Intelligent hospital operation method and system based on medical information

Abstract

The invention relates to the technical field of digital medical treatment, in particular to an intelligent hospital operation method and system based on medical information, and the method comprises the following steps: carrying out the statistics of a bed occupation amount field and a nursing scheduling time period field based on a hospitalization nursing resource distribution record, accumulating the occupation amount and time period labels, and carrying out the calculation of the time period labels; and checking the bed retention condition field in the intensive care area, and comparing the retention mark with the available bed. According to the method and the system, the dynamic relationship between the bed occupation amount field and the nursing scheduling period field is counted through multi-dimensional data association and staged processing of the hospitalization nursing resource allocation record, and the resource use state is mastered in real time. The bed retention condition field and the available bed field in the intensive care area are compared one by one, and linkage analysis of the nursing manpower input amount field and the patient admission time field is combined, so that the allocation efficiency of the intensive care resources and the nursing manpower configuration are optimized.

Description

Technical Field

[0001] The present invention relates to the field of digital medical technology, and in particular to a smart hospital operation method and system based on medical information. Background Art

[0002] The field of digital medical technology is an emerging technology field that combines information technology, data analysis and medical services. It provides efficient medical services and resource management for patients, medical institutions and related parties by collecting, storing, analyzing and sharing medical information. However, the existing technology lacks the ability to associate multidimensional data in medical information management. Most data fields are stored in a single way and simply classified, which makes it difficult to cope with dynamic needs during resource allocation. For example, in the allocation of nursing resources, the existing technology fails to match bed occupancy with nursing staff scheduling information in real time, resulting in the inability of resource allocation plans to respond to sudden demands in a timely manner, which may cause insufficient or idle nursing resources in critical areas. Therefore, improvements are needed. Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a smart hospital operation method and system based on medical information.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution, a smart hospital operation method based on medical information, comprising the following steps:

[0005] Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling time field are counted and the occupancy and time period labels are accumulated. The intensive care unit bed reservation field is checked and the reservation mark is compared with the available beds. The nursing manpower input field is compared with the patient admission time field and the input and admission time are calculated to generate nursing scheduling statistics.

[0006] Based on the nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling time period field and the time tag is compared with the scheduling period record, the operating room available capacity field is compared with the personnel deployment quantity field and the available space and the number of personnel are counted, the surgical instrument demand field is summarized and the instrument types are listed, and the operating room scheduling record is generated;

[0007] Based on the operating room scheduling records, the drug usage category field and the surgical instrument demand field are cross-checked to confirm the correspondence between the category and the instrument, the remaining inventory quantity field and the emergency replenishment quantity field are compared and the difference between the remaining quantity and the replenishment is calculated, the special drug control field is marked and the special type is recorded, and the drug and instrument demand allocation is generated.

[0008] Preferably, it also includes:

[0009] Based on the drug and equipment demand allocation, the inpatient ward number field and the drug issuance time field are queried and compared with the ward mark and the issuance period, the issuance serial number field and the usage detail field are checked and confirmed to correspond to the issuance sequence number and the details, the drug batch attribute field is archived and the batch information is recorded, and the ward drug issuance record is generated;

[0010] Based on the drug distribution record of the ward, the drug cost field and the hospitalization settlement form number field are associated and searched, and the cost number and the settlement form number are cross-matched. The settlement amount field and the patient payment amount field are compared and the bill value is compared with the actual payment. The hospitalization days field is jointly calculated and the day interval is extracted to generate income and expenditure allocation information;

[0011] Based on the income and expenditure allocation information, the hospitalization cost abnormal value field and the drug cost abnormal value field are compared and the differences between hospitalization and drug are listed. The arrears amount field and the insurance reimbursement amount field are cross-checked and the difference between the arrears amount and the reimbursement amount is identified. The overdue days field is compared and the overdue range is marked to generate financial risk warning elements.

[0012] Preferably, the steps for obtaining the nursing scheduling statistics are:

[0013] Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling period field are counted and the occupancy and period tags are accumulated. By reading the occupancy field values ​​one by one and matching them with the scheduling period field values, the occupancy and tags are merged after integrating the matching information of the two types of fields to generate occupancy and period data;

[0014] Based on the occupancy and time period data, the reservation status field of the intensive care unit bed is checked and the reservation mark is compared with the available beds, and the bed numbers corresponding to the reservation marks are recorded and mapped item by item with the list of available beds, and the items without reservation marks and bed conflicts are screened out to generate bed availability comparison information;

[0015] Based on the bed availability comparison information, the nursing manpower input field is compared with the patient admission time field and the input and check-in time are calculated. By summarizing the manpower input and the corresponding admission time interval and performing numerical calculations, nursing scheduling statistics are generated.

[0016] Preferably, the steps for obtaining the operating room scheduling record are:

[0017] Based on the nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling time period field and the time label is compared with the scheduling period record. By synchronously viewing the application time list and the scheduling period interval and comparing the labels one by one, the corresponding data of time and scheduling are generated;

[0018] Based on the time and shift corresponding data, the available capacity field of the operating room is compared with the personnel deployment quantity field and the available space and personnel quantity are counted, and the available capacity value and the corresponding deployment quantity are decomposed, compared and summarized item by item to generate available space and personnel information;

[0019] Based on the available space and personnel information, the surgical instrument requirement fields are summarized and the instrument types are listed. An instrument list is generated by reading the requirement list and classifying it by category, and an operating room scheduling record is generated.

[0020] Preferably, the steps for obtaining the distribution of drug and equipment requirements are:

[0021] Based on the operating room scheduling record, the drug usage category field and the surgical instrument requirement field are cross-compared and the category and instrument correspondence are confirmed, and the category and instrument correspondence information is generated by sequentially reading the drug category value and the instrument requirement tag and mapping and matching them;

[0022] Based on the corresponding information of the category of equipment, the remaining inventory quantity field and the emergency replenishment quantity field are compared and the difference between the remaining quantity and the replenishment is calculated, and the inventory replenishment statistics are generated by comparing the registered inventory value and the replenishment value and calculating the difference;

[0023] Based on the inventory replenishment statistics, the special drug control fields are marked and the special types are recorded. By checking the control mark values ​​one by one and classifying and archiving the corresponding information, the drug and equipment demand allocation is generated.

[0024] Preferably, the steps for obtaining the ward drug dispensing record are:

[0025] Based on the drug and equipment demand allocation, an association query is performed between the inpatient ward number field and the drug issuance time field and the ward mark and issuance time period are compared. By recording the ward number and the corresponding issuance time period and comparing the records, a ward time period mapping is generated;

[0026] Based on the ward time period mapping, the issuance serial number field and the usage detail field are checked and confirmed to correspond to the issuance serial number and the details, and the issuance detail comparison data is generated by locking the serial number value and the usage detail list and comparing the identification item by item;

[0027] Based on the distribution detail comparison data, the drug batch attribute fields are archived and the batch information is recorded. The attribute records are traversed and bundled with the batch labels to generate the ward drug distribution record.

[0028] Preferably, the steps for obtaining the income and expenditure allocation information are:

[0029] Based on the drug distribution records in the ward area, perform an associated retrieval of the drug cost field and the hospitalization settlement form number field, cross-match the cost number and the settlement form number, and generate cost and settlement cross data by reading the cost number list and the settlement form number list and comparing and pairing the successful items;

[0030] Based on the cost and settlement cross data, compare the total settlement amount field with the amount paid by the patient field, compare the bill value with the actual payment, and generate a cost difference comparison result by matching the total bill amount and the paid amount and calculating the difference;

[0031] Based on the cost difference comparison result, perform a joint calculation on the length of hospitalization field and extract the day interval, and generate revenue and expenditure distribution information by reading the day records and performing accumulation in adjacent intervals.

[0032] Preferably, the steps for obtaining the financial risk warning elements are as follows:

[0033] Based on the revenue and expenditure distribution information, compare the abnormal hospitalization cost field with the abnormal drug cost field, list the differences between hospitalization and drugs, and generate a cost anomaly comparison by sequentially taking out the anomaly labels and pairing the hospitalization and drug values;

[0034] Based on the cost anomaly comparison, perform an interactive verification on the overdue amount field and the insurance reimbursement amount field, identify the gap between the overdue amount and the reimbursement amount, and generate a difference between overdue and reimbursement by comparing the registered overdue amount and the reimbursement amount value and calculating the gap;

[0035] Based on the difference between overdue and reimbursement, compare the overdue days field and mark the overdue range, and generate financial risk warning elements by counting the overdue days and the range interval and marking the registration.

[0036] The present invention provides an intelligent hospital operation system, including:

[0037] A bed resource management module, through the inpatient nursing resource allocation records, statistically analyzes the bed occupancy field and the nursing shift scheduling period field, accumulates the occupancy and the period label, checks the bed reservation situation field in the intensive care unit area, and compares the reservation mark with the available beds to obtain a bed resource statistical report;

[0038] A nursing manpower allocation module, based on the bed resource statistical report, compares the nursing manpower input field with the patient admission time field, calculates the matching degree of the nursing staff input and the patient admission time, and generates a nursing manpower allocation plan;

[0039] The operating room scheduling module, based on the nursing manpower allocation plan, matches the operating room application time field with the nursing scheduling time field, compares the time tag with the scheduling period record, counts the operating room available capacity field and the personnel deployment quantity field, summarizes the surgical instrument demand field, lists the instrument types, and obtains the operating room scheduling record;

[0040] The drug and equipment management module, based on the operating room scheduling records, cross-checks the drug usage category field with the surgical equipment demand field, confirms the correspondence between the category and the equipment, compares the remaining inventory quantity field with the emergency replenishment quantity field, calculates the difference between the remaining quantity and the replenishment, records the special drug control field, and generates a drug and equipment demand list;

[0041] The system's comprehensive monitoring module allocates and monitors resources based on the drug and equipment requirement list and obtains a resource allocation efficiency report.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] The present invention realizes real-time mastering of resource usage status by associating and processing multi-dimensional data of inpatient nursing resource allocation records in stages, and statistically analyzing the dynamic relationship between the bed occupancy field and the nursing scheduling time field. The bed reservation field of the intensive care unit is compared with the available bed field one by one, and the linkage analysis of the nursing manpower input field and the patient admission time field is combined to optimize the allocation efficiency of critical resources and the allocation of nursing manpower. The operating room resource management link introduces the matching operation of the surgery application time field and the scheduling time field, and compares the operating room capacity field and the personnel allocation quantity field. Through the synchronous accounting of multi-dimensional data, the rationality of operating room utilization and personnel allocation is improved. In terms of drug management, based on the cross-analysis of the drug use category field and the surgical instrument demand field, combined with the difference accounting of the remaining inventory quantity field and the emergency replenishment quantity field, the utilization efficiency of the drug inventory is comprehensively optimized, and insufficient supply or resource redundancy is avoided. At the same time, the special drug control field is marked and classified and archived, which enhances the safety and compliance of drug management. In summary, the present invention constructs a dynamically optimized medical resource management framework by associating and integrating resource allocation with real-time data, realizes refined control of hospital operation links, and improves resource utilization and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] See also Figure 1 The present invention provides a technical solution, a smart hospital operation method based on medical information, comprising the following steps:

[0047] Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling time field are counted and the occupancy and time period labels are accumulated. The intensive care unit bed reservation field is checked and the reservation mark is compared with the available beds. The nursing manpower input field is compared with the patient admission time field and the input and admission time are calculated to generate nursing scheduling statistics.

[0048] Based on nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling period field and the time tag is compared with the scheduling period record. The operating room available capacity field is compared with the personnel deployment quantity field and the available space and personnel quantity are counted. The surgical instrument demand field is summarized and the instrument types are listed to generate the operating room scheduling record.

[0049] Based on the operating room scheduling records, cross-check the drug usage category field with the surgical instrument demand field and confirm the correspondence between the category and the instrument. Compare the remaining inventory quantity field with the emergency replenishment quantity field and calculate the difference between the remaining quantity and the replenishment. Mark the special drug control field and record the special type to generate drug and instrument demand allocation.

[0050] Based on the allocation of drug and equipment demand, the inpatient ward number field and the drug issuance time field are queried and compared with the ward mark and the issuance period. The issuance serial number field and the usage detail field are checked and the issuance sequence number and details are confirmed to correspond. The drug batch attribute field is archived and the batch information is recorded to generate the ward drug issuance record.

[0051] Based on the ward drug distribution records, the drug cost field and the hospitalization settlement form number field are associated and searched, and the cost number and settlement form number are cross-matched. The settlement amount field and the patient payment amount field are compared and the bill value is compared with the actual payment. The hospitalization days field is jointly calculated and the day interval is extracted to generate income and expenditure allocation information.

[0052] Based on the income and expenditure distribution information, the abnormal value field of hospitalization expenses is compared with the abnormal value field of drug expenses, and the differences between hospitalization and drugs are listed. The arrears amount field and the insurance reimbursement amount field are cross-checked to identify the difference between the arrears amount and the reimbursement amount. The overdue days field is compared and the overdue range is marked to generate financial risk warning elements.

[0053] The steps to obtain nursing scheduling statistics are as follows:

[0054] Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling period field are counted and the occupancy and period tags are accumulated. By reading the occupancy field values ​​one by one and matching them with the scheduling period field values, the occupancy and tags are merged after integrating the matching information of the two types of fields to generate occupancy and period data;

[0055] Based on the occupancy and time period data, the reservation status field of the intensive care unit bed is checked and the reservation mark is compared with the available beds. By recording the bed number corresponding to the reservation mark and mapping it item by item with the list of available beds, the items with no reservation mark and bed conflicts are screened out to generate bed availability comparison information;

[0056] Based on the bed availability comparison information, the nursing manpower input field is compared with the patient admission time field and the input and admission time are calculated. By summarizing the manpower input and the corresponding admission time interval and performing numerical calculations, nursing scheduling statistics are generated.

[0057] Specifically, based on the inpatient nursing resource allocation record, the values ​​of the bed occupancy field in each record are first read one by one and compared with the accommodation range obtained based on past statistics and current nursing standards, such as 0 to 300 beds. The values ​​beyond the range are marked and added to the exception list. Then the nursing scheduling time period field is read, and each time period value is compared with the benchmark range from 0 to 24 o'clock, and the records not in this interval are marked as time period exceptions. The nursing scheduling time period can be divided into three segments: 0 to 8 o'clock, 8 to 16 o'clock, and 16 to 24 o'clock. When recording specifically, each occupancy data is matched with its time period, and then the occupancy field value is linked to the corresponding time period field value in the same record to generate a mapping table. The total occupancy quantity in each time period is obtained by traversing and counting the occupancy and time period sequences in the mapping table. At the same time, the entries with abnormal marks are recorded and special marks are added to them in the overall summary. Then, all time period occupancy statistics and scheduling label fields are aligned and merged into a comprehensive data table. The comprehensive data table uses the time period as the index and associates the corresponding total occupancy and abnormal marks, and finally obtains the occupancy and time period data.

[0058] Based on the occupancy and time period data, the intensive care unit bed reservation status field is first read and compared with the previously obtained time period occupancy records. The bed numbers in the intensive care unit are classified and identified in the range of 0 to 200. This number range is determined according to the hospital organization and department planning. The bed numbers without registered reservation marks are queried against the time period occupancy and included in the standby list. The occupancy records of the bed numbers with registered reservation marks are checked one by one. If the occupancy value exceeds the range of 0 to 10, it is regarded as abnormal occupancy and marked. 0 to 10 is the range set with reference to the normal load of critical care beds. Then, the beds with reservation marks are cross-judged with the previously recorded standby list to screen out those entries with conflicting occupancy or repeated registration. Subsequently, all records without conflict are summarized and available tags are attached to them to generate a comparison detail with bed numbers and availability tags, which are mapped item by item with the intensive care unit bed reservation status field, and finally the bed availability comparison information is output.

[0059] Based on the bed availability comparison information, the nursing manpower input field and the patient admission time field are read and paired one by one. The range of 0 to 50 people for each nursing manpower input value is taken as the usually available reference value. This range is estimated based on the usual scheduling plan and the average nursing demand. If it is found to be beyond this range, it is marked as an abnormal shift. By comparing the patient admission time field, the actual admission time of each patient is associated with the corresponding nursing manpower input. For those admission times between 0 and 24 hours, a one-to-one mapping is maintained. After the mapping is completed, all nursing manpower input and admission time records are summarized and the time difference between the two is calculated. If this time difference is between 0 and 2 hours, it is classified as closely connected. Otherwise, it is considered that the nursing schedule is inconsistent with the admission rhythm and is additionally marked. Subsequently, all records are sorted in order of nursing manpower input from small to large and the corresponding admission time period labels are attached to integrate them into a calculation table covering the total manpower input and patient admission time information, and finally form nursing scheduling statistics.

[0060] The steps to obtain the operating room scheduling records are as follows:

[0061] Based on nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling time period field and the time label is compared with the scheduling period record. By synchronously viewing the application time list and the scheduling period interval and comparing the labels one by one, the corresponding data of time and scheduling are generated;

[0062] Based on the corresponding data of time and shift, the available capacity field of the operating room is compared with the personnel deployment quantity field, and the available space and personnel quantity are counted. By decomposing the available capacity value and the corresponding deployment quantity, they are compared and summarized item by item to generate the available space and personnel information;

[0063] Based on the available space and personnel information, the surgical instrument requirement fields are summarized and the instrument types are listed. The instrument list is generated by reading the requirement list and classifying it by category to generate the operating room scheduling record.

[0064] Specifically, based on the nursing scheduling statistics, the operating room application time field and the scheduling segment record in the previous step are read, and the feasible combination is determined by comparing each application time with the interval of the scheduling segment. Then, the application time is segmented for the determined combination, and 0:00 to 8:00 is regarded as the first period, 8:00 to 16:00 is regarded as the second period, and 16:00 to 24:00 is regarded as the third period. If an application time exceeds 24:00, it is marked as not meeting the daytime scheduling requirements and listed separately. Then, the association between each application time and the corresponding scheduling segment is summarized into a matching detail table, and an abnormal mark is added to all records that do not meet the requirements in the detail table. Then, the matching detail table is analyzed line by line and the proportion of application time that meets the time period requirements is counted. This proportion is compared with the pre-set standard range, where the lower limit of the standard range is set by 20 estimated by past surgical statistical data, and the upper limit is inferred to be 90 based on the actual operation and maintenance situation. When the statistical proportion is lower than 20 or exceeds 90, it is classified as an extreme distribution phenomenon and marked as a special entry. Finally, all matching entry information generation time and scheduling corresponding data are merged.

[0065] Based on the corresponding data of time and shift scheduling, the available capacity field of the operating room is first read and compared with the segmented application information in the previous step one by one. The available capacity value in the range of 0 to 5 rooms is considered to be in a tight state, and the range of 5 to 10 rooms is considered to be in a general state. If it exceeds 10 rooms, it is considered to be in a sufficient state. The above ranges are set up by the hospital after statistics on the scheduling of operating rooms in the past year. Then the personnel allocation quantity field is extracted and matched with the corresponding operating room segmented application list. Each allocation quantity value is compared with the standard range of 0 to 10 people. The data below 0 people are marked as abnormal, and the data above 10 people are classified as over-allocation. Then all normal allocation records are summarized and organized into an available space list and a personnel quantity list. Finally, the available space list and the personnel quantity list are vertically merged and the status category of each record is marked to form available space and personnel information.

[0066] Based on the available space and personnel information, the surgical instrument demand fields are summarized and the instrument name, quantity and usage period are read one by one. The quantity of the same instrument name in the same period is merged and the cumulative value is calculated. If the cumulative value is in the range of 1 to 5 pieces, it is considered a low-volume demand. If it exceeds 5 to 20 pieces, it is considered a medium-volume demand. If it exceeds 20 pieces, it is considered a high-volume demand. Each interval is set based on past instrument usage statistics and hospital specifications. A three-way comparison is performed between the above demand and the available space status and personnel allocation in the previous step. Insufficient marks are attached to records that cannot be met, and redundant marks are noted for records of excess resources. Finally, the demand data in all time periods and the corresponding space and personnel allocation information are merged into a scheduling list to generate an operating room scheduling record.

[0067] The steps to obtain drug and equipment demand allocation are:

[0068] Based on the operating room scheduling records, the drug usage category field and the surgical instrument requirement field are cross-checked to confirm the correspondence between the category and the instrument. By sequentially reading the drug category value and the instrument requirement tag and mapping and matching, the category and instrument correspondence information is generated;

[0069] Based on the corresponding information of the category equipment, the remaining inventory quantity field and the emergency replenishment quantity field are compared and the difference between the remaining quantity and the replenishment is calculated. By comparing the registered inventory value and the replenishment value and calculating the difference, the inventory replenishment statistics are generated;

[0070] Based on inventory replenishment statistics, special drug control fields are marked and special types are recorded. By checking the control mark values ​​one by one and classifying and archiving the corresponding information, the drug and equipment demand allocation is generated.

[0071] Specifically, based on the operating room scheduling records, the entries of the drug usage category field and the surgical instrument requirement field are first extracted and data mapping is established one by one. The category number and instrument name in each record are read, and a neural network for matching the drug category and instrument requirement characteristics is trained using pre-prepared training samples. During training, the category number and instrument name are combined as an input vector and labeled with the expected output label. Then, iterative learning is performed against multiple sets of known corresponding relationships. The convergence threshold for each iteration is set based on empirical statistics. Usually, the average matching success rate of the actual operation records of the previous year minus three percentage points is taken as the initial value. If the neural network completes the specified rounds in training and still fails to reach the threshold, the training data is randomly split into new batches for retraining until the correct recognition rate within the iterative round is greater than the set threshold before the training is terminated. Subsequently, the trained model is used to infer and match the new drug category value with the instrument requirement label. If the inference result is consistent with the corresponding relationship already existing in the original record, it is regarded as a legal record. If there is an inconsistency, it is marked as a suspected abnormality and written into a list to be checked. Finally, all legal records and suspected abnormal records are counted and output in the order of entries to generate category instrument corresponding information.

[0072] Based on the corresponding information of the category of equipment, read the remaining inventory quantity field and the emergency replenishment quantity field for numerical comparison. First, group the drugs by category in the inventory quantity set and record the remaining inventory of each category. Match the emergency replenishment quantity with these remaining inventory quantities one by one. If it is found that the inventory of a certain category is less than a set threshold during the corresponding process, the emergency replenishment quantity of the category will be deducted from a temporary replenishment resource pool. The initial value of the set threshold is determined by the average daily consumption of the past three months plus a peak value. The resource pool balance must be recalculated after each deduction. When the resource pool balance is lower than the average daily consumption, its status is marked as shortage. If the sum of the replenishment quantity and the remaining inventory is greater than a maximum reserve upper limit, it is identified as excess and marked as a waste record. The maximum reserve upper limit is set by the medical department on a quarterly basis and is usually higher than the maximum peak consumption. Finally, the replenishment difference corresponding to various categories is counted and saved as an inventory difference detail to generate inventory replenishment statistics.

[0073] Based on inventory replenishment statistics, special drug control fields are read in turn and the key drug categories involved are marked. The list of key drug categories is summarized according to the hospital management system and agreed to have a number range of 100 to 199. Then the control mark value in the corresponding number range is cross-checked with the inventory difference details. If the inventory difference is too low and the difference is less than 0, the record is marked as an emergency control item. If the difference is too high and exceeds a certain overflow limit set in advance, it is marked as an abnormal backlog. This overflow limit is determined by the maximum acceptable redundancy during the most recent inventory count. All key drug items are screened one by one and filed in the corresponding management list. Finally, a complete list of abnormal control records and normal records is formed and output in field order to generate drug and equipment demand allocation.

[0074] The steps to obtain the ward drug distribution record are as follows:

[0075] Based on the allocation of drug and equipment demand, the inpatient ward number field and the drug issuance time field are queried and compared with the ward mark and issuance period. By recording the ward number and the corresponding issuance period and comparing the records, a ward time period mapping is generated;

[0076] Based on the ward time period mapping, the issuance serial number field and the usage detail field are checked and the issuance serial number and detail correspondence are confirmed. By locking the serial number value and the usage detail list and comparing the identification item by item, the issuance detail comparison data is generated;

[0077] Based on the distribution details comparison data, the drug batch attribute fields are archived and the batch information is recorded. By traversing the attribute records and bundling them with the batch labels, the ward drug distribution records are generated.

[0078] Specifically, based on the allocation of drug and equipment demand, the inpatient ward number field and the drug issuance time field are read for associated query. The ward number is first limited to the range of 001 to 999. This range is determined by the hospital's department classification number. Any number outside this range is considered invalid and directly marked as abnormal. Each valid number is compared with the issuance time one by one and the corresponding relationship is recorded. When the time field is divided into three time periods, it is processed according to 00:00 to 08:00, 08:00 to 16:00 and 16:00 to 24:00. The time value not in this range is considered abnormal and separated into a special list. Then, a mapping data table is constructed between the ward number and the time period. Each record is sorted according to the ward number and then aligned with the corresponding time period. If there is a conflict or duplication in adjacent records, it is marked as a duplicate allocation record and the reason for the duplication is annotated. By comparing the drug category and ward demand label in the time period, it can be identified whether there is cross-department occupation or invalid registration, and then combined with the key drug control information obtained in the previous step for secondary confirmation. After sorting, all valid mapping entries are finally output to generate a ward time period mapping.

[0079] Based on the ward time period mapping, the issuance serial number field and the usage details field are checked one by one. First, all issuance records under each time period are extracted from the ward time period mapping and their serial number values ​​are read. Then, they are matched according to the association between the serial number and the usage details. Each serial number should correspond to at least one usage detail. If the serial number is found to be non-existent or cannot be matched to the corresponding detail, the record is considered missing and marked in the exception list. Otherwise, the serial number and the usage details are summarized into a paired list, and then these paired data are arranged in ascending order according to the serial number. In the case of multiple usage details with the same serial number, its usage data needs to be accumulated to compare whether it exceeds the set maximum usage limit. The maximum usage limit is based on the monthly statistics of the material management department and is generally not more than twice the peak amount of a single ward. If it exceeds, it is further marked as an over-limit record. Finally, all normal pairing information and abnormal marking information are summarized to generate issuance detail comparison data.

[0080] Based on the distribution detail comparison data, read the drug batch attribute field and traverse the batch labels in each record one by one. All entries with the same batch number are integrated and the complete attribute information is listed. If a batch number does not exist in the registration library, it is directly registered as an unknown batch and marked as possibly requiring manual verification. If batch information is repeated, it is necessary to compare in chronological order and merge the time fields. Then, the drug category and numerical fields of each batch are cross-checked. If the usage displayed in the numerical field exceeds the peak standard provided by the hospital in the past, it is marked as a questionable entry. The peak standard is obtained by adding an additional coefficient to the highest usage record of the same category batch in the past year, usually between 1.2 and 1.5 times. The specific coefficient is determined according to the hospital's management intentions. In this way, a unified batch index table is compiled and covers the key attributes of each record. All information is then integrated and output to generate ward drug distribution records.

[0081] The steps to obtain income and expenditure allocation information are as follows:

[0082] Based on the ward drug distribution records, the drug cost field and the inpatient settlement form number field are associated and searched, and the cost number and settlement form number are cross-matched. By reading the cost number list and the settlement form number list, and comparing and matching the successful items, the cost and settlement cross data are generated;

[0083] Based on the cross-data of costs and settlements, the settlement amount field is compared with the patient payment amount field and the bill value is compared with the actual payment. By matching the bill total and the payment amount and calculating the difference, the cost difference comparison result is generated;

[0084] Based on the cost difference comparison results, the hospitalization days field is jointly calculated and the day intervals are extracted. By reading the day records and performing accumulation in adjacent intervals, the income and expenditure distribution information is generated.

[0085] Specifically, based on the ward drug issuance record, the drug cost field is first read and the cost number value is identified one by one. The corresponding inpatient settlement form number field content is extracted at the same time and a list to be matched is established. If the cost number in each record is missing or not in the valid number range, it is marked as an abnormal number. The valid number range is provided by the hospital's financial department and is usually between 10000 and 99999. If the inpatient settlement form number is missing, it is also marked as an abnormal number. Then, a cross-search operation is performed on all valid numbers, and the cost number and the inpatient settlement form number are compared two by two to confirm the records that can match both parties. If an expense number corresponds to only one single settlement order number, it is considered a normal match. If it corresponds to multiple settlement order numbers, it is recorded as a multiple association. This multiple association will be included in the special marking range and further reviewed. During the comparison, the uniqueness of the match will be checked to ensure that each settlement order number will not be cross-matched with other expense numbers after a successful match. If it is found that a settlement order number corresponds to multiple expense numbers, it will be determined as a duplicate entry and appended to the suspected conflict list. Finally, all successfully matched entries and abnormal entries are grouped and summarized and rearranged in ascending order of the expense numbers to generate expense and settlement cross data.

[0086] Based on the cross-data of expenses and settlements, the total settlement amount field is first read and its value is matched one-to-one with the patient payment amount field. The total settlement amount field is usually in the range of 1 yuan to 999,999 yuan. If it exceeds this range, it is marked as an abnormal bill. The range is obtained by the hospital financial management system based on the maximum bill amount in previous years plus a safety redundancy. Then, the patient payment amount field is checked for validity in the same interval, and invalid records are listed for review. If the difference between the total settlement amount and the payment amount in each valid record is greater than 0, it means that the actual payment is less than the bill value. If the difference is less than 0, it means that overpayment has occurred. Both situations are marked as difference values ​​and the difference amount is written into the difference details. Records with a difference in the range of 0 to 10 yuan are regarded as minor errors and special marks are set. This range is specified by the accuracy requirements of medical insurance and in-hospital expenses. If it exceeds 10 to 100 yuan, it is judged as a general difference. If it exceeds 100 yuan, it is classified as an obvious difference. Finally, all difference information is summarized together with the corresponding expense number and settlement form number to generate the expense difference comparison result.

[0087] Based on the cost difference comparison results, the hospitalization days field is read and a joint calculation is performed for each record. All hospitalization days values ​​are grouped by patient identification and then accumulated one by one. If a patient has multiple admissions and corresponds to multiple day records, first determine whether the time periods of these day records are continuous. If adjacent intervals overlap or differ by no more than one day, they are combined and calculated as an overall interval. Otherwise, they are divided into multiple independent intervals and recorded separately. For each complete interval, the total statistics are performed and the patient's cumulative hospitalization days are obtained. If this cumulative number of days exceeds the upper limit of the hospitalization cycle compiled by the hospital in advance with reference to similar diseases, it is marked as abnormal hospitalization. The upper limit is the average hospitalization days for the corresponding disease in the previous year plus a 20% safety factor. If it is lower than the minimum cycle standard, it is classified as a possible early discharge situation and is also marked as a special case. After all records have completed the accumulation of adjacent intervals, a list containing the hospitalization day intervals for each patient is summarized to generate income and expenditure distribution information.

[0088] The steps to obtain financial risk warning factors are as follows:

[0089] Based on the income and expenditure distribution information, compare the abnormal value fields of hospitalization expenses with the abnormal value fields of drug expenses and list the differences between hospitalization and drugs. Generate an abnormal cost comparison by taking out the abnormal value labels in turn and matching the hospitalization and drug values;

[0090] Based on the comparison of abnormal expenses, the arrears amount field and the insurance reimbursement amount field are interactively verified to identify the difference between the arrears amount and the reimbursement amount. By comparing the registered arrears amount with the reimbursement amount value and calculating the difference, the arrears and reimbursement differences are generated;

[0091] Based on the difference between arrears and reimbursements, the overdue days field is compared and the overdue range is marked. By counting the overdue days and range intervals and marking them for registration, financial risk warning factors are generated.

[0092] Specifically, based on the income and expenditure distribution information, the hospitalization cost abnormal value field is first read and compared with the drug cost abnormal value field one by one, and each record is determined in turn whether it exceeds two abnormal value thresholds at the same time. Here, the judgment standard for hospitalization cost abnormal values ​​is set to twice the reference annual average cost level in the hospital, and the judgment standard for drug cost abnormal values ​​is set to the highest single drug cost in the past multiplied by a coefficient of 1.5. These two standards are set by financial personnel by analyzing historical consumption data and combining management needs. If they are exceeded at the same time, they are marked as double abnormalities. Records with only abnormal hospitalization costs or only abnormal drug costs are classified as single abnormalities, respectively. The abnormal value labels in the records are then sorted into a comparison table, and the double abnormal and single abnormal items are clearly distinguished in the comparison table. For records without abnormal values, normal record marks are made. If it is found during the comparison process that some values ​​are not updated in the hospitalization cost abnormal value field or the drug cost abnormal value field, they are included in the information missing list and reminded to supplement them. After completing the comparison and classification of all records, the corresponding data sequence is output to generate a cost abnormality comparison.

[0093] Based on the comparison of abnormal expenses, the arrears amount field and the insurance reimbursement amount field are first read and interactive verification is performed. For each record, the arrears amount and the reimbursement amount are extracted and compared numerically. If the arrears amount is greater than the reimbursement amount, it is recorded as the difference between the remaining arrears amount. If the reimbursement amount is greater than or equal to the arrears amount, it means that the arrears can be offset by the reimbursement part and the remaining part is recorded as the surplus amount. The reimbursement amount here depends on the maximum reimbursable amount provided to the hospital in advance by the medical insurance or commercial insurance department, which is usually determined according to the patient's insurance type or medical insurance level. The arrears amount field is also limited in range, and the range between 0 yuan and 999,999 yuan is considered a normal range. If it exceeds the range, it is marked as an account abnormality. When comparing, each record result is sorted according to the difference size and the entries with a difference exceeding 1,000 yuan are placed in a special attention list. The 1,000 yuan limit is the trigger value set by the finance department for the average hospitalization cost. Finally, all normal verification records and records with differences are summarized and distinguished by severity to generate the arrears and reimbursement differences.

[0094] Based on the difference between arrears and reimbursements, the overdue days field is read and the overdue days value of each record is compared with the account situation calculated previously. If the overdue days are between 0 and 3 days, it is considered a short-term overdue, if it is between 3 and 7 days, it is considered a medium-term overdue, and if it exceeds 7 days, it is considered a long-term overdue. These intervals are specified in reference to the hospital's collection management regulations. Subsequently, based on the difference between the patient's arrears and reimbursement, patients with larger arrears are registered in the long-term overdue watch list first. Patients with shorter overdue days and arrears in a smaller range are classified as regular overdue. If there are multiple accounts that meet the conditions of high arrears and long overdue days, they are marked as high-risk overdue records. In the recording step, the fields in the arrears and reimbursement difference table are also verified twice. If the overdue days are abnormal or not registered, a data missing mark is added and such records are listed collectively. After completing all day comparisons and markings, financial risk warning elements are generated.

[0095] The present invention provides a smart hospital operation system, comprising:

[0096] The bed resource management module records the allocation of inpatient nursing resources, counts the bed occupancy field and the nursing scheduling time field, accumulates the occupancy and time period labels, checks the intensive care unit bed reservation status field, compares the reservation mark with the available beds, and obtains the bed resource statistics report;

[0097] The nursing manpower allocation module, based on the bed resource statistical report, compares the nursing manpower input field with the patient admission time field, calculates the matching degree between the nursing staff input and the patient admission time, and generates a nursing manpower allocation plan;

[0098] The operating room scheduling module, based on the nursing manpower allocation plan, matches the operating room application time field with the nursing scheduling time field, compares the time tag with the scheduling period record, counts the operating room available capacity field and the personnel deployment quantity field, summarizes the surgical instrument demand field, lists the instrument types, and obtains the operating room scheduling record;

[0099] The drug and equipment management module, based on the operating room scheduling records, cross-checks the drug usage category field with the surgical equipment demand field, confirms the correspondence between the category and the equipment, compares the remaining inventory quantity field with the emergency replenishment quantity field, calculates the difference between the remaining quantity and the replenishment, records the special drug control field, and generates a drug and equipment demand list;

[0100] The system's comprehensive monitoring module allocates and monitors resources based on the drug and equipment requirement list and obtains a resource allocation efficiency report.

[0101] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A smart hospital operation method based on medical information, characterized in that: The following steps are involved: Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling time field are counted and the occupancy and time period labels are accumulated. The intensive care unit bed reservation field is checked and the reservation mark is compared with the available beds. The nursing manpower input field is compared with the patient admission time field and the input and admission time are calculated to generate nursing scheduling statistics. Based on the nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling time period field and the time tag is compared with the scheduling period record, the operating room available capacity field is compared with the personnel deployment quantity field and the available space and the number of personnel are counted, the surgical instrument demand field is summarized and the instrument types are listed, and the operating room scheduling record is generated; Based on the operating room scheduling records, the drug usage category field and the surgical instrument demand field are cross-checked to confirm the correspondence between the category and the instrument, the remaining inventory quantity field and the emergency replenishment quantity field are compared and the difference between the remaining quantity and the replenishment is calculated, the special drug control field is marked and the special type is recorded, and the drug and instrument demand allocation is generated.

2. The smart hospital operation method based on medical information according to claim 1 is characterized in that: Also includes: Based on the drug and equipment demand allocation, the inpatient ward number field and the drug issuance time field are queried and compared with the ward mark and the issuance period, the issuance serial number field and the usage detail field are checked and confirmed to correspond to the issuance sequence number and the details, the drug batch attribute field is archived and the batch information is recorded, and the ward drug issuance record is generated; Based on the drug distribution record of the ward, the drug cost field and the hospitalization settlement form number field are associated and searched, and the cost number and the settlement form number are cross-matched. The settlement amount field and the patient payment amount field are compared and the bill value is compared with the actual payment. The hospitalization days field is jointly calculated and the day interval is extracted to generate income and expenditure allocation information; Based on the income and expenditure allocation information, the hospitalization cost abnormal value field and the drug cost abnormal value field are compared and the differences between hospitalization and drug are listed. The arrears amount field and the insurance reimbursement amount field are cross-checked and the difference between the arrears amount and the reimbursement amount is identified. The overdue days field is compared and the overdue range is marked to generate financial risk warning elements.

3. The smart hospital operation method based on medical information according to claim 1 is characterized in that: The steps for obtaining the nursing scheduling statistics are as follows: Based on the inpatient nursing resource allocation records, the bed occupancy field and the nursing scheduling period field are counted and the occupancy and period tags are accumulated. By reading the occupancy field values ​​one by one and matching them with the scheduling period field values, the occupancy and tags are merged after integrating the matching information of the two types of fields to generate occupancy and period data; Based on the occupancy and time period data, the reservation status field of the intensive care unit bed is checked and the reservation mark is compared with the available beds, and the bed numbers corresponding to the reservation marks are recorded and mapped item by item with the list of available beds, and the items without reservation marks and bed conflicts are screened out to generate bed availability comparison information; Based on the bed availability comparison information, the nursing manpower input field is compared with the patient admission time field and the input and check-in time are calculated. By summarizing the manpower input and the corresponding admission time interval and performing numerical calculations, nursing scheduling statistics are generated.

4. The smart hospital operation method based on medical information according to claim 1 is characterized in that: The steps for obtaining the operating room scheduling record are: Based on the nursing scheduling statistics, the operating room application time field is matched with the nursing scheduling time period field and the time label is compared with the scheduling period record. By synchronously viewing the application time list and the scheduling period interval and comparing the labels one by one, the corresponding data of time and scheduling are generated; Based on the time and shift corresponding data, the available capacity field of the operating room is compared with the personnel deployment quantity field and the available space and personnel quantity are counted, and the available capacity value and the corresponding deployment quantity are decomposed, compared and summarized item by item to generate available space and personnel information; Based on the available space and personnel information, the surgical instrument requirement fields are summarized and the instrument types are listed. An instrument list is generated by reading the requirement list and classifying it by category, and an operating room scheduling record is generated.

5. The smart hospital operation method based on medical information according to claim 1 is characterized in that: The steps for obtaining the drug and equipment demand allocation are as follows: Based on the operating room scheduling record, the drug usage category field and the surgical instrument requirement field are cross-compared and the category and instrument correspondence are confirmed, and the category and instrument correspondence information is generated by sequentially reading the drug category value and the instrument requirement tag and mapping and matching them; Based on the corresponding information of the category of equipment, the remaining inventory quantity field and the emergency replenishment quantity field are compared and the difference between the remaining quantity and the replenishment is calculated, and the inventory replenishment statistics are generated by comparing the registered inventory value and the replenishment value and calculating the difference; Based on the inventory replenishment statistics, the special drug control fields are marked and the special types are recorded. By checking the control mark values ​​one by one and classifying and archiving the corresponding information, the drug and equipment demand allocation is generated.

6. The smart hospital operation method based on medical information according to claim 2 is characterized in that: The steps for obtaining the ward drug distribution record are as follows: Based on the drug and equipment demand allocation, an association query is performed between the inpatient ward number field and the drug issuance time field and the ward mark and issuance time period are compared. By recording the ward number and the corresponding issuance time period and comparing the records, a ward time period mapping is generated; Based on the ward time period mapping, the issuance serial number field and the usage detail field are checked and confirmed to correspond to the issuance serial number and the details, and the issuance detail comparison data is generated by locking the serial number value and the usage detail list and comparing the identification item by item; Based on the distribution detail comparison data, the drug batch attribute fields are archived and the batch information is recorded. The attribute records are traversed and bundled with the batch labels to generate the ward drug distribution record.

7. The smart hospital operation method based on medical information according to claim 2 is characterized in that: The steps for obtaining the income and expenditure allocation information are as follows: Based on the ward drug dispensing record, the drug cost field and the inpatient settlement form number field are associated and retrieved, and the cost number and the settlement form number are cross-matched. By reading the cost number list and the settlement form number list, comparing and matching the successful items, the cost and settlement cross data are generated; Based on the cross-data of the fees and settlements, the settlement amount field is compared with the patient payment amount field and the bill value is compared with the actual payment, and the fee difference comparison result is generated by matching the bill total amount with the payment amount and calculating the difference; Based on the cost difference comparison result, the hospitalization days field is jointly calculated and the day intervals are extracted. The income and expenditure distribution information is generated by reading the day records and performing accumulation in adjacent intervals.

8. The smart hospital operation method based on medical information according to claim 2 is characterized in that: The steps for obtaining the financial risk warning elements are as follows: Based on the income and expenditure allocation information, compare the hospitalization cost abnormal value field with the drug cost abnormal value field and list the differences between hospitalization and drug, and generate a cost abnormality comparison by taking out the abnormal value labels in turn and matching the hospitalization and drug values; Based on the cost anomaly comparison, the arrears amount field and the insurance reimbursement amount field are interactively verified and the difference between the arrears amount and the reimbursement amount is identified. By comparing the registered arrears amount with the reimbursement amount value and calculating the difference, the arrears and reimbursement difference is generated; Based on the difference between the arrears and the reimbursements, the overdue days field is compared and the overdue range is marked. By counting the overdue days and the range interval and marking and registering them, a financial risk warning factor is generated.

9. The smart hospital operation system according to any one of claims 1 to 8, characterized in that: include: The bed resource management module records the allocation of inpatient nursing resources, counts the bed occupancy field and the nursing scheduling time field, accumulates the occupancy and time period labels, checks the intensive care unit bed reservation status field, compares the reservation mark with the available beds, and obtains the bed resource statistics report; The nursing manpower allocation module, based on the bed resource statistical report, compares the nursing manpower input field with the patient admission time field, calculates the matching degree between the nursing staff input and the patient admission time, and generates a nursing manpower allocation plan; The operating room scheduling module, based on the nursing manpower allocation plan, matches the operating room application time field with the nursing scheduling time field, compares the time tag with the scheduling period record, counts the operating room available capacity field and the personnel deployment quantity field, summarizes the surgical instrument demand field, lists the instrument types, and obtains the operating room scheduling record; The drug and equipment management module, based on the operating room scheduling records, cross-checks the drug usage category field with the surgical equipment demand field, confirms the correspondence between the category and the equipment, compares the remaining inventory quantity field with the emergency replenishment quantity field, calculates the difference between the remaining quantity and the replenishment, records the special drug control field, and generates a drug and equipment demand list; The system's comprehensive monitoring module allocates and monitors resources based on the drug and equipment requirement list and obtains a resource allocation efficiency report.

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