodiments, the active learning component can evaluate which pressure prediction is most uncertain or influential using uncertainty sampling, diversity sampling, or Bayesian dropout methods. The active learning component can also track conceptual shifts and adaptively adjust which data points to highlight for human review based on changes in hospital operations or policies. The output of the active learning component can be directly fed to the prediction component 116 and the ensemble modeling component to maintain accuracy over time.

[0104] The active learning component can also provide audit logs of all marked cases, user inputs, and retraining events, enabling governance and traceability of model progress. Furthermore, when integrated into the broader pressure management system 102, the active learning component can support continuous learning while preserving clinician supervision and context-based decisions.

[0105] The pressure management system 102 may further include an audit trail component capable of recording all data entries, pressure calculations, forecasts, action recommendations, user interactions, and manual overrides in a secure and queryable format. The audit trail component can assign a unique identifier and timestamp to each data element or user event, thereby ensuring complete traceability and data history throughout the pressure management workflow. This functionality may be particularly important for regulatory compliance, operational audits, and post-event analysis.

[0106] For example, if patient transport was delayed due to an overridden pressure recommendation, the audit trail component could record when, by whom, and why the override occurred, and how the delay affected downstream operational metrics. These logs can be filtered and queried by department, user, action type, or time window, enabling retrospective investigations and compliance audits.

[0107] The audit trail component can also support audit export, data visualization, and reporting tools for hospital management, quality improvement teams, or external regulatory bodies. These outputs can help identify bottlenecks across the organization, assess policy adherence, and evaluate the frequency and rationale of manual interventions. Integrating with the override verification component can further enhance audit capabilities by linking system-generated recommendations to real-world user behavior.

[0108] The audit trail component may be designed with secure storage, access control, and encryption to meet hospital data governance and HIPAA requirements. When integrated into the pressure management system 102, the audit trail component can facilitate accountability, reproducibility, and trust in system-guided decision-making.

[0109] The pressure management system 102 may further include a warning prioritization component that can intelligently filter, rank, and send pressure-related warnings based on the user's specific background and operational urgency. The warning prioritization component can evaluate various factors such as the user's role, shift timing, past warning history, and departmental response capabilities to determine which warnings are most relevant or actionable for the user. For example, a transport operations coordinator nearing the end of their shift might receive only the most critical warnings affecting the next 30 minutes, while a supervisor on an overnight shift might receive a broader overview of expected pressures for the next 8 hours.

[0110] The alert prioritization component can use a scoring algorithm that takes into account both the importance of the alert and the user's available time or workload. The alert prioritization component can also reduce cognitive overload and alert fatigue by suppressing or batching lower-priority alerts. Alerts can be color-coded, grouped by theme, or aggregated by unit, enabling users to quickly sort through and focus their attention where needed. The alert prioritization component can also integrate user feedback to fine-tune the prioritization logic over time.

[0111] The alert prioritization component works with the user interface component 122 to control how and when alerts are displayed and which may be particularly useful in environments with limited staffing or high operational complexity. The alert prioritization component can also be integrated with the escalation workflow component to ensure that unresolved alerts are handed over between shifts in a timely manner. When combined with other components of the pressure management system 102, the alert prioritization component can improve the signal-to-noise ratio and enhance the efficiency of clinical and operational decision-making.

[0112] The pressure management system 102 may further include a capacity-based recommendation filter that can assess the real-time feasibility of prescriptive actions generated by the action recommendation component 120. Before highlighting proposed interventions for the user, the capacity-based recommendation filter can assess whether the required resources, such as personnel, beds, equipment, or transport availability, are currently prepared to support the implementation of the recommendation. If not, the capacity-based recommendation filter can suppress the recommendation or replace it with a more viable alternative.

[0113] For example, even if the action recommendation component 120 suggests reallocating environmental service personnel to speed up bed turnover, this recommendation may be downgraded or blocked if the capacity-based recommendation filter detects that all environmental personnel are currently occupied or below a minimum threshold. Instead, the system may suggest postponing non-urgent (selective) admissions, reactivating floating resources, or reprioritizing discharge order entries. This capacity-focused logic reduces wasted effort, increases user confidence in recommendations, and ensures that suggested actions are aligned with real-world constraints.

[0114] The capacity-based recommendation filter can continuously monitor the operational data stream captured by the data ingestion component 112 to assess the availability of the most critical resources. The capacity-based recommendation filter can also verify feasibility by referring to predetermined operational rules, such as minimum staffing levels or regulatory limits. Integration with the user interface component 122 allows users to understand why certain recommendations are suppressed or modified.

[0115] When used in conjunction with the behavioral recommendation component 120, the capacity-based recommendation filter can create a closed-loop recommendation system that adapts not only to pressure predictions but also to underlying operational context. This capability can be critically important for highly reliable hospital environments where inadequate recommendations can lead to confusion, attention fatigue, or unforeseen consequences.

[0116] When combined, the components of the pressure management system 102 can function as an integrated real-time decision support platform capable of foreseeing, interpreting, and responding to operational pressures across diverse healthcare environments. By continuously incorporating dynamic hospital data and applying advanced modeling and predictive techniques to recommend context-aware actions with traceable justification and feasibility testing, the system can enable healthcare organizations to proactively manage capacity constraints, mitigate bottlenecks, and maintain healthcare quality under evolving circumstances. The system's modularity anticipates scalability, expandability, and integration with existing hospital workflows, while rich interpretability, override accountability, and simulation capabilities ensure that human decision-makers remain in control. Collectively, these features can support a shift from reactive, fragmented operations to collaborative, proactive pressure management.

[0117] Figure 2 shows a flowchart of an example of a non-limiting computer-implemented method 200 that facilitates predictions by one or more embodiments described herein and enables the management of operational pressures across multiple healthcare entities within a single hospital system.

[0118] In operation 202, method 200 may include receiving operational data from multiple medical entities within a single hospital system by a system coupled to the processor (e.g., processor 108) in terms of operation (e.g., via a data acquisition component 112). These medical entities may include ward units, emergency departments, imaging departments, operating rooms, intensive care units, treatment and recovery areas, and auxiliary services such as transport departments, inventory management departments, or laboratory departments. Operational data may be received from a variety of sources and interfaces, including real-time HL7 feeds, FHIR APIs, EHR data extracts, telemetry sensors, or secure batch uploads. The data acquisition component 112 can process both structured and semi-structured data and may include logic for normalizing timestamps, mapping unit names to standard identifiers, filtering outliers, and resolving incomplete records using interpolation or substitution. For example, if a transport completion timestamp is missing for a particular patient, the system may infer the timing based on observed changes in position.

[0119] In operation 204, method 200 may include the system calculating a real-time pressure score for a medical entity based on a weighted sum of normalized pressure factor values ​​(e.g., via a pressure modeling component 114). Each medical entity may be associated with department-specific pressure factors that reflect key operational attributes related to the entity's operation. These factors may include staffed bed availability, admission delays, discharge immediacy, environmental service availability, throughput delays, or surgical schedule density. The pressure modeling component 114 can convert raw values ​​to normalized percentiles or z-scores based on the historical distribution for each department, enabling cross-unit comparisons. The component can then combine these normalized scores to obtain a real-time pressure score by applying configurable weightings. For example, a treatment unit may have a higher weighting for recovery delays, while an emergency department may have emphasis on waiting room length and diagnostic preparation time. The output of operation 204 may be a pressure score representing the operational stress experienced by a given entity at a given time.

[0120] In operation 206, method 200 may include predicting future pressure scores for a medical entity using an artificial intelligence model trained on historical pressure data, via the system (e.g., via predictive component 116). The artificial intelligence model may include one or more neural networks, temporal convolutional models, or dendritic regressors trained on multidimensional time-series data derived from previous operational conditions. The predictive component 116 may be configured to learn both intra-departmental and inter-departmental dependencies over time. For example, the predictive component 116 may recognize that delays in the radiology department often lead to downstream bottlenecks in emergency medicine, or that a reduction in environmental service staffing levels foreshadows room turnover delays and rising ward pressures. The model may generate pressure predictions at fixed intervals (e.g., hourly) over a given prediction window (e.g., the next 24 or 48 hours). These predictions can serve as early warnings of anticipated congestion and support proactive mitigation measures.

[0121] In operation 208, method 200 may include assigning pressure levels to medical entities by the system (e.g., via the score-scoring component 118) using override logic for critical factors and proportional logic for collective entities. The score-scoring component 118 can convert raw or predicted pressure scores to discrete levels such as "normal," "medium," "elevated," or "high." Override logic can be applied when certain critical factors, such as the minimum staffing threshold or ICU occupancy rate, exceed safety limits, resulting in automatic escalation regardless of the overall score. In parallel, proportional logic can be used to calculate aggregate pressure levels by weighting and combining the scores of lower-level units or services. For example, if the telemedicine room, cardiac recovery room, and catheterization room all report high pressure scores, the system can assign a high pressure score to the cardiac service line. The score-scoring component 118 can apply smoothing techniques, such as moving median, to reduce noise and prevent attention fatigue from transient fluctuations.

[0122] In operation 210, method 200 may include the system (e.g., via the action recommendation component 120) identifying one or more prescriptive actions to address current or predicted pressure. Each prescriptive action may be associated with specific trigger conditions (e.g., pressure level exceeds “rising”), an estimated impact score representing the expected decrease in pressure, and a cost score representing the operational difficulty or resource utilization associated with the action. The action recommendation component 120 may pick action candidates from a given playbook or it may dynamically generate recommendations using historical intervention-outcome pairs. For example, if a treatment recovery unit is expected to experience high pressure in four hours, the system may recommend early discharge, reallocation of float personnel, or rescheduling of treatment. Each proposed action may be ranked by priority, efficiency (e.g., impact-to-cost ratio), and feasibility, and may be validated through a recommendation filter based on capacity.

[0123] In operation 212, method 200 may include displaying a graphic user interface by the system (e.g., via user interface component 122) that includes pressure forecast visualizations, a high-incidence site overview, and one or more selectable recommended actions. The graphic interface can present historical trends and expected future pressure values ​​using line graphs, heatmaps, or color-coded indicators. The high-incidence site overview can list the most important departments or units along with key pressure-driving elements identified by the pressure explanation component. Selectable recommended actions may be displayed as clickable cards or dropdown options, each with annotations indicating estimated impact, urgency, and expected time to benefit. Users can interact with the interface to explore what-if scenarios, adjust timeframes, and approve proposed interventions. The graphic user interface can be deployed on both desktop dashboards and mobile devices and can be configured to suit different user roles, such as operations managers, nursing supervisors, or hospital general practitioners. By aggregating data, clarifying the background context, and highlighting actionable information, action 212 can support timely, well-informed decision-making that enhances operational flexibility.

[0124] In summary, the operation of Method 200 can enable a data-driven predictive framework for hospital capacity management and real-time operational decision support. The method can be implemented using a combination of rule-based logic, machine learning, and internal staff workflows, and can be deployed in a modular manner to facilitate integration with existing hospital IT systems.

[0125] Next, Figure 3 shows an example of a non-limiting method 300 that may facilitate the prediction and management of operational pressures in a healthcare system. This method may begin by receiving operational data from multiple healthcare entities within a single hospital system, as shown in Block 202. This data may include real-time and historical information on staffing levels, bed occupancy, patient throughput, treatment schedules, discharge procedures, imaging preparation time, and other operational metrics. The received data can be pre-processed, standardized, and validated to ensure consistency and accuracy across multiple healthcare entities that may operate in different information systems or with different reporting intervals.

[0126] In Block 204, the method may include a step of calculating a pressure score for each medical entity based on a weighted sum of normalized pressure factor values. These pressure factors may be constructed to suit the specific operational characteristics of each entity, and the normalization step may include percentile ranking, z-score transformation, or historical distribution fitting to enable appropriate comparisons across multiple time periods and sectors. The weighting scheme may reflect a settable priority or empirically derived influence weights, allowing the pressure score to represent a composite perspective of the current operational burden.

[0127] In Block 206, the method can predict future pressure scores using an artificial intelligence model trained on historical pressure data. The model can learn temporal patterns, correlations between operational variables, and inter-departmental cascading effects to predict future states under different conditions. Predictions can be generated hourly, at shift levels, or at daily intervals and may include confidence scores or forecast intervals to support downstream decision-making.

[0128] Block 208 includes assigning pressure levels to medical entities using both override logic and proportional logic. Override logic can be triggered regardless of the overall pressure score when the most critical operational factor exceeds a predetermined threshold. For example, if the number of ICU personnel falls below the minimum safe level, the method can automatically assign a higher pressure level. Proportional logic can aggregate pressure scores from lower entities, such as hospital units or treatment areas, to calculate a composite score and assign a facility-level pressure level.

[0129] In block 302, assigned pressure levels can be categorized into discrete labels such as "normal," "moderate," "elevated," or "high." These categories can be used to standardize reporting, trigger alerts, or drive color coding of the user interface. Categorization thresholds may be static or dynamically adjusted based on recent trends or seasonal standards. This step ensures that end users are presented with an intuitive overview to support rapid triage and intervention.

[0130] Next, the method can proceed to a decision point, where operational update inputs are evaluated. If the input data has been updated or the system is operating at the scheduled update interval, the method can proceed to block 306. In block 306, the method can update the predicted pressure score using the most recent operational data and store these scores as part of the historical dataset. This long-term storage process can support model retraining, audit trails, and long-term performance monitoring.

[0131] If the operational inputs have not been updated, the method can proceed using the most recent predictive data for downstream analysis. In block 210, the method may identify prescriptive actions that can reduce or prevent current or predicted operational pressures. Each action may be associated with predetermined trigger conditions, a quantitative impact score representing the expected pressure reduction, and a cost score representing resource utilization, implementation effort, or workflow disruption.

[0132] In Block 308, the method can prioritize candidate actions based on one or more prioritization criteria. These criteria may include absolute impact, cost-effectiveness (e.g., impact-to-cost ratio), urgency, resource availability, and policy consistency. Prioritized actions may allow users to focus on high-leverage interventions that provide meaningful pressure relief with manageable resource investments.

[0133] In Block 212, the method may display pressure forecast visualizations, a high-risk area overview, and a set of selectable recommended actions via a graphical user interface. The graphical user interface may include dynamic charts, interactive maps, alert banners, and control elements to facilitate exploration and decision-making. Forecasts can be visualized over time to show estimated pressure trajectories, and the high-risk area overview can identify entities at risk of entering pressure-increasing or high-pressure conditions.

[0134] In Block 310, the method can display predicted pressure using a bar graph color-coded with a configurable gradient. This visualization technique can translate pressure ranges into an intuitive visual representation using a color-coding scheme such as green to red or blue to orange. The gradient scale can be configured to match intuitive preferences, pressure score distributions, or accessibility needs. The visualization can help stakeholders quickly assess the severity and location of pressure across multiple departments or multiple time windows.

[0135] In block 314, the method may allow users to click on displayed actions or alerts to access linked operational data such as patient lists, bed availability reports, staffing dashboards, or intervention audit logs. This clickability may provide actionable context to support the implementation, verification, or escalation of recommended actions. For example, a user may click on a suggested discharge prioritization action to review a list of patients who are medically eligible for immediate discharge, along with anticipated discharge barriers and the assigned medical team.

[0136] In summary, each step of the method in Figure 3 can enable a comprehensive and continuously adaptive approach to operational pressure management in a medical setting.

[0137] Next, Figure 4 shows an example of a non-limiting user interface 400 that visualizes pressure forecasts, current operational pressure levels, and prescriptive actions across multiple healthcare facilities within a regional healthcare system. The interface can be configured to provide centralized situational awareness to support clinical and operational decision-making at the system, hospital, and departmental levels.

[0138] In various embodiments, the top of the interface may include a configurable pressure forecast chart that visualizes the estimated pressure trend over a predetermined assumed period, such as the next 72 hours. The forecast visualization can be presented as a color-coded bar graph, where each bar represents a pressure estimate over an hourly or multi-hour period, and the height and color of the bar reflect the severity of the foreseen operational pressure. For example, the bars may be displayed in a gradient of green for "normal" pressure, yellow for "medium," orange for "rising," and red for "high." This color gradient is configurable and can enable the user to quickly identify pressure spikes over time. The user can interact with the time axis by selecting specific increments (e.g., "now" and "+4 hours") to filter and compare pressure estimates across the system.

[0139] Below the chart, the interface can display a collapsible tabular view of pressure data per hospital or facility. Each row corresponds to an individual hospital and can show the name, current pressure level, predicted pressure in the future time window, the number of relevant prescriptive actions, and a list of departments or service lines experiencing pressure increases. These indicators can be updated in real time to reflect both the current situation and predictions. For example, the row for "General Hospital" shows that it currently has "medium" pressure, but could reach "high" predicted pressure in 4 hours, along with relevant warnings for the emergency department, imaging department, and various therapy departments. Predicted pressures can be summarized both numerically and visually, with badge-type icons labeled with severity (e.g., "medium," "high," "standard") to facilitate visual scanning.

[0140] In some implementations, the interface may allow users to delve deeper into the hospital flow to expand and observe further details. These details may include pressure metrics for individual departments (e.g., ICU, non-ICU, transfer center), lists of active or recommended mitigation actions, and links to deeper dashboards or patient flow data. Each department-level pressure tag can be interactive, enabling users to quickly identify high-risk points in operations and explore the sources of pressure.

[0141] The interface can also support filtering users within service lines (e.g., surgical, imaging, and treatment services), allowing operations managers to focus on subsets of systems relevant to their roles. In addition, sorting mechanisms such as "default" or "pressure" can sort hospital rows based on a given logic or the severity of current / future pressure.

[0142] User Interface 400 can be a command center-style visualization that enables healthcare administrators and clinical personnel to foresee bottlenecks, assess resource constraints, and take proactive steps to mitigate risks. This dashboard format can combine AI-driven predictions with a clear and actionable display, empowering healthcare systems to manage pressures holistically across numerous hospitals and service lines.

[0143] Visualization styles, including color-coded bars, icons, and departmental badges, can also support rapid recognition and contextual triage in high-pressure environments. Prediction periods, severity thresholds, and prescriptive behavioral tags can all be adjusted based on intuitive preferences, operational playbooks, or user access levels.

[0144] In summary, Figure 4 illustrates how complex multi-center pressure models can be transformed into an intuitive, role-dependent graphic interface that presents the right data to the right stakeholders at the right time.

[0145] Figures 5(A) and 5(B) together show a non-limiting set of 500 examples of department-specific pressure layers and corresponding predicted pressure factors, which can be used by a pressure prediction system to generate department-level pressure scores across a hospital or healthcare system. Each row in Figures 5(A) and 5(B) identifies a hospital department or functional area, the relevant institutions or service layers within that department, and predicted features or data elements that may contribute to the department's pressure score.

[0146] In Figure 5(A), the first sector shown is "beds," which can be modeled across multiple layers such as adult, pediatric, gynecological specialty beds (e.g., psychiatry), clinical level (e.g., internal medicine / surgery, telemedicine, intensive care), service lines, and specific units. Expected pressure factors for this sector may include net vacant beds and a calculated staffing delta representing the deviation from expected or target staffing levels. These variables can be used to assess whether sufficient staffed beds are available to meet anticipated patient demand.

[0147] The “ED” (Emergency Department) row in Figure 5(A) identifies the compartments and waiting areas as related sub-layers. Pressure in the ED can be driven by several predictive factors, including the number of patients waiting for treatment (patients waiting for hospital beds) in both the ICU and non-ICU areas, the average waiting time for treatment for each group, the staffing delta, the percentage of patients who left without being seen (LWBS), the rise process time (time from arrival to healthcare provider assessment or from healthcare provider assessment to placement), and the current conversion status of the ED. These features can provide insights into ED overcrowding, throughput bottlenecks, and medical delays.

[0148] Figure 5(A) also shows the “Various Therapies” category, which may include physical therapy (PT), occupational therapy (OT), and speech-language pathology (SLP). The primary pressure factor identified for this category is the preparation time from instruction to consultation, reflecting the time elapsed between the clinical consultation instruction and the treatment assessment or initiation of treatment. Delays in this measure may indicate staffing constraints, inefficient work coordination, or excessive demand.

[0149] Figure 5(B) then shows the framework for other departments. The “Procedure Recovery” row includes service layers such as the Postoperative Anesthesia Care Unit (PACU), endoscopy rooms, invasive radiology rooms, and cardiac catheterization rooms. Expected pressure factors for this department may include the number of patients waiting for procedures (ICU and non-ICU), the waiting time for these patients, the number of patients waiting in the operating room, and the estimated number of patients waiting for procedures across multiple days. These features may indicate strain on post-procedure capacity, especially when discharge or transfer workflows are delayed.

[0150] The "Imaging" section can be modeled across multiple layers, including imaging modality (MRI, CT, ultrasound, echocardiography), patient type (inpatient or ED), and priority level (e.g., urgent vs. routine). Pressure can be anticipated based on scan preparation time (from input to scan start) and preliminary image preparation time (from scan completion to initial image availability). Any long delay may reflect imaging delays or the radiologist's capacity limits.

[0151] Finally, the "Transfer Acceptance" line reflects the workflow for external patient referrals or system-level transfer coordination. Relevant layers may include patient type (e.g., specialty services, severity of condition), transfer type (life-threatening, emergency, non-emergency), and transfer priority. Expected pressure features may include waiting time from referral to arrival, current specialty-based transfer status, and number of refusals (i.e., number of transfers rejected due to insufficient capacity or resource mismatch). These features can help assess the system's real-time capacity to absorb external demand.

[0152] Furthermore, Figures 5(A) and 5(B) show that pressure is calculated and predicted using adjustable forecast variables that are modified to suit the unique operational dynamics of each sector. This framework enables pressure modeling at both the granular and aggregate levels, supporting visibility across the entire system and sector-specific mitigation strategies.

[0153] Figure 6 shows an example of a non-restrictive flowchart 600 illustrating how operational pressure is processed and translated into prescriptive action recommendations using both real-time and predictive inputs. This process can span three temporal dimensions—present, historical, and future—and can support pressure-driven decision-making at both the local and system levels. Each data transformation stage in the flow can be designed to preserve interpretability, traceability, and modularity.

[0154] On the far left of the figure, data model 602 may collect and organize operational input data from one or more healthcare entities. This data may include real-time feeds of pressure-related measures from departmental systems, such as imaging preparation time, staffed beds, or treatment delays. These inputs can form the basis for calculating pressure factors.

[0155] The calculated pressure factors block 604 represent the immediate processing of current state data and can generate department-specific and region-specific pressure indicators such as current action wait time, ED conversion status, or preparation time from instruction to scan. These pressure factors can be normalized to standardized scores using statistical transformation or percentile mapping in block 606, enabling consistent interpretation over time across multiple departments.

[0156] In block 608, normalized scores can be aggregated to obtain a pressure score weighted by actual weights. This step allows for the calculation of a composite pressure score for a department, unit, or hospital entity by applying set weights to each pressure factor. For example, the score for a treatment recovery unit may be weighted more heavily on delayed discharge than on staffing levels, depending on operational priorities.

[0157] Using the resulting scores, block 610 can determine a logical pressure classification at the entity level, which may be calculated using proportional thresholding and override logic. These classifications may include labels such as "normal," "medium," "rising," or "high," and may be assigned at multiple institutional levels, such as unit level, service line level, or facility level. In some embodiments, the inclusion or exclusion of certain subentities or data elements may be dynamically set based on operational rules or seasonal variations.

[0158] Once the current pressure level is established, the system can generate action recommendations in block 612 that are designed to mitigate or respond to the detected pressure conditions. These recommendations may take into account the severity of contributing factors, the feasibility of the action, and the potential downstream impact. Examples include reallocating float personnel, expediting consultation orders, or initiating escalation workflows.

[0159] In parallel, the system can track and store historical pressure factor values ​​in block 614, and use these values ​​to construct an 8-hour tracking data window for trend visualization and current situation recognition of background conditions, as shown in block 616. This historical data can be fed into downstream prediction models to contribute to pressure stability calculations, such as calculations for hysteresis smoothing.

[0160] Predictive pressure processing can be initiated in block 618, where one or more artificial intelligence or machine learning models can generate estimates of future pressure factors using trends observed in historical input. These predictions can cover the next 1 to 72 hours and can be continuously updated as new data is received.

[0161] The predicted pressure factors can be normalized as pressure scores in block 620 and processed in the same manner as the logic of the current state. In block 622, weighted scores can be calculated for each predicted entity, and in block 624, logical pressure classifications can be assigned using proportional and override methods. These outputs can then be fed into future state action recommendations in block 626, enabling clinical or operational teams to plan interventions proactively for foreseeable constraints or bottlenecks.

[0162] Throughout the entire flow, each processing path, including current, historical, and future data, can provide data for shared visualizations, reports, or dashboards. The dashed arrows in Figure 6 may represent information feedback loops that support re-prediction, cross-validation, or fine-tuning of pressure classifications. All transformation stages can be logged and made auditable to ensure that both the raw data and the resulting recommendations are explainable and reproducible. The entire flow shown in Figure 6 can support pressure-responsive decision-making across multiple operational domains and assumed timeframes, enabling both reactive and proactive hospital management.

[0163] Figure 7 shows a hierarchical pressure aggregation model 700 that illustrates how operational pressure scores can be derived and aggregated from high-granularity pressure factors into a higher-level hospital system pressure index. This model can support multi-layer pressure modeling and visualization across multiple units, departments, hospitals, and systems.

[0164] The hierarchy can begin at the system level with the hospital system pressure score represented in block 702. This pressure score may reflect the overall operational burden across all facilities included in the healthcare system. The hospital system pressure score may be derived by aggregating individual pressure scores for each hospital, such as hospital 1 in block 704, hospital 2 in block 706, and hospital n in block 708. These individual hospital pressure scores may be based on numerous departmental and unit scores calculated within each respective facility.

[0165] For example, hospital pressure 1 in block 704 may be generated by aggregating numerous departmental-level and departmental-level pressures, including clinical level I pressure in block 710, emergency department (ED) pressure in block 712, and one or more other departmental-specific pressures in blocks 714 and 716. Each departmental or departmental level may reflect a distinct clinical or operational function within the hospital and may contribute independently to the overall pressure.

[0166] Within the range of clinical level I pressures in block 710, further lower-level pressures can be modeled. As shown in block 718, these pressures may include lower-level pressure 1, lower-level pressure 2, and lower-level pressure 3, each reflecting operational pressures within a distinct category or classification, such as by patient urgency or service type. These lower-level pressures can further be broken down into specific unit-level pressures, such as unit 1 pressure and unit 2 pressure, as shown in block 722. These unit pressures may represent the highest-granularity layer in the hierarchy and often correspond to actual physical spaces or clinical units such as wards or intensive care units.

[0167] Similarly, the emergency department pressure in block 712 can be broken down into individual components. Block 724 shows an example of unit layers such as a waiting room, section 1, and further sections, each of which can be treated as an area independently monitored within the emergency department. Local pressure values ​​can be calculated for these areas, and these pressure values ​​are aggregated into an overall ED pressure score. Further services such as service 1, service 2, and service n are shown in block 726, demonstrating how modular service lines or support functions can also contribute to discrete pressure values.

[0168] At the highest level of granularity, pressure values ​​can be generated based on operational measures and real-time signals. For example, compartment 1 within the emergency department may monitor a set of defined pressure factors shown in block 728. These factors may include measures such as median wait time (LOS) for patients awaiting ICU treatment, median wait time LOS for patients not awaiting ICU treatment, percentage of patients discharged without consultation (LWBS), and mean time from arrival to consultation with a physician. These factors can be normalized, weighted, and aggregated to generate a composite pressure score for the unit or compartment.

[0169] Next, each upstream layer can calculate a weighted sum or logical aggregate of the pressure scores of its constituent units, enabling a progressive aggregation from pressure factors to unit pressure, departmental pressure, hospital-level pressure, and ultimately system-level pressure. This structure can support traceability and interpretability by allowing users to delve from high-level pressure indicators to their lower-level contributing factors.

[0170] The framework illustrated in Figure 7 can enable hospital administrators and operational teams to detect bottlenecks, predict system pressure, and coordinate cross-departmental interventions. The hierarchical model can also support real-time dashboard displays, visual heatmaps, or escalation workflows linked to pressure thresholds at any level of the hierarchy.

[0171] Next, Figure 8 shows an example of a hierarchical framework 800 for calculating, aggregating, and interpreting pressure within a healthcare network. This multi-layered pressure modeling structure can facilitate the classification and propagation of pressure signals across multiple institutional levels, from individual clinical settings to system-level aggregation. The illustrated pressure factors, units, departments, hospitals, and systems can be used to support real-time pressure scoring, visualization, and prescribing action recommendations across multiple clinical and operational contexts.

[0172] Starting from the lower left of the figure, a set of region-specific pressure factors can be defined for each clinical region. For example, blocks 802, 804, 806, 808, and 810 show pressure factors associated with various medical settings such as ward units (IP), emergency departments (ED), imaging modalities (IM), treatment recovery (PR), and therapy departments (TH). These pressure factors may include real-time measures such as staffing delta, treatment wait time, scan preparation time, and patient throughput metrics. These values ​​can be calculated continuously or at fixed intervals and can be used as input to a pressure score scoring routine.

[0173] On each set of pressure factors, a clinical setting such as a unit or modality may be defined. As shown in block 812, the IP area may include multiple units (e.g., unit 1, unit 2, unit 3, unit 4), and each unit may be scored independently based on the corresponding pressure factors of that unit. Similarly, blocks 814 to 820 show the propagation of ED pressure factors to the ED compartment, the propagation of imaging pressure factors to modalities (CT, MRI, US, ultrasound), the propagation of treatment and recovery pressure factors to service areas (PACU, IR, catheterization room, endoscopy room), and the propagation of therapy pressure factors to service lines (PT, OT, SLP).

[0174] The pressure values ​​for each of these relatively lower-level entities can be normalized and then passed through a logical pressure aggregation method, as shown in blocks 824, 826, 828, 830, and 832. This logical method can proportionally combine the lower-level entity pressure values ​​using variable weights, inclusion rules, or critical thresholds. The output of these aggregations can become a departmental-level pressure index.

[0175] The departments are indicated at the E2 level, as seen in blocks 834 through 842. These departments may include groupings such as adult wards, emergency department, imaging, treatment and recovery, and various therapies. Each department receives pressure values ​​from its constituent subunits and propagates this pressure upward if it exceeds a threshold or if the most critical services are affected. For example, if both CT and MRI are experiencing high pressure, the imaging department as a whole can be classified as being under elevated pressure.

[0176] Next, departmental-level pressures can be aggregated at the hospital level. As shown in blocks 844 through 850, the logical pressure method can again be used to calculate an overall hospital pressure score. These scores can reflect current operational pressures across the facility and can simultaneously incorporate pressure signals from multiple areas. For example, rising pressures in ED, imaging, and procedure recovery can all contribute to the total pressure score of Hospital 1.

[0177] At the top level of the hierarchy, Block 854 shows how multiple hospitals (e.g., Hospital 1, Hospital 2) can be aggregated into a system-level pressure index. Block 852 shows that this system-level score can be derived using the same logical pressure methodology applied to the previous levels, enabling a consistent interpretation across the entire entity. The system-level score can be useful for healthcare system leaders, regional operations coordinators, or state-level managers when managing capacity and resource allocation across multiple hospitals.

[0178] Finally, the figure distinguishes between computational pressure methodologies and logical aggregation methods, as shown in explanatory block 856. Computational methods can be applied when pressure factors exist for a given entity and these pressure factors can be used to predict pressure based on AI or statistical models. Logical aggregation methods can be applied when pressure is derived from subordinate child entities that have direct pressure inputs. For example, a treatment and recovery department may use computational methods, while a system-level pressure score may use logical aggregation.

[0179] This hierarchical approach enables pressure tracking and escalation across multiple granular levels, supporting both current state assessment and future state prediction. The structure shown in Figure 8 can be embodied to provide context-based situational awareness and trigger escalation workflows, enabling proactive system management in complex healthcare environments.

[0180] To provide additional background for the various embodiments described herein, Figure 9 and the following discussion are intended to provide a brief general description of a suitable computing environment 900 in which the various embodiments described herein may be realized. Although each embodiment is described above in the general background of computer-executable instructions that can be run on one or more computers, those skilled in the art will recognize that these embodiments may also be realized in combination with other program modules or as a combination of hardware and software.

[0181] Generally, a program module includes routines, programs, components, and data structures that perform a specific task or embody a specific abstract data type. Furthermore, those skilled in the art will recognize that the method of the invention can be implemented in conjunction with other computer system configurations, such as single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, and distributed computing systems, as well as personal computers, handheld calculators, and microprocessor systems or programmable consumer electronic circuits, each of which can be operationally coupled to one or more related devices.

[0182] The illustrated embodiments of the embodiments described in this book can also be implemented in a distributed computing environment in which several tasks are performed by remote processing units connected via a communication network. In a distributed computing environment, program modules can reside in both local and remote memory storage.

[0183] Computing devices typically include a variety of media, which may include computer-readable storage media, machine-readable storage media, or communication media, and the two terms are used distinctly in this text as follows: Computer-readable storage media or machine-readable storage media may be any available storage media that can be accessed by a computer, and may include both volatile and non-volatile media, and removable and non-removable media. To give an example rather than an limitation, computer-readable storage media or machine-readable storage media may be embodied in any method or technique for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0184] Computer-readable storage media may include, but are not limited to, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital general-purpose discs (DVD), Blu-ray discs (BD), or other optical disc storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices, or other magnetic storage devices, solid-state drives, or other solid-state storage devices, or other tangible or non-transient media used to store desired information. In this regard, the terms “tangible” or “non-transient” as used herein in relation to storage devices, memory, or computer-readable media should be understood as modifiers that exclude only the propagating transient signal itself, and not as a waiver of rights to all standard storage devices, memory, or computer-readable media that do not consist solely of the propagating transient signal itself.

[0185] Computer-readable storage media can be accessed by one or more local or remote computing devices via, for example, access requests, queries, or other data retrieval protocols for a variety of operations relating to the information stored by the media.

[0186] Communication media typically include any information delivery or transmission medium that embodies computer-readable instructions, data structures, program modules, or other structured or unstructured data as modulated data signals, such as carrier waves or other transmission mechanisms. The term “modulated data signal” refers to a signal whose characteristics one or more are set or changed in such a manner that information is encoded as one or more signals. To give an example without limitation, communication media include wired media such as wired networks or direct wired connections, as well as wireless media such as sound waves, RF, infrared, and other wireless media.

[0187] Again with respect to Figure 9, example 900 of an environment embodying various embodiments of the perspectives described herein includes a computer 902, which includes a processing unit 904, system memory 906, and a system bus 908. The system bus 908 connects system components, including, but not limited to, system memory 906, to the processing unit 904. The processing unit 904 may be any of the various commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 904.

[0188] The system bus 908 may be any of several forms of bus architecture, which may further interconnect with memory buses (with or without memory controllers), peripheral buses, and local buses using any of the various commercially available bus architectures. The system memory 906 includes ROM 910 and RAM 912. The basic input / output system (BIOS) may be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, and the BIOS includes basic routines that help transfer information between internal elements of the computer 902, such as during startup. RAM 912 may also include high-speed RAM, such as static RAM, for caching data.

[0189] Computer 902 further includes an internal hard disk drive (HDD) 914 (e.g., EIDE, SATA), one or more external storage devices 916 (e.g., magnetic floppy disk drive [FDD] 916, memory stick, or flash drive reader and memory card reader, etc.), and drives 920, such as optical disc drives that can read and write to disks 922, such as solid-state drives, CD-ROMs, DVDs, and BDs. Alternatively, if a solid-state drive is included, disks 922 may not be included unless they are separate. Although the internal HDD 914 is shown as being located inside the computer 902, the internal HDD 914 may also be configured for external use in a suitable chassis (not shown). In addition, although not shown in environment 900, a solid-state drive (SSD) may be used in addition to or instead of the HDD 914. The HDD 914, external storage device 916, and drive 920 may be connected to the system bus 908 by the HDD interface 924, external storage device interface 926, and drive interface 928, respectively. Interface 924 for an embodiment of an external drive may include at least one or both of the Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within the scope of the embodiments described herein.

[0190] Drives and computer-readable storage media attached to these drives provide non-volatile storage of data, data structures, and computer-executable instructions. For computer 902, drives and storage media are suitable for storing any data in a suitable digital format. While the above descriptions of computer-readable storage media refer to the respective types of storage devices, those skilled in the art will understand that other types of computer-readable storage media, whether currently existing or to be developed in the future, may also be used in this example operating environment, and furthermore, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0191] Many program modules, including the operating system 930, one or more application programs 932, other program modules 934, and program data 936, may be stored in each drive and RAM 912. All or part of the operating system, applications, modules, or data may also be cached in RAM 912. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0192] Computer 902 may optionally include emulation techniques. For example, a hypervisor (not shown) or other intermediary means may emulate a hardware environment for operating system 930, and the emulated hardware may optionally differ from the hardware shown in Figure 9. In such embodiments, operating system 930 may include one virtual machine from a number of virtual machines (VMs) hosted on computer 902. Furthermore, operating system 930 may provide a runtime environment for application 932, such as a Java runtime environment or a .NET framework. The runtime environment is a consistent execution environment that enables application 932 to run on any operating system that includes the runtime environment. Similarly, operating system 930 may support containers, and application 932 may exist in the form of a container, which is a lightweight, standalone executable package of software including, for example, code, runtime, system tools, system libraries, and configuration for the application.

[0193] Furthermore, the computer 902 can also be made available with security modules such as a Trusted Processing Module (TPM). For example, according to the TPM, a boot component hashs the next boot component in time and waits for the result to be matched against a guaranteed value before loading the next boot component. This process can occur at any layer of the computer 902's code execution stack, for example, at the application execution level or the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0194] The user can input commands and information to the computer 902 through one or more wired / wireless input devices, such as a keyboard 938, a touch screen 940, and a pointing device such as a mouse 942. Other input devices (not shown) include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls or other remote controls, joysticks, virtual reality controllers or virtual reality headsets, gamepads, stylus pens, image input devices such as cameras, gesture sensor input devices, field of view movement sensor input devices, emotion or facial expression detection devices, or biometric authentication input devices such as fingerprint or iris scanners. These and other input devices are often connected to the processing unit 904 through an input device interface 944 which can be coupled to the system bus 908, but can also be connected through other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, and BLUETOOTH® interfaces.

[0195] In addition, a monitor 946 or other type of display device may be connected to the system bus 908 via an interface such as a video adapter 948. In addition to the monitor 946, the computer typically includes other peripheral output devices (not shown), such as speakers and printers.

[0196] Computer 902 may operate in a networked environment using logical connections via wired or wireless communication to one or more remote computers, such as remote computers 950. Remote computers 950 may be workstations, server computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and typically include many or all of the elements described for computer 902, although for brevity only memory / storage 952 is illustrated. The illustrated logical connections include wired / wireless connections to a local area network (LAN) 954 or a larger network, such as a wide area network (WAN) 956. Such LAN and WAN network environments are prevalent in offices and businesses, facilitating enterprise-wide computer networks such as intranets, and all of these networks may connect to global communication networks, such as the Internet.

[0197] When used in a LAN network environment, the computer 902 may be connected to the local area network 954 via a wired or wireless communication network interface or adapter 958. The adapter 958 can facilitate wired or wireless communication to the LAN 954 and may also include a wireless access point (AP) for communication with the adapter 958 in wireless mode.

[0198] When used in a WAN network environment, computer 902 may include a modem 960 or connect to a communication server located on the WAN 956 via other means, such as the Internet, to establish communication via the WAN 956. The modem 960 may be an internal or external wired or wireless device and may be connected to the system bus 908 via an input device interface 944. In a network environment, program modules illustrated with respect to computer 902 or a part of computer 902 may be stored in a remote memory / storage device 952. The illustrated network connection is an example, and other means may be used to establish communication connections between computers.

[0199] Whether used in a LAN or WAN network environment, computer 902 can access, in addition to or instead of the external storage device 916 described above, a cloud storage system or other network-type storage system, such as a network virtual machine, which provides one or more aspects of information storage or processing. Generally, the connection between computer 902 and the cloud storage system can be established via LAN 954 or WAN 956, for example, by an adapter 958 or modem 960, respectively. When computer 902 is connected to the relevant cloud storage system, the external storage interface 926 can manage the storage provided by the cloud storage system, as with other forms of external storage, with the help of the adapter 958 or modem 960. For example, the external storage interface 926 may be configured to provide access to cloud storage sources as if these sources were physically connected to computer 902.

[0200] Computer 902 may be capable of communicating with any wireless device or entity operating in a wireless communication network, such as a printer, scanner, desktop or portable computer, personal digital assistant, communication satellite, any equipment or location associated with a wirelessly discoverable tag (e.g., a kiosk, newspaper stand, and merchandise display shelf), and a telephone. This may include Wireless Fidelity (Wi-Fi) wireless technology and Bluetooth® wireless technology. Thus, the communication may be a predetermined structure similar to conventional networks, or simply ad-hoc communication between at least two devices.

[0201] Figure 10 is a schematic block diagram of an example computing environment 1000 in which the disclosed subject matter may interact. The example computing environment 1000 includes one or more clients 1010. Client 1010 may be hardware or software (e.g., threads, processes, computing devices). The example computing environment 1000 also includes one or more servers 1030. Server 1030 may also be hardware or software (e.g., threads, processes, computing devices). Server 1030 may house threads that perform translations using one or more embodiments, such as those described herein. One possible communication between client 1010 and server 1030 may be in the form of data packets configured to be transmitted between two or more computer processes. The example computing environment 1000 includes a communication framework 1050 that may be used to facilitate communication between client 1010 and server 1030. Client 1010 may operate connected to one or more client data stores 1020 that may be used to store information locally for client 1010. Similarly, server 1030 may also operate connected to one or more server data stores 1040 that can be used to store information locally for server 1030.

[0202] Various embodiments may be systems, methods, apparatus, or computer program products at any possible level of technical detail of integration. A computer program product may include a computer-readable storage medium having computer-readable program instructions that cause a processor to carry out aspects of various embodiments. The computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random-access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random-access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital general-purpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooves containing instructions, and any appropriate combination thereof. As used in this document, computer-readable storage media should not be interpreted as radio waves or other free-propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through optical fiber cables), or transient signals themselves, such as electrical signals transmitted through lines.

[0203] The computer-readable program instructions described in this book can be downloaded from a computer-readable storage medium to each computer / processor, or they can be downloaded to an external computer or external storage device via a network, such as the Internet, local area network, wide area network, or wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers. Each computer / processor's network adapter card or network interface receives computer-readable program instructions from the network and transfers them for storage in the computer-readable storage medium built into each computer / processor. Computer-readable program instructions for performing the operations of various embodiments may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk or C++, and procedural programming languages ​​such as the C programming language or similar programming languages. Computer-readable program instructions may run entirely on the user's computer, partially on the user's computer, run as a standalone software package, partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, including a local area network (LAN) or wide area network (WAN), or the connection may be made to an external computer (e.g., via the Internet using an Internet Service Provider).In some embodiments, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can execute computer-readable program instructions by specially configuring the electronic circuit using computer-readable program instruction state information to perform various functions.

[0204] Various perspectives are described in this document with respect to flowcharts or block diagrams of methods, apparatus (systems), and computer program products in various embodiments. It will be understood that each block of a flowchart or block diagram, and each combination of blocks in a flowchart or block diagram, can be embodied by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to generate a machine such that instructions operating through the processor of the computer or other programmable data processing device generate means to embody the actions / operations specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium to instruct a computer, a programmable data processing device, or other device to operate in a particular manner, and the computer-readable storage medium storing the instructions constitutes a product containing instructions that embody each perspective of the actions / operations specified in one or more blocks of the flowchart or block diagram. Computer-readable program instructions can also be loaded into a computer, other programmable data processing device, or other device to perform a series of actions in the computer, other programmable device, or other device to generate a computer-implemented process, where the instructions operating in the computer, other programmable device, or other device embody the actions / operations specified in one or more blocks of a flowchart or block diagram.

[0205] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products in various embodiments. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions that implement a specified logical action. In some alternative implementations, the actions described in a block may occur in an order other than that shown in the drawing. For example, two blocks shown consecutively may actually be executed substantially simultaneously depending on their related actions, or each block may be executed in reverse order. It should also be noted that each block in a block diagram or flowchart, and each combination of blocks in a block diagram or flowchart, may be implemented by a special-purpose hardware system that performs a specified action or operation, or a combination of special-purpose hardware and computer instructions.

[0206] While the subject matter is described in the general context of computer-executable instructions for computer program products running on one or more computers, those skilled in the art will recognize that this disclosure can also be embodied in combination with other program modules. Generally, a program module includes routines, programs, components, and data structures, etc., that perform a specific task or embody a specific abstract data type. Furthermore, those skilled in the art will recognize that various aspects can be implemented in combination with single-processor or multi-processor computer systems, small calculators, mainframe computers, and other computer system configurations, including computers, handheld calculators (e.g., PDAs, telephones), and microprocessor-based electronic circuits or programmable consumer or industrial electronic circuits. The illustrated aspects can also be implemented in distributed computing environments where tasks are performed by remote processing units connected via a communication network. However, some, if not all, aspects of this disclosure can be implemented in standalone computers. In distributed computing environments, program modules can reside in both local and remote memory storage devices.

[0207] As used in this application, terms such as “component,” “system,” “platform,” and “interface” may refer to or include computer-related entities or entities relating to operating machines having one or more specific functions. The entities disclosed herein may be hardware, a combination of hardware and software, software, or runtime software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. For example, an application running on a server, or a server itself, may be a component. One or more components may reside within a single process or execution thread, or a single component may be localized in a single computer or distributed across two or more computers. In another example, each component may be executed from various computer-readable media storing various data structures. These components may communicate via local or remote processes, for example, by following signals containing one or more data packets (e.g., data from one component interacting with other components across a network such as the Internet, in a local or distributed system, or via signals to other systems). As another example, a component may be a device having specific functionality provided by mechanical parts operating through electrical or electronic circuits that operate through software or firmware applications executed by a processor. In such an example, the processor may be built into or external to the device and may execute at least part of the software or firmware application. As yet another example, a component may be a device that provides specific functionality through electronic components without having mechanical parts, and these electronic components may include a processor or other means that execute software or firmware that at least partially imparts the functionality of the electronic components.From one perspective, components can emulate electronic parts via virtual machines, for example, within a cloud computing system.

[0208] In addition, the term “or” shall mean an immanent “or” rather than an exclusive “or.” That is, unless otherwise specified or evident from the context, “X uses A or B” means any of the natural immanent permutations. That is, if X uses A, or X uses B, or X uses both A and B, then “X uses A or B” is satisfied under any of these examples. The term “and / or” as used herein shall have the same meaning as “or.” Furthermore, the indefinite article used in the subject matter specification and accompanying drawings shall generally mean “one or plural” unless otherwise specified or evident from the context. The terms “example” or “exemplary” as used herein are used to mean an example, example, or explanatory example. To avoid doubt, the subject matter disclosed herein is not limited by such examples. In addition, any viewpoint or design described herein as “example” or “exemplary” should not necessarily be interpreted as being superior or more advantageous than other viewpoints or designs, nor should it preclude equivalent exemplary structures and techniques known to those skilled in the art.

[0209] The disclosures in this book provide non-restrictive examples. For ease of description or explanation, various parts of the disclosures use the terms “each,” “one by one,” or “all” when discussing various examples. Such use of the terms “each,” “one by one,” or “all” is non-restrictive. In other words, whereever the disclosures in this book provide a statement that applies to “each,” “one by one,” or “all” of several specific subjects or components, this statement should be understood as a non-restrictive example, and furthermore, it should be understood that in various other examples, such statements may apply to fewer specific subjects or components than “each,” “one by one,” or “all.”

[0210] Where used in the subject matter specification, the term “processor” can refer to substantially any computing unit or device, such unit or device including, but not limited to, single-core processors, single-processors with software multithreading capabilities, multi-core processors, multi-core processors with software multithreading capabilities, multi-core processors with hardware multithreading technology, parallel platforms, and parallel platforms with distributed shared memory. In addition, a processor may refer to integrated circuits, application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic controllers (PLCs), complex programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Furthermore, a processor may utilize nanoscale architectures such as molecular and quantum dot transistors, switches, and gates, but not limited to, to optimize space utilization or enhance the performance of user equipment. A processor may also be embodied as a combination of computing units. In this disclosure, terms such as “store,” “storage device,” “data store,” “data storage device,” and “database,” as well as substantially any other information storage component relating to the operation and function of the component, refer to entities embodied as “memory component,” “memory,” or components constituting memory. Please note that the memory or memory component described herein may be either volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. To give an example rather than an limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random-access memory (RAM) (e.g., ferroelectric RAM [FeRAM]). Volatile memory may include RAM that can operate as external cache memory, for example.To give examples rather than limitations, RAM is available in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), data-double-speed SDRAM (DDR SDRAM), extended SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). In addition, the memory components disclosed in this document for system or computer implementations include, but are not limited to, these and any other suitable forms of memory.

[0211] The above description contains only examples of systems and computer-implemented methods. Needless to say, it is impossible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing this disclosure, and many further combinations and permutations of this disclosure are possible. Furthermore, wherever terms such as “includes,” “has,” and “possesses” are used in the detailed description, claims, appendices, and drawings, such terms are intended to be inclusive in the same manner as the terms “comprising” are used when “comprising” is used as a substitute term in the claims.

[0212] For illustrative purposes, various embodiments have been described, but these descriptions are neither exhaustive nor limited to the embodiments disclosed. Many modifications and variations will become apparent without departing from the scope and spirit of the embodiments described herein. The terminology used herein has been selected to best describe the principles, practical applications, or technical improvements to the technologies available on the market, or to enable those skilled in the art to understand the embodiments disclosed herein. [Explanation of Symbols]

[0213] 100 A system that can facilitate real-time prediction, scoring, and mitigation of operational pressures. 200 Methods that can facilitate forecasting and manage operational pressures 300 Methods that can facilitate the prediction and management of operational pressures in healthcare systems 400 User interface that visualizes pressure forecasts, current operating pressure levels, and prescriptive actions. 500 Pressure layers specific to each sector and corresponding expected pressure factors 600 A flowchart illustrating how operational pressures are processed and transformed into prescriptive behavioral recommendations. 700-level pressure aggregation model 800 A hierarchical framework for calculating, aggregating, and interpreting pressure within the healthcare network. Computational environment for realizing the 900 embodiment 902 Computer 1000 Practical Calculation Environments

Claims

1. A processor that executes computer-executable components stored in non-transient, computer-readable memory. A system comprising the computer executable component, A data ingestion component that receives operational data associated with multiple medical entities within the hospital system, A pressure modeling component that calculates a real-time pressure score for the medical entity based on department-specific pressure factors and weightings, A predictive component that applies an artificial intelligence model to predict the pressure score for the medical entity over a future time window, A scoring component that assigns pressure levels to medical entities using proportional logic or override logic based on critical factors or critical entities, A behavior recommendation component that identifies one or more prescriptive actions that reduce or prevent predictive pressure, each of which is associated with an influence score, a cost score, and trigger conditions; A user interface component that displays a graphic interface including pressure prediction visualization, dynamic multiple location overview, and one or more selectable actions. A system that includes this.

2. The system according to claim 1, wherein the pressure modeling component normalizes the pressure factor values ​​to percentile scores.

3. The system according to claim 1, wherein the prediction component generates hourly pressure score predictions for each eligible medical entity over a predicted period.

4. The system according to claim 1, wherein the score-scoring component propagates pressure levels from lower entities to parent entities.

5. The system according to claim 1, wherein the score scoring component overrides the pressure score based on a critical factor that exceeds a set threshold.

6. The system according to claim 1, wherein the behavior recommendation component ranks the prescriptive behaviors based on a composite value of the influence score and the cost score.

7. The system according to claim 1, wherein the user interface component displays a number of multiple locations at once, each including an outline of one or more pressure-driven elements.

8. The system according to claim 1, wherein the user interface component includes a selectable icon for launching a chat agent for scenario analysis based on predicted pressure.

9. The system according to claim 1, wherein the selectable actions include cleaning a patient bed, initiating transport, reallocating personnel, or lowering the level of medical care.

10. The steps include receiving operational data from multiple medical entities within the hospital system, A step of calculating a pressure score for the medical entity based on a weighted sum of normalized pressure factor values, The steps include: predicting future pressure scores using an artificial intelligence model trained on historical pressure data, and The process involves assigning pressure levels to medical entities using override logic for critical factors and proportional logic for collective entities, and A step of identifying prescriptive actions that address current or anticipated pressures, each of which has trigger conditions, an impact score, and a cost score. Steps include displaying pressure prediction visualizations, an overview of high-incidence locations, and selectable recommended actions via a graphical user interface. A computer-implemented method equipped with [a specific feature / feature].

11. The method according to claim 10, further comprising the step of updating the predicted pressure score at predetermined time intervals and storing historical pressure data.

12. The method according to claim 10, wherein the step of assigning the pressure level includes categorizing the pressure as “normal,” “medium,” “rising,” or “high” based on the pressure score.

13. The method according to claim 10, further comprising the step of generating a ranked list of actions based on priority level and impact-to-cost ratio.

14. The method according to claim 10, further comprising the step of enabling a user to click to view linked patient lists, bed availability, or staffing resources.

15. The method according to claim 10, further comprising the step of displaying predicted pressure using a bar graph color-coded with a variable gradient.

16. A computer program product for predicting and managing operational pressures in a medical system, comprising non-transient, computer-readable memory that embodies program instructions, said program instructions We receive operational data from multiple medical entities. Based on department-specific pressure factor scores and weightings, a real-time pressure score is generated. Using an artificial intelligence model trained on historical data, we predict future pressure conditions for each entity. Using override logic and proportional logic, assign a pressure level to each entity. Identify prescriptive actions based on relevant trigger conditions, influence scores, and cost scores. Display pressure forecasts, high-pressure locations, and selectable action recommendations within a graphical user interface. A computer program product that is executable by a processor.

17. The computer program product according to claim 16, wherein the pressure factor score includes scores for net vacant beds, staffing delta, imaging preparation time, or patient transport delays.

18. The computer program product according to claim 16, wherein the artificial intelligence model includes a deep neural network trained on historical hospital operation data.

19. The computer program product according to claim 16, wherein the prescriptive action includes opening an emergency treatment unit, adjusting staffing, reprioritizing treatment schedules, or initiating a downgrade in the ICU class.

20. The computer program product according to claim 16, wherein the graphic user interface enables the user to explore predicted pressures hourly, by department, or by unit, and to simulate alternative operational scenarios using an integrated AI agent.

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