Oxygen energy-saving metering and cost accounting system and method based on physiological parameters of patient

Abstract

The invention discloses an oxygen energy-saving metering and cost accounting system and method based on physiological parameters of a patient. An intelligent acquisition unit is arranged at an oxygen using bed, parameters such as oxyhemoglobin saturation, end-expiratory carbon dioxide and respiration are monitored in real time, an SpO2-respiration double-phase closed-loop control algorithm is adopted, oxygen is supplied according to needs in the inspiration phase, oxygen supply is stopped in a reduced mode in the expiration phase, and precise oxygen therapy is achieved. According to the system, medical advice flow is taken as a benchmark, actual flow high-frequency collection is combined, and actual oxygen consumption, oxygen saving amount and cost saving of each bed are accurately calculated through time integration. Based on a hierarchical database and an intelligent engine, multi-level and multi-dimensional data aggregation, visualization and benefit analysis from patients to the whole hospital are supported, the problems that traditional oxygen supply is extensive, lagged and difficult to trace are solved, refined, real-time and valuable management of oxygen use is achieved, and the resource efficiency is remarkably improved while the curative effect is guaranteed.

Description

Technical Field

[0001] This invention relates to the fields of smart healthcare, hospital resource management, and the Internet of Things. Specifically, it relates to a system and method that enables on-demand oxygen supply through closed-loop feedback control, automatically and accurately measures energy-saving physical quantities and equivalent economic value, and supports multi-level refined management. Background Technology

[0002] Oxygen consumption in a hospital's central oxygen supply system represents a significant portion of the hospital's daily operating costs. Currently, mainstream oxygen management models face two major bottlenecks: 1. Inefficient Clinical Oxygen Supply: Healthcare professionals often set a fixed, conservative oxygen flow rate for patients based on experience to ensure safety. However, once a patient's blood oxygen saturation (SpO2) stabilizes, a continuous, fixed flow rate not only lacks physiological necessity but also results in significant resource waste and may lead to iatrogenic risks such as oxygen toxicity. Existing automatic regulation equipment is also mostly based on a single blood oxygen parameter, without considering the respiratory cycle, leading to ineffective waste of expiratory oxygen.

[0003] 2. Management statistics are lagging and lack granularity: Current management methods rely heavily on manual labor or simple master meter readings. The logistics department can usually only read the total flow meter readings of the oxygen station on a monthly basis to obtain the total consumption of the entire hospital, and then roughly estimate the expenses of each department through cost allocation.

[0004] The applicant has successively proposed several technical solutions, including ZL 201510234314.2 and ZL 201810324681.5, which dynamically monitor blood oxygen levels and adjust oxygen flow based on a set target blood oxygen value, effectively addressing the shortcomings of fixed-flow oxygen therapy in terms of clinical treatment effectiveness and safety. However, these methods do not solve the problem of refined management of oxygen consumption and still have the following deficiencies: (1) Untraceable: It is completely impossible to distinguish between “necessary treatment consumption” and “management waste”.

[0005] (2) Unable to locate: The consumption cannot be accurately associated with a specific patient, bed or treatment event.

[0006] (3) Cannot be real-time: The statistical period is long (usually monthly), and it cannot provide dynamic data at the daily or even hourly level to support real-time decision-making.

[0007] (4) Inability to assess the effectiveness of interventions: The effectiveness of any energy-saving measures (such as education and equipment upgrades) cannot be quantitatively assessed due to the lack of fine-grained data.

[0008] (5) Single control dimension: Key respiratory metabolic parameters such as end-tidal carbon dioxide (etCO2) and respiratory status information are not integrated, oxygen supply is not synchronized with the patient's respiratory cycle, and energy-saving potential is not fully explored.

[0009] It is evident that hospital oxygen management has long been a "black box," lacking the technological foundation for truly refined and data-driven management. Therefore, although the concept of oxygen therapy equipment with automatic flow regulation exists, systematically integrating it and constructing a method and system capable of automatically and accurately measuring energy-saving physical quantities and their economic value based on multiple physiological parameters, synchronized with the respiratory cycle, remains a challenge that current technology has not yet solved. Summary of the Invention

[0010] To address the shortcomings of existing hospital oxygen management practices, such as inefficient clinical supply, limited control dimensions, coarse statistical granularity, outdated data, and the inability to quantify energy savings into economic value, this invention provides an oxygen energy-saving metering and cost accounting system and method. This system and method can achieve on-demand oxygen supply synchronized with the respiratory cycle based on individual patient blood oxygen levels and respiratory status. It accurately measures oxygen savings at all levels from the bedside to the entire hospital in real time and automatically calculates the cost savings simultaneously. This solves the problems that conventional manual statistical methods cannot accomplish in terms of data real-time performance, computational complexity, measurement dimensions, and economic value mapping.

[0011] The technical solution adopted in this invention is as follows: An oxygen energy-saving metering and cost accounting system, characterized in that it includes: 1. Patient-level intelligent control and data acquisition unit: Deployed at each oxygen bed, including a flow regulating actuator, a blood oxygen saturation sensor, an embedded processor, and a first communication module; the embedded processor is configured to execute a SpO2 closed-loop control algorithm, including: dynamically adjusting the output flow of the flow regulating actuator with a preset target blood oxygen saturation range as the control target and an initial set flow rate as the control upper limit, so that the patient's blood oxygen is maintained within the preset target range; used to realize closed-loop control of oxygen flow based on the patient's physiological parameters, and to acquire, with high time resolution, the initial set flow rate value, real-time actual flow rate value, real-time blood oxygen saturation value, pulse rate value, and their corresponding timestamps, forming raw data packets for each oxygen bed in the patient-level unit and uploading them to the server.

[0012] Furthermore, the patient-level intelligent control and data acquisition unit also includes one or both of an end-tidal carbon dioxide (etCO2) sensor or a respiratory status monitoring module. The respiratory status monitoring module is used to monitor the patient's respiratory cycle in real time, distinguishing between the inspiratory and expiratory phases. The embedded processor is configured to execute a SpO2-respiratory biphasic closed-loop control algorithm: using a preset target blood oxygen saturation range and a target etCO2 range as control targets, when the patient's inspiratory phase is detected, the output oxygen is dynamically adjusted according to the deviation of physiological parameters; during the expiratory phase, the flow rate output is stopped or reduced to maintain the patient's blood oxygen and etCO2 within the preset target range. Simultaneously, each oxygen bed acquires data at high temporal resolution, including the initial set flow rate value, real-time actual flow rate value, real-time blood oxygen saturation value, pulse rate value, etCO2, respiratory status, and their corresponding timestamps, forming raw data packets for each oxygen bed in the patient-level unit and uploading them to the server.

[0013] 2. Data Layer Aggregation and Storage Unit: Composed of a server cluster, database system, and communication module, it is responsible for receiving, classifying, and storing data from the patient layer unit, including: Receive and store clinical data, including real-time oxygen saturation (SpO2), pulse rate, end-tidal carbon dioxide (etCO2), respiratory status (inspiratory / expiratory) and timestamps, for clinical monitoring and retrospective analysis.

[0014] Receive and store flow calculation data, including the initial set flow value for each bed, the real-time actual flow value and its timestamp, as the raw input for energy saving metering and cost accounting.

[0015] The database system adopts a hierarchical architecture, including local databases at the department / ward level and a central database at the hospital level, to achieve hierarchical storage, backup and secure sharing of data.

[0016] 3. Data Layer Intelligent Computing Unit: Composed of a computing engine deployed on a server, responsible for retrieving data from the aggregation and storage unit and performing core calculations for energy-saving metering and cost accounting. The calculations include: calculating the theoretical consumption based on the initial set flow rate value, calculating the actual consumption based on the real-time actual flow rate value, calculating the amount of oxygen saved, and calculating the cost savings based on the pre-stored oxygen price information.

[0017] The computing engine is configured to perform the following core computing methods: (1) Basic energy-saving measurement model: used to calculate the theoretical consumption, actual consumption and oxygen saving of any bed in the calculation period T, and to calculate energy-saving related physical quantities through the following formulas: - Theoretical consumption Q_t = initial set flow rate × cycle duration T; - Actual consumption Q_a = Time integral of all real-time actual flow values ​​collected within period T; - The amount of oxygen saved is ΔQ = Q_t - Q_a.

[0018] (2) Economic benefit mapping model: This model uses pre-stored or real-time oxygen price information P (unit: yuan / liter or yuan / cubic meter) to calculate the cost savings based on the amount of oxygen saved and the unit price of oxygen. - Cost savings ΔC = ΔQ × P.

[0019] (3) Multi-level aggregation calculation model: Based on the bed affiliation relationship (such as department, ward, hospital), the oxygen saving amount ΔQ and cost saving amount ΔC of each level are aggregated upwards to generate the total saving amount and total cost saving amount of each level; used to aggregate the oxygen saving amount and cost saving amount according to the hospital's organizational structure.

[0020] (4) Clinical data correlation analysis: Energy-saving data (ΔQ, ΔC) can be correlated with clinical data (SpO2 trend, etCO2 trend, respiratory rate, etc.) collected at the same time to evaluate the treatment effect, ventilation status and energy-saving safety.

[0021] 4. Application Layer Visualization and Management Unit: This unit includes various management terminals (such as computers, large-screen displays, and mobile terminals) for displaying multi-dimensional data dashboards, analytical reports, and early warning information. The displayed content covers oxygen consumption data at the individual patient, department, ward, and hospital-wide levels, oxygen savings, cost savings ΔC, and trend charts of clinical physiological parameters related to the savings data, including SpO2-etCO2 trend charts and respiratory waveforms. This allows managers to monitor oxygen usage, patient respiratory physiological status, and energy conservation in real time, enabling refined management that integrates clinical and logistical aspects.

[0022] This invention also provides a method for oxygen energy-saving metering and cost accounting, characterized in that the method is implemented by the aforementioned system and differs from any feasible manual statistical method in its methodological structure. The method comprises the following inseparable combination of steps: S1: High-frequency real-time multi-parameter data acquisition layer: Synchronously acquires "initial set flow rate value", "real-time changing actual flow rate value", and "real-time physiological parameters of clinical patients such as real-time blood oxygen saturation, pulse rate, end-tidal carbon dioxide concentration and respiratory state (inspiration / expiration)" from each oxygen bed.

[0023] S2: SpO2 closed-loop control or SpO2-breathing biphasic dynamic closed-loop control and benchmark separation layer: Based on the real-time physiological parameters, execute the SpO2 closed-loop control or SpO2-breathing biphasic closed-loop control algorithm, taking the "initial set flow rate" as the theoretical consumption benchmark, corresponding to the "medical order intention". According to the deviation of one or both of the measured SpO2 or etCO2 data from the target value, dynamically adjust the actual oxygen output. Through this design, the "theoretical value" and "actual value" are separated at the physical level, laying the foundation for subsequent measurement comparison; or, based on real-time monitoring of the patient's respiratory cycle, adjust the oxygen supply flow rate in the inspiratory phase according to the deviation of blood oxygen saturation and end-expiratory carbon dioxide value from the target range, and stop or minimize the oxygen supply flow rate in the expiratory phase.

[0024] S3: Data Classification, Storage, and Real-Time Calculation Layer: The collected data is classified and stored. Based on the initial set flow rate and the real-time actual flow rate, the amount of oxygen saved is calculated through time integration and difference calculation. This, combined with the oxygen unit price, is then used to calculate the cost savings. Specific steps are as follows: S31: Classify the uploaded data streams, and store clinical data (blood oxygen, pulse rate, etCO2, respiratory status) and flow calculation data (set flow rate, actual flow rate) in the corresponding locations in the database respectively.

[0025] S32: For each bed, the result of time integration of all "real-time actual flow values" within the calculation period is the continuous actual consumption Q_a.

[0026] S33: The product of the constant "initial set flow rate value" and the calculation cycle duration is used as the theoretical consumption Q_t.

[0027] S34: Perform the difference calculation ΔQ=Q_t-Q_a to obtain the savings amount with physical meaning.

[0028] S35: Introducing the unit price of oxygen P, performing the multiplication calculation ΔC=ΔQ×P, instantly mapping the physical quantity to economic value. This step achieves a direct and automatic link between resource consumption and financial management.

[0029] S4: Multidimensional and Multilevel Aggregation Analysis Layer: Based on the hospital's organizational structure, the oxygen savings and cost savings are aggregated and correlated at multiple levels according to time (hour, day, week, month, year), spatial (bed, ward, area, department, hospital-wide), and management (patient, disease, doctor group), and the results are visualized. The correlation analysis includes linking energy-saving data with concurrently collected trends in blood oxygen saturation and end-tidal carbon dioxide to assess treatment safety and energy-saving effects. Manual statistics cannot perform such complex, real-time aggregation calculations across so many dimensions.

[0030] The beneficial effects of this invention are that, compared with conventional manual statistical methods, it achieves breakthrough improvements in measurement accuracy, management dimensions, value transformation, and management models. Specific beneficial effects are as follows: 1. Improved measurement accuracy and real-time performance Traditional manual methods rely on manually copying the summary table at the end of the month, resulting in data lag of at least one month and only static summaries. This invention achieves second-level data acquisition and minute-level calculation updates, and can present the energy-saving data of any accounting unit (such as beds or departments) for the current hour and from the present day in real time, providing timely and accurate data support for dynamic management.

[0031] 2. The leap in measurement dimensions from "chaotic summation" to "clear source tracing" Traditional manual methods can only obtain monthly total data for the entire hospital or department, failing to distinguish between therapeutic consumption and waste, or pinpoint specific beds or patients. This invention can precisely trace the savings and costs of each patient and each oxygen administration event, clearly answering refined management questions such as "Which ward has the best energy-saving effect?" and "Which patient's treatment plan adjustment brought the greatest energy-saving benefit?", providing data support for optimizing treatment plans and improving management efficiency. This step requires parallel processing of massive multi-dimensional data streams from terminals, which is impossible with existing manual statistics.

[0032] 3. Achieve lossless conversion and automatic accounting of physical quantities into economic value. Traditional manual methods can only count the total consumption, and the cost needs to be allocated and estimated by the finance department later, which deviates from the actual savings. This invention, through a built-in economic benefit mapping model, automatically and synchronously converts the saved oxygen volume (liters) into RMB (yuan) in real time, providing direct and accurate financial data for departmental cost accounting and energy-saving benefit assessment, and realizing the efficient transformation of technical management results into economic management language.

[0033] 4. Dual enhancement in control dimensions and security Traditional or single-parameter control methods do not consider the respiratory cycle and etCO2, resulting in wasted oxygen during the expiratory phase and insufficient monitoring of ventilation status. This invention introduces etCO2 monitoring and respiratory status sensing to achieve biphasic closed-loop control of SpO2 and respiration, synchronizing oxygen delivery with the patient's inspiratory needs and actively saving energy during the expiratory phase. Simultaneously, etCO2 monitoring ensures ventilation safety, achieving a fundamental transformation from "continuous gas supply" to "synchronized respiratory gas supply" and from "single blood oxygen feedback" to "multi-parameter feedback of blood oxygen and ventilation." This improves treatment safety while unlocking deeper energy-saving potential.

[0034] 5. A fundamental shift in management model from "reactive after the fact" to "proactive during the process". Traditional manual accounting methods are reactive and lack real-time intervention and optimization. This invention enables proactive performance management during the process. Managers can set departmental energy-saving targets and conduct year-on-year and month-on-month analyses by monitoring the "Cost Savings Dashboard" and "Respiratory Physiology Dashboard" in real time. This makes oxygen management a measurable, assessable, and optimizable daily operational process, effectively improving the level of lean management of hospital resources.

[0035] In summary, this invention surpasses conventional manual statistical methods in three aspects: data generation source (high-frequency multi-parameter acquisition and respiratory synchronization closed-loop control), core calculation process (integral calculation and benchmark comparison), and output results (multidimensional aggregation and value mapping). It solves tasks that traditional methods cannot accomplish in principle, promotes the digital, refined, and value-oriented transformation of hospital oxygen resource management, and has significant economic and social value. Attached Figure Description

[0036] Figure 1 : A schematic diagram of the overall system architecture and data flow of this invention.

[0037] Figure 2 Flowchart of the working logic of the patient-level intelligent control unit (SpO2-breathing biphasic closed-loop control and data generation).

[0038] Figure 3 Flowchart of the core algorithm (energy saving metering and cost accounting model) of the data layer intelligent computing unit.

[0039] Figure 1 This invention reveals how it solves the problems of high-frequency data collection, real-time integral calculation, and refined cost accounting that are impossible to achieve through traditional manual methods, by using a four-layer decoupled architecture (patient-layer intelligent control and data acquisition unit, data layer aggregation and storage unit, data layer intelligent computing unit, and application layer visualization and management unit) and a real-time data value mapping engine. Core data flow description: - Downlink configuration command (dashed line): Sends the target blood oxygen range, etCO2 range and initial flow settings from the application layer to the specific bed device.

[0040] - Uplink high-frequency multi-parameter data stream (thick solid line): The core data stream of the system, in which each bed device continuously uploads multi-dimensional data such as initial settings, real-time actual flow, blood oxygen saturation, pulse rate, end-tidal carbon dioxide, respiratory status and timestamps to the data aggregation and storage unit.

[0041] - Core calculation process (within the engine): The intelligent computing unit retrieves data from the storage unit and performs parallel calculations of the theoretical consumption Q (constant initial flow rate × time) and the actual consumption Q_a (integrating the real-time flow rate); calculates the oxygen saving ΔQ = Q_t - Q_a; and automatically calculates the cost saving ΔC = ΔQ × P based on the preset unit price P.

[0042] - Results aggregation and display: The calculation results are aggregated in multiple times and in space by bed, ward, and hospital as a whole, and the physical savings ΔQ and economic value ΔC are pushed to each management terminal at the same time.

[0043] - Local SpO2-breathing biphasic closed-loop control (circular arrow): Each terminal runs the biphasic control algorithm independently. Based on the measured respiratory state, blood oxygen and the deviation of etCO2 from the target range, the flow output is dynamically adjusted during the inspiratory phase and the output is stopped during the expiratory phase, forming a local closed loop.

[0044] This block diagram illustrates how the present invention solves the problems of high-frequency data collection, real-time integral calculation, and refined cost accounting that cannot be achieved by traditional manual methods through a multi-layer decoupled architecture and a real-time data value mapping engine.

[0045] Figure 2 This paper reveals how the device achieves dynamic regulation of SpO2-breathing biphasic processes and generates key measurement data. The core steps of the process are explained below: - Parameter settings: Key parameters are input by medical staff: - Initial flow rate (F_set): serves as the theoretical benchmark for energy-saving calculations.

[0046] - Target blood oxygenation range (SpO2_target) and target etCO2 range (etCO2_target).

[0047] - SpO2-breathing biphasic closed-loop control of the main circulation: 1. Respiratory state assessment: Real-time monitoring of the respiratory cycle to determine whether the current phase is in the inspiratory phase (Insp) or the expiratory phase (Exp).

[0048] 2. Expiratory phase processing: If it is the expiratory phase, the control flow valve output is zero or extremely low maintenance flow to achieve energy saving in the expiratory phase.

[0049] 3. Getter phase treatment: If it is an getter phase (Insp): - Monitoring: Real-time blood oxygen (SpO2_now) and real-time etCO2 (etCO2_now) are collected.

[0050] - Decision and Execution: Compare SpO2_now and etCO2_now with their respective target ranges.

[0051] - If any parameter is below the target: rapidly increase oxygen supply at a flow rate not exceeding F_set.

[0052] - If all are within the target range: intelligently adjust to a lower maintenance flow (F_maintain).

[0053] - If the oxygen level is higher than the target: reduce or suspend oxygen supply.

[0054] This control logic of "supplying only during the intake phase on demand" is the fundamental reason for the additional energy-saving effect.

[0055] - Parallel data generation: - In each control loop, the system synchronously and independently records and packages two types of data: 1. Clinical data: real-time blood oxygen saturation (SpO2_now), pulse rate (PR_now), end-tidal carbon dioxide level (etCO2_now), respiratory state (Phase) and its timestamp.

[0056] 2. Flow calculation data: constant theoretical baseline (F_set) and actual flow value after each adjustment (F_actual).

[0057] - Data is bound to timestamps and bed IDs to form structured data packets with complete traceability information.

[0058] - Data Upload: The encapsulated data packets are uploaded to the data aggregation and storage unit in real time via wireless network.

[0059] The core features that cannot be accomplished manually, as illustrated in this flowchart: - Real-time control of synchronized breathing: Control decisions and execution are made in rhythm with the respiratory cycle (usually on the order of seconds), which cannot be achieved manually.

[0060] - Multi-parameter fusion and biphasic logic: Simultaneously integrates three parameters: blood oxygen, etCO2, and respiratory state, and executes biphasic logic of "inspiratory supply, expiratory stop", which has control precision and energy efficiency far exceeding that of single-parameter continuous control.

[0061] - Control the synchronization of multi-dimensional data acquisition.

[0062] - Structured separation and classification of data.

[0063] - High-frequency, multi-dimensional data generation.

[0064] Figure 3 This reveals how intelligent computing units transform massive amounts of raw data into energy-saving and cost-effective results that have management value. Key process steps explained: - Data retrieval: Retrieve flow calculation data (for measurement) and clinical data (for correlation analysis) from the data aggregation and storage unit on demand.

[0065] - Single bed cycle calculation (core algorithm): - Dual-path parallel computing: theoretical consumption Q (simple multiplication) and actual consumption Q_a (time integration) - Difference calculation: ΔQ = Q_t - Q_a, to obtain the physical energy saving. - Value mapping: ΔC = ΔQ × P, converting physical quantities into economic value. - Multidimensional aggregation analysis: - Spatial dimension: From the bed level upwards to the entire hospital level - Time dimension: Summarized from hourly to grade level. - Supports cross-analysis by management dimensions such as disease type and physician group. - Optional comprehensive analysis can be performed by linking clinical data trends (such as SpO2-etCO2 trends, respiratory rate) to the corresponding time period. - Results distribution and application: - Real-time push to management terminals at all levels - Automatically generate standardized analysis reports - Rule-based early warnings (such as abnormally low energy efficiency, abnormal blood oxygen or etCO2 events, sleep apnea). The core innovation of this algorithm model lies in: - Real-time integration calculation.

[0066] - Automatic value mapping.

[0067] - Large-scale real-time aggregation and multi-physiological parameter correlation analysis.

[0068] This calculation process fundamentally surpasses any possible manual statistical method in terms of computational complexity, processing speed, and analytical dimensions, realizing the digital and intelligent leap of hospital oxygen management. Detailed Implementation

[0069] The present invention will now be described in detail with reference to the accompanying drawings. This description is intended to explain the invention, not to limit it.

[0070] Example 1: Overall System Implementation See Figure 1This system is deployed in a real hospital environment. Each oxygen therapy bed is equipped with a smart oxygen therapy device (i.e., the patient-level unit). This device is medically certified and integrates an electronically controlled flow valve, a high-precision flow meter, a blood oxygen probe, a mainstream or bypass etCO2 sensor, a respiratory impedance or flow sensing module (for respiratory status monitoring), and an IoT communication module. All devices continuously collect clinical data (blood oxygen, pulse rate, etCO2, respiratory status) and flow calculation data (set flow rate, actual flow rate) and send them in packages to the data aggregation and storage unit of the hospital data center via the hospital's Wi-Fi or IoT private network. This unit receives and categorizes all data. The intelligent computing unit retrieves the required data from the storage unit and runs the core algorithm described in this invention in real time. Computers or large screens (i.e., the application-level units) in the hospital's logistics management department, departmental nurse stations, and hospital management offices access the visual interface provided by the server through a browser.

[0071] Example 2: Detailed description of the complete application scenario based on SpO2-respiratory biphasic closed-loop control Combination Figure 2 and Figure 3 Taking the case of postoperative patient Wang Wu undergoing 6 hours of assisted oxygen therapy in bed 709 of the general surgery department as an example, the application process of this invention is fully demonstrated. Postoperatively, patients need to maintain a stable oxygenation and ventilation status.

[0072] 1. Parameter settings and system startup (corresponding to S1 and S2 layer initialization) Doctor's procedure: On the intelligent oxygen therapy device in bed 709 or the associated nurse station terminal, set the treatment parameters: Initial flow rate (F_set): Based on conventional experience, it is set to 3 L / min. This value will serve as a constant baseline for calculating the theoretical consumption (Q_t) throughout the entire metering cycle.

[0073] Target blood oxygenation range (SpO2_target): set to 96%~99%.

[0074] The target end-tidal carbon dioxide range (etCO2_target) is set to 35–45 mmHg to monitor ventilation status and prevent over-ventilation or under-ventilation.

[0075] Device startup: After the parameters are sent, the intelligent oxygen therapy device starts. Its integrated blood oxygen probe, bypass etCO2 sampling tube (connected to the nasal oxygen tube), and built-in respiratory sensing module (based on changes in flow rate or impedance) begin to work continuously.

[0076] 2. SpO2-breathing biphasic closed-loop control and multi-parameter data generation (core innovation, corresponding to dynamic operation of S2 layer) The device enters a continuous closed-loop control main cycle (the cycle period can be set to 100ms), and its working logic is as follows: Step A: Real-time assessment of respiratory status The respiratory sensing module continuously analyzes airflow or thoracic impedance signals to accurately determine whether the patient is currently in the inspiratory phase (Insp) or expiratory phase (Exp), and marks each sampling point with a respiratory state label (Phase).

[0077] Step B: Phase split control execution Scenario 1: The current phase is the expiratory phase (Phase = Exp). Control action: The flow regulating actuator (precision electronic valve) immediately shuts off or outputs an extremely low maintenance flow (e.g., 0.1 L / min) solely to keep the line clear. This design directly avoids the complete waste of expiratory oxygen in the traditional mode.

[0078] Data recording: Record actual traffic (F_actual) = 0 (or 0.1).

[0079] Scenario 2: The current phase is the inspiratory phase (Phase = Insp) Data Acquisition: Real-time blood oxygen saturation (SpO2_now), real-time end-tidal carbon dioxide (etCO2_now), and pulse rate (PR_now) are collected simultaneously.

[0080] Control Decision: The embedded processor compares SpO2_now with SpO2_target and etCO2_now with etCO2_target to execute intelligent decisions. Safety First: If SpO2_now is below 96% or etCO2_now is above 45 mmHg, the controller will supply oxygen at the maximum F_set (3 L / min) to quickly correct hypoxia or hypercapnia.

[0081] Precise maintenance: If SpO2_now is between 96% and 99% and etCO2_now is between 35 and 45 mmHg, the controller calculates and outputs a lower "maintenance flow rate" (F_maintain), such as 1.2 L / min. This flow rate is sufficient to offset the patient's metabolic oxygen consumption and maintain the target range.

[0082] To prevent over-conversion: If SpO2_now is above 99%, the controller will further reduce the flow rate, for example, to 0.5 L / min.

[0083] Data logging: Record the actual flow rate (F_actual) after the decision, such as 1.2 L / min.

[0084] Step C: Parallel data packaging and uploading At the end of each control cycle, a structured raw data packet is generated and uploaded in real time via Wi-Fi. The data packet is clearly divided into two categories: clinical data and flow calculation data, which are synchronized with the bed ID and timestamp respectively.

[0085] 3. Data aggregation, storage, and real-time computing (corresponding to the S3 layer) Data aggregation and storage unit: Continuously receives data packets from bed 709 and all other beds. The system automatically categorizes "flow calculation data" and "clinical data" and stores them in the corresponding tables in the database, indexing them by time and bed ID.

[0086] Intelligent computing unit: Automatically triggers computing tasks after the 6-hour treatment cycle ends (or in real-time rolling calculation).

[0087] Data retrieval: Retrieve the "flow calculation data" of bed 709 within 6 hours (360 minutes) from the storage unit, totaling approximately 216,000 data points (360min*60s*10Hz).

[0088] Core Computing: The theoretical consumption Q_t is calculated as follows: Q_t = F_set × duration = 3L / min × 360min = 1080 liters.

[0089] Calculate the actual consumption Q_a: Perform high-precision numerical integration on 216,000 F_actual data points. Due to the "expiratory phase oxygen withdrawal" strategy, approximately two-thirds of the time (expiratory phase) values ​​in the F_actual sequence are close to 0. Assume the integration result is Q_a ≈ 432 liters.

[0090] Calculate the amount of oxygen saved ΔQ: ΔQ = Q_t - Q_a = 1080 - 432 = 648 liters.

[0091] Calculate the cost savings ΔC: Assuming the oxygen cost P = 0.04 yuan / liter, then ΔC = ΔQ × P = 648 × 0.04 = 25.92 yuan.

[0092] Comparative calculation: If respiratory synchronization control is not used (based solely on continuous blood oxygen regulation), assuming an average actual flow rate of 1.8 L / min, then Q_a' ≈ 648 liters, ΔQ' = 432 liters, and ΔC' = 17.28 yuan. It is evident that the respiratory synchronization control added in this invention, in this case, saves an additional 216 liters of oxygen, worth 8.64 yuan, during just 6 hours of oxygen therapy, improving energy efficiency by 50%.

[0093] 4. Multi-level aggregation analysis and visualization (corresponding to S4 layer) Automatic data aggregation: A new entry has been added to Wang Wu's personal medical record: {Bed: 709, Cycle: 6h, ΔQ: 648L, ΔC: 259.2 yuan}.

[0094] This data is automatically added to the daily cumulative total for the "General Surgery" department. Assuming the General Surgery department has 30 beds, the system calculates in real time the total oxygen savings for the department that day, ΣΔQ_General Surgery = 15,200 liters, and the total cost savings, ΣΔC_General Surgery = 6,080 yuan.

[0095] Meanwhile, the data continues to be aggregated upwards to the "entire hospital" level.

[0096] Manager view (application layer): The head nurse of the general surgery department saw a real-time updated dashboard on the office's large screen, displaying the following information: Key metrics: "Today's total savings for this department: 6,080 yuan", "12% increase compared to yesterday".

[0097] Bed Ranking: Shows the top five most energy-efficient beds today. Bed 709, Wang Wu's bed, may be among them. Click to view its detailed SpO2-etCO2 trend chart and flow curve to confirm treatment safety.

[0098] Clinical-related early warning: The system indicated that "the average etCO2 in this department is stable within the normal range and no low oxygen alarm has occurred," confirming the safety of the energy-saving process.

[0099] From a hospital-wide perspective: The hospital management office dashboard shows that "the hospital has saved 28,500 yuan in oxygen costs today," with the data compiled in real time from various departments such as general surgery.

[0100] 5. Scenarios Summary and Technical Effects This embodiment fully demonstrates the closed loop from "medical order setting" to "economic value realization". It proves that: Technical feasibility: The system can stably achieve closed-loop control of respiratory synchronization based on multiple parameters (SpO2, etCO2, respiratory state).

[0101] Management precision: Energy consumption can be traced back to a single patient and a single treatment event, and is converted into financial data in real time.

[0102] Additional energy-saving benefits: By introducing the key control strategy of expiratory phase oxygen cessation, energy-saving potential that cannot be reached by traditional methods is explored and accurately measured while ensuring safety.

[0103] Decision support: Provides unprecedented data granularity and timeliness for optimizing clinical treatment plans (such as adjusting target ranges) and implementing precise incentives for logistical support (such as ΔC-based departmental rewards).

[0104] This application scenario vividly demonstrates how the present invention transforms a traditional logistical expense into a refined management process that can be measured in real time, deeply optimized, and closely linked to clinical quality and safety.

[0105] Example 3: Application of Economic Benefits and Clinical Safety Management – ​​Detailed Description of a Hospital-wide Multi-level Collaborative Management Scenario This embodiment demonstrates how hospital management can deeply integrate the multi-dimensional data generated by the system of this invention into daily operations, performance evaluation, and quality and safety management, thereby achieving a management paradigm shift from a "cost center" to a "value creation and safety assurance center".

[0106] Scene setting On the large screen of the "Smart Hospital Operation and Management Center" of a top-tier hospital, a comprehensive dashboard for hospital-wide oxygen management, provided by the system of this invention, is displayed in real time. The operations management team, the director of the medical affairs department, the director of the nursing department, the director of the logistics department, and the directors of relevant departments are holding a monthly resource management joint meeting.

[0107] Part 1: Application of Economic Benefit Management – ​​Incentive and Decision-Making Closed Loop Based on Precise Data 1. Goal Management and Budgeting Operation: At the beginning of the year, based on historical data and the annual budget, the hospital management used the system's forecasting module to set differentiated "annual oxygen cost saving targets (ΔC_target)" for each clinical department. For example, the target for the respiratory medicine department was to save 180,000 yuan, while the target for the intensive care unit (ICU), due to the severity of patients' conditions and high oxygen consumption, was set at 120,000 yuan.

[0108] Basis: The target setting is not a "one-size-fits-all" approach, but is based on scientific calculations using the "theoretical consumption (Q_t) base", "average energy saving rate (ΔQ / Q_t)" and "disease structure change prediction" of each department in the previous year provided by the system.

[0109] Key change: It changed the previous extensive cost-sharing model based solely on the number of beds, and achieved precise budget management based on actual treatment needs and energy-saving potential.

[0110] 2. Real-time performance dashboards and transparent competition Data Display: The conference dashboard displays dynamic dashboards, with core modules including: Departmental Energy Saving Leaderboard: Sorted by cumulative ΔC this month. It shows that "the Department of Cardiothoracic Surgery saved 32,000 yuan this month, achieving 125% of its target, ranking first; the Department of Neurology saved 18,000 yuan, achieving 72% of its target, ranking last."

[0111] Progress tracking: Displays the percentage of annual targets achieved by the entire hospital and key departments in the form of a dashboard.

[0112] Benefit details: Click on any department to drill down and view the details of its ward, beds, and even the savings contribution of the attending physician group.

[0113] Management Interaction: The head of cardiothoracic surgery shared his experience: "Through system data, we found that using the synchronized breathing mode (as described in Example 2) during the stable period of postoperative patients saves more than 30% more energy than the traditional mode. This method has now been incorporated into the department's standard operating procedure." The head of neurology was able to immediately access the data and analyze that the reason for the low energy saving rate was that the blood oxygen target range for some elderly patients was set too conservatively, and indicated that optimization would be carried out.

[0114] 3. Automated calculation and precise incentives Policy Implementation: The hospital has formulated the "Management Measures for Sharing the Savings of Medical Oxygen", which stipulates that "30% of the verified savings (ΔC) will be returned to the clinical department that generated the savings as a special performance reward".

[0115] The system automatically executes the following process: Data locking: At 00:00 on the 1st of each month, the system automatically summarizes the ΔC data of the entire hospital and departments for the previous month, and confirms it online by the finance and audit departments.

[0116] Automatic calculation: The system automatically calculates the departmental reward based on the formula: Departmental Reward = ΣΔC - Departmental Reward × 30%. For example, if the Cardiothoracic Surgery Department's ΣΔC was 32,000 yuan last month, then the reward amount would be 9,600 yuan.

[0117] Report generation: The system automatically generates a reward distribution report with a detailed data traceability chain (accurate to the calculation process of ΔQ and ΔC for each bed) and pushes it to the finance department and department heads.

[0118] Incentive distribution: Rewards are distributed based on monthly performance, and the data source is publicized in the department.

[0119] Key change: It completely changed the previous situation where "consumable savings could not be quantified and rewards were not based on evidence," and established a transparent and precise positive incentive loop of "whoever saves benefits," which greatly mobilized the enthusiasm of front-line clinicians to actively save energy.

[0120] 4. Procurement and Cost Decisions Data Analysis: The system provides long-term oxygen consumption trends, energy saving rate curves, and price (P) correlation analysis.

[0121] Decision Support: The Logistics Director reported, "Data shows that after implementing this system, the hospital's actual oxygen consumption (Q_a) decreased by 25% quarter-on-quarter, and the trend is stable. Considering the current energy saving rate, we recommend that when negotiating next year's contract with oxygen suppliers, the estimated total purchase volume be reduced by 20%, and that we utilize our accurate consumption data to negotiate a better unit price." This approach bases procurement on dynamic and accurate internal data, rather than vague historical data.

[0122] Part Two: Clinical Safety Management Applications – Data-Driven Quality Control and Treatment Optimization 1. Multi-parameter safety early warning and real-time intervention System intelligent early warning rules: Rule 1 (Hypoxia Risk): If a patient's SpO2 is below the target lower limit for 5 consecutive minutes and the system is supplying oxygen at the maximum set flow rate, the system will automatically trigger a "red alarm". The information will be pushed to the central monitoring screen at the nurse station, the responsible nurse's PDA, and the doctor's workstation in real time and recorded as a "hypoxia event requiring clinical intervention".

[0123] Rule 2 (Ventilation Safety): If a patient in a bed has an etCO2 that is consistently higher than 45 mmHg and is accompanied by a decrease in respiratory rate, the system will trigger a "yellow warning", indicating that there may be a risk of inadequate ventilation or respiratory depression.

[0124] Rule 3 (Ineffective Treatment): If the "actual flow rate (F_actual)" of a certain bed continues to approach the "set flow rate (F_set)", but the SpO2 is still below the target, the system will prompt "The current oxygen therapy plan may be ineffective. Please assess the patient's condition".

[0125] Application Scenario: In the ICU, the head nurse noticed a bed triggering a "yellow alert" on the dashboard. She immediately checked the patient's etCO2 trend chart and found it had slowly risen from 38 mmHg to 48 mmHg. Upon examination, the patient was found to be deeply sedated with weak breathing. The doctor was immediately notified to adjust the sedation regimen, preventing an adverse respiratory event. The system became an "electronic sentinel" ensuring treatment safety.

[0126] 2. Evaluation of Treatment Model Effectiveness and Optimization of Standards Retrospective study: The medical department initiated a retrospective analysis using the massive amounts of structured clinical and traffic data stored in the system.

[0127] Analysis topic: "Comparison of the efficacy and economic differences between synchronized breathing control mode and traditional constant flow mode in patients with AECOPD (acute exacerbation of chronic obstructive pulmonary disease). Analysis process: Researchers can easily access the treatment records of all AECOPD patients in the past year. The system has automatically linked data such as ΔQ, ΔC, SpO2 target achievement time percentage, and etCO2 stability index for each patient.

[0128] Analysis results: Statistical reports can be generated quickly, such as "The respiratory synchronization mode group has a 40% higher median energy saving rate than the traditional group while ensuring the same oxygenation target achievement rate, and there is no difference in the incidence of hypercapnia". Management Decision-Making: Based on this evidence, hospital pharmacy administration and hospital management can demonstrate and publish the "Recommended Pathway for Oxygen Therapy Management of AECOPD Patients," listing the respiratory synchronization control mode of this system as the preferred option. This achieves a closed loop from experience-based medicine to evidence-based medicine and data-driven clinical pathway optimization.

[0129] 3. Linking departmental and individual performance quality dimensions. Quality and safety indicators: In the performance evaluation of departmental oxygen management, not only ΔC is considered, but also the "quality and safety factor (K)" is introduced.

[0130] K is calculated from the following data: blood oxygenation target achievement rate, etCO2 abnormal event incidence rate, and respiratory alarm response timeliness rate.

[0131] Final performance score = ΣΔC×K.

[0132] Application Effects: This prevents departments from relaxing blood oxygenation targets or neglecting ventilation monitoring in the pursuit of cost savings. For example, even if a department has a high ΔC, if the system records multiple instances where the set target range was too wide, resulting in patients' SpO2 remaining at a consistently low level of 94%-95%, the department's K value will be lowered, thus reducing its overall performance score. This ensures the alignment of economic efficiency goals with medical quality and safety goals.

[0133] Scenario Summary and Value Enhancement At the conclusion of the joint meeting described in this embodiment, the operations management team summarized: "In the past, oxygen management was a 'muddled mess' for us; there were only total costs, no details; only post-event allocation, no process management; it was only seen as a cost, not its value. Now, through this system: We now have a 'microscope': we can see clearly the clinical value and economic destination of every liter of oxygen.

[0134] We now have a 'guiding principle': we can use precise data to motivate departments to save energy safely and effectively.

[0135] We have a 'safety net': while pursuing resource efficiency, we have strengthened the bottom line of patient safety through multi-parameter monitoring.

[0136] We now have a 'smart brain': data is not only used for performance evaluation, but also for optimizing treatment standards and decision-making. This invention transcends mere technical solutions, becoming a core engine driving hospitals to achieve integrated, data-driven, and refined collaborative management of "clinical-operational-financial-quality" processes. Its management and social benefits are as significant as the direct savings in oxygen costs. The above embodiments demonstrate that the method proposed in this invention is not only an innovation in technical tools but also an upgrade in management methodology. It fills the long-standing gap between precision clinical treatment, lean logistical management, and quantitative assessment of economic benefits and safety and quality, achieving deeper resource conservation through respiratory synchronization control.

[0137] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An oxygen energy-saving metering and cost accounting system, characterized in that, include: The patient-level intelligent control and data acquisition unit is deployed at each oxygen bed. It is used to achieve closed-loop control of oxygen flow based on the patient's physiological parameters, using an initial set flow rate value as a constant theoretical consumption benchmark independent of closed-loop control. At the same time, it collects data in parallel and independently with high temporal resolution, including the initial set flow rate value, real-time actual flow rate value, real-time blood oxygen saturation value, pulse rate value and their corresponding timestamps. The initial set flow rate value remains constant throughout the calculation cycle and serves as a fixed reference for subsequent measurement comparisons. The data layer aggregation and storage unit is used to receive the data from all oxygen beds and classify, index, and distribute the data according to its source and data type. The data layer intelligent computing unit is used to retrieve data from the aggregation and storage unit and perform core calculations for energy-saving metering and cost accounting. These calculations include: using the constant initial set flow rate as input, generating a theoretical consumption trajectory through time multiplication; using high-frequency sampled real-time actual flow rate as input, generating an actual consumption trajectory through numerical integration; calculating the difference between the two trajectories to obtain a time-continuous oxygen-saving sequence, and mapping the oxygen-saving sequence to a cost-saving sequence in real time based on pre-stored oxygen price information; and... The application layer visualization and management unit is used to dynamically and multi-level aggregate and reconstruct the oxygen saving sequence and cost saving sequence based on the hospital's real physical organizational structure and time dimension, and to display the trend of clinical physiological data in the same period.

2. The system according to claim 1, characterized in that, The patient-level intelligent control and data acquisition unit includes: Flow control actuator, blood oxygen saturation sensor, and embedded processor; The embedded processor is configured to execute a SpO2 closed-loop control algorithm, with a preset target blood oxygen saturation range as the control target and the initial set flow rate as the control upper limit, to dynamically adjust the output flow rate of the flow regulation actuator.

3. The system according to claim 2, characterized in that, The patient-level intelligent control and data acquisition unit also includes one or both of an end-tidal carbon dioxide sensor or a respiratory status monitoring module. The embedded processor is configured to execute a SpO2-breathing biphasic closed-loop control algorithm, with a preset target range of blood oxygen saturation and a target range of end-tidal carbon dioxide as control targets. When the patient's inspiratory phase is detected, the oxygen output is dynamically adjusted according to the deviation of physiological parameters, and the oxygen output is stopped or reduced during the expiratory phase.

4. The system according to claim 1, characterized in that, The data layer intelligent computing unit is configured to execute the following computing model: The basic energy-saving metering model is used to calculate the theoretical consumption, actual consumption, and oxygen savings of any bed within the calculation period. An economic benefit mapping model is used to calculate the cost savings based on the amount of oxygen saved and the unit price of oxygen. A multi-level aggregation calculation model is used to summarize oxygen savings and cost savings level by level according to the hospital's organizational structure.

5. The system according to claim 1, characterized in that, The application layer visualization and management unit provides the following display content: oxygen consumption data, oxygen savings, cost savings, and clinical physiological parameter trend charts related to the savings data, categorized by individual patient, department, ward, and hospital-wide level.

6. A method for energy-saving metering and cost accounting of oxygen, characterized in that, Implemented by the system according to any one of claims 1-5, the method comprises: S1: Simultaneously collect the initial set flow rate value, the real-time changing actual flow rate value, and the patient's real-time physiological parameters from each oxygen bed; S2: Based on the real-time physiological parameters, execute closed-loop control, using the initial set flow rate value as the theoretical consumption benchmark, and dynamically adjust the actual oxygen output flow rate; S3: Classify and store the collected data, and based on the initial set flow rate value and the real-time actual flow rate value, calculate the amount of oxygen saved by time integration and difference calculation, and then calculate the cost savings by combining the oxygen unit price. S4: Perform multi-level aggregation and correlation analysis on the oxygen savings and cost savings according to time, space and management dimensions, and visualize the results.

7. The method according to claim 6, characterized in that, The closed-loop control described in step S2 includes: real-time monitoring of the patient's respiratory cycle, adjusting the oxygen supply flow rate during the inspiratory phase based on the deviation of blood oxygen saturation and end-tidal carbon dioxide value from the target range, and stopping or minimizing the oxygen supply flow rate during the expiratory phase.

8. The method according to claim 6, characterized in that, The formula for calculating the oxygen saving amount ΔQ in step S3 is: ΔQ = Q_t - Q_a, where Q_t = initial set flow rate value × calculation cycle duration, and Q_a = the result of time integration of all real-time actual flow rates within the calculation cycle.

9. The method according to claim 6, characterized in that, The formula for calculating the cost savings ΔC in step S3 is: ΔC = ΔQ × P, where P is the unit price of oxygen.

10. The method according to claim 6, characterized in that, The multi-level aggregation in step S4 includes: summarizing by spatial dimensions such as beds, wards, wards, departments, and the entire hospital, and accumulating by time dimensions such as hours, days, weeks, months, and years; the correlation analysis includes correlating energy-saving data with the blood oxygen saturation trend and end-tidal carbon dioxide trend collected in the same period to assess treatment safety and energy-saving effect.

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