Hospital Management Planning System

Abstract

Conventional hospital management support systems are limited to a relative evaluation of patient attributes and medical departments, and do not accurately grasp the profitability and costs of each disease or treatment group. This makes it difficult to perform specific data analysis that directly leads to optimal allocation of medical resources, improved treatment efficiency, and management improvements. [Solution] This invention provides a "patient grouping management module" that groups patients based on specific treatments or diseases, and calculates and visualizes the profitability, costs, and profit margins of each group. Furthermore, by calculating management indicators for each patient group based on medical fees, drug price margins, and material margins, and visualizing and analyzing the results in real time, it builds a management support system that allows doctors and managers to specifically plan resource allocation and treatment strategies.

Description

[Technical Field]

[0001] The present invention relates to a support system for medical management and hospital management, and in particular to a hospital management tool (hospital management tool) that outputs and visualizes the management status of each medical department and medical group in a hospital, and also outputs and visualizes the management status of patients in a hospital classified into specific groups, and proposes improvements thereto. plan (Systems, programs, methods) [Background technology]

[0002] Traditionally, efforts have been made to analyze basic indicators related to hospital management (such as bed occupancy rates, average length of stay, cost per patient, profitability, etc.) and use them as basic data for developing specific improvement plans.

[0003] Patent Document 1 describes a system that analyzes the treatment details for each patient based on their attributes (e.g., age, gender, disease), and presents information useful for improving management by suggesting profitable treatments and measures with excellent management efficiency. Specifically, the system focuses on the treatment details of each patient and identifies the degree of profit / loss (good / average / poor) for each patient, thereby attempting to obtain information useful for improving management at the granularity of each patient. However, it is difficult to say that the system uses aggregated data for the entire hospital or department to assess the management situation. This method divides patients into groups based on predetermined attributes (e.g., disease classification, gender, age, etc.), obtains profit / loss information for each group, and then selects the most profitable treatment for each patient based on that information. However, because the groups are based on a relative evaluation of good / poor, and because invalid data that does not belong to any group exists, the groups are formed from a relative perspective.

[0004] Patent Document 2 makes it extremely easy to compare with other hospitals and between medical departments within the same hospital by conducting evaluations based on RMP (medical revenue per yen of labor cost) and RIP (invested capital per yen of labor cost). RMP increases profits with low labor costs, and RIP contributes to numerical improvement by making efficient use of capital with low labor costs (departments with high capital efficiency and departments that require capital investment). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Application No. 2010-258130 [Patent Document 2] Japanese Patent Application Laid-Open No. 2010-218448 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the system disclosed in Patent Document 1 does not use data based on patient attributes to analyze treatment profit and loss information. When patients are classified into absolute groups based on specific medical procedures or diseases (e.g., arrhythmia, heart failure, catheter treatment, breast cancer surgery), specialized analysis of specific diseases or treatment groups allows for the formulation of treatment strategies based on clinical guidelines and the latest medical evidence. This not only improves efficiency by enabling the allocation of medical resources optimized according to the characteristics and needs of each group, but also allows for accurate understanding of the profitability of each group and clarification of which treatment areas are performing well or in need of improvement. 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 optimal treatment methods and equipment to each group, treatment efficiency per patient improves. In this way, analyzing the absolute relationship between costs and revenues for each patient group, rather than a relative evaluation of good / bad, enables transparent data analysis. In other words, the calculation method is clear using data collected from electronic medical records and medical fee data, and even complex calculations can be easily understood with charts and graphs. Analysis without any particular bias leads to an intuitive understanding of the results.

[0008] Furthermore, while patient granularity analysis and indicators (RMP and RIP) are desirable as a means of indirectly achieving both the quality of medical services and ensuring profitability in terms of profitability and cost efficiency, they are different from directly improving profit variability through the efficiency of medical resources. In other words, appropriate resource allocation while maintaining medical quality is expected to increase patient satisfaction and have a positive impact on profits through an increase in repeat patients and referred patients.

[0009] Furthermore, resource efficiency not only affects specific departments and patients, but also extends to the operation of the entire hospital. For example, optimizing hospital beds, medical equipment, and treatment space can increase revenue while reducing costs, and is expected to improve the speed and responsiveness of treatment in all departments.

[0010] Furthermore, focusing on granular patient analysis and indicators (RMP and RIP) may lead to temporary improvements in profits, but resource efficiency is expected to bring medium- to long-term stability to the hospital as a whole. Optimizing resource allocation and improving or eliminating bottlenecks that result in low-profit operations and unnecessary costs can be expected to stabilize hospital management and improve profitability over the long term.

[0011] In addition, efficient resource allocation reduces the burden on medical staff. For example, if hospital bed occupancy rates are appropriate, the burden of patient care is distributed, reducing 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 medical staff.

[0012] In addition, resource efficiency can reduce unnecessary costs, enabling more strategic investments. The appropriate use of medical equipment and clinical space reduces facility maintenance and renewal costs, making it easier to reinvest in necessary resources and introduce new medical technologies.

[0013] To solve these problems, the purpose of this invention is to propose the most desirable method for improving management by improving resource efficiency, which will have a direct impact on the quality of service and sound management of the entire hospital. This method can simultaneously achieve improvements in multiple aspects, such as medical quality, operational efficiency, and financial stability, and is an excellent method for the sustainable growth of hospitals.

[0014] The present invention was made in consideration of these issues and provides a tool that appropriately evaluates resources within a hospital, simulates resource reallocation (resource movement and resource addition) to reduce undesirable resource usage, and simultaneously improves management efficiency and maintains the quality of medical services. The present invention also provides a tool that visualizes the impact on profitability and service quality using graphs and dashboards, and visualizes where resource bottlenecks exist, making it easier to intuitively understand resources with room for improvement and the effects of such improvements.

[0015] Furthermore, by managing profit data by grouping patients based on specific treatments, diseases, and DPC information, this invention makes it easier for medical professionals and management to identify which groups to focus on, maximizing profits by focusing on highly profitable treatments and patients, and contributing to the prioritized allocation of personnel and equipment resources. In particular, it enables doctors to select treatment plans that balance profitability and medical appropriateness, and makes it easier for doctors to plan and implement specific measures to achieve business goals based on the 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 dashboard format using a display device. This allows managers and administrators to grasp at a glance the performance, resource usage, profitability, workload, and other information for each department and treatment group. Indicators are also updated in real time during visualization, allowing for constant up-to-date understanding of the management situation.

[0017] The differences between running a general company and running a hospital lie in the unique constraints on revenue structure, cost management methods, and business operations. <Differences in revenue structure> Hospitals' income is regulated by the medical fee system, and they cannot freely set prices, so there are limits to how much they can expand their profits. While private companies can freely adjust the prices and content of the services they provide to increase profits, hospitals must find ways to maximize profits within the system. Since they cannot charge separately for consumables such as bandages and gauze used by patients and their profits depend on medical fees, it is essential that they find ways to reduce the costs of consumables and equipment. <Cost control constraints> Treatment costs include direct costs such as pharmaceutical costs, testing costs, and labor costs, but reducing these costs can easily affect patient safety and the quality of medical services. Because simple cost reductions are difficult, we focused on efficient resource allocation without waste. In addition, indirect cost reductions are required through inventory management of pharmaceuticals, medical equipment, and consumables, as well as workload leveling. Rather than simply reducing workloads, resource management is required to distribute workloads while maintaining high-quality medical services. Workload and human resource management The number of medical professionals, such as doctors and nurses, is limited, and there is a risk of overwork due to long working hours and employee turnover. Management improvement emphasizes efficient allocation of personnel and appropriate distribution of workload. In addition, rather than simply reducing the workload, resource management is required to distribute the workload while maintaining high-quality medical services. <Achieving both patient satisfaction and profitability> While a typical company can adjust the costs of customer service while balancing them with profitability, hospitals prioritize patient safety and quality of care. Therefore, they must optimize operating costs while maintaining patient satisfaction. While shortening patient response times and improving medical efficiency contribute to increased profits, they must also do so within the scope of not compromising patient convenience or satisfaction, maintaining a balance between medical quality and profitability. Legal and ethical constraints Hospital management is subject to strict regulations regarding medical fees and medical procedures, and unlike ordinary businesses, the freedom to increase profits is limited. This requires management strategies that are premised on compliance with laws and regulations and ethical considerations, rather than simply pursuing profits. Furthermore, hospitals are highly public institutions, and their mission is not just to make profits, but also to meet the medical needs of the local community and ensure the quality of medical care. Therefore, it is necessary to manage sustainably while balancing profits and the public interest. The present invention takes these perspectives into consideration as a management improvement measure, and it is important to realize efficient resource allocation, optimization of workload, reduction of indirect costs, and improvement of patient satisfaction. It also proposes a hospital management improvement method that involves data-based analysis and adjustment, such as effective utilization of resources, identification and improvement of bottlenecks, and presentation of profit improvement measures through simulation.

[0018] (B) A revenue management database that calculates and registers management indicator data (= revenue data) for each medical department or medical group within the hospital. (C) A resource management database that registers the resources allocated to each medical department or each medical group. The revenue management database calculates and records sales, costs, and profits, accumulating sales figures and profit margins for use in revenue analysis and management improvement. The resource management database registers resources (personnel, facilities, medical equipment, hospital beds, etc.) allocated to each medical department or medical group, calculating workload and resource utilization status and supporting efficient resource allocation. Additionally, by utilizing the patient database, which records data such as each patient's medical history, age, gender, illness, treatment details, and number of visits, it is possible to analyze patient trends and understand the utilization status and repeat visit rate of specific medical departments. Additionally, by utilizing the cost management database, which records direct costs (labor costs, pharmaceutical costs, testing costs, equipment maintenance costs, etc.) and indirect costs associated with operating each medical department or medical group, it is possible to calculate the balance between revenue and costs and evaluate the profit margin for each medical department. It is used as basic data for cost reduction and profitability improvement measures. In addition, by utilizing a work schedule database that records information such as shifts, work schedules, and appointment status for each department and staff member, the system can be used to level out staff utilization rates and workloads, preventing excessive staff burden and resource waste, thereby streamlining bed and equipment scheduling. In addition, by utilizing an inventory management database that records the inventory status of medicines, consumables, medical equipment, etc. and manages usage frequency and replenishment timing, the system can ensure the appropriate supply of necessary supplies without shortages, contributing to cost reduction and improved operational efficiency. In addition, by utilizing an equipment management database that manages data such as the utilization rate, usage frequency, maintenance history, and lifespan of the equipment and medical equipment used by each department, the system can be used to efficiently operate equipment and develop appropriate maintenance plans, maximizing utilization rates while minimizing the risk of operational shutdowns due to equipment failure or end of life. In addition, by utilizing an external factors database that includes seasonal information such as influenza epidemics, local infectious disease situations, and social and economic impacts (legal changes, economic indicators), the system can help adjust hospital operations and optimize resource allocation based on external factors, enabling predictive responses to fluctuations in patient numbers and revenue.In other words, the resources include one or more of the following resource data: number of waiting patients, number of patients, number of doctors, number of nurses, number of medical assistants, number of nursing assistants, number of hospital beds, number of operating rooms, number of surgery slots, number of surgical materials, number of medications, number of anesthesiologists, number of available working hours for the anesthesiology department, and working hours for each medical department.

[0019] Furthermore, by equipping the profit index calculation module (= profit simulation module) with a data collection function, these databases enable efficient simulations by collecting only the necessary data in real time for simulations of revenue and resource allocation. The profit index calculation module (profit simulation module)'s data collection function for revenue data identifies the data collection targets by selecting and collecting data such as revenue, costs, resource utilization, patient numbers, and patient groups. A trigger function is provided to automatically collect data when a simulation is run or when data is updated, ensuring that the latest data is always reflected. During this process, data consistency checks are performed to check for missing or outliers in the collected data. If any abnormalities are found, the data will not be reflected in the simulation, or if abnormally high costs or revenues are included, the data will be automatically corrected or supplemented as necessary.

[0020] (D) A profit index calculation module that calculates and visualizes unit management indexes for each resource based on revenue data for each medical department or medical group and allocated resource data. Alternatively, a profit index calculation module that calculates the increase in profit by transferring a unit resource from a medical department with a low unit management index to a medical department with a high unit profit. Alternatively, a profit index calculation module that calculates a unit management index for each resource based on the revenue data of each medical department or medical group and the allocated resource data, and simulates profit fluctuations that occur by transferring or adding resources between medical departments or medical groups based on the unit management index.

[0021] Here, the management indicator (resource unit profit) is the profit of each medical department or medical group (sales minus costs). Coarse It is calculated by dividing the total profit (revenue) by the number of allocated resources (e.g., personnel, hospital beds, medical equipment, etc.). In other words, resource unit profit = profit / resource quantity, and it is possible to quantify how much profit each resource generates and compare the efficiency of each medical department. The comparison of unit profit magnitudes involves comparing the calculated unit management index values ​​for each medical department or medical group, identifying medical departments with low and high unit profits, and predicting how overall profits will fluctuate if resources are shifted from medical departments with low profit efficiency to those with high profit efficiency. Alternatively, it predicts how profits will fluctuate if resources are added to medical departments with high profit efficiency.

[0022] "Profit indicators" and "unit management indicators" "Management Indicators" is used to evaluate profitability for each resource (number of doctors, number of nurses, number of hospital beds, etc.), specifically to calculate profit for each resource allocated to each medical department or medical group. Profit per resource is calculated by calculating the profit per resource unit (e.g., one doctor, one doctor hour, one hospital bed, one hospital bed x 1 day, one anesthesiologist hour, one anesthesiologist shift, one operating room surgery slot, etc.), and the profit score is calculated by scoring how much profit is generated for each resource, and is used as a "profit index" and "unit management index." "Management Indicators" By using this, we can calculate the change in overall profit when changing the allocation of resources between medical departments or medical groups.

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

[0024] Resource transfer simulation involves simulating the transfer of unit resources (e.g., one doctor, one doctor's hour, one hospital bed, one hospital bed per day, one anesthesiologist's hour, one anesthesiologist shift, one operating room surgery slot, etc.) from departments with low profit efficiency to departments with high efficiency over a specified period. For example, by allocating medical personnel and equipment utilization to departments with high unit profits, it is possible to estimate the extent to which overall profits will increase.

[0025] Profit increase forecasts calculate the expected increase in profit due to resource transfers or resource expansion. The amount of profit improvement that would occur if resources were reallocated is quantified, and the optimal resource allocation is proposed along with the numerical information. In other words, the calculation is Predicted Increased Profit = Unit Profit of Destination Department x Amount of Resources Transferred - Unit Profit of Source Department x Amount of Resources Transferred. This calculation allows you to change the simulation conditions as needed and try different patterns of resource allocation.

[0026] Resource transfer methods can be flexibly selected based on management efficiency and department needs. Transferring resources (units) permanently or for a set period of time can lead to stable management and efficient resource utilization throughout the hospital. When there are long-term imbalances in department profitability or workload, concentrating resources in profitable departments to improve long-term profits, or departments that require highly utilized equipment, clinical space, or personnel with specific skills, permanent resource reallocation is preferable. On the other hand, when seasonal or temporary demand, such as during influenza epidemics or when there is a need for specific treatments, or when new medical services or treatments are being introduced, transferring unit resources for a set period of time is preferable. Furthermore, permanent and temporary transfers can be combined to flexibly respond to fluctuations in demand in line with department growth and changes in the external environment. By allocating the necessary resources appropriately, resource waste and excessive staff burden can be prevented. If the effectiveness of a fixed-term resource transfer is confirmed and stable results are achieved, switching to permanent allocation can be used. These measures can be selected appropriately depending on the situation to improve hospital management efficiency, maximize profits while maintaining the quality of patient service.

[0027] Furthermore, resource transfers or additions over a specified period enable dynamic simulations, which differ from static simulations based on accumulated data. They allow for flexibility in responding to real-world changes and improve simulation accuracy. Resource transfers over a specified period allow for real-time collection of actual operational status, fluctuations in patient numbers, and impacts on revenue. Obtaining dynamic data makes it easier to understand discrepancies between the effects predicted by static simulations and actual data. Resource transfers or additions over a specified period have the advantage of speeding up the hypothesis verification process, allowing for the rapid confirmation of the revenue impact and workload reduction effects of increasing resources in a specific department, thereby verifying whether the hypothesis is correct. The verification results can help determine whether to implement permanent resource reallocation or try a different approach.

[0028] The resource management database may be in either a list format, where resource data such as the number of doctors, nurses, hospital beds, operating rooms, anesthesiologists, available anesthesiology work hours, or work hours for each department is registered, or in a table format, where unit resources and their numerical information (quantity) for each doctor, nurse, hospital bed, operating room, anesthesiologist, available anesthesiology work hours, and department are registered. Profit indicators may be calculated using unit resources such as equipment-related resources, staff-related resources, facility-related resources, other resources, external resources, patient service-related resources, and IT-related resources. Equipment-related resources include the number of diagnostic devices (e.g., MRI, CT, ultrasound devices), treatment devices (e.g., radiation therapy devices, laser treatment devices), testing devices (e.g., blood testing devices, electrocardiogram monitors, endoscopes), and pharmaceutical inventory (e.g., specific drug inventory or storage volume). Staff-related resources include the number of rehabilitation staff (physical therapists, occupational therapists), pharmacists, radiologists, clinical laboratory technicians, administrative staff, and counselors / social workers. Equipment-related resources include the number of examination rooms, examination rooms, waiting room capacity, intensive care unit (ICU) beds, emergency response equipment, rehabilitation facility equipment, and treatment rooms. Other resources include the number of ambulances and transportation vehicles, the number of patient parking spaces, the number of mobility equipment such as beds and stretchers, the availability of each resource (e.g., examination room and examination room operating hours, and the use times of specific medical equipment), and the number of infection control equipment (e.g., disinfection equipment, infection protection equipment inventory). External resources include the number of partner facilities (other hospitals, nursing homes, specialized medical facilities, etc.) and the number of external specialists (the number of specialists outside the hospital who can cooperate). Patient service-related resources include the number of private / multi-bed patient rooms, 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 operating electronic medical record systems, data server capacity, and the number of security devices (firewalls, antivirus equipment). These resources fall within the scope of the present invention or its equivalents.

[0029] In particular, when it comes to the allocation of medical administrative assistants, it's possible to calculate bottlenecks by factoring in how much a doctor's productivity will increase as a result of that allocation. It's estimated that 30% of a doctor's work is administrative work, and if half that time could be spent issuing instructions to medical administrative assistants to complete administrative tasks, then a 17% productivity improvement of 1 / (1 - 0.3 / 2) = 1.17 can be expected. Similarly, when it comes to nursing assistants for nurses, it's estimated that 30% of a nurse's work involves paperwork and transporting patients. If half that time could be spent issuing instructions to nursing assistants to complete nursing tasks, then a 17% bottleneck reduction effect of 1 / (1 - 0.3 / 2) = 1.17 can be expected.

[0030] (G) The profit index calculation module utilizes the simulation results of the resource transfer or resource expansion as budget compilation materials for the hospital management in the next and subsequent periods. (H) The profit index calculation module manages the results of the simulation of the resource movement or the resource expansion. (I) The profit index calculation module calculates the increase in productivity of doctors or nurses by adding more medical assistants or nursing assistants, and simulates the management index of the increased resources. (J) The resource management database has a function for registering the potential number of resources that can be added, and has an added function for calculating the potential profit or sales that can be added based on the potential number of resources that can be added. (K) The resource management database has a function to visualize whether or not there are limitations on other resources when simulating one or more of resource increases or replacements, and at the same time has a function to visualize a list of measures to resolve resource limitations.

[0031] <Use as budget compilation material> The profit index calculation module can be used as budget preparation material for hospital management from the next fiscal year onwards, based on the results of simulations of resource transfers and expansions. <Use in budget and actual management> The profit index calculation module manages budgets and actual results based on the results of simulations of resource transfers and expansions, thereby comparing business plans with actual performance and identifying discrepancies. <Productivity improvement simulation> The profit index calculation module calculates the effect of increasing the productivity of doctors and nurses by adding medical administrative assistants and nursing assistants, and simulates how the increased resources will affect management indicators. This simulation function makes it possible to quantitatively evaluate the effect that adding medical administrative assistants and nursing assistants will have on improving efficiency and productivity in actual medical settings, and to predict the effect of increasing resources on improving management. <Registering the number of potential additions> The resource management database has a function for registering the potential number of additional resources that can be added, and uses this data to calculate potential additional profits and sales. <Visualization of resource limitations and listing of solutions> The resource management database has the function of visualizing whether there are limitations on other resources when simulating the addition or replacement of resources, and displaying a list of measures to resolve the limitations. With these functions, the hospital management support system can utilize the simulation results in management decisions and planning, supporting optimal resource management and improved productivity.

[0032] (L) The profit index 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 simulation of an increase in the number of patients using hospital beds and an increase in the unit price per patient. The bed occupancy rate management module of the present invention visualizes bed occupancy rates and performs simulations related to optimizing bed utilization in order to evaluate the efficient use of hospital beds, which are part of the hospital's resources. The bed occupancy rate calculates the utilization status of each bed (e.g., utilization rate, number of available beds), and then simulates the increase in the number of patients that can be admitted based on this utilization rate by increasing the number of beds or expediting patient admissions and discharges. This simulates the profit improvement that would result from increasing the unit price per patient when bed utilization is maximized. For example, it estimates the increase in the unit price per patient by improving the length of hospital stay or the content of medical treatment. The results are reflected in a profit simulation, and the simulation results of the utilization rate, predicted patient increase, and unit price increase obtained by the bed occupancy rate management module are reflected in the profit simulation to calculate the profit improvement effect for the entire hospital.

[0033] The function of the "profit index calculation module" of the present invention corresponds to the function of reallocating resources and predicting profits as profit simulation means in relation to the profit simulation means (295) described later.

[0034] (M) The 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 early hospitalization or discharge include medical reasons based on the recovery status or changes in treatment policy, the possibility of receiving appropriate care at home after discharge, whether early discharge is desirable when there is a shortage of hospital beds, the tightness of hospital beds, and the patient's or family's wishes for early discharge.Medical professionals will be able to enter the reasons using a selection method (e.g., pull-down menu or check box) or in free text format, and will also be able to enter detailed information and supplementary comments as necessary.

[0035] 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 association with the resource management DB 283C and the profit simulation means (295) described later.

[0036] (N) A fixed cost utilization optimization module that visualizes bottleneck resources, such as fixed costs for equipment within the hospital, and helps propose improvements to the allocation of those resources. (O) If the bottleneck is either the number of patients or the number of waiting patients, a function has been added to display a list of patient increase measures and calculate the cost-effectiveness of one or more of the patient increase measures. The fixed cost utilization optimization module of the present invention visualizes areas where equipment and other fixed costs within a hospital are causing resource bottlenecks (obstacles to management and operations) and provides functions for proposing improvements to their allocation. It has functions such as a bottleneck resource visualization function, a resource allocation optimization simulation function, a fixed cost cost evaluation function, a resource usage forecast and demand analysis function, and an implementation result monitoring function for improvement proposals, which improves the operating status of fixed cost resources, reduces the overall operating costs of the hospital, and enables a stable supply of necessary resources. If the bottleneck is related to the number of patients or the number of waiting patients, a function has been added that displays a list of measures to increase patients and calculates the cost-effectiveness of each measure, allowing you to select efficient measures to increase patients and supporting decisions to increase revenue.

[0037] (P) The fixed cost utilization optimization module has a function of proposing the placement of multiple physician administrative assistants and supporting the operation of multiple operating rooms.

[0038] The target resources are identified by collecting the utilization status (utilization rate, utilization frequency, standby time) and maintenance costs of each resource from a database, and then selecting those with high fixed costs, such as operating rooms, hospital beds, medical equipment, waiting rooms, and specialized medical facilities. Resources with low utilization rates or prone to overload, or low or very high utilization rates, are identified. Resources with very high utilization rates tend to be concentrated in specific medical departments or tasks, potentially disrupting other tasks. Long standby times for resource use indicate a shortage of supply relative to demand. Resources with very high maintenance or operating costs require cost review. Resources with abnormally low utilization rates or extremely uneven utilization are identified and included as bottleneck candidates. Time-series data is analyzed, and significant changes in resource utilization trends are identified as bottleneck candidates. Furthermore, because a specific resource is linked to other resources, a bottleneck in one resource can affect overall operations, resource interrelationships are also analyzed. For example, in cases where the utilization rates of operating rooms and doctors are linked, a bottleneck in either resource will reduce surgical efficiency, so both are identified. Since there are various reasons for bottlenecks, we will list and prioritize potential bottlenecks, and then list the resources extracted using the above indicators based on their utilization rate, cost, frequency of use, and waiting time. We will then prioritize and present improvement proposals starting with the highest priority ones, and propose reallocation, distribution of utilization rates, and the introduction of additional resources as necessary.

[0039] Bottlenecks are calculated by identifying areas of the overall data where load is particularly concentrated, or specific medical departments or equipment where delays are likely to occur. This differs from average values, which only show overall trends and are not suitable for identifying areas where load is extremely concentrated (e.g., specific time periods or specific equipment). Bottlenecks identify points in the data where load is locally high, so it is also possible to focus on abnormal data that deviates significantly from the average. Furthermore, visualizing bottlenecks makes it possible to prioritize the allocation of resources to areas where improvement is most necessary, allowing measures to be taken starting from the areas that will have the greatest impact, even with limited budgets and personnel.

[0040] The function of the "fixed cost utilization optimization module" of the present invention corresponds to a function that supports the detection of bottleneck fixed cost resources and improvement proposals in association with a bottleneck processing unit 295B described later.

[0041] (Q) A score generation module is provided that has a scoring function that converts the workload and profitability of each medical department and medical department group into a score, and a visualization function that visualizes the score. (R) A fluctuation history recording module is provided to periodically record fluctuations in the workload score and profitability score generated by the score generation module and grasp fluctuation trends in workload and profitability for each medical department. (S) A notification module is provided that notifies medical managers when (significant) fluctuations such as excessive workload or declining profits for each medical department are detected 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 medical department / medical group into scores, and a visualization function that visualizes the generated scores on a dashboard or graph. This quantifies the performance of each medical department, allowing resource allocation and profitability to be understood at a glance. Scoring makes complex data such as workload and profitability easier to understand, facilitating a quick understanding of the performance of each medical department. Scored indicators also facilitate comparisons between medical departments. This allows for comparisons of workload and profitability between different medical departments or medical groups, helping to analyze differences in resource allocation and efficiency between departments and identify areas requiring improvement or areas where resources should be prioritized. Furthermore, scoring and periodic recording allows for tracking the effectiveness of improvement measures as specific numerical values, and evaluating score fluctuations allows for the determination of the success of measures and the need for further improvement. Furthermore, regular score monitoring allows for early detection of risks such as workload imbalances and declining profits. Issuing a warning when scores fall below the reference value or when sudden fluctuations occur contributes to early detection of management risks and prompt response, contributing to maintaining management stability.

[0043] In addition, the fluctuation history recording module regularly records fluctuations in the workload scores and profitability scores generated by the score generation module, and can be used to understand long-term trends in workload and profitability for each medical department, making it possible to analyze the effects of improvements and changes in resource demand, which is useful for making management decisions.

[0044] Furthermore, if the notification module detects significant fluctuations in workload and profitability, such as excessive workload or declining profits, in a specific medical department based on the trends in workload and profitability obtained from the fluctuation history recording module, it will automatically notify medical managers, enabling them to take immediate action and reduce management risks.

[0045] The "score generation module" of the present invention corresponds to the score evaluation unit 295C described later, and is related to functions related to score conversion and visualization of workload and profitability. The "fluctuation history recording module" of the present invention corresponds to the data storage area 283 described later, and periodically records and saves score fluctuations in workload and profitability in the data storage area. The "general history module" of the present invention corresponds to the alert processing means 293 and notification means 250 described later, and is related to functions for notifying medical managers when abnormalities in workload or profitability are detected.

[0046] (T) The profit index calculation module has the function of periodically monitoring and updating one or more of the results, including the revenue improvement rate, workload reduction rate, bed occupancy rate improvement rate, patient response time reduction rate, and cost reduction rate, for the simulation results of each medical department or medical group. According to the present invention, the effects of management improvements can be continuously assessed as one or more of the following outcomes: revenue improvement rate, workload reduction rate, improvement rate of bed occupancy rate, shortened 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 that is difficult to see with a single indicator, making it possible to provide measures that can be expected to produce greater synergistic effects. By taking into account not only economic indicators such as revenue and cost, 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 toward short-term profits.

[0047] (U) A hospital management support system for visualizing the status of hospital management, A revenue management database in which management index data for one or more of sales, costs, number of patients, and revenue for each medical department or medical group within the hospital is registered; a patient information management database for allocating patients to patient groups; A hospital management support system comprising a profit index calculation module that calculates unit management indexes for each patient group.

[0048] (V) The revenue management database registers the calculated profit margins for each patient group for each department or group within the hospital, and the profit simulation module has the function of visualizing the profit margins for each patient group. (V') The revenue management database has a patient grouping management function that allocates (manages) patients into groups, and the results of calculating one or more of the profits or sales per patient for each group of patients are registered for each medical department or medical group within the hospital, and the profit index calculation module has a function to simulate the profits or sales for each medical department or medical group when treating a certain number of patients for each patient group.

[0049] According to the present invention, by visualizing the profit margins for each patient group, it is possible to identify patient groups that generate high profits and those that are high-cost and low-profit. This allows for the strengthening of services and measures appropriate for each group, and allows for efficient countermeasures, making it easier to develop strategies for optimally allocating management resources. When a low-profit group is identified, the cause may be due to the content of medical treatment, patient care, or a lack of facilities. Visualization highlights the problem, and appropriate service improvement measures can be implemented to improve patient satisfaction. Furthermore, further improving services for high-profit patient groups can lead to an increase in repeat customers. Furthermore, based on the profitability of each patient group, it becomes easier to plan the efficient allocation of resources (doctors, nurses, equipment, etc.) between medical departments and medical groups. Increasing medical capacity for high-profit groups can increase profits and support efficient management.

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

[0051] The revenue management database has a function to calculate profits for each patient by adding the DPC margin to the drug price margin and material cost margin. It also groups patients according to specific criteria, calculates the profit for each group, and registers it for each medical department or medical 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 for each medical department or medical group. In particular, for profits that include the drug price margin, the average value is calculated using a statistically sufficient number of patients, making it possible to evaluate the stability and profitability of revenue.

[0052] (X) A function to input text requesting doctors to input the target number of patients, a function to register the time periods when medical personnel are most likely to receive phone calls, and a function to notify medical personnel by vocalizing the text information during the time periods when medical personnel are most likely to receive calls.

[0053] The system is equipped with a function that creates a text requesting doctors to input the target number of patients. It also has a function that allows medical professionals to register the time periods when they are most likely to receive phone calls, and the request text information can be spoken to the medical professional at the registered time period. Furthermore, to ensure that the target number of patients is properly entered each month, the system is equipped with a function that makes phone calls as needed to remind doctors, as well as a chat reminder function, providing efficient support for reminders using both chat and phone. This series of functions creates a system that ensures medical professionals can reliably enter the target number, supporting efforts toward achieving goals.

[0054] (1) A management support system for calculating one or more management indicators (sales or gross profit) of a medical institution (hospital, etc.) from among sales by disease group or treatment category, gross profit on medical fees, or gross profit with drug price margins added to that, or gross profit with material margins added to that, characterized by requiring the following components 1 and 5 and combining them with one or more of 2, 3, 4, 6, and 7. Here, "disease group" refers to the group classified by element 5, and "medical care category" refers to each medical department or medical care group within the hospital. Sales, gross profit on medical fees, gross profit including drug price margins, and material margins are stored in the "revenue management database" of the present invention, which includes data tables such as a patient data table, a DPC information table, and a management data table. This database enables detailed analysis of management status and proposals for improvements, helping healthcare professionals maintain an optimal balance between medical care provision and management within limited resources. While healthcare professionals, particularly physicians, have a responsibility to prioritize treatment selection based on medical indications, in today's world of medical cost reduction, we can expect them to focus on management aspects, consider management efficiency, and make management efforts to select cost-effective treatments. The system includes a patient grouping management module that groups and manages hospital patients based on specific treatments and diseases, and has the function of calculating profit data for each patient group based on past data. The patient grouping management module also includes a classification module that classifies patients into specific groups, such as arrhythmia, heart failure, catheterization, and breast cancer surgery, based on the DPC information. The "patient grouping management module" (element 5) of the present invention includes components including a DPC information acquisition module, a patient classification module, and a profit data calculation module. This module contributes to the efficiency of hospital operations and patient management. By understanding the profitability of each patient group in detail, medical resources can be optimized, and low-profit treatment areas can be identified and improved. The "classification module" is based on a patient attribute-based approach, which applies specific criteria to group patients based on their attribute information (diagnosis, treatment details, hospitalization status, disease characteristics, etc.); a medical database that analyzes medical data such as DPC information and treatment details and classifies patients using pattern recognition and rule-based methods; and a segmentation-based approach, which analyzes patient data to create groups based on their characteristics and attributes. Groupings include diagnosis, disease characteristics, treatment details, individual patient attributes, and hospitalization and treatment patterns.

[0055] By utilizing the patient grouping management module and revenue management database, doctors can understand the importance of patient groups that should be focused on from a business perspective, and by presenting treatment targets that are also important from a business perspective, doctors can be encouraged to attract those groups. By visualizing the profitability, costs, and profit margins of each patient group, doctors can be supported 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 and medical insurance claim data> It stores one or more of Diagnosis Procedure Combination (DPC) data information or prescription information, and includes patient information, disease name information, sales information, or payment information. Patient information: patient ID, age, gender, number of days hospitalized, etc.; disease name information: main diagnosis, comorbidities, complications, etc.; sales information: revenue data based on medical fees and medical procedures, etc.; payment information: insurer share and patient share, etc. <2: Database that stores sales and payment information other than DPC and prescription data> It stores uninsured income, drug purchase information, material purchase information, or any one or more of these, and includes information on sales, costs, and margins. Non-insurance income: Revenue from self-paid medical treatment (e.g. health checkups, vaccinations, cosmetic medical treatments); 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-insurance medical treatment and in-hospital sales, cost information: Cost of purchasing medicines and materials, profit margin information: Profit as the difference between sales and costs, etc. The two databases, Element 1 and Element 2, function as the management foundation for hospital management data covering both insured medical treatment elements and uninsured medical treatment elements, drug costs, material costs, and labor costs (collectively referred to as "uninsured medical treatment").

[0057] <3: Module for classifying sales and payment information> A classification module that classifies sales and payment information obtained from elements 1 and 2, as well as from databases such as the Revenue Management DB, as a specific patient or affiliation information associated with that patient, where the classification is based on one or more of the following for the patient: The medical department the patient visited The bed or ward where the patient stayed The medical group to which the doctor who examined the patient belongs Classification of patient treatment as inpatient or outpatient

[0058] <4: Module for allocating costs and sales> A module for allocating costs to a medical department, bed or ward, or medical group to a patient based on the affiliation information. <5: Classification module for classifying patients> A module that uses DPC data, medical receipt data, medical records, or a combination of these to classify patients into specific disease groups or medical care categories.

[0059] <6: Calculation and Analysis Module> A calculation module or an analysis module for calculating sales, gross profit, drug price margin, material margin, number of patients, or an indicator combining these, for each of the classified disease groups or medical treatment categories.

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

[0061] The classification module has a function of subdividing and grouping patients based on one or more conditions of medical treatment or surgery details, medication information, length of hospital stay, and whether or not there are complications. The "classification module" of the present invention performs multidimensional analysis of patient characteristics and medical information, and groups patients according to specific purposes. This segmentation makes it possible to identify patient groups with high profitability and cost performance, which is useful for considering profitable treatment strategies. Segmentation based on medical treatment details, surgical details, medication information, length of hospital stay, and the presence or absence of complications contributes to both providing medical care tailored to patient characteristics and improving management. - Medical treatment and surgery details: Resource allocation becomes possible according to these details, improving treatment efficiency. By understanding the revenue structure for each medical treatment and surgery, hospitals can focus on treatments with high profitability. In addition, by creating patient groups for each medical treatment, hospitals can provide specialized care for specific diseases and treatments, allowing them to focus resources on treatments that contribute to hospital management. Drug Information: Understanding patterns of high-cost and generic drug use can help promote appropriate drug selection. By segmenting drug use, standardization of treatment pathways can be promoted, reducing variations in treatment outcomes among patients. By identifying patient groups who use drugs with high drug price differentials, profitable treatment plans can be promoted. Length of stay: Distinguishing between short-term, medium-term, and long-term hospitalization patients will improve bed occupancy rates and optimize patient flow. Since length of stay affects medical fees, grouping by length of stay will enable you to identify profitable lengths of stay. Complications: Prevent an increase in the number of patients at risk of complications and strengthen management, and plan and implement appropriate preventive measures for patient groups at high risk of complications.

[0062] The calculation module has the function of updating the drug price margin and medical revenue for each patient group in real time and predicting future profits based on the past data. It forecasts profits by calculating hypothetical scenarios of drug usage patterns and the impact of changes in treatment methods using algorithms such as regression analysis and time series forecasting models based on real-time data obtained from electronic medical records, drug management systems, and medical fee billing systems, including DPC information, drug usage data, medical treatment details, and surgery details, as well as historical data on past revenues, costs, and drug price margins, as well as revenue patterns including seasonality and trends.By integrating real-time and past data and using advanced algorithms, it is a system that balances hospital management and the quality of medical care provided, and can support improved management efficiency, optimized treatment strategies, and improved medical quality.

[0063] The analysis module has a function of presenting a management plan based on the total profit for each patient group and prioritizing resource allocation to high-profit groups based on the total profit. A management plan is formulated using gross profit data for each patient group, and resources are allocated preferentially to highly profitable patient groups. Gross profit is calculated by subtracting costs such as hospitalization fees, personnel costs, drug costs, and medical equipment fees from medical revenue and revenue from drug price differentials. This is then tallied for each patient group. Management strategies are proposed based on the gross profit data for each patient group, including measures to expand highly profitable areas and improvement proposals for low-profit groups (cost reductions and revenue improvement measures). Resource allocation to high-profit groups is prioritized in terms of personnel (specialists, nurses, technical staff), facilities (operating rooms, specialized equipment (catheterization devices, etc.), and budget (necessary drug procurement and equipment renewal costs).Resource allocation can be adjusted in real time based on total profit and fluctuations in profitability and demand.

[0064] It has a user interface that executes each function for each patient group, and is equipped with a graph display module for visually displaying the time trends of profit margins, revenues, costs, and drug price margins for each patient group. The system is equipped with a user interface (UI) and graph display module for visually displaying the time trends in profit margins, revenue, costs, and drug price differentials for each patient group, making it intuitively designed for easy operation by medical professionals (doctors and managers).Options such as filtering patient groups, setting the data range (6 months, 1 year), and the ability to freely switch between indicators such as profit margins, revenue, costs, and drug price differentials enable management plans and treatment policy decisions to be made in a short amount of time.

[0065] (2) A hospital management support system characterized by further comprising a planning module that calculates management indicators per patient for a disease group or medical category by dividing the management indicators by the number of patients for each disease group or medical category, and plans the management indicators by having the user who planned the planned number of patients for each disease group or medical category input or register the planned number of patients and multiplying it by the management indicators for each patient for each disease group or medical category, or inputs or registers the planned number of patients and correlating the planned number of patients with the management indicators for each patient for each disease group or medical category.

[0066] This invention is a projected value planning module based on management indicators per patient. It calculates management indicators per patient taking into account the number of patients in each disease group or treatment category, and the user inputs or registers the planned number of patients. The planned number of patients is multiplied by the management indicator for each patient to calculate the planned value of the management indicator. This makes it possible to simulate management plans that take into account future increases or decreases in the number of patients, and provides flexible revenue forecasts based on the planned number of patients input by the user. "Multiplication" refers to the process of multiplying to calculate a total management indicator based on the number of patients. For example, total management indicator = management indicator per patient x planned number of patients.

[0067] (3) The target number of patients is the planned number of patients input or registered by the user who has planned the number of patients for each disease group or medical treatment category. The target number of patients refers to the planned number of patients, and includes replacing the word "planned" with terms such as "anticipation," "expectation," "prediction," "anticipation," "plan," "estimate," "target," "prospect," "supposition," and "future."

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

[0069] This management support system, equipped with a comparative analysis function for actual values, clarifies the difference between planned and actual values, can evaluate 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 margins and material margins, and enables the formulation of future management plans that accurately predict future revenue and profits using the planned number of patients. By comparing actual values ​​with planned values, management issues and areas for improvement are visualized. [Effects of the Invention]

[0070] The aim of this invention is to efficiently utilize resources within a hospital, simultaneously improving management soundness and the quality of medical services. In particular, by comprehensively evaluating multiple indicators such as workload, profitability, bed occupancy rate, and cost reduction, and deriving improvement proposals through simulation, it enables managers to make quick, sustainable decisions based on data.In addition, by visualizing each indicator and providing notifications, it helps to identify resource surpluses and shortages and bottlenecks at an early stage, thereby optimizing the management efficiency and service quality of the entire hospital.

[0071] In addition, patient grouping will enable focus on profitable areas, improve the quality of treatment, reduce costs, and strengthen risk management, helping to achieve both management and medical efficiency. [Brief explanation of the drawings]

[0072] [Figure 1] 1 is a diagram showing a network configuration of a hospital management support system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a block diagram showing an example of the system configuration of a hospital management support system. [Figure 3] FIG. 2 is a block diagram showing an example of the functional configuration of the server 20. [Figure 4] 3 is a flowchart showing a processing procedure executed by a program of the hospital management support system according to the embodiment of the present invention. [Figure 5] 10 is a flowchart showing a processing procedure for bottleneck processing. [Figure 6] Flowchart showing the surgical procedure for patient grouping and classification. [Figure 7] FIG. 10 is a diagram for explaining before and after a simulation. [Figure 8] A diagram to explain the surgical flow at a hospital. BEST MODE FOR CARRYING OUT THE INVENTION

[0073] Figure 1 shows the network configuration centered on the hospital management support system 2, which is connected as needed to in-hospital terminals 1 (PCs, tablet terminals, etc.) and in-hospital medical systems 3 (e.g., electronic medical record systems, accounting systems, DPC systems, etc.) via telecommunications lines via communication networks such as the Internet, intranet, or in-hospital LAN. Sis Tem 2 The network is configured to allow access to the medical system 3 when referring to data in the medical system 3.

[0074] Figure 2 shows hospital management support Sis FIG. 1 is a block diagram showing an example of a system configuration of the server 20 of the system 2 and other devices; Sis The system 2 performs display control to visualize the status of hospital management analyzed and evaluated by the server 20 on the in-hospital terminals 10, 30. The computer of the server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29.

[0075] The communication IF 22 is an interface for inputting and outputting signals so that the server 20 can communicate with external devices. The input / output IF 23 functions as an interface with an input device for receiving input operations from a user and an output device for presenting information to the user. The memory 25 is for temporarily storing programs and data processed by the programs, etc., and is a volatile memory such as a DRAM. The storage 26 is a storage device for saving data, such as a flash memory or HDD. The processor 29 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0076] The hospital terminals 10 and 30 are communicatively connected to the server 20 via a network 80 and have a display means for visualizing the status of hospital management analyzed and evaluated by the server 20. They also have a display means or a notification means for notifying the user in real time of any significant fluctuations in the monitoring score via a visual screen or voice. The hospital terminal 10 is connected to the network 80 by communicating with a wireless base station 81 compatible with various communication standards such as LTE, or with other communication devices such as a wireless LAN router compatible with IEEE or wireless LAN standards. The 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 hospital terminal 10 is a desktop or laptop PC, and the hospital terminal 30 is a mobile terminal such as a tablet or smartphone.

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

[0078] <Functional configuration of server 20> 3 is a block diagram showing an example of the functional configuration of the server 20. The server 20 includes 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, which are electrically connected via a bus.

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

[0080] The input device 230 is a device through which an administrator who operates the server 20 inputs instructions or information as needed, and may be a keyboard, mouse, reader, or touch-sensitive device. The input device 230 converts 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 receives electrical signals input from an external input device. The display means 240 is a device such as a display 241 of an LCD or organic EL display for presenting information to an administrator who operates the server 20 as needed. The display 241 can display data according to the control content of the control means 290, and can confirm the communication status between the server 20 and other external devices 10 and 30.

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

[0082] The work area 281 is secured when the system is started up, and is an area in which various data input and output by the system are temporarily stored. The data storage area 283 is an area in which data temporarily stored in the work area 281 is semi-permanently stored through write control when a save request is made for the data. The screen definition area 284 is an area in which 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 screen settings to be displayed by the display control means 292.

[0083] The screen setting format information included in the screen definition area 284 includes a function for visualizing profit margins for each patient group and a graph display module for visually displaying the time trends in 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 intuitive understanding of management efficiency. It displays profit margins in bar graphs or line graphs, and uses color schemes and labels to facilitate comparison between groups. The graph display module visualizes the time trends in revenue, costs, and drug price margins for each patient group (e.g., arrhythmia, heart failure, breast cancer surgery). It has a user interaction function for switching 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 or report creation.

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

[0085] The evaluation foundation DB283A is a database that stores information indicating the status of hospital management using items and numerical values. This includes information on hospital beds and their occupancy rates, patients and patient unit prices, patients in bed and the number of days they stay in bed, fixed cost items and their amounts, doctors and administrative assistants and their working hours, number of working days, and fees. The resource management DB283C is a database that stores information indicating the status of hospital management using items and numerical values ​​or allocation levels. For facilities and equipment such as operating rooms and waiting rooms that are directly linked to revenue, this includes information on operating room occupancy rates, number of surgeries, revenue, waiting times, number of medical staff deployed, number of waiting room users, waiting times, and the provision status 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, and indicates numerically how many resources are allocated to facilities in need and how frequently the allocated resources are used. Staff allocation frequency refers to the frequency of the number of doctors and nurses assigned to a facility (for example, an operating room). If two doctors and three nurses are assigned to the operating room in the morning, the morning staff allocation frequency for the operating room is recorded as "2 doctors, 3 nurses." Equipment allocation frequency indicates how frequently medical equipment and examination space are used in which facilities. For example, the allocation frequency for CT scan equipment might be recorded as "operating room: 3 times a week, emergency room: 2 times a week." This allows us to understand which facilities use equipment frequently and helps optimize allocation. Bed allocation frequency indicates the occupancy and frequency of patient use of hospital beds, indicating the bed utilization rate. If the allocation frequency of a certain waiting room is "an average of 10 people per day," this can be used as a basis for deciding whether to increase, decrease, or reallocate hospital beds. By introducing the concept of allocation frequency, even for resources that are difficult to intuitively quantify, it is possible to more precisely manage resource allocation within the hospital and to concretely visualize improvement measures aimed at improving management efficiency.

[0087] The evaluation basic DB283A also includes a patient grouping management module that groups and manages patients at the hospital based on specific treatments or diseases, and has a classification module that classifies patients into specific groups (such as arrhythmia, heart failure, catheter treatment, and breast cancer surgery) based on DPC information. The patient grouping management module has the function of grouping patients within the hospital based on specific treatments or diseases and managing them efficiently, as well as the function of integrated management of medical treatment details, treatment outcomes, and cost / profit information for each group. Classification is performed by treatment method (such as catheter treatment or breast cancer surgery) based on medical treatment details, or by disease (such as arrhythmia, heart failure, and cancer treatment) based on disease details. The classification module uses DPC information to associate patients with medical departments and treatment details based on DPC codes. It also performs more detailed classification based on factors such as the main diagnosis, presence or absence of complications, length of hospital stay, and surgery / procedure details. In addition to fixed conditions, it is possible to adjust classification in real time based on data fluctuations.

[0088] The Revenue Management DB283B is a database that stores the calculated sales and profits for each medical department or medical group within the hospital. This database stores management indicator data for each medical department or medical group within the hospital, including revenue status and profit margins, for one or more of the following key management indicators: sales, costs, number of patients, and revenue. Sales and profit data are used to evaluate the profitability of each medical department and calculate unit management indicators (profit indicators), which are useful for identifying medical departments that need improvement and those that are highly profitable. This data can be used to review resource allocation and perform simulations for improving profits. It also serves as the base data for calculating profit per resource unit for each medical department.

[0089] The revenue management DB283B also functions as a calculation module that calculates medical revenue, drug price margins, raw material costs, and total profits for each patient group. The calculation module functions based on each data input, with revenue information acquired from medical systems 3 such as medical fee billing systems and electronic medical records, and drug price margin data from the drug management system as real-time data, and past revenue data and raw material costs (equipment costs, consumable costs, etc.) as fixed data. The calculation module is primarily responsible for the processing function of the calculation unit 295D, and calculates and aggregates data for each patient group using the following calculation methods, and can also reflect past trends by utilizing time-series data. Medical revenue: Calculated from medical fees (comprehensive points, fee-for-service points) Drug Price Margin: Calculate the difference between prescription drug prices and procurement costs · Raw material costs: medical equipment usage costs, drug costs, and other consumable costs. Gross profit: Medical revenue + Drug price margin - 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 therapy, and breast cancer surgery based on the DPC information in the revenue management DB 283B. Since DPC codes include information such as the primary diagnosis, surgery / procedure details, and severity, I49.x (abnormal heart rhythm) 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, surgery / procedure details, length of hospitalization, and patient attributes is extracted from electronic medical records and DPC claim data, and patients are grouped based on predefined rules. The patient grouping management module calculates the revenue, costs, drug price margin, and profit margin for each group and manages the integrated data.

[0091] The classification and characteristics of each patient group, and the management plan to be presented, are as follows: Management plans and management goals are not fixed, and AI can be used to generate plans tailored to the circumstances of patient groups or individual patients. <Arrhythmia Treatment Group> Primary diagnosis: I49.x Surgery: Catheter ablation Features: High profitability, short-term hospitalization Management plan: Resource allocation (increasing the number of specialists, expanding catheterization facilities) Management goal: Increase in the number of catheter treatments <Heart failure patient group> Main diagnosis: I50.x Characteristics: Many patients are hospitalized for mid- to long-term periods, and there is a high proportion of elderly patients. Management plan: Implementing a home care program to shorten hospital stays <Breast Cancer Surgery Group> Primary diagnosis: C50.x Surgery: Mastectomy, reconstructive surgery Characteristics: Post-operative care is important, and chemotherapy drug use affects profits Management plan: Strengthening postoperative follow-up system, using highly effective and low-cost chemotherapy drugs Management goal: Reduce postoperative complications and maximize drug price differentials to improve profitability

[0092] In addition, by including medical cost data, you can record direct treatment costs (e.g., pharmaceutical costs, testing costs, surgery costs) and indirect costs (e.g., labor costs, equipment usage costs) for each department or medical group, understand the balance between revenue and medical costs, and analyze which departments are profitable and have high profit indicators, or which are overly expensive and have low profit indicators. Furthermore, by including patient volume data, you can record the number of patients, visits, hospitalizations, and repeat visit rates for each department or medical group, analyze fluctuations in patient volume and their impact on revenue, and understand which departments have stable patient volumes. Including average length of stay and bed turnover rates allows you to record the average length of stay and bed turnover rate for each department, especially for departments that require hospitalization, analyze how length of stay affects revenue, and determine whether hospital beds are being used efficiently. Including patient cost (cost per consultation) allows you to record the average cost per consultation and the average patient cost (average revenue per patient), understanding the profitability of medical departments and medical procedures that generate high revenue and utilizing this information to introduce and strengthen high-profit medical treatment options. Including treatment time and staff working time allows you to record the average treatment time and staff working time for each medical department and measure the balance of profitability against treatment time, allowing you to identify areas for improvement to improve operational efficiency and revenue. Including cancellation rates and missed appointment rates allows you to record the number of appointments, actual visits, and cancellation and missed appointment rates for each medical department, helping you plan countermeasures and improve revenue for medical departments with high cancellations and missed appointments. Including revenue data by medical procedure allows you to record revenue data for specific treatments, procedures, and tests, allowing you to analyze the profitability of each medical department and treatment, enabling strategic decisions such as focusing on high-profit treatments. By including seasonality and trend data, seasonal fluctuations by medical department and annual revenue transition data can be recorded, and seasonal fluctuations and annual trends can be understood, allowing for use in seasonal management strategies such as increasing resources during periods when revenue is likely to increase. Furthermore, by analyzing the interrelationships between these data, specific measures for improving revenue can be made clearer.

[0093] In the control means 290, various processes of the input means 291, display control means 292, alert processing means 293, user IF 294, and profit simulation means 295 are realized by a processor. The processor is one or more processors. The 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). The at least one processor may be a single-core or multi-core processor. Furthermore, the at least one processor may be a processor in a broad sense, such as a hardware circuit (for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing.

[0094] The display control means 292 controls the display of the results of the simulations performed by the system on the in-hospital terminals 10 and 30. Key indicators, such as sales, profits, resource utilization, and workload for each department and treatment group, as well as profit margins for each patient group, are displayed in a dashboard format for at-a-glance confirmation. Color coding and icons highlight abnormal values ​​of key indicators (e.g., departments with excessive workloads or declining profitability). Simulation results are displayed in graphical formats, such as line graphs, bar graphs, and pie charts, to facilitate intuitive understanding by showing the impact on revenue of changes in profits due to increases or decreases in resources, as well as the impact on revenue of changes in the number of patients and profit margins for each patient group, over time. Providing a dashboard for simultaneously comparing multiple simulation results, such as profits resulting from resource reallocation and changes in bed occupancy rates, helps users compare different patterns and consider optimal strategies. Different simulation conditions can also be manually adjusted using interactive formats such as sliders and checkboxes.

[0095] The alert processing means 293 has a function of executing a process to notify the medical manager when a significant change, such as an excessive workload or a decline in profits, is detected in the workload and profitability fluctuation trends obtained by the fluctuation history recording module, and can issue a command to issue a warning on the display or speaker of the in-hospital terminal 10, 30. It may also send an immediate notification to the manager via email or SMS. Furthermore, as a preventative management improvement, AI can detect significant fluctuations early, enabling preventative measures to be taken before they develop into serious management risks.

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

[0097] The profit simulation means 295 has a function to calculate an increase in profit by moving or adding unit resources based on the data of each database in the data storage area 283, a function to calculate a simulation of an increase in the number of patients using hospital beds and an increase in the unit price per patient based on the bed occupancy rate, a function to calculate a simulation of an increase in the number of patients and an increase in the unit price per patient by promoting early admission and discharge of patients, a function to calculate a simulation by proposing improvements to resources that are bottlenecks due to facilities and other fixed costs within the hospital, a function to propose the placement of multiple medical office assistants and calculate a simulation of the operation of multiple operating rooms, a function to convert the workload and profitability for each medical department and medical department group into scores and calculate a simulation with a score value, a function to regularly record fluctuations in the workload score and profitability score and calculate fluctuation trends in the workload and profitability for each medical department, a function to group patients into patient groups and group them by each medical department or medical group, a function to calculate one or more of profit per patient or sales per patient for each grouped patient group, and a function to simulate and calculate current profit data for each patient group based on the above-mentioned past data. That is, the profit simulation means 295 has a multi-function of carrying out various simulations in hospital management and providing data for management improvement.

[0098] The simulation results can provide specific directions for management improvement based on data, helping managers to make data-based decisions quickly. It also maximizes profits and reduces workloads through efficient resource allocation (resource transfer, resource addition). In addition to its original function of grouping and managing patients, the patient grouping management module also simulates patient groupings that are optimal for hospital management. ShoIt may also be possible to specify specific conditions for grouping by conducting a simulation. Furthermore, it contributes to supporting mid- to long-term management strategies, enabling the setting of mid- to long-term management goals through trend analysis and the evaluation of the degree of achievement of those goals. For example, it can be linked to a function that outputs the simulation results so that they can be used as budget compilation materials for hospital management from the following year onwards.

[0099] The profit increase simulation by unit resource transfer simulates the increase in profits that can be obtained by reallocating resources between medical departments based on each database in the data storage area 283. For example, it calculates the extent to which overall profits will improve if personnel or hospital beds are transferred from a less profitable medical department to a more profitable medical department, or if resources are added to the more profitable medical department. It also performs a simulation to prioritize resource allocation to high-profit groups based on the total profit for each patient group.

[0100] The simulation of patient numbers and patient cost increases based on bed occupancy rates uses bed occupancy rate data to simulate changes in revenue due to increases in the number of patients and increases in cost. For example, it calculates the increase in revenue when more patients are accommodated through efficient use of hospital beds, and the fluctuations in profit when the cost per patient is increased. In this case, the results of calculating profit indicators such as total profit and profit margin for each patient group, which are grouped into patients, are registered for each medical department or medical group within the hospital, and the profit simulation module visualizes the profit margin for each patient group.

[0101] The simulation of increasing patient numbers and patient unit prices by promoting early admission and discharge is carried out to improve bed turnover and profitability by promoting early admission and discharge of patients. For example, by speeding up admission and discharge, bed utilization becomes more efficient, and the change in profits is predicted as the number of patients that can be accommodated increases. In this case, too, the simulation takes into account the profit margin for each patient group.

[0102] Profit simulation based on improvement proposals for fixed cost resources simulates profit changes based on improvement proposals when fixed cost resources such as equipment are bottlenecks. For example, it calculates the effects of expected cost reductions and profit increases due to equipment reallocation or improved utilization efficiency.

[0103] The optimization simulation for medical administrative assistants and operating room operations simulates the profit and efficiency improvements that can be obtained by optimizing the placement of medical administrative assistants and supporting the operation of multiple operating rooms. For example, it analyzes the possibility of increasing profits by reducing the workload of doctors and improving the utilization rate of operating rooms.

[0104] The workload and profitability score simulation for each medical department and medical group scores the workload and profitability of each medical department and medical group, and calculates the score changes after resource reallocation and implementation of measures. For example, it provides simulation results showing that adjusting the workload balance will increase profitability.

[0105] The record of changes in workload and profitability scores and trend calculations involves periodically recording changes in workload and profitability scores, analyzing trends for each department, and evaluating the long-term effects of improvements. For example, the long-term trends in workload and profitability can be calculated to predict the sustainability of management improvements.

[0106] The functions of each block in FIG. 3 correspond to the following components. <1> The database storing DPC and receipt data stores information on medical treatment details, main diagnoses, length of hospital stay, and medical fee information, and corresponds to the evaluation basic DB 283A in the storage means 280 as providing basic data for analysis. <2> The database that stores sales and payment information other than DPC and prescription data corresponds to the revenue management DB283B, which stores non-insurance information such as private medical treatment, drug purchases, and material purchases, and grasps overall income and expenditures. <3> The module for classifying sales and payment information corresponds to the processing of S603 by the patient grouping unit 295E, and classifies sales and payment information by medical department, disease category, and treatment content. <4> The module for allocating costs and revenues corresponds to the calculation unit 295D, which allocates revenues and costs for each group and calculates the profit margin. <5> The classification module for classifying patients corresponds to the processing of S604 by the patient grouping unit 295E, and classifies patients based on medical department, disease group, and medical treatment category (inpatient / outpatient). <6> The calculation and analysis module corresponds to the calculation unit 295D and the profit simulation means 295, and executes calculation and analysis of revenue, gross profit, drug price margin, and material margin. <7> The module for outputting or visualizing the analysis results visualizes the analysis results in graph or table format and provides them to the user, and corresponds to the processing of S606 by the display control means 292 and the user IF 294.

[0107] FIG. 4 shows various processing procedures executed by the application program of the hospital management support system according to the embodiment of the present invention. flowchart This system collects, evaluates, and simulates data, and provides users with visualized information necessary for business improvement.

[0108] <System startup, data preparation, data collection, and updates> When the server 20 is started, the system connects to each terminal and medical system in the hospital via the communication IF 22 and the network. The system 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 stores the necessary initial data in memory. Resource and business data collection involves collecting management data such as sales, profits, and resource utilization rates for each medical department and medical group, and storing this data in the revenue management DB 283B and resource management DB 283C (step S401). Next, if real-time data updating is required, new data and updated information from the 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 most recent data within the expiration date can be used for the evaluation analysis so that old data does not affect the evaluation. In other words, collecting and analyzing data within a specified period contributes to understanding the real-time situation of management. The expiration date for data updates can also be set by AI, dynamically adjusting the optimal deadline based on the fluctuation patterns of past data and the accuracy of the evaluation. Analysis of fluctuations in past data is performed in chronological order to evaluate the frequency and stability of fluctuations. If the data fluctuates frequently, a short expiration date should be used, and if the fluctuations are small, a long expiration date should be used. Trend detection is performed, and AI uses anomaly detection algorithms and trend analysis to evaluate the stability of the data. Trends and anomalies can be identified based on whether revenue data fluctuates from week to week or is stable from month to month.

[0109] <Evaluation of workload and profitability> Next, to generate workload and profitability scores, the score generation module converts the workload and profitability into scores, and the score evaluation unit 295C evaluates them (step S403). In addition, to record scores and analyze trends, the fluctuation history is periodically recorded, and trends in the workload and profitability are identified. The profitability score is calculated by subtracting costs from total revenue based on the main indicators related to profitability: total revenue for each medical department, costs associated with operating the department, such as labor and equipment maintenance costs, average revenue per patient, and unit revenue per medical procedure, examination, or procedure. The profit margin (profitability) is then calculated and standardized to allow for comparison with other medical departments. Adjustments and seasonal corrections are made during scoring, and if the impact of seasonality differs by medical department, a forecast model can be used to correct for seasonality and smooth fluctuations in the profit score. Evaluating and managing workload and profitability as a score that can be grasped at a glance contributes to management improvements and resource allocation by enabling comparisons across the entire hospital or with other hospitals beyond the medical department level.

[0110] <Profit Simulation> The simulation is also performed on profits and the bed occupancy rate and medical cost resources that are directly related to profits (step S404). Alternatively, the profit rate for each patient group is calculated for each department or treatment group within the hospital, and simulations are performed by grouping patients. As a resource reallocation simulation, the resource reallocation section 295A of the profit simulation means 295 calculates the resource unit profit for each medical department, and simulates an increase in profit due to resource movement. As a bed occupancy rate simulation, we simulate an increase in the unit price per patient and an increase in the number of patients based on the bed occupancy rate. To optimize fixed cost resources, the bottleneck processing unit detects bottlenecks in facility utilization and performs simulations for improvements.

[0111] <Alert processing> a The alert processing means 293 monitors sudden or significant fluctuations in workload and profitability, and if a reference value is exceeded, an alert is displayed on the hospital terminals 10 and 30, or a notification is sent to the manager's mobile terminal by email or SMS (step S405).

[0112] Display and Visualization The display control means 292 performs processing to display the management status of each medical department or medical group in dashboard format on the in-hospital terminals 10, 30 (step S406), and visually displays each simulation result as a line graph or bar graph, etc., to enable comparison for management improvement (step S407). Note that by operating the user IF 294, the user IF 294 may receive input from the in-hospital terminal, change the simulation conditions or check the details of alerts, or the profit simulation means 295 may record feedback based on the simulation results in a database, thereby managing the budget and actual results of simulation results for resource movement or resource addition and supporting continuous improvement measures.

[0113] Figure 5 shows the bottleneck processing procedure implemented by the hospital management support system. flowchart is.

[0114] <Data Collection> When obtaining resource usage data, data such as resource usage status, equipment utilization rate, and workload for each medical department and medical group is obtained from the revenue management database and resource management database. When acquiring fixed cost data, data related to fixed cost resources, such as the utilization rate, maintenance cost, and operating time of each piece of equipment, is acquired from the evaluation basis DB (step S501).

[0115] <Analysis of resource usage> Next, the utilization rate is calculated by calculating the utilization frequency and utilization rate of each resource. For example, the utilization rate of operating rooms and medical equipment, the utilization rate of hospital beds, etc. are analyzed. When evaluating the workload of medical staff such as doctors and nurses, the workload of staff and equipment is calculated and areas where excessive strain is placed are identified (step S502). In this way, to detect bottlenecks, data is first collected from the revenue management DB and resource management DB, and an analytical process is performed to analyze the utilization rate and utilization frequency of resources.

[0116] <Comparison with standard values> A threshold is set for determining whether a resource is a bottleneck (step S503), and resources that exceed this threshold are identified as bottleneck candidates (step S504). The bottleneck threshold is set for each resource (e.g., an availability rate of less than 80%, excessive concentration of utilization on a specific resource, etc.), and a resource whose availability rate or utilization rate exceeds the threshold is flagged as a bottleneck candidate. The threshold can also be set to vary depending on factors such as seasonality and social phenomena, and AI can automatically set the bottleneck threshold. In this case, data from various data sources, such as data for each department within the hospital (availability rate, number of patients, waiting time, revenue, etc.), external seasonal data (influenza epidemics, climate change, seasonal events), and social factor data (economic conditions, infectious disease epidemics, etc.), can be integrated to obtain data. Long-term time-series data on seasonal and social factor changes can be compiled, enabling resource usage trends to be extracted by season or during specific event periods. As a time-series forecasting model, the AI ​​model can learn resource usage patterns using time-series analysis models such as Long Short-Term Memory (LSTM) and Seasonal ARIMA (SARIMA) to predict how seasonal and social factors affect resource usage trends. As a factor analysis model, in cases where resource usage fluctuates due to external factors (e.g., epidemics or economic fluctuations), models such as XGBOOST and Random Forest can be used to incorporate external variables that can respond to changes in seasonal and social factors into the forecasting model, measuring the influence of the variables and reflecting them in the baseline setting. Baseline setting can also be dynamically adjusted to set different bottleneck baseline values ​​for different seasons and factors. Baseline values ​​can be flexibly changed according to the situation, such as raising the bed occupancy baseline during a winter influenza epidemic or adjusting the revenue baseline when a worsening economic situation is predicted. Additionally, methods for resolving bottlenecks can be managed as a checklist, which can be used as input for a large-scale language model and displayed as advice.

[0117] To identify bottleneck resources, resources that exceed the standard value are added to a bottleneck list to generate a bottleneck list. This lists facilities with too low utilization rates, such as operating rooms and waiting rooms, and medical departments where staff are under an excessive workload. Bottleneck resources can also be prioritized and sorted in order of the greatest impact.

[0118] <Improvement proposals based on simulation> The profit simulation means simulates a number of scenarios for eliminating the bottleneck (step S505). Improvement measures, such as adding medical clerical work assistants, increasing or decreasing the number of operating rooms, and promoting patient admissions and discharges, are presented. The results of each simulation may be evaluated to evaluate the effectiveness of the improvement, and the most effective improvement plan for eliminating the bottleneck may be presented.

[0119] Visualization to the user (step S507) is performed in accordance with display control (step S506). Display of improvement proposals is performed by visualization on a dashboard using display control means, and the improvement proposals and the impact of bottleneck resources are displayed on the dashboard. It is advisable to display the impact and priority in an easy-to-understand manner using graphs or color coding. In addition, an alert notification function is provided, and for bottlenecks that require particularly urgent resolution, the alert processing means may be used to notify the manager.

[0120] The specific method of converting scores involves quantifying the workload and profitability of medical departments and medical groups. <Calculation of workload score> The workload is calculated based on indicators such as the number of patients in each department, consultation time, number of medical cases, working hours of doctors and nurses, and complexity of medical treatment. The number of consultations and working hours are standardized, and the deviation from the average is calculated to determine the score. The score is normalized to a range of 1 to 100. If a specific indicator has a large impact on workload (e.g., a department with a large number of patients), weight each indicator accordingly. For example, set a weight of 40% for the number of patients, 30% for working hours, and 30% for the number of consultations. <Calculation of profitability score> The revenue of a medical department is calculated by multiplying the medical fee (unit price) by the number of medical treatments. Expenses include personnel costs, medical supplies costs, and equipment maintenance costs. -Evaluate the profitability of each department by dividing revenue by costs. This profitability ratio is used to generate a score, which is then standardized for comparison with other departments. Convert scores into a 100-point scale or a 5-point scale to see the profitability of each medical department at a glance. For example, a profitability rate of 20% or more would be assigned a score of 100, and a profitability rate of 5% or less would be assigned a score of 50. <Regular score updates and trend analysis> Since the score fluctuates depending on business conditions and revenue, the score is recalculated monthly or quarterly to maintain the score based on the most up-to-date information. By recording the history of score fluctuations, we can analyze how the workload and profitability of each department changes over time, thereby predicting 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 an overall score. For example, if the workload score is 60 points and the profitability score is 80 points, the workload score will be weighted at 40% and the profitability score will be weighted at 60%, resulting in an overall score of 70 points. -Using the overall score allows for a comprehensive evaluation of each medical department, making it easier to compare with other medical departments.

[0121] Quantitative evaluation using scores is useful for evaluating the results of resource transfers or expansions. Comparing the changes in workload scores and profitability scores for each department or treatment group before and after a resource transfer makes it possible to measure the impact of the resource transfer on operational efficiency and revenue. Setting a target score according to the purpose of the resource transfer and comparing it with the actual results clarifies the degree of goal achievement and contributes to budget-to-actual management. The score serves as an indicator of whether the workload score for the entire department or treatment group has decreased as a result of the resource expansion, i.e., whether operational efficiency has improved. The profitability score can also be used to evaluate how much new revenue has increased as a result of the resource expansion, or whether there has been an increase in the number of patients or the average patient price. Continuous management improvement is possible by regularly evaluating the effects of resource transfers or expansions using scores and establishing a PDCA cycle to plan the next resource allocation based on the results.

[0122] Figure 6 is a flowchart showing the processing operation of grouping and classifying patients, and shows the procedure for classifying patients by specific treatment content or disease based on DPC information and patient data, and classifying patient affiliation information. The hospital management support system 1 first performs a data collection process to acquire patient information such as DPC information (principal diagnosis, surgery details, complication information), medical treatment details, treatment history, length of hospital stay, and medication use data from the medical system 3, such as an electronic medical record or medical fee claim system (step S601). Patients are classified based on one or more of the following information regarding their affiliation: the department visited, the bed or ward where they stayed, the medical treatment group to which the examining doctor belongs, and the classification of medical treatment, such as inpatient or outpatient.

[0123] Next, a classification condition setting process is performed (step S602). Classification rules are determined, and the length of hospital stay and patient attributes (age, sex) are set as auxiliary conditions based on disease classification based on the primary diagnosis code (ICD-10 code) and classification based on the details of surgery and treatment. In condition comparison (step S603), patients who meet the conditions are grouped (step S604), and patients who do not meet the conditions are provisionally processed as "unclassified," and classification conditions are set again (step S602) to change the classification rules. Classification conditions are set by setting filtering conditions based on classification criteria. 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 the matched / mismatched filtered data. Each patient's data is assigned to a corresponding group (e.g., arrhythmia, heart failure). If a patient falls into multiple groups, they are prioritized based on the primary criteria.

[0125] Next, the group data is tabulated (step S605), and the revenue, cost, drug price margin, and profit margin for each group are calculated. The tabulated results are saved in a database (evaluation base DB or profit management DB) (step S606). These tabulated results are visualized to the user (step S607). The revenue, cost, and profit margin for each group are visualized in a graph and exported (PDF, Excel) for presentation as needed. Note that step S607 can also be an interrupt process that is executed only when requested.

[0126] This invention utilizes both department- or treatment group-specific revenue management and patient group-specific management. Department- or treatment group-specific revenue management, which provides a comprehensive understanding of revenue and costs for each department (e.g., cardiology, oncology, etc.) or treatment group (e.g., cardiovascular disease, cancer treatment, etc.), and patient group-specific management, which categorizes patients based on specific diseases or treatments to support efficient treatment and management improvements, achieve both medical care delivery and management by managing overall management at the department level and making micro-improvements at the patient group level. By linking the two, the hospital's overall sustainability can be enhanced. Specifically, if the analysis shows that the arrhythmia treatment group accounts for a major portion of revenue while the cardiology department has high revenue, the revenue structure can be identified from both an overall and detailed perspective by analyzing the profitability of each department and the profitability of each patient group. Furthermore, measures can be implemented to improve treatment efficiency for each patient group while balancing overall revenue at the department level. By promoting short-term hospitalization for arrhythmia treatment and reducing costs while maintaining the revenue of the cardiology department as a whole, both profitability and cost reduction can be achieved.

[0127] Strategic resource allocation becomes possible, and priorities for resource allocation can be clarified. High-profit medical departments can be identified, specialists and equipment can be added, and resources can be focused on specific diseases and treatments through patient group analysis. In addition, overall strategies (adjusting the number of hospital beds, expanding the scope of medical care) can be planned for each medical department, and specific measures (changing medications, revising treatment protocols) can be implemented on a group basis, allowing for flexible management responses.

[0128] It also contributes to improving the accuracy of management decisions by taking a bird's-eye view of overall management at the department level and proposing specific measures based on the results of patient group analysis. For example, strengthening the treatment system for breast cancer patients based on the results of profit analysis of cancer treatment. Alternatively, it contributes to swift problem-solving by identifying issues in the entire department or specific patient groups early based on data. For example, identifying that the declining profit margin in the cardiology department is due to the long-term hospitalization of heart failure patients and working to resolve the issue.

[0129] It is possible to respond to revenue fluctuations by medical department, grasp revenue trends for each medical department, and take measures to respond to DPC revisions and drug price revisions. For example, in the cardiology department, the amount of high-priced drugs used that are affected by drug price revisions can be adjusted. Alternatively, safety measures can be implemented by patient group, and measures specific to patient groups with a high risk of complications or readmission can be implemented. For example, the home care system can be strengthened to reduce the readmission rate for heart failure patients.

[0130] Examples of combinations of required components 1 and 5 with optional components 2, 3, 4, 6, and 7 are shown below. <Example 1: 1+5+2+6> Configuration: Based on DPC data and patient classification, non-insurance revenue data (2) is additionally stored, and revenue indicators are calculated in the calculation and analysis module (6). Effect: It will be possible to conduct business analysis that includes not only insured medical treatment but also non-insured revenue such as health checkups and vaccinations, identify highly profitable private medical treatment services, and develop 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 in the visualization module (7). Effect: Revenue and payment data can be organized by medical category, making it easier to understand. <Example 3: 1+5+4+6> Structure: In addition to DPC data and patient classification, the module (4) allocates costs and sales, and the calculation and analysis module (6) calculates detailed management indicators. Effect: By clarifying the cost allocation for each medical category, you can focus on medical fields with high profit margins. For example, consider cost reduction measures for medical categories with high material costs. <Example 3: 1+5+2+3+6> Structure: Based on a database (2) containing insured medical treatment and non-insured income, sales and payment information is classified (3), and income is calculated using a calculation and analysis module (6). Effect: By conducting a detailed analysis of sales and costs by medical category, we can evaluate the growth potential of non-insured medical care. We can then develop a strategy that takes into account the revenue balance between insured and non-insured medical care. <Example 4: 1+5+4+6+6> Configuration: Based on DPC data and patient classification, cost allocation (4), analysis (6), and visualization (7) are performed. Effect: Visualize profitability and cost structure for each medical category in a graph. Identify high-profit categories and areas requiring improvement at a glance, and formulate resource allocation plans.

[0131] Figure 7 is a diagram to explain the before and after profit simulation of hospital rooms. (A) shows the profit simulation by medical department, (B) shows the additional profit that can be made by each medical department, (C) shows the results of the hospital room movement simulation, and (D) shows the total profit before and after the simulation. This provides a concrete evaluation of how the movement of resources (hospital rooms) affects the profitability of each medical department.

[0132] Figure 7(A) shows the monthly profit per hospital room for each medical department (internal medicine, general surgery, ophthalmology, otolaryngology), visualizing the differences in profitability between medical departments. The total profit with the current hospital room layout is 1,600 million yen, but after simulating a revised hospital room layout, the total profit is expected to increase to 1,700 million yen, an increase of 100 million yen. During the simulation, the profit simulation module calculates the breakdown by medical department and uses AI to generate resource transfers or resource additions that will increase profits. In other words, the profit fluctuations for each medical department are as follows: Internal Medicine: Reducing the number of hospital rooms by one will reduce profits from 200 million yen to 180 million yen (-20 million yen). General surgery: There is no change in the number of rooms, and profits remain unchanged at 1,000 million yen. Ophthalmology: By increasing the number of rooms by one, profits increased from 360 million yen to 480 million yen (+120 million yen). Otolaryngology: No change in number of rooms or profits. This simulation shows the effect of increasing total profits by increasing the number of ophthalmology rooms in particular. The profit increase due to a review of the layout is visually shown, and resource allocation is optimized for each department. The increase or decrease in the number of hospital beds is based on the bed occupancy rate (not shown) calculated by the bed occupancy rate management module, which simulates the relocation of hospital rooms based on the current number of hospital rooms and the number of additional rooms that can be calculated by calculating the physical maximum number of rooms. While the number of hospital rooms will not increase or decrease when viewed from the perspective of the hospital as a whole, an increase in profits can be expected by shifting resources between medical departments. A medical group is a team formed by combining various medical departments in collaboration, or a group made up of doctors and medical professionals selected from each medical department.

[0133] Figure 7(B) shows the amount of additional profit that each department can make, visualizing the potential profit that an increase or decrease in resources will have on each department. It can be seen that focusing on ophthalmology based on the additional profit that can be made is rational. Ophthalmology has a high profit per room (Figure 7(C)), and the additional profit from adding rooms is also greater than for other departments, so it is expected to have an effect of increasing profitability. The simulation results show that adding one room to the ophthalmology department is expected to increase profits by 120 million yen, a greater profit-increase effect than for other departments.

[0134] Figure 7(D) visualizes the change in total profit after resource reallocation through simulation: total profit before resource transfer was 1,600 million yen, but by reallocating resources (specifically increasing the number of ophthalmology rooms by one and decreasing the number of internal medicine rooms by one), total profit after simulation is 1,700 million yen, an expected increase in profit of 100 million yen. This result shows that resource reallocation improves the profitability of the entire hospital, and visually demonstrates the profitability improvement that can be achieved by concentrating resources on specific medical departments.

[0135] Figure 8 is a diagram illustrating a hospital's surgical flow, showing information related to the surgery, such as the name of the surgery, planned date of admission, preoperative tests, surgeon, medication discontinuation, surgical tools, surgery date, anesthesiology consultation, consent form acquisition and its type, and other restrictions. In the simulation of the present invention, this information is an important factor influencing resource allocation and scheduling, as described below. These factors function as basic data for enabling appropriate resource allocation and schedule adjustment through 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 each patient's planned date of admission, preoperative tests, surgery date, surgeon, and scheduled anesthesiology consultation. The resource management database stores information on hospital beds, operating rooms, surgical tools, and medications, and manages the usage and reservation status of each resource. This database enables optimal resource allocation based on simulation. The medical procedure database stores information on the status of consent form acquisition, type of consent form, and other restrictions. This allows 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 schedules. This allows for optimization so that surgery and examination schedules do not overlap with other patients. Through these databases, the hospital management support system can comprehensively manage the status of each department and patient, and perform resource allocation and scheduling based on simulations. Furthermore, by updating and synchronizing data, simulations are always based on the latest information.

[0136] Name of surgery and surgeon The type of surgery and the qualifications and skills of the surgeon involved affect the appropriate allocation of resources, as the required resources vary. Scheduled admission date and surgery date These dates directly affect resource scheduling and are a key factor in optimizing bed and operating room allocation. Preoperative examination and anesthesiology consultation By allocating resources at the appropriate time to patients who require these preparations, we help ensure that surgery proceeds smoothly. Drug discontinuation If certain medications need to be discontinued, different preoperative preparation periods will affect resource planning. ·Types of surgical instruments and consent forms Securing the necessary surgical tools and preparing consent forms are essential for performing surgery, so it is important to track the status of resource acquisition and document preparation through simulation.

[0137] The profit index calculation module of the present invention optimally manages hospital resources and evaluates the profit contribution of each resource. Specifically, it handles a wide range of resource data, such as the number of waiting patients, number of patients, number of doctors, number of nurses, number of medical assistants, number of nursing assistants, number of hospital beds, number of operating rooms, number of surgery slots, number of surgical materials, number of medications, number of anesthesiologists, number of available working hours in the anesthesiology department, and working hours by department. Based on this resource data and the profit data of each department or group of medical treatments, it calculates management indexes (e.g., profit margins and cost efficiency) for each resource. This calculation visualizes the profit contribution of each resource, which can be used to optimize resource allocation. This function plays an important role in supporting decision-making for efficient resource utilization and increased profitability in hospital management. [Industrial Applicability]

[0138] This invention is a system for supporting the management of hospitals and medical institutions, and is useful in simultaneously improving the efficiency of hospital management and the quality of medical services by visualizing the evaluation and improvement of resource utilization efficiency, profitability, and workload of each medical department and medical group.

[0139] Management improvement proposals based on a combination of multifaceted indicators, i.e., by analyzing multiple indicators in a comprehensive manner, such as increased profits, reduced workload, bed occupancy rates, shorter patient response times, and cost reductions, more accurate management decisions can be made 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 a 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), and solid-state drives (SSDs), dedicated devices and terminals such as POS terminals, vending machines, ATMs, and medical equipment, and game consoles (home and portable).When the program is installed in a medical device, it may be linked to a hospital's electronic medical record (EHR / EMR) system.

[0142] [Appendix B1] operating a computer having a processor and a memory; A program for calculating one or more management indicators of sales by disease group or treatment category, gross profit on medical fees, gross profit taking into account drug price margins, and gross profit taking into account material margins at a medical institution, characterized in that the program requires the following components 1 and 5, and is configured by combining them with one or more of 2, 3, 4, 6, and 7 to configure the processor or memory. 1: A database that stores one or more of the following data: DPC and prescription data 2: Database that stores sales and payment information other than DPC and prescription data 3: Ability to categorize sales and payment information 4: Ability to allocate costs and revenues 5: Classification function for categorizing patients 6: Calculation and analysis functions 7: Function to output or visualize analysis results [Appendix B2] The program described in [Appendix B1], As a management indicator for each disease group or medical category, divide by the number of patients for each disease group or medical category to calculate the management indicator per patient for the obtained disease group or medical category; A program characterized by calculating a target management index by combining the management index per patient and the target number of patients for each disease group or treatment category. [Appendix B3] The program described in [Appendix B2] is characterized in that the target number of patients is the planned number of patients input or registered by the user who planned the number of patients for each disease group or medical treatment category. [Appendix B4] A program according to any one of [Appendix B1] to [Appendix B3], A program that causes the processor to perform processing to analyze actual profits for each disease group or treatment category based on actual values ​​such as the actual number of patients, sales, gross profit on medical fees, drug price margins, and material margins, and to perform a performance analysis function that enables comparison with planned values, and a visualization function that visualizes the analysis results.

[0143] [Appendix C1] A method for supporting hospital management obtained by operating a computer having a processor and a memory, comprising: A method for calculating one or more management indicators of sales by disease group or treatment category, gross profit on medical fees, gross profit taking into account drug price margins, and gross profit taking into account material margins at a medical institution, characterized in that the processor or memory is configured to require the following components 1 and 5, and to combine them with one or more of 2, 3, 4, 6, and 7. 1: A database that stores one or more of the following data: DPC and prescription data 2: Database that stores sales and payment information other than DPC and prescription data 3: Ability to categorize sales and payment information 4: Ability to allocate costs and revenues 5: Classification function for categorizing patients 6: Calculation and analysis functions 7: Function to output or visualize analysis results [Appendix C2] The method according to [Appendix C1], As a management indicator for each disease group or medical category, divide by the number of patients for each disease group or medical category to calculate the management indicator per patient for the obtained disease group or medical category; A method for causing the processor to perform a function of calculating a target management index by performing an operation that combines the management index per patient with the target number of patients for each disease group or medical treatment category. [Appendix C3] The method described in [Appendix C2] is characterized in that the target number of patients is the planned number of patients input or registered by a user who has planned the number of patients for each disease group or medical treatment category. [Appendix C4] A method according to any one of [Appendix C1] to [Appendix C3], A method for having the processor perform processing to analyze actual profits for each disease group or treatment category based on actual values ​​such as the actual number of patients, sales, gross profit on medical fees, drug price margins, and material margins, and to perform a performance analysis function that enables comparison with planned values, and a visualization function that visualizes the analysis results.

Claims

1. A hospital management planning system that manages patients by disease group or medical treatment category (hereinafter collectively referred to as "group") in a medical institution, A hospital management planning system comprising a processor and a memory, wherein the processor executes the following processes (A) to (E): (A) a process for inputting the target number of patients for each group; (B) a process of storing past data related to the management indicators for each group or acquiring real-time data related to the management indicators; (C) A process of calculating a target management index by combining the target number of patients for each group and the management index based on (A) and (B). (D) a process of aggregating the calculation results for each group to calculate an aggregate result; (E) A process of outputting the calculation results and / or the tabulation results in a table format.

2. A hospital management planning system as described in claim 1, further having the function of importing performance data based on DPC or prescription data, automatically updating the management indicators for each group as real-time data based on the performance data, and outputting in tabular format a comparison between planned values ​​calculated using the updated management indicators and the performance values ​​related to the imported performance data.

3. A hospital management planning system according to claim 1 or 2, A hospital management planning system characterized in that the management indicators are sales amount, costs, and margins including at least drug price margins and material margins.

4. A hospital management planning system according to claim 1 or 2, A hospital management planning system characterized in that the calculation calculates the target sales and target gross profit for each group using the following formulas. Target sales = Target number of patients x Sales amount Target gross profit = Target number of patients x (sales - cost)

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