Hospital management planning methods and hospital management planning programs

Abstract

Conventional hospital management support systems were limited to relative evaluations based on patient attributes and departmental units, failing to accurately grasp the profitability and costs of each disease or treatment group. This made it difficult to perform concrete data analysis directly related to optimal allocation of medical resources, improved treatment efficiency, and management improvement. [Solution] The present invention provides a "patient grouping management module" that groups patients based on specific treatment content or diseases, and calculates and visualizes the profitability, cost, and profit margin of each group. Furthermore, it calculates management indicators for each patient group based on medical fees, drug price differences, and material price differences, and visualizes and analyzes the results in real time, thereby constructing a management support tool that enables doctors and managers to concretely plan resource allocation and treatment strategies.

Description

Technical Field

[0001] The present invention relates to a support system for medical management and hospital management. In particular, it outputs and visualizes the management status of each medical department and medical group in the hospital, and also outputs and visualizes the management status of patients classified into specific groups in the hospital, and proposes improvements thereto. It relates to a hospital management tool (hospital management planning system, program, method).

Background Art

[0002] Conventionally, efforts have been made to analyze basic indicators related to hospital management (for example, bed occupancy rate, average length of stay, patient unit price, profitability rate, etc.) and utilize them as basic materials for formulating specific improvement plans.

[0003] Patent Document 1 is a system that analyzes the treatment content for each patient based on the attributes of the patient (age, gender, disease, etc.) and presents useful information for management improvement by presenting highly profitable treatments and measures with excellent management efficiency. That is, by focusing on the treatment content for each patient and grasping the goodness or badness (good / standard / bad) of the profit and loss level for each patient, it aims to obtain information beneficial for management improvement at the granularity of each patient, and it is difficult to say that it judges the management status using aggregated data for the entire hospital or at the medical department unit level. The method of grouping patients based on predetermined attributes (for example, disease classification, gender, age, etc.), obtaining profit and loss information for each group, and selecting a treatment with better profit for each patient based on that information is grouped by relative evaluation of good / bad, and there is invalid data that does not belong to any group, so the group is constructed from a relative perspective.

[0004] Patent Document 2 describes a method that makes it extremely easy to compare hospitals with other hospitals and compare departments within the same hospital by performing evaluations based on RMP (Revenue per yen of personnel costs) and RIP (Return on Investment per yen of personnel costs). RMP contributes to improving the figures by achieving high revenue with low personnel costs, and RIP contributes to improving the figures by efficiently utilizing capital with low personnel costs (departments with high capital efficiency or departments that require equipment investment). [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Patent Application No. 2010-258130 [Patent Document 2] Japanese Patent Publication No. 2010-218448 [Overview of the project] [Problems that the invention aims to solve]

[0006] However, the system disclosed in Patent Document 1 is not a method for analyzing the profit and loss information of treatments using data based on patient attributes. When patients are classified into absolute groups based on specific medical procedures or diseases (e.g., arrhythmias, heart failure, catheter treatment, breast cancer surgery), it is possible to formulate treatment strategies based on clinical guidelines and the latest medical evidence through analysis specific to particular diseases or treatment groups, and to allocate medical resources in an optimized manner according to the characteristics and needs of each group. Furthermore, it is possible to accurately grasp the profitability of each group and clearly identify which treatment areas are performing well or need improvement from a business perspective. For example, catheter ablation for arrhythmia patients may be highly profitable, while outpatient management of heart failure patients may be costly.

[0007] Furthermore, by allocating treatment methods and equipment optimized for each group, the treatment efficiency per patient improves. In this way, rather than using relative evaluations of good / bad, analyzing the absolute cost-revenue relationship for each patient group enables transparent data analysis. Specifically, the calculation method is clear, using data collected from electronic medical records and medical fee data, and even complex calculations can be easily understood through diagrams and graphs. Analysis without any particular bias leads to an intuitive understanding of the results.

[0008] Furthermore, while granular patient analysis and metrics (RMP and RIP) are desirable as means of indirectly achieving both quality and profitability in healthcare services from the standpoint of profitability and cost efficiency, they do not directly improve profit volatility through the efficiency of healthcare resources. In other words, appropriate resource allocation while maintaining the quality of healthcare is expected to increase patient satisfaction and have a positive impact on revenue through an increase in repeat patients and referrals.

[0009] Furthermore, optimizing resources has ripple effects not only on specific medical departments or patients, but also on the overall operation of the hospital. For example, optimizing hospital beds, medical equipment, and treatment spaces can be expected to increase revenue while keeping costs down, and to improve the speed and responsiveness of treatment in all medical departments.

[0010] Furthermore, while focusing on patient granularity analysis and metrics (RMP and RIP) may yield temporary revenue improvements, resource efficiency is expected to lead to medium- and long-term stability for the hospital as a whole. By optimizing resource allocation and improving or reducing bottlenecks that result in low-profit operations and unnecessary costs, long-term stability and improved profitability of the hospital can be expected.

[0011] Furthermore, efficient resource allocation reduces the burden on healthcare professionals. For example, if the bed occupancy rate is appropriate, the workload of patient care is distributed, easing the burden on doctors and nurses and making it easier to provide high-quality medical care. This also contributes to lower turnover rates and improved morale among healthcare professionals.

[0012] Furthermore, by optimizing resource utilization, unnecessary costs can be reduced, enabling more strategic investments. Proper use of medical equipment and treatment spaces reduces equipment maintenance and replacement costs, making it easier to reinvest in necessary resources and introduce new medical technologies.

[0013] To address these challenges, the objective of this invention is to propose the most desirable method of management improvement by directly impacting the overall service quality and financial soundness of the hospital through the efficient use of resources. It can simultaneously achieve improvements in multiple aspects, such as the quality of medical care, operational efficiency, and financial stability, and is an excellent method for the sustainable growth of the hospital.

[0014] This invention has been made in view of these challenges and provides a tool that appropriately evaluates resources within a hospital, simulates resource reallocation (resource movement and resource expansion) to reduce undesirable resource use, and simultaneously achieves improved management efficiency and maintenance of the quality of medical services. Furthermore, it provides a tool that makes it easier to intuitively understand resources with room for improvement and the effects of improvements by visualizing the impact on profitability and service quality using graphs and dashboards, and by visualizing where resource bottlenecks exist.

[0015] Furthermore, by grouping patients based on specific treatments, diseases, and DPC information and managing profit data accordingly, this invention makes it easier for healthcare professionals and management to identify groups that should be focused on. This helps maximize revenue by focusing on high-profit treatments and patients, and facilitates the priority allocation of personnel and equipment resources. In particular, it enables physicians to select treatment policies that balance profitability and medical appropriateness, and makes it easier for physicians to plan and implement specific measures to achieve management goals based on profit data for each patient group. [Means for solving the problem]

[0016] (A) A hospital management support tool for visualizing the status of hospital management. This tool is a system, program, and method that visualizes the status of hospital management in a dashboard format using display devices. This allows managers and administrators to grasp the performance, resource usage, profitability, and workload of each clinical department and clinical group at a glance. Furthermore, each indicator is updated in real time during visualization, ensuring that the latest management status is always understood.

[0017] The differences between general company management and hospital management lie in the revenue structure, cost management methods, and unique constraints on the scope of operations. <Differences in revenue structure> Hospitals have limited revenue potential because their income is determined by the medical fee system, preventing them from freely setting prices. While general companies can freely adjust the prices and content of their services to increase profits, hospitals must devise ways to maximize profits within the system. Since they cannot individually charge for consumables such as bandages and gauze used by patients, and their revenue depends on medical fees, it is essential for hospitals to devise ways to reduce the costs of consumables and equipment. <Cost control constraints> Treatment costs include direct costs such as pharmaceuticals, tests, and labor, but reducing these costs can easily impact patient safety and the quality of medical services. Because simple cost reduction is difficult, the focus was on efficient resource allocation without waste. Furthermore, indirect cost reductions are required through inventory management of pharmaceuticals, medical devices, and consumables, as well as workload leveling. This requires not just reducing the workload, but managing resources to distribute the workload while maintaining high-quality medical services. <Management of workload and human resources> The number of medical professionals, such as doctors and nurses, is limited, leading to risks of overwork due to long working hours and high turnover rates. Effective management improvements emphasize the efficient allocation of personnel and the appropriate distribution of workload. Furthermore, it's not simply a matter of reducing the workload; resource management is required to distribute the workload while maintaining high-quality medical services. <Balancing patient satisfaction and profitability> While general businesses can adjust customer service costs to balance profitability, hospitals prioritize patient safety and quality of care. Therefore, they must optimize operating costs while maintaining patient satisfaction. Reducing patient response times and improving the efficiency of medical care contribute to increased revenue, but these must be done in a way that does not compromise patient convenience or satisfaction, thus maintaining a balance between the quality of medical care and profitability. <Legal and ethical constraints> Hospital management is subject to strict regulations on medical fees and medical procedures, and unlike general companies, the degree of freedom to increase profits is limited. Therefore, management strategies must be based not only on profit maximization, but also on legal compliance and ethical considerations. Furthermore, hospitals are highly public institutions, and their mission is not only to generate profits, but also to meet the healthcare needs of the community and ensure the quality of medical care. Therefore, it is necessary to manage hospitals sustainably while balancing profit and public interest. This invention proposes hospital management improvements that take these aspects into consideration, emphasizing the importance of efficient resource allocation, optimization of workload, reduction of indirect costs, and improvement of patient satisfaction. These improvements involve data-driven analysis and adjustments, including effective resource utilization, identification and improvement of bottlenecks, and presentation of profit improvement measures through simulations.

[0018] (B) A revenue management database in which management indicator data (= revenue data) for each clinical department or clinical group within the hospital is calculated and registered. (C) Resource management database in which resources assigned to each clinical department or clinical group are registered. The revenue management database calculates and records sales, costs, and profits, accumulating data such as sales figures and profit margins, which are then used for revenue analysis and business improvement. The resource management database registers resources (personnel, equipment, medical devices, hospital beds, etc.) allocated to each clinical department or clinical group, allowing for the calculation of workload and resource utilization, and supporting efficient resource allocation. In addition, the patient database, which records data such as each patient's medical history, age, gender, disease, treatment content, and number of visits, allows for the analysis of patient trends and the understanding of utilization and repeat visit rates for specific clinical departments. Furthermore, the cost management database, which records direct costs (personnel costs, pharmaceutical costs, testing costs, equipment maintenance costs, etc.) and indirect costs related to the operation of each clinical department or clinical group, allows for the calculation of the balance between revenue and costs and the evaluation of profit margins for each clinical department. This data is then used as foundational information for measures to reduce costs and improve profitability. Furthermore, by utilizing a work schedule database that records the shifts, work schedules, and appointment status of each clinical department and staff, as well as the status of appointments for consultations and examinations, the hospital can improve staff utilization rates and workload levels, preventing excessive burden on staff and wasted resources, thereby streamlining the scheduling of hospital beds and equipment. In addition, by utilizing an inventory management database that records the inventory status of pharmaceuticals, consumables, and medical equipment, and manages usage frequency and replenishment timing, the hospital can ensure that necessary supplies are supplied appropriately without shortages, contributing to cost reduction and improved operational efficiency. Furthermore, by utilizing an equipment management database that manages data such as the utilization rate, usage frequency, maintenance history, and lifespan of equipment and medical devices used in each clinical department, the hospital can use this to efficiently operate equipment and develop appropriate maintenance plans, maximizing utilization rates while minimizing the risk of operational downtime due to equipment failure or lifespan. In addition, by utilizing an external factors database that contains seasonal information such as influenza outbreaks, regional infectious disease situations, and social and economic impacts (legal revisions, economic indicators), the hospital can adjust hospital operations and optimize resource allocation based on external factors, enabling responses that can predict fluctuations in patient numbers and revenue.That is, the resources include any one or more of the resource data of the number of waiting patients, the number of patients, the number of doctors, the number of nurses, the number of medical office assistants, the number of nursing assistants, the number of hospital beds, the number of operating rooms, the number of surgical slots, the number of surgical materials, the number of drugs, the number of anesthesiologists, the number of possible working hours of anesthesiologists, and the working hours for each medical department.

[0019] Note that these databases enable efficient simulation by providing the profit index calculation module (= profit simulation module) with a data collection function to collect only the necessary information in real time for the simulation regarding revenue and resource allocation. The data collection function of the profit index calculation module (profit simulation module) for revenue data identifies the data collection targets by selecting and collecting patient data such as revenue, cost, resource utilization status, the number of patients, and patient groups. A trigger function is provided to automatically collect data during simulation execution or data update so that the latest data is always reflected. At that time, data integrity checks are performed to confirm whether the collected data contains missing values or abnormal values. If there are abnormalities, they are not reflected in the simulation. Or, if the data contains extremely high costs or revenues, automatic correction and supplementation of the data are performed as necessary.

[0020] (D) A profit index calculation module that calculates and visualizes unit management indicators for each resource unit based on the revenue data and allocated resource data of each medical department or medical group. Alternatively, a profit index calculation module that calculates the increased profit by moving unit resources from medical departments with fewer of the unit management indicators to medical departments with higher unit profits. Or, a profit index calculation module that calculates unit management indicators for each resource unit based on the revenue data and allocated resource data of each medical department or medical group, and simulates the profit fluctuations caused by resource movement or resource addition between medical departments or medical groups based on the unit management indicators.

[0021] Here, the unit management indicator (resource unit profit) is calculated by dividing the profit of each medical department or medical group (gross profit obtained by subtracting costs from sales) by the unit of allocated resources (for example, personnel, hospital beds, medical equipment, etc.). That is, resource unit profit = profit / resource quantity, which numerically represents how much profit each resource generates and enables comparison of the efficiency of each medical department. The comparison of the magnitudes of unit profits involves comparing the calculated values of the unit management indicators for each medical department or medical group, identifying medical departments with low unit profits and those with high unit profits, and predicting how the overall profit will change when resources are transferred from medical departments with low profit efficiency to those with high profit efficiency. Or, predict the profit change when resources are added to medical departments with high profit efficiency.

[0022] The "profit indicator", "unit management indicator", and "management indicator" are used to evaluate profitability for each unit of resources (such as the number of doctors, nurses, hospital beds, etc.). Specifically, they are used to calculate the profit for each resource allocated to each medical department or medical group. The profit per resource calculates the profit per resource unit (e.g., per doctor, per doctor-hour, per hospital bed, per hospital bed per day, per anesthesiologist-hour, per anesthesiologist shift, per operating room surgery slot, etc.), and the profit score scores how much profit is generated for each resource. By using the "profit indicator", "unit management indicator", and "management indicator", the change in the overall profit when the allocation of resources is changed between medical departments or medical groups is calculated.

[0023] (E) The profit indicator calculation module has the function of calculating one or more management indicator data of sales, costs, number of patients, and revenue from one or more of DPC information, receipt information, drug price margin, and material cost margin. (F) Based on the unit management indicator standard, the profit indicator calculation module transfers resources between medical departments or medical groups, or adds and expands the resources allocated to each medical department or each medical group.

[0024] Resource movement simulations simulate the movement of unit resources (e.g., 1 doctor, 1 hour of doctor work, 1 hospital bed, 1 hospital bed per day, 1 hour of anesthesiologist work, 1 anesthesiologist shift, 1 operating room surgery slot, etc.) from less profitable departments to more efficient ones over a predetermined period. For example, it estimates how much overall revenue would increase by allocating physician personnel and equipment to departments with high unit profits.

[0025] Profit increase forecasts calculate the expected increase in profit due to resource relocation or resource expansion. It quantifies how much profit improves when resources are reallocated and proposes the optimal resource allocation along with numerical information. Specifically, the predicted increase in profit is calculated as: Predicted Increase in Profit = Unit Profit of the Relocating Department × Amount of Resources Relocated - Unit Profit of the Original Department × Amount of Resources Relocated. This calculation allows for experimentation with different resource allocation patterns by changing simulation conditions as needed.

[0026] Furthermore, it is possible to flexibly select the method of resource movement according to management efficiency and the needs of each clinical department. Moving (unit) resources permanently or only for a predetermined period can lead to stable management of the entire hospital and efficient resource utilization. When there is a long-term imbalance in the profitability or workload of clinical departments, or when concentrating resources in highly profitable clinical departments to improve long-term profits, permanent resource reallocation is preferable for departments with high utilization of equipment or clinical space, or departments that require personnel with specific skills. On the other hand, when it is necessary to respond to seasonal or temporary demands, such as during influenza outbreaks or when there is a demand for specific treatments, or when introducing new medical services or treatments, it is preferable to move unit resources only for a predetermined period. In addition, it is possible to combine permanent and temporary movements. This allows for flexible responses to fluctuations in demand in line with the growth of clinical departments and changes in the external environment, prevents resource waste and excessive staff burden by allocating necessary resources without excess or deficiency, and allows for switching to permanent allocation if stable results are obtained after confirming the effectiveness of resource movements for a predetermined period. These measures can be appropriately selected according to the situation to improve the efficiency of hospital management and maximize profits while maintaining the quality of patient services.

[0027] Furthermore, moving or adding resources over a predetermined period enables dynamic simulations, which have a different significance from static simulations based on data accumulation. This allows for greater flexibility in responding to real-world changes and improved simulation accuracy. Moving resources over a predetermined period allows for real-time collection of data on actual work conditions, fluctuations in patient numbers, and impacts on revenue. Dynamically obtaining data makes it easier to identify discrepancies between the effects predicted by static simulations and actual data. Moving or adding resources over a predetermined period is advantageous because it allows for rapid hypothesis testing. The revenue effects and workload reduction effects of increasing resources in a specific clinical department can be verified in a short period, allowing for confirmation of whether the hypothesis is correct. Based on the verification results, it is helpful in deciding whether to implement permanent resource reallocation or try other measures.

[0028] The resource management database may be in either a list format, where each resource data entry includes either the number of physicians, nurses, hospital beds, operating rooms, anesthesiologists, available anesthesiology hours, or working hours per medical department; or a table format, where unit resources and their numerical information (numbers) for each medical department are registered. Profit indicators may be calculated using equipment-related resources, staff-related resources, facility-related resources, other resources, external resources, patient service-related resources, and IT-related resources as unit resources. Equipment-related resources include the number of diagnostic devices (e.g., MRI, CT, ultrasound equipment), treatment devices (e.g., radiotherapy equipment, laser therapy equipment), testing devices (e.g., blood testing equipment, electrocardiogram monitors, endoscopes), and pharmaceutical inventory (e.g., specific drug inventory or stock). Staff-related resources include the number of rehabilitation staff (physical therapists, occupational therapists), pharmacists, radiologic technologists, clinical laboratory technologists, administrative staff, and counselors / social workers. Equipment-related resources include the number of examination rooms, the number of testing rooms, the capacity of waiting rooms, the number of intensive care unit (ICU) beds, the number of emergency response facilities, the number of rehabilitation facilities, and the number of treatment rooms. Other resources include the number of ambulances and means of transportation, the number of patient-only parking spaces, the number of mobility devices such as beds and stretchers, the available hours for each resource (e.g., operating hours of examination rooms and testing rooms, usage times for specific medical equipment), and the number of infection control facilities (e.g., disinfection equipment, inventory of personal protective equipment). External resources include the number of partnerships with other facilities (other hospitals, nursing homes, specialized medical facilities, etc.) and the number of external specialists (the number of specialists who can provide cooperation from outside the hospital). Patient service-related resources include the number of private / multi-bed rooms for patients, the number of services provided to patients (e.g., interpretation services, nutritional counseling), and telemedicine equipment (equipment for online consultations and remote monitoring). IT-related resources include the number of operational electronic medical record systems, data server capacity, and security devices (firewalls, antivirus equipment). These resources fall within the scope of the present invention or its equivalents.

[0029] In particular, regarding the placement of medical office assistants, the bottleneck calculation can be performed by incorporating the perspective of how much the physician's productivity will increase as a result of their placement. It is said that 30% of a physician's work involves administrative tasks, and if half of that time can be dedicated to directing medical office assistants to complete the administrative tasks, then a productivity improvement of 17% can be expected (1 / (1-0.3 / 2) = 1.17). Similarly, regarding nursing assistants for nurses, it is said that 30% of a nurse's work involves paperwork and patient transport, and if half of that time can be dedicated to directing nursing assistants to complete nursing tasks, then a bottleneck reduction effect of 17% can be achieved (1 / (1-0.3 / 2) = 1.17).

[0030] (G) The profit indicator calculation module utilizes the simulation results of resource movement or resource expansion as budgeting data for hospital management in subsequent periods. (H) The profit indicator calculation module manages the simulation results of resource movement or resource expansion against the actual results. (I) The profit indicator calculation module calculates the productivity improvement of physicians or nurses by adding the number of medical office assistants and nursing assistants and simulates the management indicators of the increased resources. (J) The resource management database has a function to register the number of potential additional resources that can be added, and a function to calculate potential additional profit or potential additional sales based on the number of potential additional resources that can be added. (K) The resource management database has a function to visualize whether there are any limitations on other resources when simulating either an increase or replacement of one or more resources, and at the same time has a function to visualize a list of measures to resolve resource limitations.

[0031] <Use as budget formulation material> The profit indicator calculation module can be used as budgeting material for hospital management in subsequent periods, based on simulation results of resource relocation and expansion. <Application to budget management> The profit indicator calculation module manages budgets and actual results based on simulation results of resource movement and expansion. This allows for a comparison of business plans with actual performance and identification of discrepancies. <Productivity Improvement Simulation> The profit indicator calculation module calculates the productivity improvement effect on doctors and nurses resulting from the addition of medical office assistants and nursing assistants, and simulates how the increased resources will affect management indicators. This simulation function allows for a quantitative evaluation of how the addition of medical office assistants and nursing assistants will affect efficiency and productivity in actual medical settings, and makes it possible to predict the business improvement effect of increased resources. <Registration of potential additional slots> The resource management database has a function to register the potential number of additional resources that can be added, and uses this data to calculate potential additional profits and sales. <Visualizing resource limitations and listing solutions> The resource management database has a function that visualizes whether there are limitations on other resources when simulating resource increases or replacements, and displays a list of measures to resolve those limitations. These features enable the hospital management support system to utilize simulation results in management decisions and planning, supporting optimal resource utilization and productivity improvement.

[0032] (L) The profit indicator calculation module includes a bed occupancy rate management module that visualizes the bed occupancy rate for hospital beds, which are part of the resources, and enables simulations of an increase in the number of patients using the hospital beds and an improvement in the average patient cost. The bed occupancy rate management module of the present invention visualizes the bed occupancy rate and performs simulations for optimizing bed utilization in order to evaluate the efficient use of hospital beds, which are a part of the resources. The bed occupancy rate calculates the utilization status of each hospital bed (utilization rate, number of empty beds, etc.) and simulates how much the number of patients that can be accepted will increase if the number of hospital beds is increased or patient admissions and discharges are accelerated based on this occupancy rate. This allows for a simulation of profit improvement when the unit price per patient is increased when bed utilization is maximized. For example, it estimates how much the unit price per patient can be increased by improving the length of hospital stay and the content of medical treatment. As a result, the profit simulation is reflected, and the occupancy rate, patient increase prediction, and unit price increase simulation results obtained from the bed occupancy rate management module are reflected in the profit simulation to calculate the overall profit improvement effect of the hospital.

[0033] Furthermore, the function of the "profit indicator calculation module" of the present invention corresponds to the function of reallocating resources and forecasting revenue as a profit simulation means (295) described later.

[0034] (M) The aforementioned hospital bed occupancy rate management module has a function to promote early admission and discharge of patients, and a function to allow medical personnel to input the reasons for such admission and discharge. Reasons for requiring early admission or discharge include medical reasons based on the patient's recovery status or changes in treatment plan, the possibility of home care where appropriate care can be received at home after discharge, the severity of the hospital bed shortage and whether early discharge is desirable, and the patient's or family's wishes for early discharge. Healthcare professionals will be able to input the reasons for admission or discharge using a selection format (e.g., a dropdown menu or checkboxes) or free-text format, and will also be able to input detailed information and supplementary comments as needed.

[0035] Furthermore, the function of the "bed occupancy rate management module" of the present invention corresponds to the function of visualizing and simulating the bed occupancy rate in relation to the resource management DB283C and profit simulation means (295) described later.

[0036] (N) A fixed cost utilization optimization module that visualizes resources that are bottlenecks in hospital equipment and other fixed costs, and supports suggestions for improving the allocation of those resources. (O) If the bottleneck is either the number of patients or the number of patients on the waiting list, a function has been added to display a list of measures to increase the number of patients and to calculate the cost-effectiveness of one or more of these measures. The fixed cost utilization optimization module of the present invention visualizes areas where hospital facilities and other fixed costs are becoming resource bottlenecks (obstacles to management and operations), and provides functions for proposing improvements to their allocation. It has functions for visualizing bottleneck resources, simulating resource allocation optimization, evaluating fixed costs, predicting resource utilization and analyzing demand, and monitoring the results of improvement proposals, thereby improving the utilization of fixed cost resources, reducing overall hospital operating costs, and enabling a stable supply of necessary resources. If the bottleneck is related to the number of patients or the number of patients on the waiting list, a function has been added that displays a list of measures to increase the number of patients and calculates the cost-effectiveness of each measure. This allows for the selection of efficient measures to increase the number of patients and supports decisions aimed at expanding revenue.

[0037] (P) The fixed cost utilization optimization module has the function of proposing the placement of multiple medical office support staff and supporting the operation of multiple operating rooms.

[0038] The resources to be targeted are those with high fixed costs, such as operating rooms, hospital beds, medical equipment, waiting rooms, and specialized medical equipment. These resources are selected based on their utilization status (uptime, frequency of use, waiting time) and maintenance costs collected from a database. Resources with low or excessive uptime, or those with low or very high usage frequency, tend to be concentrated in specific departments or tasks, potentially disrupting other operations. Long waiting times for resource use indicate insufficient supply relative to demand. Resources with very high maintenance or operating costs should be reviewed. Resources with abnormally low uptime or extremely uneven usage should be identified and included as potential bottlenecks. Time-series data should be analyzed, and resources showing significant changes in utilization trends should be included as potential bottlenecks. Furthermore, if a particular resource is linked to others, a bottleneck in one resource can affect overall operations, so resource interrelationship analysis should also be conducted. For example, in cases where the uptime of operating rooms and physicians are linked, a bottleneck in either will reduce surgical efficiency, so both should be identified. Since there are various reasons for bottlenecks, we will create a list of potential bottlenecks and prioritize them. The resources extracted using the above indicators will then be listed and prioritized based on utilization rate, cost, frequency of use, and waiting time. Improvement proposals will be presented starting with the highest priority items, and reallocation, distribution of utilization rates, and introduction of additional resources will be suggested as needed.

[0039] Bottleneck calculation differs from averages in that it only shows the overall trend and is not suitable for identifying areas where the load is particularly concentrated (e.g., specific time periods or specific equipment), as it identifies specific departments or equipment prone to delays, rather than the overall load. Bottlenecks identify points in the data where the load is locally high, allowing attention to be paid to abnormal data that deviates significantly from the average. Furthermore, visualizing bottlenecks makes it possible to prioritize resource allocation to areas that require improvement, enabling measures to be taken starting with the areas with the greatest impact, even with limited budgets and personnel.

[0040] Furthermore, the functions of the "fixed cost utilization optimization module" of the present invention, in relation to the bottleneck processing unit 295B described later, correspond to functions that support the detection of bottleneck fixed cost resources and the suggestion of improvements.

[0041] (Q) The system includes a score generation module that has a scoring function for converting the workload and profitability of each clinical department and clinical department group into a score, and a visualization function for visualizing the said score. (R) The system includes a change history recording module that periodically records changes in the workload score and profitability score generated by the score generation module, and allows for understanding the trends in changes in workload and profitability for each clinical department. (S) The system includes a notification module that notifies medical administrators when significant fluctuations, such as excessive workload or decreased revenue, are detected in each medical department, based on the fluctuation trends in workload and profitability obtained by the fluctuation history recording module.

[0042] According to the present invention, the score generation module has a scoring function that converts the workload and profitability of each clinical department / clinical group into a score, and a visualization function that visualizes the generated scores on a dashboard or graph. As a result, the performance of each clinical department is quantified, and resource allocation and profitability can be grasped at a glance. By scoring, complex data such as workload and profitability are made easy to understand, making it easier to quickly understand the performance of each clinical department. In addition, the scored indicators make it easy to compare between clinical departments. That is, it is possible to compare the workload and profitability of different clinical departments or clinical groups, analyze differences in resource allocation and efficiency between clinical departments, and help identify areas that need improvement or areas where resources should be prioritized. Furthermore, by scoring and recording regularly, the effects of improvement measures can be tracked in concrete numbers, and by evaluating the fluctuations in scores, the success of the measures and the need for further improvement can be judged. Moreover, by regularly monitoring scores, risks such as workload imbalances and declining profits can be detected early. By issuing warnings when scores fall below the standard value or when sudden fluctuations occur, efforts can be made to detect management risks early, and it contributes to maintaining management stability through prompt response.

[0043] Furthermore, the fluctuation history recording module periodically records fluctuations in the workload score and profitability score generated by the score generation module. This can be used to understand long-term fluctuation trends in workload and profitability for each medical department, enabling analysis of improvement effects and changes in resource demand, which is useful for management decisions.

[0044] Furthermore, the notification module, based on the fluctuation trends in workload and profitability obtained from the fluctuation history recording module, automatically notifies medical administrators if significant fluctuations such as excessive workload or decreased profits are detected in a particular medical department. This allows for immediate countermeasures and helps mitigate business risks.

[0045] Furthermore, the "score generation module" of the present invention corresponds to the score evaluation unit 295C described later and is related to functions related to the conversion and visualization of scores for workload and profitability. In addition, the "variation history recording module" of the present invention corresponds to the data storage area 283 described later and periodically records fluctuations in workload and profitability scores, which are stored in the data storage area. Furthermore, the "overview module" of the present invention corresponds to the alert processing means 293 and notification means 250 described later and is related to the function of notifying medical managers when an anomaly in workload or profitability is detected.

[0046] (T) The profit indicator calculation module has the function of periodically monitoring and updating one or more outcomes, including the rate of revenue improvement, the degree of workload reduction, the degree of improvement in bed occupancy rate, the reduction in patient response time, and the cost reduction rate, for each clinical department or clinical group based on the simulation results. According to this invention, the effects of management improvements can be continuously monitored as one or more outcomes, including revenue improvement rate, reduction in workload, improvement in bed occupancy rate, reduction in patient response time, and cost reduction rate, enabling more accurate management decisions. Furthermore, by combining multiple indicators, it is possible to improve overall management performance, which is difficult to see with a single indicator, and to provide measures that can be expected to have a greater synergistic effect. By taking into account not only economic indicators such as revenue and costs, but also operational indicators such as workload and patient response time, it is possible to derive strategic and sustainable improvement proposals that are not biased towards short-term profits.

[0047] (U) A hospital management support system for visualizing the status of hospital management, A revenue management database containing data on one or more management indicators for each clinical department or clinical group within the hospital, such as sales, costs, patient numbers, and revenue. It has a patient information management database that assigns patients to patient groups, A hospital management support system characterized by having a profit indicator calculation module that calculates individual management indicators for each patient group.

[0048] (V) The revenue management database has the results of calculating the profit margin for each patient group, which is a group of patients, registered for each clinical department or clinical group within the hospital, and the profit simulation module has a function to visualize the profit margin for each patient group. (V') The revenue management database has a patient grouping management function that distributes (manages) patients into groups, and the results of calculating one or more of either patient-specific profit or patient-specific sales for each grouped patient are registered for each clinical department or clinical group within the hospital, and the profit indicator calculation module has a function to simulate the profit or sales for each clinical department or clinical group when treating a certain number of patients from each patient group.

[0049] According to this invention, by visualizing the profit margin for each patient group, it becomes possible to identify high-revenue patient groups and low-revenue patient groups with high costs. This allows for strengthening services and measures tailored to each group, enabling efficient countermeasures and facilitating the development of strategies for optimal allocation of management resources. When low-revenue groups are identified, the cause may be related to the content of medical treatment, patient care, or insufficient equipment. Visualization highlights these problems, allowing for appropriate service improvements and potentially increasing patient satisfaction. Furthermore, for high-revenue patient groups, further service improvements can lead to an increase in repeat patients. In addition, based on the profitability of each patient group, it becomes easier to plan the efficient allocation of resources (doctors, nurses, equipment, etc.) between departments and medical groups. Increasing the capacity for medical care for high-revenue groups can increase revenue and support efficient operations.

[0050] (W) The revenue management database is capable of calculating profits by adding drug price profits and material cost profits to the DPC profit for each patient, and the results of calculating modules for grouping patients are registered for each clinical department or clinical group within the hospital, and the calculation module (the profit indicator calculation module) has a function to visualize the profit margin for each patient group.

[0051] The revenue management database has a function to calculate the profit for each patient by adding the profit margin on drug prices and material costs to the profit margin on the DPC (Diagnosis Procedure Combination) system. It also groups patients according to specific criteria, calculates the profit for each group, and registers this information for each clinical department or clinical group within the hospital. Furthermore, the profit indicator calculation module has a function to visualize the profit margin for each patient group, making it easier to understand the profitability of each clinical department or clinical group. In particular, for profits including drug price differences, the system calculates the average using a statistically sufficient number of patients, allowing for the evaluation of revenue stability and profitability.

[0052] (X) The system has a function to input text requesting doctors to enter the target number of patients, a function to register the times when healthcare professionals are most likely to receive phone calls, and a function to notify healthcare professionals by speaking the text information aloud during the aforementioned times when they are most likely to receive calls.

[0053] This system includes a function to generate text requests for doctors to input their target patient numbers. It also has a function to register times when healthcare professionals are most likely to receive phone calls, and can notify healthcare professionals by speaking the request text information aloud at the registered times. Furthermore, it incorporates a function to send reminders by phone as needed to ensure that target patient numbers are entered correctly each month, as well as a chat-based reminder function, efficiently supporting reminders using both chat and phone. This series of functions ensures that healthcare professionals can reliably input their target numbers, supporting efforts toward achieving those goals.

[0054] (1) A management support system for calculating one or more management indicators (sales or gross profit) from among sales by disease group or treatment category, gross profit from medical fees, gross profit including drug price differences, or gross profit including material differences, for medical institutions (hospitals, etc.), characterized in that components 1 and 5 below are required, and one or more of components 2, 3, 4, 6, and 7 are combined with them. Here, "disease group" refers to a group classified by element 5, and "treatment category" refers to each clinical department or treatment group within the hospital. Sales, gross profit from medical fees, gross profit including drug price differences, and material price differences are stored in the "revenue management database" of the present invention. The database includes data tables such as a patient data table, a DPC information table, and a management data table, enabling detailed analysis of the management situation and proposals for improvement measures, thereby helping healthcare professionals maintain an optimal balance between providing medical care and managing the business within limited resources. Healthcare professionals, especially physicians, have the responsibility to prioritize treatment selection based on medical indications. However, in today's environment where medical cost reduction is required, physicians can be expected to focus on management aspects, consider management efficiency, and make management efforts to select cost-effective treatments. The invention has a patient grouping management module that groups and manages patients in a hospital based on specific treatment content or diseases, and has a function to calculate profit data for each patient group based on past data. The patient grouping management module has a classification module that classifies patients into specific groups such as arrhythmia, heart failure, catheter treatment, and breast cancer surgery based on the DPC information. The "Patient Grouping Management Module" (Element 5) of the present invention includes components such as a DPC information acquisition module, a patient classification module, and a profit data calculation module. It contributes to the efficiency of hospital operations and patient management, enables the optimization of medical resources by understanding the profitability of each patient group in detail, and allows for the identification of treatment areas with low profit margins and the implementation of improvement measures. The "Classification Module" is based on patient attribute-based grouping, which applies specific criteria based on patient attribute information (diagnosis, treatment content, hospitalization status, disease characteristics, etc.), a medical database that analyzes medical data such as DPC information and treatment content and classifies patients using pattern recognition or rule-based methods, and a segmentation-based grouping, which analyzes patient data and creates groups according to their characteristics. Grouping can be based on diagnosis / disease characteristics, treatment content, individual patient attributes, hospitalization / treatment patterns, etc.

[0055] By utilizing a patient group management module and a revenue management database, this system helps physicians understand the importance of patient groups that should be prioritized from a business perspective and encourages their inclusion by presenting treatment targets that are also business-critical. By visualizing the profitability, costs, and profit margins of each patient group, it can support physicians in setting specific goals, such as increasing the number of patient groups that should be prioritized from a business perspective or focusing on specific diseases.

[0056] Details of each element are as follows: <1: Database for storing DPC / medical claim data> It stores either Diagnosis Procedure Combination (DPC) data information or claims information, and includes patient information, disease name information, sales information, or payment information. Patient information: Patient ID, age, gender, length of hospital stay, etc.; Disease information: Primary diagnosis, comorbidities, complications, etc.; Revenue information: Revenue data based on medical fees and medical procedures, etc.; Payment information: Insurer's share and patient's share, etc. <2: Database for storing sales and payment information other than DPC and medical claim data> It stores non-insurance revenue, drug purchase information, material purchase information, or one or more of these, and includes information on sales, costs, and profit margins. Non-insured income: Revenue from self-pay medical services (e.g., health checkups, vaccinations, cosmetic surgery); drug purchase information: drug purchase price, usage amount, inventory data; material purchase information: purchase price and usage information of medical equipment and consumables, etc. Sales information: Revenue from non-insured medical services and in-house sales; Cost information: Procurement costs for drugs and materials; Profit margin information: Profit as the difference between sales and costs, etc. The two databases, Element 1 and Element 2, function as a management platform for hospital management data, covering both elements related to insured medical services and elements related to uninsured medical services, drug costs, material costs, and personnel costs (collectively referred to as "uninsured medical services").

[0057] <3: Module for classifying sales and payment information> This classification module classifies sales and payment information obtained from element 1, element 2, and databases such as revenue management DB, as specific patients or affiliation information associated with those patients, and the classification is performed based on one or more of the following for the patient. The medical department the patient visited The hospital bed or ward in which the patient stayed. The medical group to which the doctor who examined the patient belongs Classification of patient care (inpatient, outpatient, etc.)

[0058] <4: Module for allocating costs and revenue> A module for allocating costs to patients based on the aforementioned affiliation information, specifically for clinical departments, hospital beds or wards, and medical groups. <5: Classification module for classifying patients> A module that uses DPC data, claims data, medical records, or a combination of these data to classify patients into specific disease groups or treatment categories.

[0059] <6: Calculation and Analysis Module> A calculation module or analysis module for calculating sales, gross profit, drug price difference, material price difference, number of patients, or a combination thereof for each of the classified disease groups or treatment categories.

[0060] <7. Modules for outputting or visualizing analysis results> A module that outputs or visualizes the results from the aforementioned analysis module as the aforementioned management indicators by clinical department, disease group, period, or for the entire medical institution.

[0061] The aforementioned classification module has the function of subdividing and grouping patients based on one or more of the following conditions: medical treatment or surgical procedure, medication information, length of hospital stay, and presence or absence of complications. The "classification module" of this invention analyzes patient characteristics and medical information in a multidimensional manner and groups them according to specific purposes. This subdivision allows for the identification of patient groups with high profitability and cost-effectiveness, which is useful for considering highly profitable treatment strategies. Subdivision based on medical treatment content, surgical procedures, medication information, length of hospital stay, and presence or absence of complications contributes to achieving both patient-specific medical care and improved management. • Treatment and surgical procedures: Resources can be allocated accordingly, improving treatment efficiency. Understanding the revenue structure for each treatment and surgery allows for focus on high-profit treatments. Furthermore, creating patient groups based on treatment type enables specialized care for specific diseases and treatments, allowing resources to be concentrated on treatments that contribute to hospital management. • Drug Information: Understand the usage patterns of high-cost drugs and generic drugs to facilitate appropriate drug selection. By segmenting drug use, standardization of treatment pathways is promoted, and variability in treatment effectiveness among patients is reduced. Identifying patient groups that use drugs with high drug price margins allows for the promotion of more profitable treatment plans. • Length of hospital stay: Differentiate between short-term, medium-term, and long-term inpatients to improve bed occupancy rates and optimize patient flow. Since length of hospital stay affects medical fees, grouping by length will allow for the identification of the most profitable length of hospital stay. • Presence or absence of complications: Strengthen management and prevent an increase in patients at risk of complications, and plan and implement appropriate preventive measures for patient groups at high risk of complications.

[0062] The aforementioned calculation module has the function of updating drug price difference profits and medical revenues for each patient group in real time and making future profit predictions based on the aforementioned past data. This system predicts profits by using real-time data obtained from electronic medical records, drug management systems, and medical fee billing systems, including DPC information, drug usage data, treatment details, and surgical details, along with historical data on past revenue, costs, and drug price margins, as well as revenue patterns including seasonality and trends. It then uses algorithms such as regression analysis and time-series forecasting models to calculate the impact of drug usage patterns and treatment changes as hypothetical scenarios. By integrating real-time and historical data and utilizing advanced algorithms, this system aims to improve both hospital management and the quality of healthcare delivery, supporting improved management efficiency, optimized treatment strategies, and enhanced healthcare quality.

[0063] The aforementioned analysis module presents a management plan based on the total profit for each patient group and has the function of prioritizing resource allocation to high-profit groups based on total profit. We will develop a business plan using total profit data for each patient group and prioritize resource allocation to the most profitable patient groups. Total profit is calculated by subtracting costs such as hospitalization fees, personnel costs, drug costs, and medical equipment usage fees from revenue from medical services and drug price margins. This will be aggregated for each patient group. As for business strategy proposals, based on the total profit data for each patient group, we will implement measures to expand high-profit areas and improvement plans (cost reduction and profit improvement measures) for low-profit groups. Resource allocation to high-profit groups is prioritized in terms of personnel (specialists, nurses, technical staff), equipment (operating rooms, specialized equipment such as catheter devices), and budget (necessary drug procurement and equipment upgrade costs). Resource allocation can be adjusted in real time based on gross profit and in response to fluctuations in profitability and demand.

[0064] It has a user interface that allows each function to be executed for each patient group, and includes a graphing module for visually displaying the time-dependent changes in profit margins, revenue, costs, and drug price margins for each patient group. By providing a user interface (UI) and graph display module that visually displays the time-dependent trends of profit margins, revenue, costs, and drug price margins for each patient group, the system features an intuitive design that is easy for healthcare professionals (doctors, management personnel) to operate. Options such as filtering patient groups, setting data ranges (6 months, 1 year), and freely switching between indicators such as profit margins, revenue, costs, and drug price margins enable quick decision-making regarding management plans and treatment policies.

[0065] (2) A hospital management support system further comprising a planning module for which, as a management indicator for each disease group or medical category, the system calculates a management indicator per patient for each disease group or medical category obtained by dividing by the number of patients for each disease group or medical category, and a user who has planned the number of patients for each disease group or medical category inputs or registers the planned number of patients, multiplies it by the management indicator per patient for each disease group or medical category, or inputs or registers the planned number of patients, and correlates the planned number of patients with the management indicator per patient for each disease group or medical category to plan the management indicators.

[0066] This invention is a project value planning module based on per-patient management indicators. It calculates per-patient management indicators considering the number of patients for each disease group or medical category, allows the user to input or register the projected number of patients, and calculates projected values ​​for management indicators by multiplying the projected number of patients by the per-patient management indicators. This enables simulation of business plans in response to future increases or decreases in the number of patients and provides flexible revenue forecasts based on the projected number of patients entered by the user. "Multiplication" refers to the process of multiplying to calculate the total management indicator based on the number of patients. For example, the total management indicator = per-patient management indicator × projected number of patients.

[0067] (3) The target number of patients is the planned number of patients entered or registered by the user who planned the number of patients for each disease group or medical category. The target number of patients refers to the planned number of patients, and may be replaced with terms such as "expected," "assumed," "predicted," "expected," "planned," "estimated," "guideline," "likely to be reached," "assumption," or "future."

[0068] (4) A hospital management support system characterized by further comprising a performance analysis module that analyzes the actual profit of each disease group or medical category based on actual values ​​such as the actual number of patients, sales, gross profit from medical fees, drug price difference profit, and material price difference profit, and enables comparison with planned values, and a visualization module that visualizes the analysis results.

[0069] This invention, as a management support system equipped with a comparative analysis function with actual values, clarifies the difference between planned and actual values, enables evaluation of the degree of achievement of management goals, and contributes to supporting continuous management improvement based on actual values. It calculates multidimensional management indicators that take into account not only sales and gross profit, but also drug price difference profit and material difference profit, enabling the formulation of future management plans that accurately predict future revenue and profit using the planned number of patients, and visualizes management challenges and areas for improvement by comparing actual and planned values. [Effects of the Invention]

[0070] The present invention aims to efficiently utilize resources within a hospital and simultaneously achieve sound management and improved quality of medical services. In particular, it comprehensively evaluates multiple indicators such as workload, profitability, bed occupancy rate, and cost reduction, and derives improvement plans through simulation, thereby enabling managers to make quick and sustainable data-driven decisions. Furthermore, it supports the early identification of resource surpluses or shortages and bottlenecks through the visualization and notification functions of each indicator, thereby optimizing the overall management efficiency and service quality of the hospital.

[0071] Furthermore, patient grouping helps to achieve both operational and medical efficiency by enabling focus on highly profitable areas, improving the quality of treatment, reducing costs, and strengthening risk management. [Brief explanation of the drawing]

[0072] [Figure 1] A diagram showing the network configuration of a hospital management support system according to an embodiment of the present invention. [Figure 2] A block diagram showing an example of the system configuration of a hospital management support system. [Figure 3] A block diagram showing an example of the functional configuration of Server 20. [Figure 4] A flowchart showing the processing procedure executed by the program of the hospital management support system according to an embodiment of the present invention. [Figure 5] A flowchart illustrating the processing steps for bottleneck removal. [Figure 6] A flowchart illustrating the surgical procedures for grouping and classifying patients. [Figure 7] A diagram illustrating the before and after of the simulation. [Figure 8] A diagram illustrating the surgical procedure flow in a hospital. [Best Mode for Carrying Out the Invention]

[0073] Figure 1 shows the network configuration centered on the hospital management support system 2. It is connected to in-hospital terminals 1 (such as PCs and tablet devices) and in-hospital medical systems 3 (for example, electronic medical record systems, accounting systems, and DPC systems) via telecommunication lines and communication networks such as the Internet, intranet, and in-hospital LAN, as needed. The network is configured to allow the hospital management support system 2 to access medical systems 3 when it needs to refer to data within those systems.

[0074] Figure 2 is a block diagram showing an example of the system configuration of the server 20 of the hospital management support system 2 and other equipment. The hospital management support system 2 performs display control to visualize the status of hospital management, which has been analyzed and evaluated by the server 20, on in-hospital terminals 10 and 30. The computer of the server 20 is equipped with a communication IF 22, an input / output IF 23, memory 25, storage 26, and a processor 29.

[0075] Communication IF22 is an interface for inputting and outputting signals so that the server 20 can communicate with external devices. Input / Output IF23 functions as an interface to an input device for receiving input operations from the user and an output device for presenting information to the user. Memory 25 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as DRAM. Storage 26 is a storage device for saving data, such as flash memory or an HDD. Processor 29 is hardware for executing the instruction set written in the program, and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0076] The in-hospital terminals 10 and 30 are connected to the server 20 via the network 80 and have display means that visualize the status of hospital management analyzed and evaluated by the server 20. They also have display means or notification means that provide real-time notification via a visual screen or audio if there is a significant change in the monitored score. The in-hospital terminal 10 is connected to the network 80 by communicating with communication equipment such as a wireless base station 81 that supports various communication standards such as LTE, and a wireless LAN router that supports IEEE and wireless LAN standards. The in-hospital terminal 10 includes a communication IF 12, an input device 13, an output device 14, a memory 15, a storage unit 16, and a processor 19. The in-hospital terminal 10 is a desktop or laptop PC, while the in-hospital terminal 30 is a mobile terminal such as a tablet or smartphone.

[0077] The communication interface 12 is an interface for the in-hospital terminal 10 to communicate with the server 20 and input / output signals. The input device 13 is an input device (such as a keyboard, touch panel, touchpad, mouse, or other pointing device) for receiving input operations from in-hospital users. The output device 14 is an output device (such as a display or speaker) for presenting information to in-hospital users. The memory 15 is for temporarily storing programs, data processed by programs, etc., and is a volatile memory such as DRAM. The storage unit 16 is a storage device for saving data, such as flash memory or an HDD. The processor 19 is hardware for executing the instruction set written in the program, and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0078] <Functional configuration of Server 20> Figure 3 is a block diagram showing an example of the functional configuration of server 20. Server 20 is electrically connected by a bus to a communication means 220, an input device 230, a display device 240, a storage means 280, a control means 290, an input means 291, a display control means 292, an alert processing means 293, a user IF 294, and a profit simulation means 295.

[0079] The communication means 220 performs modulation and demodulation processing for the server 20 to communicate with user terminals 10 and 30, processes the signal calculated by the control means 290 for transmission, and transmits it to external devices and equipment. The communication means 220 processes the signal received from the outside and outputs it to the control means 290. In this way, the communication means 220 interprets commands or input content and provides them to each means, and also functions as an interface that interprets various display commands issued from the storage means 280 and performs output control.

[0080] The input device 230 is a device used by an administrator to input instructions or information to operate the server 20 as needed, and may be a keyboard, mouse, reader, or touch-sensitive device. The input device 230 also converts the instructions input by the administrator into electrical signals and outputs the electrical signals to the control means 290. The input device 230 also includes a receiving port that accepts electrical signals input from external input devices. The display means 240 is a display device such as an LCD or organic EL that presents information to an administrator who operates the server 20 as needed. The display 241 can display data corresponding to the control content of the control means 290 and can check the communication status between the server 20 and other external devices 10, 30.

[0081] The storage means 280 is implemented by memory (RAM) 25 and storage 26 such as a disk device (floppy disk, hard disk, or magneto-optical disk, etc.) and stores data, programs, etc. used by the server 20. The storage means 280 stores the application program 282 of this system, as well as data for the work area 281, data storage area 283, and screen definition storage area 284.

[0082] The work area 281 is a region that is secured when the system is started and where various data input and output by the system are temporarily stored. The data storage area 283 is a region where data temporarily stored in the work area 281 is semi-permanently stored through write control when a save request is made. The screen definition area 284 is a region where screen definition information for various screens to be output and displayed on the in-hospital terminals 10 and 30 is stored in advance, and includes format information for the screen settings to be displayed by the display control means 292.

[0083] The screen setting format information included in screen definition area 284 includes a function to visualize profit margins for each patient group, and a graph display module for visually displaying the temporal trends of profit margins, revenue, costs, and drug price margins for each patient group. The profit margin visualization function visualizes the profit margins for each patient group in real time, allowing for an intuitive understanding of management efficiency. It displays profit margins as bar graphs or line graphs and uses color schemes and labels to facilitate comparisons between groups. The graph display module visualizes the temporal trends of revenue, costs, and drug price margins for each patient group (e.g., arrhythmia, heart failure, breast cancer surgery). It also has a user interaction function to switch target groups using a group selection menu, a real-time update function that instantly reflects the latest data obtained from electronic medical records and medical fee systems, and a data export function that outputs analysis results in Excel or PDF format for sharing and report creation.

[0084] The data storage area 283 includes an evaluation basis DB 283A which stores information that forms the basis of the evaluation used by the profit simulation means 295, a revenue management DB 283B which calculates sales or profits, and a resource management DB 283C which registers resources.

[0085] Evaluation Base DB283A is a database that stores information indicating the status of hospital management in terms of items and numerical values. It includes information such as hospital beds and their occupancy rates, patients and patient costs, in-bed patients and length of stay, fixed cost items and their amounts, and doctors and administrative assistants and their working hours, working days, and compensation amounts. Resource Management DB283C is a database that stores information indicating the status of hospital management in terms of items and numerical values ​​or allocation frequencies. For facilities that directly affect revenue, such as operating rooms and waiting rooms, it includes information such as operating room occupancy rates, number of surgeries, revenue, waiting time, and number of medical staff allocated, as well as the number of waiting room users, waiting time, and the status of provision of revenue-related services.

[0086] Here, allocation frequency refers to the frequency and number of resources (doctors, nurses, medical equipment, etc.) allocated to a specific facility or resource, numerically indicating how much resources are allocated to facilities that need them and how frequently those resources are used. Staff allocation frequency represents the frequency of the number of doctors and nurses assigned to a facility (for example, an operating room). If "2 doctors and 3 nurses" are assigned to an operating room in the morning, the staff allocation frequency for the operating room in the morning is recorded as "2 doctors, 3 nurses." Equipment allocation frequency shows how often medical equipment and treatment spaces are used in which facilities. For example, the allocation frequency for a CT scanner might be recorded as "Operating room: 3 times a week, Emergency room: 2 times a week," allowing us to understand which facilities use the equipment frequently and helping to optimize allocation. Bed allocation frequency represents the occupancy and patient utilization frequency of hospital beds, indicating the occupancy rate of the beds. If a waiting room's frequency of use is "used by an average of 10 people per day," this can serve as a basis for deciding whether to increase, decrease, or reallocate hospital beds. By introducing the concept of frequency of use, even for resources that are difficult to quantify intuitively, it becomes possible to manage resource allocation within the hospital more precisely and to concretize and visualize improvement measures aimed at improving operational efficiency.

[0087] Furthermore, the evaluation base DB283A includes a patient grouping management module that groups and manages patients in hospitals based on specific treatments or diseases. It also has a classification module that classifies patients into specific groups (such as arrhythmias, heart failure, catheter treatment, and breast cancer surgery) based on DPC information. The patient grouping management module has the function of efficiently managing patients within a hospital by grouping them based on specific treatments or diseases, and the function of integrating and managing medical content, treatment outcomes, and cost / revenue information for each group. Classification is performed by treatment method, such as catheter treatment or breast cancer surgery, based on medical content, or by disease, such as arrhythmias, heart failure, or cancer treatment. The classification module utilizes DPC information to associate patients with medical departments and treatments based on DPC codes. Furthermore, it performs subdivided classifications according to primary diagnosis, presence or absence of complications, length of hospital stay, and surgical / procedural content. In addition to fixed conditions, it is possible to adjust classifications in real time based on data fluctuations.

[0088] Revenue Management DB283B is a database that stores the results of calculations for sales and profits for each clinical department or clinical group within the hospital. This database accumulates data on one or more key management indicators for each clinical department or clinical group, such as sales, costs, patient numbers, and revenue, including revenue status and profit margins. Sales and profit data are used to evaluate the profitability of each clinical department, calculate unit management indicators (profit indicators), identify departments that need improvement, and identify highly profitable departments. This data allows for the review of resource allocation and simulations of revenue improvement. Furthermore, it serves as foundational data for calculating profit per resource unit for each clinical department.

[0089] Furthermore, the revenue management DB283B also functions as a calculation module that calculates medical revenue, drug price difference profit, raw material costs, and total profit for each patient group. Revenue information obtained from medical systems 3 such as the medical fee billing system and electronic medical records, and drug price difference data from the drug management system are used as real-time data, while past revenue data and raw material costs (equipment costs, consumable costs, etc.) are used as fixed data, and the calculation module functions based on each data input. The calculation module is mainly handled by the processing functions of the calculation unit 295D, which calculates and aggregates the data for each patient group using the calculation method described below, and can also reflect past trends by utilizing time-series data. • Medical revenue: Calculated from medical fees (bundle points, fee-for-service points) • Drug price difference profit: Calculate the difference between the price of prescription drugs and the procurement cost. • Raw material costs: Medical equipment usage fees, drug costs, and other consumable costs. • Total profit: Medical revenue + drug price difference - raw material costs

[0090] The patient grouping unit 295E is responsible for the main processing function of the patient grouping management module and has a classification function that classifies patients into specific groups such as arrhythmia, heart failure, catheter treatment, and breast cancer surgery from DPC information in the revenue management DB 283B. Since the DPC code includes information such as the primary diagnosis, surgical / procedure content, and severity, "I49.x" (heart rate abnormality) is classified as "arrhythmia," "I50.x" (heart failure) as "heart failure," and "C50.x" (malignant breast tumor) as "breast cancer." During classification, information such as the primary diagnosis code, surgical / procedure content, length of hospital stay, and patient attributes is extracted from electronic medical records and DPC billing data, and patients are grouped according to predefined rules. The patient grouping management module calculates the revenue, cost, drug price difference profit, and profit margin for each group and integrates and manages the data.

[0091] The classification and characteristics of each patient group, along with the proposed management plan, are as follows, for example. Note that the management plan and objectives are not fixed; it is possible to generate a plan tailored to the situation of each patient group or individual patient using AI. <Arrhythmia Treatment Group> Primary diagnosis: I49.x Surgical procedure: Catheter ablation Features: High profitability, short hospital stay Business plan: Resource allocation (increase in the number of specialists, expand catheterization equipment) Business objective: Increase the number of catheterization procedures. <Heart Failure Patient Group> Primary diagnosis: I50.x Characteristics: High rate of medium- to long-term hospitalization, high proportion of elderly patients. Business plan: Introduction of a home care program to shorten hospital stays. <Breast Cancer Surgery Group> Primary diagnosis: C50.x Surgical procedure: Mastectomy, reconstructive surgery Features: Post-operative care is crucial; the use of chemotherapy drugs impacts revenue. Business plan: Strengthening post-operative follow-up system, using highly effective and low-cost chemotherapy drugs. Business objectives: Reduce postoperative complications and maximize drug price margins to improve profitability.

[0092] Furthermore, by including medical cost data, it is possible to record direct treatment costs (pharmaceutical costs, examination costs, surgery costs, etc.) and indirect costs (personnel costs, equipment usage costs) for each clinical department and clinical group, understand the balance between revenue and medical costs, and analyze which clinical departments are profitable and have high profit indicators, or which are too costly and have low profit indicators. In addition, by including patient number data, it is possible to record the number of patients, visits, hospitalizations, and repeat visitor rates for each clinical department and clinical group, analyze fluctuations in patient numbers and their impact on revenue, and understand which clinical departments have a stable number of patients. By including average length of stay and turnover rate, it is possible to record the average length of stay and bed turnover rate for each clinical department, especially for clinical departments where hospitalization occurs, analyze how length of stay affects revenue, and determine whether bed utilization is efficient. By including patient cost (consultation cost), the average cost per consultation and patient cost (average revenue per person) can be recorded, allowing for an understanding of the profitability of high-revenue departments and treatments, which can then be used to introduce and strengthen high-revenue treatment options. By including treatment time and staff working hours, the average treatment time for each department and staff working hours can be recorded, and the balance between profitability and consultation time can be measured, allowing for the identification of areas for improvement to increase operational efficiency and revenue. By including cancellation rates and unfulfilled appointment rates, the number of appointments, actual visits, cancellation rates, and unfulfilled appointment rates for each department can be recorded, which is useful for planning countermeasures and improving revenue for departments with high cancellation or unfulfilled appointments. By including revenue data by treatment content, revenue data for specific types of treatments, procedures, and examinations can be recorded, enabling analysis of profitability by department and treatment content, and allowing for strategic decisions such as focusing on high-revenue treatments. By including seasonal and trend data, it is possible to record seasonal fluctuations for each medical department and annual revenue trends, allowing for the understanding of seasonal variations and annual trends, and enabling the use of this data for seasonal management strategies, such as increasing resources during periods when revenue is likely to increase. Furthermore, by analyzing the interrelationships of this data, specific measures for improving revenue can be made clearer.

[0093] The control means 290 is configured such that various processes, including the input means 291, display control means 292, alert processing means 293, user IF 294, and profit simulation means 295, are implemented by a processor. The processor is one or more processors. At least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may be other types of processors such as a GPU (Graphics Processing Unit). At least one processor may be single-core or multi-core. Furthermore, at least one processor may be a broad-sense processor such as a hardware circuit that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array) or ASIC (Application Specific Integrated Circuit)).

[0094] The display control means 292 controls the display of the results of the evaluation and simulation of the management status by this system on the in-hospital terminals 10 and 30. Key indicators such as sales, profits, resource utilization rates, workload, and profit margins for each patient group are displayed in a dashboard format for easy viewing. Anomalies in key indicators (for example, departments with excessively high workloads or declining profitability) are highlighted using color coding and icons. The simulation results are displayed in graph formats such as line graphs, bar graphs, and pie charts, showing the impact of changes in profits when resources are increased or decreased, and the impact of changes in the number of patients and profit margins for each patient group on revenue, over time, aiding in intuitive understanding. A dashboard is provided for simultaneously comparing multiple simulation results, such as changes in profits due to resource reallocation and changes in bed occupancy rates, helping to consider the optimal strategy while comparing different patterns. Different simulation conditions can also be manually adjusted using interactive formats such as sliders and checkboxes.

[0095] The alert processing means 293 has the function of notifying medical managers when significant fluctuations such as excessive workload or decreased profits are detected in the fluctuation trends of workload and profitability obtained by the fluctuation history recording module, and can issue a command to send a warning to the displays and speakers of the in-hospital terminals 10, 30. Alternatively, an immediate notification may be sent to the managers via email or SMS. Furthermore, as a preventative measure for business improvement, by having the AI ​​detect significant fluctuations early, preventative measures can be taken before they develop into serious business risks.

[0096] User interface 294 performs processing that enables functions such as receiving user operations and commands from in-hospital terminals 10 and 30 via keyboard input or voice input, and creating a layout that is easy for the user to view based on the screen format information in the screen definition area 284.

[0097] The profit simulation means 295 has the following functions: a function to calculate the increase in profit by moving or adding unit resources based on the data in each database of the data storage area 283; a function to calculate simulations of an increase in the number of patients using hospital beds and an improvement in the average patient cost based on the bed occupancy rate; a function to calculate simulations of an increase in the number of patients and an improvement in the average patient cost by promoting early admission and discharge of patients; a function to calculate simulations of resource improvements that are bottlenecks in hospital facilities and other fixed costs; a function to propose the placement of multiple medical office assistants and calculate simulations of the operation of multiple operating rooms; a function to convert the workload and profitability of each clinical department and clinical department group into scores and calculate simulations using score values; a function to periodically record fluctuations in workload scores and profitability scores and calculate fluctuation trends in workload and profitability for each clinical department; a function to calculate the profit margin for each patient group and group them by each clinical department or clinical group; a function to calculate one or more of either patient-specific profits or patient-specific sales for each grouped patient group; and a function to simulate and calculate the current profit data for each patient group based on the aforementioned past data. In other words, the profit simulation means 295 has a complex function of performing various simulations in hospital management and providing data for management improvement.

[0098] The simulation results provide data-driven directions for improving management, supporting managers in making quick, data-driven decisions. Furthermore, efficient resource allocation (resource movement, resource expansion) maximizes revenue and reduces workload. In addition to its primary function of grouping and managing patients, the patient grouping management module may also be able to simulate optimal patient grouping for hospital management and define specific conditions for grouping. Moreover, it contributes to supporting medium- to long-term management strategies, enabling the setting of medium- to long-term management goals through trend analysis and evaluation of their achievement. For example, it could be linked to a function that outputs simulation results for use as budgeting data in hospital management in subsequent years.

[0099] The profit increase simulation through the movement of individual resources simulates the increase in profits obtained when resources are reallocated between medical departments, based on each database in the data storage area 283. For example, it calculates how much overall revenue improves when personnel or hospital beds are moved from a low-profit medical department to a high-profit medical department, or when resources are increased in a high-profit medical department. It also simulates prioritizing resource allocation to high-profit groups based on the total profit for each patient group.

[0100] The bed occupancy rate-based patient number and average patient revenue increase simulation uses bed occupancy rate data to simulate changes in revenue due to an increase in the number of patients or an increase in average patient revenue. For example, it calculates the revenue increase when accommodating more patients through efficient use of beds, and the profit fluctuation when the average patient revenue is increased. In this process, profit indicators such as total profit and profit margin for each patient group are calculated and registered for each clinical department or clinical group within the hospital, and the profit simulation module visualizes the profit margin for each patient group.

[0101] The simulation for increasing the number of patients and improving the average revenue per patient by promoting early admission and discharge aims to increase profitability by improving the turnover rate of hospital beds through earlier admission and discharge of patients. For example, it predicts the change in revenue that can be obtained by increasing the number of patients that can be accommodated as bed utilization becomes more efficient by accelerating admission and discharge. In this simulation as well, the profit margin for each patient group is taken into consideration.

[0102] Revenue simulations based on proposed improvements to fixed cost resources simulate changes in revenue when fixed cost resources such as equipment are acting as a bottleneck. For example, they calculate the effects of cost reductions and revenue increases expected from rearranging equipment or improving its utilization efficiency.

[0103] The simulation for optimizing medical office support and operating room operations simulates the revenue and efficiency improvements that can be achieved by optimizing the placement of medical office support staff and supporting the operation of multiple operating rooms. For example, it analyzes the potential for increased revenue through reduced workload for doctors and improved operating room utilization rates.

[0104] The simulation of workload and profitability scores for each clinical department and clinical group scores the workload and profitability of each department and clinical group, and calculates the change in scores after resource reallocation and the implementation of measures. For example, it provides simulation results that show increased profitability by adjusting the balance of workload.

[0105] The recording and trend calculation of workload and profitability scores involves periodically recording fluctuations in these scores, analyzing trends for each clinical department, and evaluating the long-term improvement effects. For example, it calculates how workload and profitability fluctuations have changed over a long period and predicts the sustainability of management improvements.

[0106] Each block function in Figure 3 corresponds to the following components. <1> The DPC / receipt data storage database stores information on medical treatment details, primary diagnosis, length of hospital stay, and medical fee information, and provides basic data for analysis, corresponding to the evaluation basis DB283A within the storage means 280. <2> The database that stores sales and payment information other than DPC / reimbursement data stores non-insurance information such as self-pay medical services, drug purchases, and material purchases, and is used to grasp the overall income and expenses, and corresponds to the revenue management DB283B. <3> The module for classifying sales and payment information classifies sales and payment information by medical department, disease category, and treatment content, and corresponds to the processing of S603 by the patient grouping unit 295E. <4> The module that assigns stock and sales corresponds to the calculation unit 295D, which assigns revenue and costs to each group and calculates the profit margin. <5> The classification module for classifying patients is designed to classify patients based on their medical department, disease group, and treatment category (inpatient / outpatient), and corresponds to the processing of S604 by the patient grouping unit 295E. <6> The calculation and analysis module corresponds to the calculation unit 295D and the profit simulation means 295, and is responsible for calculating and analyzing revenue, gross profit, drug price difference profit, and material price difference profit. <7> The module that outputs or visualizes the analysis results visualizes the analysis results in graph or tabular format and provides them to the user, corresponding to the processing in S606 by the display control means 292 and user IF 294.

[0107] Figure 4 is a flowchart showing various processing procedures executed by the application program of the hospital management support system according to an embodiment of the present invention. This system collects, evaluates, and simulates data, and visualizes and provides to the user the information necessary for management improvement.

[0108] <System startup, data preparation, data collection, and updates> This system connects to various terminals and medical systems in the hospital via the communication interface 22 and network when the server 20 is started. It also loads various data from the evaluation basis DB 283A, revenue management DB 283B, and resource management DB 283C from the data storage area 283 in the storage means 280 and loads the necessary initial data into memory. For resource and business data collection, it collects management data such as sales, profits, and resource utilization rates for each clinical department and clinical group and stores them in the revenue management DB 283B and resource management DB 283C (step S401). Then, if real-time data updates are necessary, new data and update information from in-hospital terminals 10 and 30 are reflected via the communication means 220 (step S402). To determine whether an update is necessary, for example, an expiration date can be set for the data, and only the latest data within the expiration date can be used for evaluation analysis to prevent old data from affecting the evaluation. In other words, collecting and analyzing data within a predetermined period contributes to understanding the business situation in real time. The AI ​​can also set the expiration date for data updates, dynamically adjusting the optimal expiration date based on past data fluctuation patterns and evaluation accuracy. Past data fluctuation analysis is performed over time to evaluate the frequency and stability of fluctuations. A shorter expiration date is used when the data fluctuates frequently, and a longer expiration date when the fluctuations are infrequent. Trend detection is performed, and the AI ​​uses anomaly detection algorithms and trend analysis to evaluate how stable the data is. Trends and anomalies are identified by whether revenue data fluctuates weekly or remains stable monthly.

[0109] <Evaluation of workload and profitability> Next, for generating workload and profitability scores, the score generation module converts workload and profitability into scores, which are then evaluated by the score evaluation unit 295C (step S403). Furthermore, for score recording and trend analysis, the fluctuation history is recorded periodically to identify trends in workload and profitability. The profitability score is calculated by subtracting costs from total revenue to determine profit, and then standardizing and scoring the profit margin (profitability) to enable comparison with other departments. This is based on key profitability indicators such as total revenue for each department, costs such as personnel expenses and equipment maintenance costs for the operation of the department, average revenue per patient, and revenue per medical procedure, examination, or treatment. Adjustments and seasonal corrections are made during the scoring process. If the impact of seasonality differs among departments, a predictive model may be used to correct for seasonality and smooth out fluctuations in the revenue score. By evaluating and managing this as a score that allows for an at-a-glance understanding of workload and profitability, it contributes to management improvement and resource allocation by enabling comparisons with the entire hospital or other hospitals, beyond the individual departments.

[0110] <Profit Simulation> The simulation also includes profits and related factors such as bed occupancy rates and medical resource costs (step S404). Alternatively, the profit margin for each patient group is calculated for each clinical department or clinical group within the hospital, and the patients are grouped together for the simulation. As part of the resource reallocation simulation, the resource reallocation unit 295A of the profit simulation means 295 calculates the resource unit profit for each medical department and simulates the increase in profit due to resource movement. As part of the hospital bed occupancy rate simulation, we will simulate improvements in patient revenue per patient and increases in the number of patients based on the hospital bed occupancy rate. As part of optimizing fixed cost resources, the bottleneck processing unit detects bottlenecks in equipment utilization and performs improvement simulations.

[0111] <Alert processing> The alert processing means 293 monitors sudden or significant fluctuations in workload and profitability, and if these exceed a threshold, it displays an alert on the in-hospital terminals 10 and 30, or notifies the manager via email or SMS on their mobile device (step S405).

[0112] <Display and Visualization> The display control means 292 processes data to display the management status of each clinical department or clinical group in a dashboard format on the in-hospital terminals 10 and 30 (step S406), and visually displays each simulation result using line graphs, bar graphs, etc., enabling comparison for management improvement (step S407). In addition, through the operation of user IF294, user IF294 may receive input from the in-hospital terminal, change simulation conditions, or check the details of alerts, and the profit simulation means 295 may record feedback based on the simulation results in a database, thereby supporting continuous improvement measures by managing the simulation results of resource movement or resource expansion against actual results.

[0113] Figure 5 is a flowchart showing the processing procedure for bottleneck resolution implemented by the hospital management support system.

[0114] <Data Collection> When acquiring resource utilization data, data such as resource usage, equipment utilization rate, and workload for each clinical department and clinical group are obtained from the revenue management DB and resource management DB. When acquiring fixed cost data, data related to fixed cost resources, such as the utilization rate, maintenance costs, and operating hours of each piece of equipment, are obtained from the evaluation base database (step S501).

[0115] <Analysis of resource utilization> Next, the utilization rate is calculated by determining the frequency of use and utilization rate for each resource. For example, it analyzes how much operating rooms and medical equipment are being used, and the occupancy rate of hospital beds. When evaluating the workload of medical personnel such as doctors and nurses, the workload of staff and equipment is calculated to identify areas where there is an excessive burden (step S502). In this way, in order to detect bottlenecks, data is first collected from the revenue management DB and resource management DB, and analytical processing is performed to analyze the utilization rate and frequency of use of resources.

[0116] <Comparison with reference values> A threshold value is established to determine a bottleneck (step S503), and resources exceeding this threshold are identified as potential bottlenecks (step S504). The bottleneck threshold value is set for each resource (e.g., utilization rate below 80%, excessive concentration of usage on a specific resource), and if a resource's utilization rate or usage frequency exceeds the threshold value, it is flagged as a potential bottleneck. The threshold value can also be adjusted based on factors such as seasonality and social phenomena, and the system may automatically set the bottleneck threshold value using AI. In this case, in addition to data from each department within the hospital (utilization rate, patient numbers, waiting times, revenue, etc.), data from various external sources such as seasonal data (influenza outbreaks, climate change, seasonal events) and social factor data (economic conditions, infectious disease outbreaks, etc.) may be integrated to acquire data, construct long-term time-series data on seasonality and social factor changes, and extract resource usage trends for each season or during specific event periods. As a time-series forecasting model, the AI ​​model can learn resource utilization patterns by using time-series analysis models such as LSTM (Long Short-Term Memory) and SARIMA (Seasonal ARIMA) to predict how seasonal and social factors affect resource usage trends. As a factor analysis model, in cases where resource utilization fluctuates based on external factors (e.g., epidemics or economic fluctuations), models such as XGBOOST and Random Forest, which incorporate external variables that can respond to changes in seasonal and social factors into the forecasting model, can be used to measure the impact of these variables and reflect them in setting baseline values. Dynamic adjustment of baseline values ​​can also be performed by setting different bottleneck baseline values ​​for each season and factor. For example, the baseline value for hospital bed occupancy rate can be raised during winter influenza outbreaks, and the revenue baseline can be adjusted when a deterioration in the economic situation is predicted. In addition, methods for resolving bottlenecks can be managed as a checklist and used as input to a large-scale language model, which can then be displayed as advice.

[0117] Identifying bottleneck resources involves generating a bottleneck list by adding resources exceeding a certain threshold. This list might include facilities with excessively low utilization rates, such as operating rooms and waiting rooms, or medical departments where staff are overloaded. Prioritizing these bottleneck resources and sorting them by impact level is also possible.

[0118] <Improvement proposals based on simulations> A profit simulation tool is used to simulate how to resolve the bottleneck under multiple scenarios (step S505). As improvement measures, for example, additional medical office support, an increase or decrease in the number of operating rooms, or acceleration of patient admission and discharge may be suggested. To evaluate the effectiveness of the improvements, the results of each simulation may be evaluated, and the improvement plan that is most effective in resolving the bottleneck may be suggested.

[0119] Visualization for the user (step S507) is performed according to display control (step S506). The display of improvement proposals is performed by visualization on the dashboard using the display control means, and the impact of the improvement proposals and bottleneck resources is displayed on the dashboard. It is advisable to display the degree of impact and priority clearly using graphs and color coding. In addition, an alert notification function may be provided, and for bottlenecks that require urgent resolution, the management may be notified by the alert processing means.

[0120] The specific method for converting scores involves quantifying the workload and profitability of clinical departments and clinical groups. <Calculation of workload score> • The workload is calculated based on indicators such as the number of patients per medical department, consultation time, number of consultations, working hours of doctors and nurses, and the complexity of medical treatment. • Standardize metrics such as the number of consultations and working hours, calculate the deviation from the mean, and determine the score. The score is then normalized to a range of 1 to 100. • If a specific indicator significantly impacts the workload (e.g., a medical department with a large number of patients), weight each indicator accordingly. For example, assign a weight of 40% to the number of patients, 30% to working hours, and 30% to the number of consultations. <Calculation of Profitability Score> • Revenue for a medical department is calculated as medical fees (unit price) x number of consultations. Expenses include personnel costs, medical supplies costs, and equipment maintenance costs. • Evaluate the profitability of each medical department by dividing revenue by expenses. This profitability rate is then used to create a score, which is standardized for comparison with other medical departments. • Convert scores into a 100-point scale or a 5-point rating system, making the profitability of each medical department immediately apparent. For example, a score of 100 would be assigned to a profitability rate of 20% or higher, and a score of 50 to a profitability rate of 5% or lower, creating a hierarchical system. <Regular score updates and trend analysis> • Scores fluctuate depending on business and revenue performance, so scores are recalculated monthly and quarterly to maintain a score based on the latest information. By recording the history of score fluctuations, we can analyze how the workload and profitability of each clinical department change over time. This allows us to predict the occurrence of risks such as increased workload and decreased profitability. <Overall Score> • The workload score and profitability score are combined and weighted to create a total score. For example, if the workload score is 60 points and the profitability score is 80 points, the workload score is weighted 40% and the profitability score 60%, resulting in a total score of 70 points. • Using a comprehensive score allows for a holistic evaluation of each medical department, making it easier to compare with other departments.

[0121] Quantitative evaluation using score values ​​is useful for assessing the effectiveness of resource relocation or resource expansion. By comparing changes in workload scores and profitability scores for each clinical department or clinical group before and after resource relocation, it becomes possible to measure the extent to which resource relocation has affected operational efficiency and profitability. Setting target scores according to the purpose of resource relocation and comparing them with actual results clarifies the degree of target achievement and contributes to budget management. Resource expansion serves as an indicator of whether the workload score for the entire clinical department or clinical group has decreased, i.e., whether operational efficiency has improved. Furthermore, the profitability score can be used to evaluate how much new revenue has increased as a result of resource expansion, or whether there has been an improvement in the number of patients or the average patient spending. By regularly evaluating the effects of resource relocation or expansion with scores and building a PDCA cycle that plans the next resource allocation based on the results, continuous management improvement becomes possible.

[0122] Figure 6 is a flowchart illustrating the processing steps for grouping and classifying patients. Based on DPC information and patient data, it shows the procedure for classifying patients by specific treatment content or disease and classifying their affiliation information. The hospital management support system 1 first performs data collection processing to acquire patient information such as DPC information (primary diagnosis, surgical procedure, complication information), treatment details, treatment history, length of hospital stay, and medication use data from medical systems 3 such as electronic medical records and medical fee billing systems (step S601). Patients are classified based on one or more of the following categories as patient affiliation information: the department they visited, the bed or ward they stayed in, the medical group to which the examining physician belongs, and the classification of treatment (inpatient, outpatient, etc.).

[0123] Next, the classification criteria setting process is performed (step S602). The classification rules are determined, and based on the primary diagnostic code (ICD-10 code) for disease classification and the content of surgery / procedures, hospitalization period and patient attributes (age, sex) are set as auxiliary conditions. In the condition comparison (step S603), patients who match the conditions are grouped together (step S604), and patients who do not match are provisionally processed as "unclassified," and the classification criteria are set again (step S602) to change the classification rules. The classification criteria are set based on the classification criteria, and filtering conditions are set. For example, filtering conditions such as "cardiology" + "ICU stay" + "inpatient" are used.

[0124] The classification process (step S604) classifies the data into groups based on matching / mismatched data. Each patient data is assigned to a corresponding group (e.g., arrhythmia, heart failure). If a patient belongs to multiple groups, priority is given based on the primary criteria.

[0125] Next, group data is aggregated (step S605) to calculate revenue, costs, drug price difference profit, and profit margin for each group. The aggregated results are saved to a database (evaluation base DB and revenue management DB) (step S606). These aggregated results are made visible to the user (step S607). Revenue, costs, and profit margins for each group are visualized in a graph and can be exported (PDF, Excel) as needed. Step S607 can also be made an interrupt process that is executed only when requested.

[0126] This invention utilizes both revenue management by clinical department or clinical group and patient group management in a two-pronged approach. Revenue management by clinical department or clinical group allows for a comprehensive understanding of the revenue and costs of each clinical department (e.g., cardiology, oncology, etc.) or clinical group (e.g., cardiovascular disease, cancer treatment, etc.), while patient group management classifies patients based on specific diseases and treatments to support efficient treatment and management improvement. By managing the overall management at the clinical department level on a macro level and making micro improvements at the patient group level, it is possible to achieve a balance between medical care provision and management, and by linking the two, the sustainability of the entire hospital can be enhanced. Specifically, if analysis shows that the arrhythmia treatment group accounts for a major portion of the revenue within the cardiology department, the revenue structure can be identified from both an overall and detailed perspective by analyzing the profitability by clinical department and the revenue by patient group. Furthermore, it is possible to implement measures to improve treatment efficiency for each patient group while maintaining the overall revenue balance at the clinical department level, and by promoting short hospitalizations for arrhythmia treatment to reduce costs while maintaining the overall revenue of the cardiology department, it is possible to achieve both profitability and cost reduction.

[0127] Strategic resource allocation becomes possible, and priority for resource allocation can be clearly defined. High-profit departments can be identified, and specialists and equipment can be increased, or resources can be focused on specific diseases and treatments through patient group analysis. Furthermore, flexible management responses are possible, such as planning overall strategies at the departmental level (adjusting the number of hospital beds, expanding the scope of services) and implementing specific measures at the group level (changing medications, revising treatment protocols).

[0128] Furthermore, by taking a comprehensive view of management at the departmental level and formulating specific measures based on the analysis results of patient groups, the accuracy of management decisions can be improved. For example, based on the revenue analysis results of cancer treatment, the treatment system for breast cancer patients can be strengthened. Alternatively, by identifying issues in the entire department or in specific patient groups early on based on data, it can contribute to rapid problem solving. For example, by identifying that the decline in profitability of the cardiology department is due to the long-term hospitalization of heart failure patients, the problem can be addressed.

[0129] It is possible to respond to revenue fluctuations at the departmental level, understand revenue trends for each department, and take measures to address DPC revisions and drug price revisions. For example, the use of high-priced drugs affected by drug price revisions can be adjusted in the cardiology department. Alternatively, safety measures can be implemented at the patient group level, with measures specifically tailored to patient groups at high risk of complications or readmission. For example, home care systems can be strengthened to reduce the readmission rate for heart failure patients.

[0130] Examples of combinations of required components 1 and 5 and optional components 2, 3, 4, 6, and 7 are shown. <Example 1: 1+5+2+6> Configuration: Based on DPC data and patient classification, non-insurance revenue data (2) is added and stored, and revenue indicators are calculated using the calculation and analysis module (6). Effect: This enables business analysis that includes not only insured medical services but also non-insured revenue such as health checkups and vaccinations, allowing for the identification of highly profitable self-pay medical services and the development of expansion strategies. <Example 2: 1+5+3+7> Configuration: Patient classification is performed based on DPC data, and sales and payment information is classified (3). The analysis results are displayed using the visualization module (7). Effect: Revenue and payment data can be organized by treatment category, making them intuitively understandable. <Example 3: 1+5+4+6> Configuration: Using a module (4) that assigns costs and sales in addition to DPC data and patient classification, a calculation and analysis module (6) calculates detailed management indicators. Effect: Cost allocation for each medical category becomes clearer, allowing for focus on high-profit areas. For example, cost reduction measures can be considered for medical categories with high material costs. <Example 3: 1+5+2+3+6> Structure: Based on a database (2) containing both insured medical services and non-insured revenue, sales and payment information is classified (3), and revenue is calculated using a calculation and analysis module (6). Benefits: Provides a detailed analysis of revenue and costs by treatment category, and evaluates the growth potential of non-insured medical services. Enables the development of strategies that consider the revenue balance between insured and non-insured medical services. <Example 4: 1+5+4+6+6> Structure: Based on DPC data and patient classification, cost allocation (4), analysis (6), and visualization (7) are performed. Effect: Visualizes profitability and cost structure for each medical category using graphs. Allows for quick identification of high-profit categories and areas requiring improvement, enabling the development of resource allocation plans.

[0131] Figure 7 illustrates the before-and-after of the hospital room profit simulation. (A) shows the profit simulation by medical department, (B) shows the additional profit for each medical department, (C) shows the results of the hospital room relocation simulation, and (D) shows the total profit before and after the simulation. This specifically evaluates how the relocation of resources (hospital rooms) affects the profitability of each medical department.

[0132] Figure 7(A) shows the monthly profit per patient room for each medical department (internal medicine, general surgery, ophthalmology, and otolaryngology), visualizing the differences in profitability among the departments. The total profit with the current patient room layout is 1,600 million yen, but after a simulation of revising the patient room layout, the total profit is expected to increase to 1,700 million yen, an increase of 100 million yen. In the simulation, the profit simulation module calculates the breakdown by medical department and the AI ​​generates resource movements or resource additions that will increase profits. In other words, the profit changes for each medical department will be as follows. Internal Medicine: Reducing the number of patient rooms by one resulted in a decrease in profit from 200 million yen to 180 million yen (-20 million yen). General Surgery: There is no change in the number of patient rooms, and profits remain unchanged at 1,000 million yen. Ophthalmology: By increasing the number of patient rooms by one, profits increased from 360 million yen to 480 million yen (+120 million yen). ENT (Ear, Nose, and Throat): No changes in the number of patient rooms or profits. This simulation demonstrates that increasing the number of ophthalmology wards, in particular, can increase overall profits. The profit increase resulting from the revised layout is visually shown, and resource allocation can be optimized for each medical department. The increase or decrease in the number of hospital beds is based on the hospital bed occupancy rate (not shown) calculated by the hospital bed occupancy rate management module. The hospital bed occupancy rate management module then performs a simulation of room relocation based on the current number of rooms and the number of additional rooms that can be added, calculated from the maximum number of rooms that can be physically accommodated. While there is no change in the number of hospital rooms as a whole hospital, an increase in profits can be expected due to resource changes between clinical departments. Furthermore, a clinical group refers to a team formed by collaboration between various clinical departments, or a group formed by gathering doctors and medical professionals selected from various clinical departments.

[0133] Figure 7(B) shows the potential benefits that increases or decreases in resources can bring to each department by indicating the amount of additional profit each department can generate. Based on the additional profit, it is reasonable to focus on ophthalmology. Ophthalmology has a high profit per patient room (Figure 7(C)), and the additional profit from increasing the number of patient rooms is also larger than in other departments, so it is expected to have an effect on increasing profitability. The simulation results show that adding one patient room to ophthalmology is expected to increase profits by 120 million yen, which is a higher revenue-generating effect than in other departments.

[0134] Figure 7(D) visualizes the change in total profit after resource reallocation through simulation. While the total profit before resource movement was 1,600 million yen, reallocating resources (particularly increasing the number of ophthalmology rooms by one and decreasing the number of internal medicine rooms by one) results in a total profit of 1,700 million yen after simulation, indicating an expected increase in profit of 100 million yen. This result demonstrates that resource reallocation improves the overall revenue of the hospital and visually illustrates the profitability improvement achieved by concentrating resources in specific medical departments.

[0135] Figure 8 is a diagram illustrating the surgical flow in a hospital, showing information related to surgery, such as the name of the surgery, the scheduled admission date, preoperative examinations, the surgeon, discontinuation of medications, surgical instruments, the date of surgery, anesthesiology consultations, obtaining and type of consent forms, and other restrictions. This information is a crucial factor in resource allocation and scheduling in the simulation of the present invention, as described below. These elements function as basic data to enable appropriate resource allocation and schedule adjustments through the simulation, and this basic data is stored in a database within the hospital management support system 2 or a database within the medical system 3. The patient management database stores basic information related to surgery and hospitalization, such as the scheduled admission date, preoperative examinations, surgery date, surgeon, and scheduled anesthesiology consultations for each patient. The resource management database stores information on hospital beds, operating rooms, surgical instruments, and medications, and manages the utilization and reservation status of each resource. This database allows for optimal resource allocation based on the simulation. The medical procedure database stores information on the status of consent form acquisition, the type of consent form, and other restrictions. This allows for management of whether necessary procedures have been completed before surgery and reflects this in the simulation. As a schedule management database, it stores schedule information related to surgeries and hospitalizations, ensuring smooth coordination of resources and appointments. This optimizes the scheduling of surgeries and examinations to avoid overlapping with other patients. Through these databases, the hospital management support system can comprehensively manage the status of each clinical department and patient, enabling resource allocation and scheduling based on simulations. Furthermore, by updating and synchronizing data, simulations are always conducted based on the latest information.

[0136] • Name of surgery and surgeon The required resources vary depending on the type of surgery and the qualifications and skills of the surgeon performing it, which affects the appropriate allocation of resources. • Scheduled date of hospitalization and date of surgery These dates directly impact resource scheduling and are crucial factors in optimizing the allocation of hospital beds and operating rooms. • Pre-operative examination and anesthesiology consultation By allocating resources at the appropriate time to patients who require these preparations, we help ensure that surgery proceeds smoothly. • Discontinue medication If discontinuation of certain medications is necessary, the pre-operative preparation period will differ, which will affect resource planning. • Types of surgical instruments and consent forms Securing the necessary surgical instruments and preparing consent forms are essential for performing the surgery, and it is important to track resource allocation and document preparation status through simulations.

[0137] The profit indicator calculation module of this invention optimally manages resources within a hospital and evaluates the revenue contribution of each resource. Specifically, it handles a wide range of data as resources, such as "number of waiting patients, number of patients, number of doctors, number of nurses, number of medical office assistants, number of nursing assistants, number of hospital beds, number of operating rooms, number of surgical slots, number of surgical materials, number of drugs, number of anesthesiologists, available working hours for anesthesiologists, and working hours for each clinical department." Based on this resource data and revenue data for clinical departments or clinical groups, it calculates management indicators (e.g., profit margin and cost efficiency) for each resource unit. This calculation visualizes how much each resource contributes to revenue and can be used to optimize resource allocation. This function plays an important role in supporting efficient resource utilization and decision-making for improving profitability in hospital management. [Industrial applicability]

[0138] This invention is a system related to supporting the management of hospitals and medical institutions. By visualizing the evaluation and improvement of resource utilization efficiency, profitability, and workload of each clinical department and clinical group, it is useful in simultaneously achieving increased efficiency in hospital management and improved quality of medical services.

[0139] By proposing management improvements through a combination of multifaceted indicators—that is, by comprehensively analyzing multiple indicators such as revenue improvement, workload reduction, bed occupancy rate, patient response time reduction, and cost reduction—it becomes possible to make more accurate management decisions that do not rely on a single indicator. [Explanation of Symbols]

[0140] 1: In-hospital terminals 2: Hospital management (support) system 3: Medical system 10,30: In-hospital terminal (13: Input device, 14: Output device, 15: Memory, 16: Storage unit, 19: Processor) 20: Server (25: Memory, 26: Storage, 29: Processor) 80: Internet network, 81: Wireless base station

[0141] The program of the present invention can be installed in computer terminals having computer functions such as CPU, memory, and storage, as well as mobile devices such as smartphones, tablets, and wearable devices, digital home appliances such as smart TVs, smart speakers, and smart home appliances, recording media such as USB memory, SD cards, hard disk drives (HDDs) / solid state drives (SSDs), dedicated equipment and terminals such as POS terminals, vending machines, ATMs, and medical equipment, and game consoles (home and portable). When this program is installed in medical equipment, it may be linked with the hospital's electronic health record (EHR / EMR) system.

[0142] [Note B1] To operate a computer that has a processor and memory, A program for calculating one or more management indicators in a medical institution, including sales by disease group or treatment category, gross profit from medical fees, gross profit considering drug price differences, and gross profit considering material differences, wherein the program is characterized in that components 1 and 5 below are required, and these are combined with one or more of components 2, 3, 4, 6, and 7 to constitute the processor or memory. 1: A database that stores one or more of either DPC or medical claim data. 2: Database for storing sales and payment information other than DPC and medical claim data. 3: Function to categorize sales and payment information 4: Function to allocate costs and revenues 5: Classification function for classifying patients 6: Calculation and Analysis Functions 7: Function to output or visualize analysis results [Note B2] The program described in [Appendix B1], As a management indicator for each disease group or treatment category, divide by the number of patients for each disease group or treatment category to calculate the management indicator per patient for the obtained disease group or treatment category. A program characterized by calculating target management indicators by combining the aforementioned management indicators per patient with the target number of patients for each disease group or treatment category. [Note B3] The program described in [Appendix B2] is characterized in that the target number of patients is the planned number of patients entered or registered by the user who planned the number of patients for each disease group or medical category. [Note B4] A program described in any of the [Appendix B1] to [Appendix B3], A program that causes the processor to perform a performance analysis function that analyzes the actual profit of each disease group or medical category based on actual values ​​such as the actual number of patients, sales, gross profit from medical fees, drug price difference profit, and material price difference profit, and enables comparison with planned values, as well as a visualization function that visualizes the analysis results.

[0143] [Note C1] A method for supporting hospital management obtained by operating a computer equipped with a processor and memory, A method for calculating one or more management indicators in a medical institution, including sales by disease group or treatment category, gross profit from medical fees, gross profit considering drug price differences, and gross profit considering material differences, characterized in that components 1 and 5 below are required, and these are combined with one or more of components 2, 3, 4, 6, and 7 to constitute the processor or memory. 1: A database that stores one or more of either DPC or medical claim data. 2: Database for storing sales and payment information other than DPC and medical claim data. 3: Function to categorize sales and payment information 4: Function to allocate costs and revenues 5: Classification function for classifying patients 6: Calculation and Analysis Functions 7: Function to output or visualize analysis results [Note C2] The method described in [Appendix C1], As a management indicator for each disease group or treatment category, divide by the number of patients for each disease group or treatment category to calculate the management indicator per patient for the obtained disease group or treatment category. A method for causing the processor to execute a function that calculates target management indicators by combining the management indicators per patient and the target number of patients for each disease group or medical treatment category. [Note C3] The method described in [Appendix C2], characterized in that the target number of patients is the planned number of patients entered or registered by the user who planned the number of patients for each disease group or medical category. [Note C4] The method described in [Appendix C1] to [Appendix C3], A method for causing the processor to perform a process that analyzes the actual profits of each disease group or medical category based on actual values ​​such as the actual number of patients, sales, gross profit from medical fees, drug price difference profit, and material price difference profit, and enables comparison with planned values, as well as a visualization function that visualizes the analysis results.

Claims

1. A hospital management planning method for managing patients in a medical institution according to disease group or treatment category (hereinafter collectively referred to as "group"), A hospital management planning method characterized by having the following steps (A) to (E). (A) A step of having the target number of patients for each group input, (B) A step of storing historical data related to the management indicators for each group or acquiring real-time data related to said management indicators. (C) A step of calculating the target management indicator by combining the target number of patients for each group and the management indicator based on (A) and (B) above. (D) A step of aggregating the calculation results for each group and calculating the aggregated result, (E) A step of outputting the calculation results and / or the aggregated results in tabular format.

2. A hospital management planning method according to claim 1, further comprising the steps of: importing actual data based on DPC or claims data; automatically updating the management indicators for each group as real-time data based on the actual data; and outputting a comparison of planned values ​​calculated using the updated management indicators with actual values ​​related to the imported actual data in tabular format.

3. A hospital management planning method according to claim 1 or 2, A hospital management planning method characterized in that the aforementioned management indicators are sales revenue and costs, as well as profit margins including at least drug price margins and material price margins.

4. A hospital management planning method according to claim 1 or 2, The aforementioned calculation is a hospital management planning method characterized by calculating the target sales and target gross profit for each group using the following calculation formula. Target sales = Target number of patients × Sales amount Target gross profit = Target number of patients × (Sales amount - Costs)

5. A hospital management plan program for managing patients in a medical institution according to disease group or treatment category (hereinafter collectively referred to as "group"), A hospital management planning program characterized by causing a computer to perform the following functions (A) through (E). (A) A function to input the target number of patients for each group. (B) A function to store historical data related to the management indicators for each group or to acquire real-time data related to said management indicators. (C) A function to calculate the target management indicator by combining the target number of patients for each group and the management indicator based on (A) and (B) above. (D) A function to aggregate the calculation results for each group and calculate the aggregated result. (E) A function to output the calculation results and / or the aggregated results in tabular format.

6. A hospital management planning program according to claim 5, further comprising the function of importing performance data based on DPC or claims data, automatically updating the management indicators for each group as real-time data based on the performance data, and outputting a comparison of the planned values ​​calculated using the updated management indicators and the actual values ​​related to the imported performance data in the tabular format.

7. A hospital management plan program according to claim 5 or 6, The aforementioned management indicators are sales revenue and costs, as well as profit margins including at least drug price margins and material price margins, making it a hospital management planning program.

8. A hospital management plan program according to claim 5 or 6, The aforementioned calculation is a hospital management planning program characterized by calculating the target sales and target gross profit for each group using the following calculation formula. Target sales = Target number of patients × Sales amount Target gross profit = Target number of patients × (Sales amount - Costs)

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