CN117894421A - Medical service automation flow optimization method - Google Patents
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Abstract
The invention relates to the technical field of automatic service, in particular to a medical service automatic flow optimization method, which comprises the following steps: patient information acquisition and analysis; intelligent diagnosis and treatment plan making, based on the data collected in the step S1, a personalized diagnosis and treatment scheme is proposed by using a machine learning algorithm; resource scheduling optimization, namely automatically coordinating medical resources according to a diagnosis and treatment plan, including scheduling a diagnosis room, medical equipment and medical staff, and optimizing resource allocation through an algorithm; automatic medicine management and delivery, automatically managing medicine inventory according to real-time conditions and prescriptions of patients, and ensuring timely delivery of medicines to a treatment area; and the interdisciplinary treatment coordination mechanism is integrated with a interdisciplinary coordination platform and is used for processing complex cases including rare diseases or multiple chronic diseases, and when detecting that the patient needs multidisciplinary coordination, the interdisciplinary coordination mechanism is automatically triggered. The invention improves the efficiency and the accuracy of the diagnosis and treatment process and reduces the operation cost.
Description
Technical Field
The invention relates to the technical field of automatic service, in particular to a medical service automatic flow optimization method.
Background
The current medical services field faces various challenges, especially in terms of resource allocation efficiency, diagnosis and treatment accuracy and patient satisfaction. Traditional medical procedures often rely on manually operated and non-integrated systems, resulting in islanding, resource allocation inefficiency, and diagnosis delays. Although some medical institutions began to employ Electronic Health Records (EHR) and some basic automated tools, these measures still do not fully address the problems of diagnosis of complex cases, medication errors, and unbalanced distribution of medical resources.
In terms of medication management, incorrect medication delivery and inventory management are common problems in the medical arts, which not only affect the effectiveness of patient treatment, but can also lead to medication wastage and additional economic burden. In addition, for the treatment of rare and multiple chronic diseases, conventional medical systems often have difficulty providing effective treatment regimens, affecting treatment outcome, due to the need for multidisciplinary knowledge and coordination.
Therefore, there is a need to develop a comprehensive solution to improve the overall efficiency and quality of medical services. The solution needs to utilize the latest data processing technology and artificial intelligence algorithm to realize the automation of the medical procedure, optimize the resource allocation and provide an accurate and effective diagnosis and treatment scheme for complex cases.
Disclosure of Invention
Based on the above purpose, the invention provides a medical service automation flow optimization method.
A medical service automation flow optimization method, comprising the following steps:
s1: when a patient enters a medical institution, the information acquisition system automatically records basic information and medical history of the patient, acquires physiological data of the patient in real time, and simultaneously performs basic examination, an examination report and a medical image report to perform preliminary diagnosis suggestion;
S2: intelligent diagnosis and treatment plan making, based on the data collected in the step S1, utilizing a machine learning algorithm to provide a personalized diagnosis and treatment scheme, and considering the illness state, historical data and real-time physiological information of a patient;
S3: resource scheduling optimization, namely automatically coordinating medical resources according to a diagnosis and treatment plan, including scheduling a consulting room, medical equipment and medical staff, optimizing resource allocation through an algorithm, and reducing waiting time of patients;
S4: automatic medicine management and delivery, automatically managing medicine inventory according to real-time conditions and prescriptions of patients, and ensuring timely delivery of medicines to a treatment area;
S5: and the interdisciplinary treatment coordination mechanism is integrated with a interdisciplinary coordination platform and is used for processing complex cases including rare diseases or multiple chronic diseases, and when detecting that the patient needs multidisciplinary coordination, the interdisciplinary coordination mechanism is automatically triggered to collect the professional opinions of doctors in different professional fields and formulate a comprehensive treatment scheme.
Further, the information acquisition system in the step S1 automatically identifies the identity of the patient through the identity verification equipment, and automatically accesses an Electronic Health Record (EHR) of the patient after the identity is confirmed, and pulls the history case, the past treatment record and the drug sensitivity information;
meanwhile, physiological data including heart rate, blood pressure and body temperature indexes are collected in real time through the rapid detection equipment.
Further, the step S2 specifically includes:
firstly, analyzing historical medical records of patients, and identifying potential health risks and disease modes;
Combining the real-time physiological data with the medical history of the patient and the medical image report, and performing data fusion and analysis by using a deep learning model to improve the accuracy of diagnosis, wherein the deep learning model is trained by clinical data to identify complex health modes and disease indexes;
The pre-trained machine learning model is utilized to classify and predict the illness state of the patient, and the current medical research and treatment guideline is combined to automatically generate personalized diagnosis and treatment advice, so that the unique medical history, the real-time physiological state and the treatment response of the patient are considered.
Further, the deep learning model adopts an improved convolutional neural network model, and introduces an attention mechanism in a traditional convolutional neural network model structure, and specifically comprises the following steps:
attention module: the introduction of the attention mechanism helps the convolutional neural network model to focus on critical areas in the medical image, including areas where abnormal cell proliferation exists in tumor detection, and the attention module is realized by the following formula:
a[l]＝A[l]⊙a[l-1]；
wherein, A [l] is the attention weight, which is obtained by convolution operation and normalized by softmax function, and the attention weight and the activation value a [l-1] of the upper layer are multiplied element by element and expressed as #;
Multi-scale feature fusion: the medical image contains information of different scales from microscopic to macroscopic, multi-scale feature fusion is integrated in the convolutional neural network model to capture the information of different scales, and features extracted on different levels are fused by the following modes:
a[l]＝f(W[l]F[l]+b[l])；
wherein F [l] is a vector fused with features of different scales, and is obtained by connecting activation values of different levels ;
integration of structured data: the structured data is processed by introducing parallel fully connected network layers and its output is fused with image features:
s＝f(WsD+bs)；
a[final]＝concat(a[l],s)；
Wherein D is structured data comprising medical history and real-time physiological data, W s and b s are weights and biases of a fully connected layer, s is output of the layer, and finally, image features a [l] and structured data features s are combined based on concat operation to form comprehensive features a [final], and a machine learning classifier is used for analyzing the comprehensive features to generate specific diagnosis and treatment suggestions.
Further, the machine learning classifier adopts a random forest model, and performs classification or regression analysis by constructing a plurality of decision trees and summarizing the results, and specifically includes:
Training a plurality of decision trees: for each decision tree, randomly selecting different sample subsets and feature subsets for training, wherein the training process of each tree is represented as starting from a root node, recursively selecting the best features to divide data until a preset stopping condition is reached, and the stopping condition comprises maximum depth and minimum sample number;
decision process of decision tree: each tree uses discriminant criteria on its nodes:
Wherein Gini split represents the non-purity of the keni after the division, gini before is the non-purity of the keni before the division, N i is the number of samples of the ith child node after the division, N is the total number of samples before the division, gini i is the non-purity of the keni of the ith child node;
results of integrating multiple trees: in the classification task, each tree gives a prediction result, the final result is the majority vote of all the tree prediction results, and in the regression task, the final result is the average of all the tree prediction values;
Taking the fused characteristic a [final] as the input of a random forest model, training the random forest type and severity by using historical medical data, and generating specific disturbance therapy advice by combining medical professional knowledge and therapy guidelines according to the prediction result of the model;
the analysis results include: the model outputs the disease type, severity or specific health indicator.
Further, the resource scheduling optimization in the step S3 adopts a queuing optimization algorithm to allocate medical resources, the queuing optimization algorithm is based on linear programming, and the algorithm inputs requirements including a patient diagnosis and treatment plan, the current state of available resources and constraints;
the method comprises the following specific steps:
And (3) data collection: collecting the service condition of a consulting room, the state of medical equipment and the work arrangement of medical staff in real time through a management system in a medical institution;
Demand analysis: determining the type and the quantity of required resources according to the diagnosis and treatment plan of the patient;
resource optimization allocation: a linear programming model is adopted, a mathematical model of the resource allocation problem is established, the optimization target is to minimize the waiting time of a patient and the idle time of the resource, the constraint comprises the availability of the resource and the working time of medical staff, and the linear programming model target function is expressed as follows:
Wherein W i is the waiting time of the ith patient, I i is the resource idle time, and the constraint condition reflects the resource limitation and the time schedule;
real-time data, including resource usage and patient arrival time, is continuously monitored, and resource allocation is dynamically adjusted according to the real-time data to optimize overall efficiency.
Further, the step S4 specifically includes:
integration of drug management system with diagnosis and treatment plan: the electronic health record and the drug management system are integrated, so that the diagnosis and treatment plan of the patient and the drug management system are synchronously updated, and the diagnosis and treatment plan of the patient contains the necessary drug types, doses and drug administration time;
automatic matching and inventory updating of drug requirements: the medication management system automatically determines the medication needed according to the patient's medical plan and checks the inventory.
Drug delivery schedule: automatically generating a drug delivery plan according to the treatment area and the administration time of the patient, and using an automatic guided vehicle AGV to deliver the drug to the appointed treatment area on time;
The automated guided vehicle AGV is equipped with a scanning device to ensure that the correct medication is delivered to the correct patient, and after delivery is complete, the medication management system automatically updates the medication usage record and synchronizes to the patient's electronic health record.
Further, the S5 specifically comprises
Establishing an integrated interdisciplinary coordination platform, and connecting doctors and health professionals in different professional fields, wherein the interdisciplinary coordination platform comprises a case management system, a collaboration tool, a real-time communication function and a professional knowledge base;
Automatically analyzing the medical records of the patient by utilizing a data analysis and artificial intelligence algorithm, identifying complex cases requiring interdisciplinary treatment, identifying the complex cases, and automatically triggering interdisciplinary coordination mechanism;
forming and cooperating of multidisciplinary expert teams, automatically inviting experts in related fields to join in a treatment team according to the characteristics and the requirements of cases, providing a cooperation platform, sharing case information, discussing treatment schemes and exchanging comments in real time;
By crossing discipline coordination platforms, team members co-review patient medical data, including diagnostic results, treatment history, and real-time health status, and co-formulate comprehensive treatment plans based on team discussions and a professional knowledge base.
Further, the data analysis is based on natural language processing technology, wherein feature extraction converts text data into numerical features using TFIDF, TFIDF formula TF-IDF (t, d) =tf (t, d) ×idf (t), wherein TF (t, d) is the frequency of word t in document d, IDF (t) is the inverse document frequency, for reducing the effect of common words;
The artificial intelligence algorithm selects a support vector machine to classify cases, trains a model by using a training data set, aims at identifying cases needing cross-disciplinary treatment, and adopts a linear support vector machine:
f(x)＝wx+b
wherein x is a feature vector, w is a weight vector, and b is a bias term;
The model applies to new cases: and applying the same natural language processing technical characteristic extraction process to the new medical records, classifying the new cases by using a trained machine learning model, judging whether the new cases belong to complex cases, automatically notifying relevant interdisciplinary treatment teams after the complex cases are identified, and providing medical records and model output information.
Further, the method also comprises the steps of fee settlement and data feedback, and after treatment is completed, fee settlement is automatically carried out, and meanwhile, treatment results and feedback of patients are collected for continuously improving medical service quality.
The invention has the beneficial effects that:
According to the invention, by integrating an advanced data processing technology and an artificial intelligence algorithm, the efficiency and the accuracy of the diagnosis and treatment process are remarkably improved, and the medical records are automatically analyzed by using a natural language processing and machine learning technology, so that the complex cases needing special attention, such as rare diseases or multiple chronic diseases, can be rapidly identified.
According to the invention, the allocation of medical resources, including the arrangement of a consulting room, medical equipment and medical staff, is optimized through an intelligent resource scheduling algorithm (such as a linear programming or genetic algorithm), so that the waiting time of a patient is reduced, the use efficiency of the medical resources is improved, the operation cost is reduced, the medicine inventory management is optimized through a prediction model, and the medicine waste and the overdue risk are reduced.
The invention provides a comprehensive and personalized treatment scheme for complex cases by adopting a interdisciplinary treatment coordination mechanism, and improves the comprehensive effect of treatment through cooperation of multidisciplinary specialists.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, a medical service automation flow optimization method includes the following steps:
s1: when a patient enters a medical institution, the information acquisition system automatically records basic information and medical history of the patient, acquires physiological data of the patient in real time, and simultaneously performs basic examination, an examination report and a medical image report to perform preliminary diagnosis suggestion;
S2: intelligent diagnosis and treatment plan making, based on the data collected in the step S1, utilizing a machine learning algorithm to provide a personalized diagnosis and treatment scheme, and considering the illness state, historical data and real-time physiological information of a patient;
S3: resource scheduling optimization, namely automatically coordinating medical resources according to a diagnosis and treatment plan, including scheduling a consulting room, medical equipment and medical staff, optimizing resource allocation through an algorithm, and reducing waiting time of patients;
s4: automatic medicine management and delivery, automatically managing medicine inventory according to real-time conditions and prescriptions of patients, ensuring timely delivery of medicines to a treatment area and ensuring quick response in emergency;
S5: and the interdisciplinary treatment coordination mechanism is integrated with a interdisciplinary coordination platform and is used for processing complex cases including rare diseases or multiple chronic diseases, and when detecting that the patient needs multidisciplinary coordination, the interdisciplinary coordination mechanism is automatically triggered to collect the professional opinions of doctors in different professional fields and formulate a comprehensive treatment scheme.
The information acquisition system in S1 automatically identifies the identity of the patient through identity verification equipment (such as facial recognition, fingerprint recognition or RFID recognition), automatically accesses an Electronic Health Record (EHR) of the patient after the identity is confirmed, and pulls historical cases, past treatment records and drug sensitivity information;
meanwhile, physiological data including heart rate, blood pressure and body temperature indexes are collected in real time through the rapid detection equipment.
S2 specifically comprises:
firstly, analyzing historical medical records of patients, and identifying potential health risks and disease modes;
Combining real-time physiological data (such as heart rate, blood pressure, body temperature and the like) with medical history and medical image report of a patient, and performing data fusion and analysis by using a deep learning model to improve diagnosis accuracy, wherein the deep learning model is trained by clinical data to identify complex health modes and disease indexes;
classifying and predicting the illness state of a patient by utilizing a pre-trained machine learning model, automatically generating personalized diagnosis and treatment advice by combining with the current medical research and treatment guidelines, and considering the unique medical history, real-time physiological state and treatment response of the patient;
In addition, a self-learning mechanism is also provided, and the algorithm can be continuously optimized according to new medical data and treatment results so as to improve the accuracy and the effectiveness of future diagnosis and treatment plans.
The deep learning model adopts an improved convolutional neural network model, and introduces a attention mechanism in the traditional convolutional neural network model structure, and specifically comprises the following steps:
attention module: the introduction of the attention mechanism helps the convolutional neural network model to focus on critical areas in the medical image, including areas where abnormal cell proliferation exists in tumor detection, and the attention module is realized by the following formula:
a[l]＝A[l]⊙a[l-1]；
Wherein, A [l] is the attention weight, which is obtained by convolution operation and normalized by softmax function, and the attention weight and the activation value a [l-1] of the upper layer are multiplied element by element and expressed as #;
Multi-scale feature fusion: the medical image contains information of different scales from microscopic to macroscopic, multi-scale feature fusion is integrated in the convolutional neural network model to capture the information of different scales, and features extracted on different levels are fused by the following modes:
a[l]＝f(W[l]F[l]+b[l])；
Wherein F [l] is a vector fused with features of different scales, and is obtained by connecting activation values of different levels ;
Integration of structured data: in addition to image data, historical health records and real-time physiological data of the patient are also important. The structured data is processed by introducing parallel fully connected network layers and its output is fused with image features:
s＝f(WsD+bs)；
a[final]＝concat(a[l],s)；
Wherein D is structured data comprising medical history and real-time physiological data, W s and b s are weights and biases of a fully connected layer, s is output of the layer, and finally, image features a [l] and structured data features s are combined based on concat operation to form comprehensive features a [final], and a machine learning classifier is used for analyzing the comprehensive features to generate specific diagnosis and treatment suggestions.
Diagnosis and treatment are performed by adopting an improved convolutional neural network model, and the specific proposal is as follows:
Data preprocessing and integration: image data processing, for medical images (such as X-rays, MRI, CT scanning, etc.), firstly performing standardization processing such as resizing, contrast enhancement, etc. to prepare for input to a convolutional neural network CNN; structured data processing, wherein the historical health record and the real-time physiological data (such as blood pressure, heart rate and blood test result) of a patient are subjected to data cleaning and normalization processing so as to be suitable for entering a parallel full-connection layer;
Feature extraction and analysis: the CNN enhanced by the attention mechanism is used for processing medical images, focusing on key areas such as abnormal tissues or lesions, capturing information of different layers from microscopic to macroscopic by utilizing a multi-scale feature fusion technology, and processing structured data (medical history, real-time data and the like) through a specially designed full-connection layer to extract key health indexes and patient features.
Generating a data fusion and personalized diagnosis and treatment scheme: the image and the structured data features are fused, the image features extracted by the CNN are combined with the structured data features to create a comprehensive feature representation, which is the key of personalized diagnosis and treatment scheme formulation, and one or more machine learning classifiers (such as a support vector machine, a random forest and the like) are used for analyzing the comprehensive features to generate specific diagnosis and treatment suggestions. These classifiers are trained based on a large number of historical case data, and can identify specific pathology patterns and take into account individual differences.
Feedback mechanism: during treatment, the system will continue to monitor the patient's physiological data, dynamically adjust the treatment regimen based on new data, if necessary, and collect treatment effects and patient feedback for continued optimization of algorithms, improving the level of accuracy and individuality of future diagnosis and treatment plans.
By means of the method, the improved CNN can comprehensively consider the illness state, the historical data and the real-time physiological information of the patient, and a highly personalized and accurate diagnosis and treatment scheme is provided. This not only improves the accuracy of diagnosis and treatment, but also provides more customized medical services for the patient.
The machine learning classifier adopts a random forest model, performs classification or regression analysis by constructing a plurality of decision trees and summarizing results, and specifically comprises the following steps:
Training a plurality of decision trees: for each decision tree, randomly selecting different sample subsets and feature subsets for training, wherein the training process of each tree is represented as starting from a root node, recursively selecting the best features to divide data until a preset stopping condition is reached, and the stopping condition comprises maximum depth and minimum sample number;
decision process of decision tree: each tree uses discriminant criteria on its nodes:
Wherein Gini split represents the non-purity of the keni after the division, gini before is the non-purity of the keni before the division, N i is the number of samples of the ith child node after the division, N is the total number of samples before the division, gini i is the non-purity of the keni of the ith child node;
results of integrating multiple trees: in the classification task, each tree gives a prediction result, the final result is the majority vote of all the tree prediction results, and in the regression task, the final result is the average of all the tree prediction values;
the fused characteristic a final is used as the input of a random forest model, the type and severity of the random forest are trained by using historical medical data (including labels of disease type, severity and the like), and specific therapeutic advice is generated by combining medical professional knowledge and therapeutic guidelines according to the prediction result (such as specific disease type and severity) of the model;
the analysis results include: the type of disease output from the model (heart disease, diabetes, etc.), severity (mild, moderate, severe) or specific health indicators (blood glucose level, cholesterol level);
according to the analysis result, specific diagnosis and treatment suggestions are generated:
if the model identifies a patient at high risk for heart disease, recommendations include medication, lifestyle adjustments, and periodic heart monitoring.
For mild diabetics, advice may include dietary adjustments, increased physical activity, and regular blood glucose testing.
According to the specific conditions of patients, such as age, sex, genetic background and the like, the diagnosis and treatment advice is adjusted to realize personalized treatment.
The resource scheduling optimization in the S3 adopts a queuing optimization algorithm to allocate medical resources, the queuing optimization algorithm is based on linear programming, the algorithm inputs requirements (such as diagnosis and treatment type and predicted time) comprising a patient diagnosis and treatment plan, and the current state and constraints (such as working time length and equipment use limit) of available resources (such as rooms, equipment and personnel);
the method comprises the following specific steps:
And (3) data collection: collecting the service condition of a consulting room, the state of medical equipment and the work arrangement of medical staff in real time through a management system in a medical institution;
Demand analysis: determining the type and the quantity of required resources according to the diagnosis and treatment plan of the patient;
resource optimization allocation: a linear programming model is adopted, a mathematical model of the resource allocation problem is established, the optimization target is to minimize the waiting time of a patient and the idle time of the resource, the constraint comprises the availability of the resource and the working time of medical staff, and the linear programming model target function is expressed as follows:
Wherein W i is the waiting time of the ith patient, I i is the resource idle time, and the constraint condition reflects the resource limitation and the time schedule;
real-time data, including resource usage and patient arrival time, is continuously monitored, and resource allocation is dynamically adjusted according to the real-time data to optimize overall efficiency.
S4 specifically comprises the following steps:
Integration of drug management system with diagnosis and treatment plan: integrating an Electronic Health Record (EHR) and a drug management system, and ensuring that the diagnosis and treatment plan of a patient and the drug management system are synchronously updated, wherein the diagnosis and treatment plan of the patient contains the necessary drug types, doses and administration time;
automatic matching and inventory updating of drug requirements: the medication management system automatically determines the medication needed according to the patient's medical plan and checks the inventory.
Drug delivery schedule: automatically generating a drug delivery plan according to the treatment area and the administration time of the patient, and using an automatic guided vehicle AGV to deliver the drug to the appointed treatment area on time;
The automated guided vehicle AGV is equipped with a scanning device to ensure that the correct medication is delivered to the correct patient, and after delivery is complete, the medication management system automatically updates the medication usage record and synchronizes to the patient's electronic health record.
S5 specifically comprises the following steps:
establishing an integrated interdisciplinary coordination platform, and connecting doctors and health professionals in different professional fields, wherein the interdisciplinary coordination platform comprises a case management system, a collaboration tool, a real-time communication function and a professional knowledge base;
Automatically analyzing the medical records of the patient by utilizing a data analysis and artificial intelligence algorithm, identifying complex cases requiring interdisciplinary treatment, identifying the complex cases, and automatically triggering interdisciplinary coordination mechanism;
forming and cooperating of multidisciplinary expert teams, automatically inviting experts in related fields to join in a treatment team according to the characteristics and the requirements of cases, providing a cooperation platform, sharing case information, discussing treatment schemes and exchanging comments in real time;
By crossing discipline coordination platforms, team members co-review patient medical data, including diagnostic results, treatment history, and real-time health status, and co-formulate comprehensive treatment plans based on team discussions and a professional knowledge base.
In the treatment implementation process, the reaction and the health condition of the patient are continuously monitored, and the treatment scheme is periodically checked and adjusted by the interdisciplinary team according to the reaction and the latest medical information of the patient, so that the interdisciplinary treatment coordination mechanism can provide comprehensive and personalized treatment scheme for complex and rare cases, and the treatment effect and efficiency are improved.
The data analysis is based on natural language processing technology, wherein the feature extraction converts text data into numerical features using TFIDF, TFIDF formula TF-IDF (t, d) =TF (t, d) ×IDF (t), wherein TF (t, d) is the frequency of word t in document d, IDF (t) is the inverse document frequency for reducing the influence of common words;
The artificial intelligence algorithm selects a support vector machine to classify cases, trains a model by using a training data set, aims at identifying cases needing cross-disciplinary treatment, and adopts a linear support vector machine:
f(x)＝wx+b
wherein x is a feature vector, w is a weight vector, and b is a bias term;
The model applies to new cases: and applying the same natural language processing technical characteristic extraction process to the new medical records, classifying the new cases by using a trained machine learning model, judging whether the new cases belong to complex cases, automatically notifying relevant interdisciplinary treatment teams after the complex cases are identified, and providing medical records and model output information.
And the method also comprises the steps of fee settlement and data feedback, and after treatment is finished, fee settlement is automatically carried out, and meanwhile, the treatment result and feedback of the patient are collected for continuously improving the medical service quality.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (10)
1. The medical service automation flow optimization method is characterized by comprising the following steps:
s1: when a patient enters a medical institution, the information acquisition system automatically records basic information and medical history of the patient, acquires physiological data of the patient in real time, and simultaneously performs basic examination, an examination report and a medical image report to perform preliminary diagnosis suggestion;
S2: intelligent diagnosis and treatment plan making, based on the data collected in the step S1, utilizing a machine learning algorithm to provide a personalized diagnosis and treatment scheme, and considering the illness state, historical data and real-time physiological information of a patient;
S3: resource scheduling optimization, namely automatically coordinating medical resources according to a diagnosis and treatment plan, including scheduling a consulting room, medical equipment and medical staff, optimizing resource allocation through an algorithm, and reducing waiting time of patients;
S4: automatic medicine management and delivery, automatically managing medicine inventory according to real-time conditions and prescriptions of patients, and ensuring timely delivery of medicines to a treatment area;
S5: and the interdisciplinary treatment coordination mechanism is integrated with a interdisciplinary coordination platform and is used for processing complex cases including rare diseases or multiple chronic diseases, and when detecting that the patient needs multidisciplinary coordination, the interdisciplinary coordination mechanism is automatically triggered to collect the professional opinions of doctors in different professional fields and formulate a comprehensive treatment scheme.
2. The automated healthcare process optimization method according to claim 1, wherein the information acquisition system in S1 automatically identifies the identity of the patient through an identity verification device, automatically accesses the electronic health record of the patient after the identity is confirmed, and pulls the historical case, the past treatment record and the drug sensitivity information;
meanwhile, physiological data including heart rate, blood pressure and body temperature indexes are collected in real time through the rapid detection equipment.
3. The method for optimizing a medical service automation process according to claim 2, wherein S2 specifically comprises:
firstly, analyzing historical medical records of patients, and identifying potential health risks and disease modes;
Combining the real-time physiological data with the medical history of the patient and the medical image report, and performing data fusion and analysis by using a deep learning model to improve the accuracy of diagnosis, wherein the deep learning model is trained by clinical data to identify complex health modes and disease indexes;
The pre-trained machine learning model is utilized to classify and predict the illness state of the patient, and the current medical research and treatment guideline is combined to automatically generate personalized diagnosis and treatment advice, so that the unique medical history, the real-time physiological state and the treatment response of the patient are considered.
4. A method of medical service automation procedure optimization according to claim 3, in which the deep learning model employs an improved convolutional neural network model, and in which attention mechanisms are introduced in a conventional convolutional neural network model structure, specifically comprising:
attention module: the introduction of the attention mechanism helps the convolutional neural network model to focus on critical areas in the medical image, including areas where abnormal cell proliferation exists in tumor detection, and the attention module is realized by the following formula:
a[l]＝A[l]⊙a[l-1]；
wherein, A [l] is the attention weight, which is obtained by convolution operation and normalized by softmax function, and the attention weight and the activation value a [l-1] of the upper layer are multiplied element by element and expressed as #;
Multi-scale feature fusion: the medical image contains information of different scales from microscopic to macroscopic, multi-scale feature fusion is integrated in the convolutional neural network model to capture the information of different scales, and features extracted on different levels are fused by the following modes:
a[l]＝f(W[l]F[l]+b[l])；
Wherein F [l] is a vector fused with features of different scales, and is obtained by connecting activation values of different levels ;
integration of structured data: the structured data is processed by introducing parallel fully connected network layers and its output is fused with image features:
s＝f(WsD+bs)；
a[final]＝concat(a[l],s)；
Wherein D is structured data comprising medical history and real-time physiological data, W s and b s are weights and biases of a fully connected layer, s is output of the layer, and finally, image features a [l] and structured data features s are combined based on concat operation to form comprehensive features a [final], and a machine learning classifier is used for analyzing the comprehensive features to generate specific diagnosis and treatment suggestions.
5. The automated healthcare process optimization method according to claim 4, wherein the machine learning classifier uses a random forest model to perform classification or regression analysis by constructing a plurality of decision trees and summarizing the results, specifically comprising:
Training a plurality of decision trees: for each decision tree, randomly selecting different sample subsets and feature subsets for training, wherein the training process of each tree is represented as starting from a root node, recursively selecting the best features to divide data until a preset stopping condition is reached, and the stopping condition comprises maximum depth and minimum sample number;
decision process of decision tree: each tree uses discriminant criteria on its nodes:
Wherein Gini split represents the non-purity of the keni after the division, gini before is the non-purity of the keni before the division, N i is the number of samples of the ith child node after the division, N is the total number of samples before the division, gini i is the non-purity of the keni of the ith child node;
results of integrating multiple trees: in the classification task, each tree gives a prediction result, the final result is the majority vote of all the tree prediction results, and in the regression task, the final result is the average of all the tree prediction values;
Taking the fused characteristic a [final] as the input of a random forest model, training the random forest type and severity by using historical medical data, and generating specific disturbance therapy advice by combining medical professional knowledge and therapy guidelines according to the prediction result of the model;
the analysis results include: the model outputs the disease type, severity or specific health indicator.
6. The automated healthcare process optimization method according to claim 5, wherein the resource scheduling optimization in S3 allocates medical resources using a queuing optimization algorithm, the queuing optimization algorithm being based on a linear program, the algorithm inputting requirements including a patient diagnosis and treatment plan, a current state of available resources and constraints;
the method comprises the following specific steps:
And (3) data collection: collecting the service condition of a consulting room, the state of medical equipment and the work arrangement of medical staff in real time through a management system in a medical institution;
Demand analysis: determining the type and the quantity of required resources according to the diagnosis and treatment plan of the patient;
resource optimization allocation: a linear programming model is adopted, a mathematical model of the resource allocation problem is established, the optimization target is to minimize the waiting time of a patient and the idle time of the resource, the constraint comprises the availability of the resource and the working time of medical staff, and the linear programming model target function is expressed as follows:
Wherein W i is the waiting time of the ith patient, I i is the resource idle time, and the constraint condition reflects the resource limitation and the time schedule;
real-time data, including resource usage and patient arrival time, is continuously monitored, and resource allocation is dynamically adjusted according to the real-time data to optimize overall efficiency.
7. The method for optimizing a medical service automation process according to claim 6, wherein S4 specifically comprises:
integration of drug management system with diagnosis and treatment plan: the electronic health record and the drug management system are integrated, so that the diagnosis and treatment plan of the patient and the drug management system are synchronously updated, and the diagnosis and treatment plan of the patient contains the necessary drug types, doses and drug administration time;
automatic matching and inventory updating of drug requirements: the drug management system automatically determines the required drugs according to the diagnosis and treatment plan of the patient and checks the inventory;
drug delivery schedule: automatically generating a drug delivery plan according to the treatment area and the administration time of the patient, and using an automatic guided vehicle AGV to deliver the drug to the appointed treatment area on time;
The automated guided vehicle AGV is equipped with a scanning device to ensure that the correct medication is delivered to the correct patient, and after delivery is complete, the medication management system automatically updates the medication usage record and synchronizes to the patient's electronic health record.
8. The method for optimizing a healthcare automated process according to claim 7, wherein S5 specifically comprises
Establishing an integrated interdisciplinary coordination platform, and connecting doctors and health professionals in different professional fields, wherein the interdisciplinary coordination platform comprises a case management system, a collaboration tool, a real-time communication function and a professional knowledge base;
Automatically analyzing the medical records of the patient by utilizing a data analysis and artificial intelligence algorithm, identifying complex cases requiring interdisciplinary treatment, identifying the complex cases, and automatically triggering interdisciplinary coordination mechanism;
forming and cooperating of multidisciplinary expert teams, automatically inviting experts in related fields to join in a treatment team according to the characteristics and the requirements of cases, providing a cooperation platform, sharing case information, discussing treatment schemes and exchanging comments in real time;
By crossing discipline coordination platforms, team members co-review patient medical data, including diagnostic results, treatment history, and real-time health status, and co-formulate comprehensive treatment plans based on team discussions and a professional knowledge base.
9. The automated healthcare process optimization method of claim 8, wherein the data analysis is based on natural language processing techniques, wherein feature extraction uses TFIDF to convert text data into numeric features, TFIDF formula TF-IDF (t, d) = TF (t, d) ×idf (t), wherein TF (t, d) is the frequency of word t in document d, IDF (t) is the inverse document frequency, for reducing the impact of common words;
The artificial intelligence algorithm selects a support vector machine to classify cases, trains a model by using a training data set, aims at identifying cases needing cross-disciplinary treatment, and adopts a linear support vector machine:
f(x)＝wx+b
wherein x is a feature vector, w is a weight vector, and b is a bias term;
The model applies to new cases: and applying the same natural language processing technical characteristic extraction process to the new medical records, classifying the new cases by using a trained machine learning model, judging whether the new cases belong to complex cases, automatically notifying relevant interdisciplinary treatment teams after the complex cases are identified, and providing medical records and model output information.
10. The automated healthcare process optimization method of claim 9, further comprising fee settlement and data feedback, wherein the fee settlement is automatically performed after the treatment is completed, and wherein the patient treatment results and feedback are collected for continuously improving the quality of the healthcare service.
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