EP4010861A1 - Système et procédé de gestion d'inventaire dans un hôpital - Google Patents

Système et procédé de gestion d'inventaire dans un hôpital

Info

Publication number
EP4010861A1
EP4010861A1 EP20850345.8A EP20850345A EP4010861A1 EP 4010861 A1 EP4010861 A1 EP 4010861A1 EP 20850345 A EP20850345 A EP 20850345A EP 4010861 A1 EP4010861 A1 EP 4010861A1
Authority
EP
European Patent Office
Prior art keywords
medical
information
inventory
forecast
procedures
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20850345.8A
Other languages
German (de)
English (en)
Other versions
EP4010861A4 (fr
Inventor
Vivek Tiwari
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Boston Ivy Healthcare Solutions Private Ltd
Original Assignee
Boston Ivy Healthcare Solutions Private Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Boston Ivy Healthcare Solutions Private Ltd filed Critical Boston Ivy Healthcare Solutions Private Ltd
Publication of EP4010861A1 publication Critical patent/EP4010861A1/fr
Publication of EP4010861A4 publication Critical patent/EP4010861A4/fr
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

Definitions

  • the present invention relates to the field of inventory management and inventory planning and specifically to inventory management and planning in hospital and forecasting stock requirements.
  • a major part of an average hospital budget accounts for medical supplies. Effectively managing medical supplies in a hospital is critical in healthcare sector. One is required to keep the inventory as lean as possible while ensuring that the required essential medical supplies are readily available all time.
  • One of the major challenges faced by hospitals is cancellation of planned surgeries or medical procedures due to missing medical supplies.
  • On the other side most of the new hospitals are clueless as how much medical supplies they should stock for certain number of procedures. This is due to the fact that the new hospitals do not have any historical data for projecting the medical supply stocks that would be required for the forecasted medical procedures that the hospital is expected to conduct.
  • stock forecast is arrived by extrapolating the movement of item by help of various existing techniques based on relationship that can be used to automatically calculate forecasts of demand. This is done using demand history data. These forecast techniques are used to calculate fresh base forecast from actual demand adjusted for seasonal and period length variations. A method is specified for each forecast technique. The technique also contains specified parameters and limits which regulate the calculation performed using the method.
  • This forecast method calculates the base forecast for the next period as the average of historic base demand for a specified number of periods. This is denoted in the equation 1 below:
  • the number of periods used determines how quickly the averaging will react to changes in actual trends and how sensitive it will be to random variations. The more periods included will make the calculation method more stable from random variations, but it will also react more slowly to changes resulting from real trends.
  • smoothing constant determines how quickly the forecast will react to changes in actual trends and how sensitive it will be to random variations. The lower the value, the more stable the calculation is from random variations, but it will also react more slowly to changes resulting from real trends. Smoothing constant (must be between 0 and 1).
  • This forecast method uses (values that are adjusted for the current systematic forecast error. A larger mean forecast error results in a higher value. This results in quicker corrections to the forecast towards reflecting actual demand.
  • M Average demand for the latest 25 % periods out of the total of (n) periods
  • MAD(i) Forecast MAD for period (i)
  • ABS( ) Absolute difference, the difference without minus sign
  • the existing techniques in prior art has various problems.
  • One such problem is that the stock movement does not incorporate any specific attributes like capacity utilization e.g. occupancy in a hospital at a given point of time, seasonality and/or trends or introduction of any new services which brought in additional Stock Keeping Units (SKU’s) to be bought or increase in required quantity of existing SKUs in stock etc.
  • capacity utilization e.g. occupancy in a hospital at a given point of time
  • seasonality and/or trends or introduction of any new services which brought in additional Stock Keeping Units (SKU’s) to be bought or increase in required quantity of existing SKUs in stock etc.
  • SKU Stock Keeping Units
  • Another problem with the existing techniques is that usually maximum of last one year’s historic sales or issue data is taken as base data for projection. Yet another problem might be issued doesn’t necessarily means consumed at the end point. It may be laying as the closing stock for example stock issued from inventory but lying at production floor or in case of hospital stock issued from central inventory but lying in inventory of operating rooms. Yet another problem with the existing techniques is use of sales data or inventory issued of the medical supplies. The sales data or the inventory issued might be different as compared to what would have been the actual consumption of medical supplies. Consumption of items is the best data because it has sustained all factors like occupancy in the hospital or introduction of any new services, disease trend etc.
  • the existing techniques also fails to disclose a lean supply chain system applied to healthcare with mass customized order fulfillment, closed loop inventory planning and management and feedback systems, real-time monitoring and data flows to connect the healthcare supply chain from patient to manufacturer, and machine learning based feedback for both clinicians and hospital administrators.
  • the existing techniques also lacks in accurately forecasting the stock requirement in a hospital.
  • KR101939106B1 titled “Inventory management and method using common prediction model” discloses inventory management by a server or a forecast demand using the stored predictive model to a public server, to minimize the portion of the people involved in inventory management. It shares predictive models to perform machine learning.
  • statistical learning techniques can be used, such as time series analysis, regression analysis, to classify learning techniques may be used, such as SVM, Naive Bayesian, Decision Tree.
  • SVM Naive Bayesian
  • Decision Tree Decision Tree
  • the objective of the invention is to obtain the information regarding the overall stock requirement in a hospital. Specifically, the invention focuses on the aspect of the stock consumption for various procedures performed in the hospital. Thereby, the invention facilitates accurate measurement to forecast the stock requirement in the hospital.
  • the objective of the invention is achieved by a system for Inventory planning and management according to claim 1.
  • the system comprises an input unit, a memory device and a processing unit.
  • the input unit receives a user input related to one or more medical procedures.
  • the memory device stores a mapping information between a medical procedure and an inventory of items required to carry out the medical procedure.
  • the memory device also stores historical database comprising a historic information which includes a consumption information related to items consumed in past for the one or more medical procedures.
  • the processing unit receives and processes the user input, and based on that retrieves and processes the mapping information for the one or more medical procedures and the historical information for the one or more medical procedures to generate at least one of an inventory forecast or a procedure forecast, or combination thereof.
  • the inventory forecast relates to the inventory of items required by the medical facility, the procedure forecast related to number of medical procedures to be taking place in the medical facility.
  • This embodiment of the invention facilitates generation of the forecasts which are accurate and efficient. Most importantly, the invention focuses on generating the forecast based on the consumption of items which is the best data to relay on, because it has sustained all factors like occupancy in the hospital or introduction of any new services, etc.
  • the processing unit generates the forecast for a predefined period.
  • the processing unit retrieves the historical information from the memory device, wherein the historical information is for a specific medical facility for which a specific medical facility forecast is generated.
  • the historical information can before more than one medical facilities.
  • the historical information is for more than one medical facility located in a predefined geographical area.
  • This embodiment of the invention enhances the likelihood to generate finest inventory forecasts, as it shall be region specific, and considers parameters and past data which is specific to the region.
  • the processing unit receives a geographic area information related to a location of the medical facility and processes the mapping information and the historical information for the one or more medical procedures to generate the at least one of the inventory forecast or the procedure forecast or a disease outbreak forecast or combination thereof.
  • the disease outbreak forecasts relate to disease outbreak tends to happen in a particular Geographical area.
  • the input unit receives an additional input comprising at least one of a medical facility infrastructure information, a medical speciality information, or a staff headcount information, or combination thereof.
  • the medical facility infrastructure information is defined by a physical capacity related data of the medical facility to carry out various procedures
  • the medical speciality information is related to various specialities the medical facility is having
  • the staff headcount information relates to number of staff of various categories are involved to carry out various procedures at the medical facility.
  • the processing unit receives and processes the additional input along with a geographical area information the mapping information and the historical information for the one or more medical procedures and generates the at least one of the inventory forecast or procedure forecast or combination thereof.
  • the processing unit retrieves and processes at least one of a seasonality of medical requirements data or a trend related data, or combination thereof with the mapping information and the historical information for the one or more medical procedures to generate the forecasts.
  • the historical information can further comprise at least one of the number of procedures done in the past by the medical facility, a number of surgeons the medical facility engages, or an availability of operating rooms, or combination thereof.
  • the processing unit receives and processes an editing input and the mapping information to generate an updated mapping information. Further, the processing unit is adapted to send the updated mapping information back to the memory device.
  • the user input may comprise an expected number of one or more types of medical procedures to be carried out.
  • the user input includes identification information related to more than one medical facilities for whom the forecast is to be generated.
  • the processing unit receives and processes the user input and the mapping information and the historical information for the one or more medical procedures and generates at least one of a cumulative inventory forecast or a cumulative procedure forecast, or combination thereof.
  • the cumulative inventory forecast relates to the inventory of items required by the medical facilities, and the cumulative procedure forecast is related to the number of procedures to be taking place in the medical facilities.
  • the processing unit retrieves a current stock information related to each of an inventory item required for one or more procedures from a stock database based on processing of the mapping information. Further, the processing unit processes the current stock information along with the at least one of an inventory forecast, or a procedure forecast, or combination thereof and generates a list of items required to be ordered, wherein the stock database is defined by quantities of each items present in the inventory.
  • the processing unit receives one or more threshold data for one or more thresholds of quantity of an item in the inventory, retrieves the quantities of each of the items from the stock database.
  • the processing unit processes the quantities against the threshold data and update an indicator for quantities of each of the items in the stock database.
  • the processing unit receives and processes the threshold data and quantities of each of the items from the stock database and generates one or more reminders for making an order for the list of items on predefined intervals.
  • the processing unit retrieves and processes the user input along with the mapping information, delivery data, and the lead time data, and the historical information for the one or more medical procedures and generates a safety inventory forecast.
  • the safety inventory forecast relates to the inventory of items required by the medical facility to be kept as buffer for unforeseen demands for one or more procedures.
  • the historical information includes at least one of a consumption deviation data related to deviation in consumption of inventory items in past, or a requirement variation data related to deviation between the inventory forecast and the order placed in past, or combination thereof.
  • the delivery data is related to delivery constraints related to probability of insufficient delivery of inventory items or quality deficiency in the delivery of inventory items or combination thereof.
  • the lead time data is defined as an expected delay for a future delivery of inventory, or a deviation in delay time for inventory delivery in past, or combination thereof.
  • the memory device stores a relational information which relates to a medical speciality to more than one procedure.
  • the processing unit retrieves and processes the relational information for one or more specialities, the mapping information and the historical information for the one or more medical procedures and generates the at least one of the inventory forecast or a procedure forecast or combination thereof.
  • the inventory forecast relates to the inventory of items required by one or more specialities of the medical facility, the procedure forecast related to the number of procedures to be taking place in one or more specialities of the medical facility.
  • a processing unit uses a decision- tree-based ensemble Machine Learning mechanism to generate the forecasts.
  • the decision- tree-based ensemble Machine Learning mechanism cab be based on an XGBoost mechanism.
  • Fig. 1 illustrates the system for Inventory management in the hospital to forecast stock requirement of the hospital.
  • the invention focuses on providing a system and method of inventory planning and management in a hospital for forecasting stock requirements in the hospital.
  • the invention focuses on achieving the results by utilizing machine learning techniques for forecasting the future stock requirements.
  • One of the major issues that is observed with respect to inventory planning and management is that emerging hospitals are clueless regarding the quantity i.e. how much medical supplies they should stock for certain number of procedures. Further, the cancellation of planned surgeries or medical procedures due to missing medical supplies are one of the major reasons which leads to degraded performance of the hospitals.
  • There are several inventory planning and management solutions those are presently available address the aforementioned issues partially but none of the solutions concentrate on the aspect of "the consumed stock" of the hospital.
  • the present invention provides a system for inventory planning and management in a hospital for forecasting stocks requirement.
  • a mechanism is provided to manage the inventory in the hospital, and more specifically predict the stock to be consumed in the hospital for various procedures, or various medical procedures to be carried out in the hospital.
  • One such implementation of the invention is presented in Fig. 1.
  • Fig. 1 shows the mechanism for predicting the stock from the systeml for Inventory management.
  • the system 1 includes an input unit 2, memory device 10, and processing unit22.
  • the input unit 2 relates to a hardware component such as keyboard, mouse, etc which accepts the data from the user, or any other kind of sensors which can receive gestures as input, or any other sensors which automatically generates data to be used by the processing unit, such as location sensors.
  • a hardware component such as keyboard, mouse, etc which accepts the data from the user, or any other kind of sensors which can receive gestures as input, or any other sensors which automatically generates data to be used by the processing unit, such as location sensors.
  • the memory device is another hardware component such as Read-Only-Memory (ROM), flash memory etc, which stores and retrieves the data by the user.
  • ROM Read-Only-Memory
  • flash memory etc
  • the processing unit relates to another hardware component which is capable for processing any data or information received by them.
  • these processing unit can be part of any regularly devices, such as laptops, mobile devices, servers, etc.
  • the processing unit 22 in the present invention is dedicated to be in communicative coupling to the input unit, memory device/s, and display. It receives data from the memory device/s and input unit, and processes the data to generate forecast, and communicate the forecast to the display.
  • the input unit 2 of system 1 receives a user input 3.
  • the user input 3 is related to information with respect to medical procedures. This information can be related to various types of medical procedures being carried out, or specific procedures being carried out, or identification of a medical facility where the medical procedures being carried out.
  • the user input 3 can also be a combination of such before-said information.
  • Some examples of medical procedures carried out are General Surgery, Cardiac Surgery, Hernia Operation, Laser removal of power, Gyneac normal delivery, Gyneac C section, Root Canal procedure and Hipec, etc.
  • the input unit 2 can receive any other inputs to further refine the forecasts to be provided by the processing unit 22.
  • the inputs which can be received are as follows: a geographic area information 4, additional information 5, and trend related data 8and editing input 9.
  • the geographic area information4 relates to a location of the medical facility such as the area where the hospital/nursing home is located.
  • the additional information 5 includes various kinds of information such as a medical facility infrastructure information, a medical speciality information, a staff headcount information.
  • the medical facility infrastructure information is defined by a physical capacity related data of the medical facility to carry out various procedures such as operating rooms
  • the medical speciality information is related to various specialities the medical facility is having such as automatic medical instruments
  • the staff headcount information relates to number of staff of various categories are involved to carry out various procedures at the medical facility such as surgeons.
  • the editing input9 relates to certain data which requires certain modifications correction of values etc.
  • the user input 3 alone is sufficient to process and generate the forecast, and no other types of inputs, as mentioned above, may be required.
  • one or more inputs can be received along the user input 3 depending on the requirement of the desired forecast.
  • the memory devices 10 of the system 1 stores various data which are retrieved by the processing unit 22 for determining the forecasts.
  • the data stored are a mapping information 11, the relational information 12, the stock database 18, the historical database 15, seasonality of medical requirements data 7, and trend data 8.
  • the mapping information 11 relates to mapping between the medical procedure and an inventory of items required to carry out the medical procedure.
  • the relational information 12 relates a medical speciality of a medical facility to more than one procedure.
  • the stock database 18 is defined by quantities of inventory items present in the inventory of the medical facility.
  • the stock databasel8 includes a threshold data 13, a current stock information 19, and indicator 21.
  • the current stock information 19 relates to quantities of each inventory items in the stock in the medical facility.
  • the threshold data 13 relates to one or more thresholds of quantity of an item required to be kept in the inventory by the medical facility.
  • the indicator 21 is a level indicator which gives a quick visual information to the user regarding the stock available for a particular item in the inventory of the medical facility.
  • the seasonality of medical requirements data 7 relates to infections, allergies or diseases occurred during a particular period such as fever, cold, malaria, chickenpox.
  • the trend related data 8 relates to a particular symptoms/sickness goes in a trend in one geographical area or sometimes it may be irrespective of geographical area.
  • One good example of trend related data is COVID 19.
  • the memory device lOfurther stores a historical database 15 which includes a historic information 16 comprising information related to items consumed in past for the one or more medical procedures. Consumption information items may be related to contains trends, seasonality, cycle, or combination thereof which is generally different for different hospitals, clinics and nursing homes.
  • the historic information 16 may also include a number of procedures done in the past by the medical facility, and/or a number of surgeons the medical facility engages, or an availability of operating rooms.
  • a processing unit 22 of the system 1 retrieves data from the input unit 2 and memory devices 10 and processes the same to generate inventory forecast 23, procedure forecast 24, specific medical facility forecast 25, disease outbreak forecast 17, safety inventory forecast 20, cumulative inventory forecast 26 and cumulative procedure forecast 27. These forecasts may be generated for a specific predefined time or multiple time intervals.
  • the inventory forecast 23 relates to predicting the inventory of items required by a medical facility in future.
  • the procedure forecast 24 relates to predicting the number of medical procedures to be taking place in the medical facility in future.
  • the specific medical facility forecast 25 relates to predicting the forecasts 23, 24 for a specific medical facility.
  • the disease outbreak forecast 17 relates to predicting the diseases which may outbreak in near future with respect to the geographical area of a particular medical facility.
  • the safety inventory forecast 20 relates to the inventory of items required by the medical facility to be kept as buffer for unforeseen demands for one or more procedures.
  • the cumulative inventory forecast 26 relates to relates to the inventory of items required by more than one medical facilities.
  • the cumulative procedure forecast 27 relates to the number of procedures to be taking place in more than one medical facilities.
  • the processing unit 22 employs a decision-tree-based ensemble Machine Learning mechanism to generate the forecast(s).
  • the decision-tree-based ensemble Machine Learning mechanism used is an XG Boost mechanism.
  • any other mechanisms can be used such as Random Forrest, SVM, LSTM, SARIMA, Gated Recurrent Units, RNN, etc.
  • the processing unit 22 generates only inventory forecast 23 or the procedure forecasts 24, or both, and no other forecasts may be required by an user, and hence the processing unit 22 shall not be configured to any other type of forecasts.
  • the input unit 2 receives the user input 3, and send it to the processing unit 22.
  • the processing unit 22 processes the user input 3, and based on such processing retrieves the mapping information 11 and the historical information 16 from memory device 10, and processes them to generate the inventory forecast 23 and the procedure forecast 29.
  • the processing unit 22 may only generate either of the forecasts 23, 24.
  • the historical information 16 is also linked with respect to a specific facility, and the processing unit generates the specific medical facility forecast 25, which can be either of the forecasts 23, 24, or both for that specific facility.
  • the system is enacted in a closed computing environment of a specific medical facility, and in such scenario the historical information 16 maybe required only for that specific medical facility.
  • the embodiment may also be implemented in a client-server architecture, where the server has the processing unit 22, and the memory device 10 storing historical database of multiple medical facilities, however while generating the forecasts 23, 24, 25 it utilizes historical information 16 of only specific medical facility.
  • the historical information 16 shall be of the medical facilities for a predefined geographical area only.
  • the geographical area can be a specific city, or a country, or can be a hyperlocal area in a city which can be of a predefined radius, or can be a municipal division of the city, etc. This helps in fine tuning the forecasts 23, 24 for the medical facility considering the past going for the medical facilities in that geographical area.
  • the input unit 2 further receives a geographical area information 4 and sends it to the processing unit 22.
  • the processing unit 22 processes this geographical area information 4 along with the mapping information 11 and the historical information 16 to additionally generate the disease outbreak forecast 17 for the geographic area, and generates more refined forecasts 23, 24 with respect to the geographical area in concern.
  • the processing unit 22 do not generate all the three forecasts 23, 24, 17, rather it generates any combination of the forecasts 23, 24, 17, as required by the end user.
  • the geographical area information 4 shall not be required, if the fine tuning of the forecasts 23, 24is not desired or the disease outbreak forecast 17 is not required.
  • the processing unit 22 generates forecast 23, 24 for a bigger range of periods. However, when a specific periods forecasts is desired, the processing unit 22 generates it only for the desired predefined period. This embodiment becomes really helpful when a medical facility may want to go in a futuristic procurement agreement for a predefined period, or human resource department may want to defined the medical practitioner required to be enrolled for a predefined period, etc.
  • the input unit 2 further receives the additional input 5 and sends it to the processing unit 22.
  • the processing unit 22 processes the additional input 5 along with the geographical area information4, the mapping information 11 and the historical information 16, and generates the inventory forecast 23, or the procedure forecast 24. It is pertinent to note that use of additional input 5, and the geographical area information4 further refines the forecasts
  • the processing unit 22 may only generate either of the forecasts 23, 24.
  • the additional input 5 and the geographical area information 4 may not be required by the processing unit 22, if a coarse level of forecasts 23, 24 are required.
  • the processing unit 22 also receives and processes the seasonality of medical requirements data 7 and the trend related data 8 along with the mapping information Hand the historical information 16, and generates the inventory forecast 23, the procedure forecast
  • the seasonality of medical requirements data 7 further refines the forecasts 23, 24, 25 considering the diseases/illness such as fever, flu which affects the people during particular season of the year
  • the trend related data 8 refines the forecasts 23, 24, 25 considering the diseases/illness such as COVID 19 occurred in 2019, Spanish Flu occurred in 1918 which might occur over ages but the illness remains for certain period and affects huge mass of people.
  • the processing unit 22 may only generate either of the forecasts 23, 24, 25, or combination thereof, and not all of the forecasts 23, 24, 25.
  • the seasonality of medical requirements data 7 may not be required by the processing unit 22, if forecasts 23, 24, 25 are not required with respect to seasonality constraints.
  • the trend related data 8 may not be required by the processing unit 22, to generate forecast 23, 24, 25 unless there is a necessity to predict the illness which occur infrequently.
  • the input unit 2 further receives the editing input 9 and sends it to the processing unit 22.
  • the processing unit 22 processes the editing input 9 along with the mapping information 11 and generates the updated mapping informationll.
  • the generated updated mapping information 11 is sent further to the memory device.
  • This updated mapping information 11 either edits only the part of the records which is changed, or replaces the entire mapping information 11. It is pertinent to note that the option for editing the mapping information helps to replace any faulty records or change the information according to change in protocol for a particular procedure which eventually changes the inventory requirements for handling a medical procedure.
  • the mapping information is hard coded, and can only be changed changing the source code on whose execution the processing unit runs, and enables the functioning of whole system.
  • the user input 3 includes the identification information related to more than one medical facilities for whom the forecast 23, 24 is to be generated.
  • the processing unit 22 processes the identification information 3 along with the mapping information 11 and the historical information 16, and generates the cumulative inventory forecast 26 related to the inventory of items required by the medical facilities, and a cumulative procedure forecast 27 related to the number of procedures to be taking place in the medical facilities.
  • the processing unit 22 may only generate either of the forecasts 26, 27, or combination thereof, and not all of the forecasts 26,
  • the identification information may not be required for multiple facilities, if the cumulative forecast 26, 27, is not required.
  • the processing unit 22 Based on processing of the mapping information 11, the processing unit 22 also retrieves the current stock information 19 from the stock database 18 stored in the memory device 10, and processes along with the inventory forecast 23, or the procedure forecast 24, or both and generates a list of items required to be ordered.
  • the knowing of list of items to be ordered further helps in execution process of the ordering of inventory, which was not alone possible for the forecasts 23, 24, as based on the forecasts 23, 24, substantial efforts shall be required to match with the inventory the items already in store, and then only a list could have been created for ordering. In case a medical facility has another system in place to make an order which takes inputs as forecasts 23, 24, such list generation shall not be required.
  • the processing unit 22 receives one or more threshold data 13 for one or more thresholds of quantity of an item in the inventory, and retrieves a current stock information 19 related to quantities of each of the inventory items in stock from the stock database 18, and thereafter processes them to update an indicator 21 for quantities of each of the items in the stock database 18.
  • threshold data 13 and stock database 18 shall not be required for processing.
  • the processing unit 22 further generates reminders 14for making an order for the list of items on predefined intervals also, based on processing the threshold data 13 and the current stock information 19. In an alternate embodiment, where the medical facility has a separate system of ordering and setting up reminders, the processing unit 22 is not required to carry out such processing and generating reminders.
  • the processing unit 22 further processes the mapping information 11, a delivery data 28, a lead time data 29, and the historical information 16 and generates the safety inventory forecast 20.
  • the historical information 16 also includes a consumption deviation data, and/or a requirement variation data, which are relevant for generating the safety inventory forecast 20.
  • the consumption deviation data relates to deviation in consumption of inventory items in past
  • the requirement variation data relates to deviation between the inventory forecast and the order placed in past.
  • the delivery data 28 is related to delivery constraints related to probability of insufficient delivery of inventory items or quality deficiency in the delivery of inventory items or combination thereof.
  • the lead time data 29 is defined as an expected delay for a future delivery of inventory, or a deviation in delay time for inventory delivery in past, or combination thereof.
  • the safety forecast 20 is sometimes important to determine, if inventory available shall be enough with respect to forecast of procedures or not. In alternate embodiment, such safety forecast 20 may not be required, if there is a manager who is manually checking and determining based on his experience and forecasts 23, 24, whether the stock is safely enough for future procedures to be carried out.
  • each speciality may be managed separately, and may require a separate forecast.
  • the system shown in figure 1 also takes care of such requirements by giving the forecast to the speciality level.
  • the processing unit 22 retrieves and process a relational information 12 which relates a medical speciality to more than one procedures, for one or more specialities, the mapping information 11 and the historical information 16, and generates the inventory forecast 23 related to the inventory of items required by one or more specialities of the medical facility, or a procedure forecast 24 related to the number of procedures to be taking place in one or more specialities of the medical facility.
  • the inventory forecast 23, or the procedure forecast 24 is carried out.
  • the hospital doesn’t have such department division, such forecasts may not be required.

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Abstract

La présente invention concerne un système (1) et un procédé (30) pour la gestion d'inventaire dans un établissement médical. Le système (1) comprend une unité d'entrée (2), un dispositif de mémoire (10) et une unité de traitement (22). L'unité d'entrée (2) est configurée pour recevoir une entrée d'utilisateur (3) associée à une ou plusieurs procédures médicales. Le dispositif de mémoire (14) est configuré pour mémoriser des informations de mappage (11) et des informations historiques (16). L'unité de traitement (22) est configurée pour extraire et traiter l'entrée d'utilisateur (3) conjointement avec les informations de mappage et les informations historiques pour générer une ou plusieurs prévisions (23-24). L'invention facilite la génération de prévisions d'inventaire qui sont précises et efficaces.
EP20850345.8A 2019-08-07 2020-08-04 Système et procédé de gestion d'inventaire dans un hôpital Pending EP4010861A4 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN201921031911 2019-08-07
PCT/IB2020/057346 WO2021024171A1 (fr) 2019-08-07 2020-08-04 Système et procédé de gestion d'inventaire dans un hôpital

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EP4010861A1 true EP4010861A1 (fr) 2022-06-15
EP4010861A4 EP4010861A4 (fr) 2023-08-23

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CN113240162B (zh) * 2021-04-28 2022-03-01 南京天溯自动化控制系统有限公司 一种基于EEMD-Prophet算法的医院能耗预测方法

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JP2005293046A (ja) * 2004-03-31 2005-10-20 Hogi Medical:Kk 手術室管理システム
US20150187035A1 (en) * 2013-12-30 2015-07-02 Cerner Innovation, Inc. Supply management in a clinical setting
CN107072739B (zh) * 2014-08-01 2020-09-11 史密夫和内修有限公司 提供用于手术程序的植入物
US20190172012A1 (en) * 2017-12-05 2019-06-06 Standvast Healthcare Fulfillment, LLC Healthcare supply chain management systems, methods, and computer program products

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EP4010861A4 (fr) 2023-08-23
AU2020326471A1 (en) 2022-03-03
WO2021024171A1 (fr) 2021-02-11

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