Hospital pharmacy dispensing information intelligent processing method and system

Abstract

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a method and system for intelligently processing hospital pharmacy dispensing information. Background Art

[0002] With the continuous development of modern medical technology, the demand for information processing in the process of hospital pharmacy dispensing is increasing. The traditional dispensing method mainly relies on manual operation, involving the management of drug information, the acquisition and analysis of patient information, the identification of drug incompatibilities, and the formulation and adjustment of medication plans. Due to the complex process and numerous links, these operations are prone to problems such as untimely information processing, inaccurate data, and insufficient identification of drug incompatibilities, which leads to increased risks of medication errors and adverse reactions. In addition, the individual differences of patients and real-time monitoring requirements also bring higher challenges to hospital pharmacy dispensing, and a more efficient and intelligent dispensing information processing system is urgently needed.

[0003] In the existing technology, most dispensing systems find it difficult to achieve comprehensive drug information management and identification of incompatibilities in a complex and ever-changing medical environment. Traditional systems usually lack the ability to update global drug data in real time, and cannot effectively integrate patients' individual physiological characteristics and historical medication information to generate personalized medication plans. In addition, the existing systems lack real-time monitoring and dynamic adjustment mechanisms during the patient's medication process, and are difficult to deal with abnormal situations during the medication process. These technical difficulties not only affect the safety of medication, but also limit the intelligent development of hospital pharmacy dispensing information processing.

[0004] Therefore, it is of great practical significance to develop an intelligent processing method and system that can comprehensively, real-time and efficiently process pharmaceutical dispensing information. Summary of the invention

[0005] Based on the above objectives, the present invention provides a method and system for intelligently processing hospital pharmacy dispensing information.

[0006] A method for intelligently processing hospital pharmacy dispensing information comprises the following steps:

[0007] S1: Obtain basic drug data in real time through the global drug database and the latest clinical research platform, and use blockchain technology to establish a decentralized database to store basic drug data;

[0008] S2: Obtain the patient's medical history information, current medication status and physiological parameters from the hospital information system, pre-process the acquired data, and associate and integrate the patient's individual information with the basic drug information in S1;

[0009] S3: Based on the associated and integrated data in S2, the existing drug combinations are analyzed through the incompatibility identification algorithm to detect potential drug incompatibility. The analysis results include the identified incompatibility combinations and their associated risk levels, and corresponding warning information is generated;

[0010] S4: Automatically generate medication regimen for patients based on the contraindication information identified in S3;

[0011] S5: Push the medication plan and contraindication information generated in S4 to the work terminals of doctors and pharmacists, provide medication suggestions, and synchronize the final plan to the patient's electronic medical record system after the doctor's confirmation;

[0012] S6: During the patient's medication process, the patient's response is continuously monitored through the physiological parameters obtained in S2. When abnormalities are detected, the medication will be adjusted through the adjustment algorithm, and the relevant data will be sent back to the decentralized database to update the medication information.

[0013] Optionally, the S1 specifically includes:

[0014] S11: Automatically import the basic data of drugs into the local system through the global drug database API interface. The basic data includes the name, chemical structure, pharmacological properties, known drug interactions and contraindications of the drug. The data is provided by multiple internationally recognized drug databases, including PubChem, DrugBank and ClinicalTrials.gov;

[0015] S12: Use an algorithm based on natural language processing to perform semantic analysis and data structuring on the drug data imported in S11, classify and organize the information on drug names, chemical structures, and pharmacological properties, and standardize the parsed data;

[0016] S13: Through the smart contract mechanism in blockchain technology, the structured drug data in S12 is stored in a decentralized database, and the decentralized database adopts distributed ledger technology to ensure the synchronous update and non-tamperability of the data between nodes;

[0017] S14: In the decentralized database, a hash function is used to generate a unique hash value for each set of drug data, and the data is encrypted and stored using public key encryption technology;

[0018] S15: Automatically obtain and synchronize the latest drug information by regularly calling the data update interface of the global drug database and clinical research platform.

[0019] Optionally, the S2 specifically includes:

[0020] S21: Through the data interface of the hospital information system, the patient's medical record information, including medical history records, diagnosis results and treatment plans, is extracted from the electronic medical record, and the patient's current medication status, including specific drug types, dosages and routes of administration, is obtained through the medication management system. At the same time, through integration with physiological monitoring equipment, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time;

[0021] S22: Clean and standardize the patient data obtained in S21, including removing duplicate records, filling in missing data, and correcting data formats to ensure that all data conform to a unified standard format;

[0022] S23: Associating the standardized individual patient information in S22 with the basic drug information stored in S1 to generate a comprehensive data set, which includes the patient's medical history information, current medication status, physiological parameters and corresponding basic drug data.

[0023] Optionally, the S3 specifically includes:

[0024] S31: Extract the patient's current medication combination information and related physiological parameters from the comprehensive data set, and match them with the pharmacological properties and known incompatibility information in the basic drug information to preliminarily screen out potential incompatibility drug combinations;

[0025] S32: Analyze the drug combinations screened in S31 using the incompatibility identification algorithm, cross-check each drug combination, identify potential drug incompatibilities, and consider the individual physiological characteristics and medication history of the patient during the analysis;

[0026] S33: Evaluate the risk level of the contraindication drug combination identified in S32. The evaluation is based on the severity of the contraindication, the probability of occurrence, and the interaction between the individual physiological characteristics of the patient and the contraindication drug. Through the preset risk assessment model, each contraindication combination is classified into different risk levels, including low-high risk, medium-high risk, and high risk.

[0027] S34: Based on the risk level assessment results of S33, the corresponding warning information is automatically generated, and the warning information includes the identified contraindication combination, the associated risk level, the potential clinical impact and the response measures.

[0028] Optionally, the S32 specifically includes:

[0029] S321: Preliminary classification of the drug combinations screened in S31 is performed, and the drug combinations are grouped according to their mechanism of action, chemical structure and pharmacological properties. Specifically, the similarity of the drug groups is measured by using a similarity calculation formula, which is: Among them, sim(A, B) represents the similarity between drug A and drug B, F A and F B Represent the feature sets of drug A and drug B respectively;

[0030] S322: After the grouping is completed, a cross-comparison is performed on the drug combinations in each group using an incompatibility recognition algorithm, wherein the incompatibility recognition algorithm specifically uses association rules to mine and detect incompatibility in drug combinations;

[0031] S323: Based on the potential contraindications, the individual physiological characteristics and medication history of the patient are used to conduct personalized risk assessment through weighted scoring. The weighted scoring formula is:

[0032] Among them, R is the risk score, w is the weight of the i-th factor, and f i is the numerical indicator of the ith physiological characteristic or medication history;

[0033] S324: Sort each potential contraindication combination according to the risk score R, and finally output a high-risk drug combination.

[0034] Optionally, the S33 specifically includes:

[0035] S331: Conduct a severity assessment of the contraindicated drug combinations identified in S32. The severity assessment is based on the known side effects of each drug in the combination and the potential harmfulness of the interaction. The severity score formula is used for quantification. The formula is: Among them, S s Indicates the severity score, v j is the side effect intensity of the jth drug, h j is the harmfulness of the drug's interaction with other drugs in the combination, and m is the number of drugs;

[0036] S332: Combine the risk score R and severity score S calculated in S323 s , a comprehensive risk assessment is performed on each contraindicated drug combination, and the final comprehensive risk score formula is: R t =α·S s +β·R, where R t is the comprehensive risk score, S s is the severity score, R is the risk score calculated in S323, and α and β are weight coefficients;

[0037] S333: Based on the comprehensive risk score R t The size of each taboo combination is classified into different risk levels. t <50, classified as low-high risk; tWhen it is between 50 and 75, it is classified as medium-high risk; when R t When >75, it is classified as high risk.

[0038] Optionally, the S4 specifically includes:

[0039] S41: Obtain the risk level information of each contraindicated drug combination from S3, and remove high-risk and medium-high-risk contraindicated drug combinations from the current medication plan;

[0040] S42: Based on the patient's condition and physiological characteristics, alternative drugs for the removed drugs are screened from the basic drug information database. The screening basis includes the therapeutic effect, chemical structure similarity and metabolic pathway of the drug. The specific screening is performed using the preset alternative drug recommendation algorithm. The formula is: Among them, S r Score alternative medicines, w k is the weight of the kth screening criterion, T k is the score of the alternative drug under the kth criterion, n is the number of screening criteria, and the drug with the highest score will be recommended as the preferred alternative drug;

[0041] S43: Integrate the recommended alternative medications into the patient's medication regimen, ensuring that the drug combination of the new regimen matches the patient's individual physiological characteristics in terms of pharmacological properties;

[0042] S44: Verify the integrated medication regimen and use the incompatibility identification algorithm to check the new regimen again to ensure that there are no new drug incompatibilities in the newly generated medication regimen. When a new contraindication combination is detected, repeat steps S42 and S43 until a final medication regimen without contraindication combinations is generated.

[0043] Optionally, the S5 specifically includes:

[0044] S51: Using the hospital's internal network, push the medication regimen and contraindication information generated in S4 to the doctor's and pharmacist's work terminals through the data transmission interface. The data transmission process is encrypted using a secure transmission protocol;

[0045] S52: The medication regimen and the identified contraindication information are presented in a visual manner on the work terminal, including the risk level of the contraindication combination and the basis for selecting alternative drugs, and medication recommendations are automatically generated, including recommended dosage, administration time and precautions;

[0046] S53: Doctors and pharmacists review, modify or confirm the provided medication plans and suggestions on the terminal;

[0047] S54: After the doctor confirms the final medication plan, the confirmed plan will be automatically synchronized to the patient's electronic medical record system through the API interface. The synchronized data includes the final confirmed medication plan, medication recommendations, contraindications and the doctor's confirmation record.

[0048] Optionally, the S6 specifically includes:

[0049] S61: During the medication process, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time through a hospital information system connected to the physiological monitoring equipment, and the collected data are uploaded to the monitoring system at fixed time intervals;

[0050] S62: Use an abnormality detection algorithm to continuously monitor the physiological parameters collected in S61, and calculate abnormal values ​​by comparing the patient's current physiological data with the normal range to detect potential adverse drug reactions in real time. The calculation formula for abnormal values ​​is: E v =|P c -P n |, where E v Indicates outliers, P c is the current physiological parameter value, P n is the median of the normal range of values ​​for the parameter;

[0051] S63: When abnormal physiological parameters are detected, the adjustment algorithm is automatically triggered to recalculate the drug dosage or replace the drug. The formula is: Among them, D a is the adjusted drug dose, D o is the original drug dose, E v is the detected outlier, w is the adjustment weight;

[0052] S64: The adjusted medication regimen will be automatically synchronized to the patient's electronic medical record system through the API interface and updated in the decentralized database at the same time.

[0053] A hospital pharmacy dispensing information intelligent processing system, used to implement the above-mentioned hospital pharmacy dispensing information intelligent processing method, includes the following modules:

[0054] Drug data collection module: used to obtain basic drug data in real time through the global drug database and clinical research platform, including drug name, chemical structure, pharmacological properties and known incompatibility information, and use blockchain technology to establish a decentralized database for storing and updating basic drug data;

[0055] Patient information acquisition module: used to extract the patient's medical history information, current medication status and physiological parameters from the hospital information system, and associate and integrate them with the basic drug information stored in the drug data acquisition module to generate a comprehensive data set;

[0056] Incompatibility Identification and Assessment Module: Based on the comprehensive data set generated by the patient information acquisition module, the incompatibility identification algorithm is used to analyze the patient's existing medication combinations, identify potential drug incompatibility, and conduct risk level assessment based on the patient's individual physiological characteristics, and classify the incompatibility combinations into low-high risk, medium-high risk, and high risk;

[0057] Medication plan generation module: used to automatically generate medication plans for patients based on the evaluation results of the incompatibility identification and evaluation module, eliminate high-risk and medium-high-risk drug combinations, and recommend alternative drugs to form a safe and effective medication plan;

[0058] Medication plan push module: used to push the generated medication plan and incompatibility information to the work terminals of doctors and pharmacists, and provide medication suggestions. When the doctor confirms the plan on the terminal, the system synchronizes the final plan to the patient's electronic medical record system and sends a confirmation notification;

[0059] Medication monitoring and adjustment module: used to monitor the patient's physiological parameters in real time during the medication process, detect abnormal conditions, automatically adjust the drug dosage or replace the drug when abnormalities are found, and update the adjusted plan and monitoring data to the decentralized database.

[0060] Beneficial effects of the present invention:

[0061] The present invention ensures the integrity of drug information and the non-tamperability of data by utilizing real-time data updates of the global drug database and clinical research platform and blockchain technology, thereby effectively reducing the risk of drug incompatibility errors. In addition, the system's real-time monitoring and dynamic adjustment functions can promptly identify and respond to abnormal situations during the patient's medication process, and adjust the medication regimen through intelligent algorithms, thereby ensuring medication safety and reducing potential adverse drug reactions.

[0062] The present invention, by integrating the patient's physiological parameters and basic drug information, uses advanced analysis algorithms to identify incompatibility contraindications and provide personalized medication recommendations, thereby greatly improving the personalization and scientific nature of medication regimens. This not only improves the treatment effect, but also optimizes resource allocation and reduces medical costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1A schematic diagram of a method for intelligently processing medication information according to an embodiment of the present invention;

[0065] Figure 2 Schematic diagram of the intelligent processing system for medication information according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0067] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0068] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0069] like Figure 1 As shown, a method for intelligently processing hospital pharmacy dispensing information includes the following steps:

[0070] S1: Obtain basic drug data in real time through the global drug database and the latest clinical research platform, and use blockchain technology to establish a decentralized database to store basic drug data to ensure data integrity and non-tamperability;

[0071] S2: Obtain the patient's medical history information, current medication status and physiological parameters from the hospital information system, pre-process the acquired data, associate and integrate the patient's individual information with the basic drug information in S1, and provide basic data support for subsequent identification of incompatibility taboos;

[0072] S3: Based on the associated and integrated data in S2, the existing drug combinations are analyzed through the incompatibility identification algorithm to detect potential drug incompatibility. The analysis results include the identified incompatibility combinations and their associated risk levels, and corresponding warning information is generated;

[0073] S4: Based on the contraindications identified in S3, the patient's medication regimen is automatically generated. The medication regimen adjusts the drug combination or dosage to ensure that the regimen is consistent with the patient's physiological characteristics and disease progression. The generated medication regimen is then transmitted to the doctor review platform;

[0074] S5: Push the medication plan and contraindication information generated in S4 to the work terminals of doctors and pharmacists, provide medication suggestions, and synchronize the final plan to the patient's electronic medical record system after the doctor's confirmation;

[0075] S6: During the patient's medication process, the patient's response is continuously monitored through the physiological parameters obtained in S2. When abnormalities are detected, the medication will be adjusted through the adjustment algorithm, and the relevant data will be sent back to the decentralized database to update the drug information to ensure the accuracy and dynamism of future identification.

[0076] The basic data of drugs acquired in real time in S1 specifically include:

[0077] S11: Automatically import basic drug data into the local system through the global drug database API interface. The basic data includes the drug's name, chemical structure, pharmacological properties, known drug interactions and contraindications. The data is provided by multiple internationally recognized drug databases, including PubChem, DrugBank and ClinicalTrials.gov;

[0078] S12: Use algorithms based on natural language processing (NLP) to perform semantic analysis and data structuring on the drug data imported in S11, classify and organize the information on drug names, chemical structures, and pharmacological properties, and standardize the parsed data to ensure its compatibility with existing systems;

[0079] S13: Through the smart contract mechanism in blockchain technology, the structured drug data in S12 is stored in a decentralized database. The decentralized database uses distributed ledger technology to ensure the synchronization and immutability of data between nodes. Smart contracts are pre-written codes that run on the blockchain network and are used to automatically execute the data storage process. Smart contracts automatically trigger data storage operations when specific conditions are met based on predefined rules and ensure that the execution of storage operations does not rely on human intervention.

[0080] S14: In the decentralized database, a hash function is used to generate a unique hash value for each set of drug data, and the data is encrypted and stored using public key encryption technology to ensure the security and privacy protection of basic drug data;

[0081] S15: By regularly calling the data update interface of the global drug database and clinical research platform, the latest drug information is automatically obtained and synchronized. The specific synchronization process uses the consensus mechanism of the blockchain to ensure the consistency and timeliness of the data. The consensus mechanism is a method for all nodes in a distributed network to reach consensus. By verifying and confirming the updated data, it ensures that the data remains consistent among all blockchain nodes to avoid data lags or omissions. Through the above steps, the real-time and accuracy of the basic drug data obtained from the global drug database and the latest clinical research platform are ensured, and the smart contract mechanism and consensus mechanism of blockchain technology are used to achieve the secure storage and effective management of data in a decentralized database, providing strong data support for the subsequent identification of incompatibility taboos.

[0082] S2 specifically includes:

[0083] S21: Through the data interface of the hospital information system (HIS), the patient's medical record information, including medical history records, diagnosis results and treatment plans, is extracted from the electronic medical record, and the patient's current medication status, including specific drug types, dosages and routes of administration, is obtained through the medication management system (PMS). At the same time, through integration with physiological monitoring equipment, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time;

[0084] S22: Clean and standardize the patient data obtained in S21, including removing duplicate records, filling in missing data, and correcting data formats to ensure that all data conform to a unified standard format, providing a basic guarantee for subsequent analysis;

[0085] S23: Associate the standardized individual patient information in S22 with the basic drug information stored in S1 to generate a comprehensive data set. The comprehensive data set includes the patient's medical history information, current medication status, physiological parameters and corresponding basic drug data, providing complete data support for subsequent identification of incompatibility and generation of personalized medication plans.

[0086] S3 specifically includes:

[0087] S31: Extract the patient's current medication combination information and related physiological parameters from the comprehensive data set, and match them with the pharmacological properties and known incompatibility information in the basic drug information to preliminarily screen out potential incompatibility drug combinations;

[0088] S32: Analyze the drug combinations screened by S31 using the incompatibility recognition algorithm, cross-check each drug combination, and identify potential drug incompatibility. The individual physiological characteristics and medication history of the patient are taken into consideration during the analysis process to ensure the accuracy of the recognition results.

[0089] S33: Evaluate the risk level of the contraindication drug combination identified in S32. The evaluation is based on the severity of the contraindication, the probability of occurrence, and the interaction between the individual physiological characteristics of the patient and the contraindication drug. Through the preset risk assessment model, each contraindication combination is classified into different risk levels, including low-high risk, medium-high risk, and high risk.

[0090] S34: Based on the risk level assessment results of S33, the corresponding warning information is automatically generated. The warning information includes the identified contraindication combination, the associated risk level, the potential clinical impact and the countermeasures. The warning information is transmitted to the doctor's and pharmacist's work terminals in real time through the integrated prompt system so that corresponding countermeasures can be taken in time. Through the above steps, based on the comprehensive data set, the incompatibility recognition algorithm can be used to accurately analyze the patient's existing medication combination, detect potential drug incompatibilities, and conduct risk level assessments on them. Finally, warning information is generated and transmitted to provide reliable data support for clinical decision-making and ensure the safety of patients' medication.

[0091] Potential drug incompatibilities identified in S32 include:

[0092] S321: Preliminary classification of the drug combinations screened in S31 is performed, and the drug combinations are grouped according to their mechanism of action, chemical structure and pharmacological properties. Specifically, the similarity of the drug groups is measured by using a similarity calculation formula, which is: Among them, sim(A, B) represents the similarity between drug A and drug B, F A and F B Represent the feature sets of drug A and drug B respectively;

[0093] S322: After the grouping is completed, the incompatibility identification algorithm is used to cross-check the drug combinations in each group. The incompatibility identification algorithm specifically uses association rule mining (such as Apriori algorithm) to detect the incompatibility in the drug combination. The mining formula of the association rule is: Among them, S represents support, C represents confidence, C(X∪Y) represents the frequency of simultaneous occurrence of drug X and drug Y, S(X) represents the support of drug X, and N is the total number of samples;

[0094] S323: Based on the individual physiological characteristics (such as liver and kidney function, metabolic rate) and medication history of the patient using potential contraindications, a personalized risk assessment is performed using a weighted score. The weighted score formula is:

[0095] Where R is the risk score, w i is the weight of the i-th factor, f i is the numerical index of the ith physiological characteristic or medication history, such as the liver function abnormality score or the previous medication adverse reaction score;

[0096] S324: Sort each potential contraindication combination according to the risk score R, and finally output the high-risk drug combination for risk level assessment in subsequent steps; through the above steps, the patient's potential drug incompatibility can be accurately identified during the analysis process, and combined with the patient's individual physiological characteristics and medication history, personalized risk analysis results can be provided to improve the accuracy and reliability of drug incompatibility identification.

[0097] S33 specifically includes:

[0098] S331: Conduct a severity assessment of the contraindicated drug combinations identified in S32. The severity assessment is based on the known side effects of each drug in the combination and the potential harmfulness of the interaction. The severity score formula is used for quantification. The formula is: Among them, S s Indicates the severity score, v j is the side effect intensity of the jth drug, h j is the harmfulness of the drug's interaction with other drugs in the combination, and m is the number of drugs;

[0099] S332: Combine the risk score R and severity score S calculated in S323 s , a comprehensive risk assessment is performed on each contraindicated drug combination, and the final comprehensive risk score formula is: R t =α·S s +β·R, where R t is the comprehensive risk score, S s is the severity score, R is the risk score calculated in S323, and α and β are weight coefficients used to balance the impact of the two scores;

[0100] S333: Based on the comprehensive risk score R t The size of each taboo combination is classified into different risk levels. t <50, classified as low-high risk; t When it is between 50 and 75, it is classified as medium-high risk; when R tWhen it is >75, it is classified as high risk. Through the above steps, the individualized risk score and the drug severity score can be combined to systematically evaluate the risk level of the identified contraindicated drug combinations, and each contraindicated combination can be accurately classified into different risk levels to ensure the safety of drug use.

[0101] S4 specifically includes:

[0102] S41: Obtain the risk level information of each contraindicated drug combination from S3, and remove high-risk and medium-high-risk contraindicated drug combinations from the current medication regimen to ensure that the drugs in the regimen do not contain the identified high-risk or medium-high-risk drug combinations;

[0103] S42: Based on the patient's condition and physiological characteristics, alternative drugs for the removed drugs are screened from the basic drug information database. The screening basis includes the therapeutic effect, chemical structure similarity and metabolic pathway of the drug. The specific screening is performed using the preset alternative drug recommendation algorithm. The formula is: Among them, S r Score alternative medicines, w k is the weight of the kth screening criterion, T k is the score of the alternative drug under the kth criterion, n is the number of screening criteria, and the drug with the highest score will be recommended as the preferred alternative drug;

[0104] S43: Integrate the recommended alternative drugs into the patient's medication regimen, ensuring that the drug combination of the new regimen matches the patient's individual physiological characteristics in terms of pharmacological properties and does not contain any high-risk or medium-to-high-risk contraindicated drug combinations;

[0105] S44: Verify the integrated medication regimen, and use the incompatibility identification algorithm to check the new regimen again to ensure that there are no new drug incompatibilities in the newly generated medication regimen. When a new contraindication combination is detected, repeat steps S42 and S43 until a final medication regimen without a contraindication combination is generated; after the final medication regimen is generated, compare it with the patient's individualized treatment needs to ensure that the regimen is optimal in terms of efficacy, drug safety and patient compliance. After the final confirmation of the regimen, submit it to the doctor for review; through the above steps, the patient's medication regimen can be automatically generated based on the contraindication information identified in S3, ensuring that the regimen avoids any drug incompatibilities while meeting the treatment needs, thereby improving the safety and effectiveness of medication.

[0106] S5 specifically includes:

[0107] S51: Using the hospital's internal network, push the medication regimen and contraindication information generated in S4 to the doctor's and pharmacist's work terminals through the data transmission interface. The data transmission process is encrypted using a secure transmission protocol (such as HTTPS) to ensure the data integrity and confidentiality of the regimen and information during transmission;

[0108] S52: The medication regimen and the identified contraindication information are presented in a visual manner on the work terminal, including the risk level of the contraindication combination and the basis for selecting alternative drugs, and medication recommendations are automatically generated, including recommended dosage, administration time and precautions. The recommendations are based on the individual physiological characteristics and current condition of the patient;

[0109] S53: Doctors and pharmacists review, modify or confirm the provided medication regimen and suggestions on the terminal. The system allows doctors to fine-tune the regimen according to the patient's latest clinical condition to ensure the personalization and accuracy of the medication regimen;

[0110] S54: After the doctor confirms the final medication plan, the confirmed plan will be automatically synchronized to the patient's electronic medical record system (EMR) through the API interface. The synchronized data includes the final confirmed medication plan, medication recommendations, contraindications and the doctor's confirmation record. The data transmission during the synchronization process also uses a secure transmission protocol (such as HTTPS) to ensure the security and integrity of the data. After the synchronization is completed, the system will send a confirmation notification to the doctor's and pharmacist's work terminals to ensure that all relevant personnel are aware of the update of the medication plan and reflect the latest treatment plan in the patient's medical record in real time.

[0111] S6 specifically includes:

[0112] S61: During the medication process, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time through the hospital information system (HIS) connected to the physiological monitoring equipment. The collected data are uploaded to the monitoring system at fixed time intervals and stored in a temporary database for preliminary analysis;

[0113] S62: Use an abnormality detection algorithm to continuously monitor the physiological parameters collected in S61, and calculate abnormal values ​​by comparing the patient's current physiological data with the normal range to detect potential adverse drug reactions in real time. The calculation formula for abnormal values ​​is: E v =|P c -P n |, where E v Indicates outliers, P c is the current physiological parameter value, P n is the median of the normal range of values ​​for the parameter;

[0114] S63: When abnormal physiological parameters are detected, the adjustment algorithm is automatically triggered to recalculate the drug dosage or replace the drug. The formula is: Among them, D a is the adjusted drug dose, D o is the original drug dose, E v is the detected abnormal value, w is the adjustment weight, and the weight is dynamically set according to the severity of the abnormal parameter and the patient's physiological characteristics;

[0115] S64: The adjusted medication regimen will be automatically synchronized to the patient's electronic medical record system (EMR) through the API interface, and will be updated in the decentralized database at the same time. The updated data will include the adjusted drug dosage or new drug combination, the monitored abnormal physiological parameters and their corresponding timestamps. Through the above steps, during the patient's medication process, the algorithm can be monitored and adjusted in real time to respond to abnormal changes in physiological parameters in a timely manner, and the drug information can be updated through the decentralized database, thereby ensuring the safety of the patient's medication and the effectiveness of the treatment.

[0116] like Figure 2 As shown, a hospital pharmacy dispensing information intelligent processing system is used to implement the above-mentioned hospital pharmacy dispensing information intelligent processing method, including the following modules:

[0117] Drug data collection module: used to obtain basic drug data in real time through the global drug database and clinical research platform, including drug name, chemical structure, pharmacological properties and known incompatibility information, and use blockchain technology to establish a decentralized database for storing and updating basic drug data to ensure data integrity and non-tamperability;

[0118] Patient information acquisition module: used to extract the patient's medical history information, current medication status and physiological parameters from the hospital information system, and associate and integrate them with the basic drug information stored in the drug data acquisition module to generate a comprehensive data set;

[0119] Incompatibility Identification and Assessment Module: Based on the comprehensive data set generated by the patient information acquisition module, the incompatibility identification algorithm is used to analyze the patient's existing medication combinations, identify potential drug incompatibility, and conduct risk level assessment based on the patient's individual physiological characteristics, and classify the incompatibility combinations into low-high risk, medium-high risk, and high risk;

[0120] Medication plan generation module: used to automatically generate medication plans for patients based on the evaluation results of the incompatibility identification and evaluation module, eliminate high-risk and medium-high-risk drug combinations, and recommend alternative drugs to form a safe and effective medication plan;

[0121] Medication plan push module: used to push the generated medication plan and incompatibility information to the work terminals of doctors and pharmacists, and provide medication suggestions. When the doctor confirms the plan on the terminal, the system synchronizes the final plan to the patient's electronic medical record system and sends a confirmation notification;

[0122] Medication monitoring and adjustment module: used to monitor the patient's physiological parameters in real time during the medication process, detect abnormal conditions, automatically adjust the drug dosage or replace the drug when abnormalities are found, and update the adjusted plan and monitoring data to the decentralized database.

[0123] Through the collaborative work of the above modules, this system can process hospital pharmacy dispensing information comprehensively and in real time, from the acquisition of drug data, the integration of patient information, the identification of incompatibility, to the generation, push, monitoring and adjustment of medication plans, to ensure the safety and effectiveness of the medication process. The modules are closely linked to ensure smooth information flow, and each module is linked with other modules to realize intelligent dispensing information processing.

[0124] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0125] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for intelligently processing hospital pharmacy dispensing information, characterized in that: The following steps are involved: S1: Obtain basic drug data in real time through the global drug database and the latest clinical research platform, and use blockchain technology to establish a decentralized database to store basic drug data; S2: Obtain the patient's medical history information, current medication status and physiological parameters from the hospital information system, pre-process the acquired data, and associate and integrate the patient's individual information with the basic drug information in S1; S3: Based on the associated and integrated data in S2, the existing drug combinations are analyzed through the incompatibility identification algorithm to detect potential drug incompatibility. The analysis results include the identified incompatibility combinations and their associated risk levels, and corresponding warning information is generated; S4: Automatically generate medication regimen for patients based on the contraindication information identified in S3; S5: Push the medication plan and contraindication information generated in S4 to the work terminals of doctors and pharmacists, provide medication suggestions, and synchronize the final plan to the patient's electronic medical record system after the doctor's confirmation; S6: During the patient's medication process, the patient's response is continuously monitored through the physiological parameters obtained in S2. When abnormalities are detected, the medication will be adjusted through the adjustment algorithm, and the relevant data will be sent back to the decentralized database to update the medication information.

2. A hospital pharmacy dispensing information intelligent processing method according to claim 1, characterized in that: The S1 specifically includes: S11: Automatically import the basic data of drugs into the local system through the global drug database API interface. The basic data includes the name, chemical structure, pharmacological properties, known drug interactions and contraindications of the drug. The data is provided by multiple internationally recognized drug databases, including PubChem, DrugBank and ClinicalTrials.gov; S12: Use an algorithm based on natural language processing to perform semantic analysis and data structuring on the drug data imported in S11, classify and organize the information on drug names, chemical structures, and pharmacological properties, and standardize the parsed data; S13: Through the smart contract mechanism in blockchain technology, the structured drug data in S12 is stored in a decentralized database, and the decentralized database adopts distributed ledger technology to ensure the synchronous update and non-tamperability of the data between nodes; S14: In the decentralized database, a hash function is used to generate a unique hash value for each set of drug data, and the data is encrypted and stored using public key encryption technology; S15: Automatically obtain and synchronize the latest drug information by regularly calling the data update interface of the global drug database and clinical research platform.

3. The intelligent processing method for hospital pharmacy dispensing information according to claim 1 is characterized in that: The S2 specifically includes: S21: Through the data interface of the hospital information system, the patient's medical record information, including medical history records, diagnosis results and treatment plans, is extracted from the electronic medical record, and the patient's current medication status, including specific drug types, dosages and routes of administration, is obtained through the medication management system. At the same time, through integration with physiological monitoring equipment, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time; S22: Clean and standardize the patient data obtained in S21, including removing duplicate records, filling in missing data, and correcting data formats to ensure that all data conform to a unified standard format; S23: Associating the standardized individual patient information in S22 with the basic drug information stored in S1 to generate a comprehensive data set, which includes the patient's medical history information, current medication status, physiological parameters and corresponding basic drug data.

4. A hospital pharmacy dispensing information intelligent processing method according to claim 3, characterized in that: The S3 specifically includes: S31: Extract the patient's current medication combination information and related physiological parameters from the comprehensive data set, and match them with the pharmacological properties and known incompatibility information in the basic drug information to preliminarily screen out potential incompatibility drug combinations; S32: Analyze the drug combinations screened in S31 using the incompatibility identification algorithm, cross-check each drug combination, identify potential drug incompatibilities, and consider the individual physiological characteristics and medication history of the patient during the analysis; S33: Evaluate the risk level of the contraindication drug combination identified in S32. The evaluation is based on the severity of the contraindication, the probability of occurrence, and the interaction between the individual physiological characteristics of the patient and the contraindication drug. Through the preset risk assessment model, each contraindication combination is classified into different risk levels, including low-high risk, medium-high risk, and high risk. S34: Based on the risk level assessment results of S33, the corresponding warning information is automatically generated, and the warning information includes the identified contraindication combination, the associated risk level, the potential clinical impact and the response measures.

5. A hospital pharmacy dispensing information intelligent processing method according to claim 4, characterized in that: The S32 specifically includes: S321: Preliminary classification of the drug combinations screened in S31 is performed, and the drug combinations are grouped according to their mechanism of action, chemical structure and pharmacological properties. Specifically, the similarity of the drug groups is measured by using a similarity calculation formula, which is: Among them, sim(A, B) represents the similarity between drug A and drug B, F A and F B Represent the feature sets of drug A and drug B respectively; S322: After the grouping is completed, a cross-comparison is performed on the drug combinations in each group using an incompatibility recognition algorithm, wherein the incompatibility recognition algorithm specifically uses association rules to mine and detect incompatibility in drug combinations; S323: Based on the potential contraindications, the individual physiological characteristics and medication history of the patient are used to conduct personalized risk assessment through weighted scoring. The weighted scoring formula is: Where R is the risk score, w i is the weight of the i-th factor, f i is the numerical indicator of the ith physiological characteristic or medication history; S324: Sort each potential contraindication combination according to the risk score R, and finally output a high-risk drug combination.

6. A hospital pharmacy dispensing information intelligent processing method according to claim 5, characterized in that: The S33 specifically includes: S331: Conduct a severity assessment of the contraindicated drug combinations identified in S32. The severity assessment is based on the known side effects of each drug in the combination and the potential harmfulness of the interaction. The severity score formula is used for quantification. The formula is: Among them, S s Indicates the severity score, v j is the side effect intensity of the jth drug, h j is the harmfulness of the drug's interaction with other drugs in the combination, and m is the number of drugs; S332: Combine the risk score R and severity score S calculated in S323 s , a comprehensive risk assessment is performed on each contraindicated drug combination, and the final comprehensive risk score formula is: R t =α·S s +β·R, where R t is the comprehensive risk score, S s is the severity score, R is the risk score calculated in S323, and α and β are weight coefficients; S333: Based on the comprehensive risk score R t The size of each taboo combination is classified into different risk levels. t <50, classified as low-high risk; t When it is between 50 and 75, it is classified as medium-high risk; when R t When >75, it is classified as high risk.

7. A hospital pharmacy dispensing information intelligent processing method according to claim 1, characterized in that: The S4 specifically includes: S41: Obtain the risk level information of each contraindicated drug combination from S3, and remove high-risk and medium-high-risk contraindicated drug combinations from the current medication plan; S42: Based on the patient's condition and physiological characteristics, alternative drugs for the removed drugs are screened from the basic drug information database. The screening basis includes the therapeutic effect, chemical structure similarity and metabolic pathway of the drug. The specific screening is performed using the preset alternative drug recommendation algorithm. The formula is: Among them, S r Score alternative medicines, w k is the weight of the kth screening criterion, T k is the score of the alternative drug under the kth criterion, n is the number of screening criteria, and the drug with the highest score will be recommended as the preferred alternative drug; S43: Integrate the recommended alternative medications into the patient's medication regimen, ensuring that the drug combination of the new regimen matches the patient's individual physiological characteristics in terms of pharmacological properties; S44: Verify the integrated medication regimen and use the incompatibility identification algorithm to check the new regimen again to ensure that there are no new drug incompatibilities in the newly generated medication regimen. When a new contraindication combination is detected, repeat steps S42 and S43 until a final medication regimen without contraindication combinations is generated.

8. The intelligent processing method for hospital pharmacy dispensing information according to claim 1, characterized in that: The S5 specifically includes: S51: Using the hospital's internal network, push the medication regimen and contraindication information generated in S4 to the doctor's and pharmacist's work terminals through the data transmission interface. The data transmission process is encrypted using a secure transmission protocol; S52: The medication regimen and the identified contraindication information are presented in a visual manner on the work terminal, including the risk level of the contraindication combination and the basis for selecting alternative drugs, and medication recommendations are automatically generated, including recommended dosage, administration time and precautions; S53: Doctors and pharmacists review, modify or confirm the provided medication plans and suggestions on the terminal; S54: After the doctor confirms the final medication plan, the confirmed plan will be automatically synchronized to the patient's electronic medical record system through the API interface. The synchronized data includes the final confirmed medication plan, medication recommendations, contraindications and the doctor's confirmation record.

9. The intelligent processing method for hospital pharmacy dispensing information according to claim 1, characterized in that: The S6 specifically includes: S61: During the medication process, the patient's physiological parameters, including blood pressure, heart rate, and liver and kidney function indicators, are collected in real time through a hospital information system connected to the physiological monitoring equipment, and the collected data are uploaded to the monitoring system at fixed time intervals; S62: Use an abnormality detection algorithm to continuously monitor the physiological parameters collected in S61, and calculate abnormal values ​​by comparing the patient's current physiological data with the normal range to detect potential adverse drug reactions in real time. The calculation formula for abnormal values ​​is: E v =|P c -P n |, where E v Indicates outliers, P c is the current physiological parameter value, P n is the median of the normal range of values ​​for the parameter; S63: When abnormal physiological parameters are detected, the adjustment algorithm is automatically triggered to recalculate the drug dosage or replace the drug. The formula is: Among them, D a is the adjusted drug dose, D o is the original drug dose, E v is the detected outlier, w is the adjustment weight; S64: The adjusted medication regimen will be automatically synchronized to the patient's electronic medical record system through the API interface and updated in the decentralized database at the same time.

10. A hospital pharmacy dispensing information intelligent processing system, used to implement a hospital pharmacy dispensing information intelligent processing method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Drug data collection module: used to obtain basic drug data in real time through the global drug database and clinical research platform, including drug name, chemical structure, pharmacological properties and known incompatibility information, and use blockchain technology to establish a decentralized database for storing and updating basic drug data; Patient information acquisition module: used to extract the patient's medical history information, current medication status and physiological parameters from the hospital information system, and associate and integrate them with the basic drug information stored in the drug data acquisition module to generate a comprehensive data set; Incompatibility Identification and Assessment Module: Based on the comprehensive data set generated by the patient information acquisition module, the incompatibility identification algorithm is used to analyze the patient's existing medication combinations, identify potential drug incompatibility, and conduct risk level assessment based on the patient's individual physiological characteristics, and classify the incompatibility combinations into low-high risk, medium-high risk, and high risk; Medication plan generation module: used to automatically generate medication plans for patients based on the evaluation results of the incompatibility identification and evaluation module, eliminate high-risk and medium-high-risk drug combinations, and recommend alternative drugs to form a safe and effective medication plan; Medication plan push module: used to push the generated medication plan and incompatibility information to the work terminals of doctors and pharmacists, and provide medication suggestions. When the doctor confirms the plan on the terminal, the system synchronizes the final plan to the patient's electronic medical record system and sends a confirmation notification; Medication monitoring and adjustment module: used to monitor the patient's physiological parameters in real time during the medication process, detect abnormal conditions, automatically adjust the drug dosage or replace the drug when abnormalities are found, and update the adjusted plan and monitoring data to the decentralized database.

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