CN104951894A - Intelligent analysis and assessment system for disease management in hospital - Google Patents
Intelligent analysis and assessment system for disease management in hospital Download PDFInfo
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Abstract
The invention discloses an intelligent analysis and assessment system for disease management in a hospital. A user inquiry terminal is used for in-hospital hospital lean management and contrast assessment among hospitals in terms of four dimensions including medical treatment quality, medical treatment efficiency, medical treatment benefit and satisfaction of patients according to assessment results of a hospital management quality assessment module. Various screening, free combination and 360-degree omni-directional dynamic contrast among different dimensions are realized, various screening, combination and omni-directional contrast in the same dimension are also realized, problems of hospital management, ranking and positioning are made clear by big data professional analysis of the hospital, hospital management policy supporting and hospital management contrast in different dimensions, comprehensive contrast and ranking in terms of ICD (international classification of diseases) diagnosis, diagnosis related grouping (DRG) diseases, clinical doctors, clinical departments and hospitals in a region are realized, advantages and disadvantages of contrasted objects in the industry are made clear, clear positioning can be realized, lean management of the hospital is promoted, and comprehensive competition capacity of the hospital is improved.
Description
Technical field
The present invention relates to lean hospital quality management field, the large data analysis of medical treatment and Decision-making of Hospital Management and support field, particularly relate to a kind of hospital disease control intellectual analysis and evaluating system.
Background technology
Compared with external advanced hospital management, domestic hospital management is substantially still in the starting stage, not only lack high-quality managerial talent, more lack scientific quality and efficiency evaluation standard, cause the huge waste of the planless enlarging of hospital and medical resource.In recent years due to the fast development of domestic hospitals IT technology, preliminarily complete the raw data accumulation of patient and disease, but suffer from and there is no methodology, these data can not be refined the decision-making foundation becoming tutorial message and hospital management effectively, cause most data can only be stored in the data warehouse of hospital, waste resource.If U.S. government can be used for reference fully to the successful pattern of hospital management and outstanding methodology, in addition localization improvement again, domestic HMOs not only can be allowed to increase effective monitoring approach and means, and hospital can also be impelled to accelerate from the extensive paces made the transition to fine management model.
Recent government actively advocates and encourages traditional industries to the transition of " internet+", make full use of the quality of medical care that digital technology improves hospital, operation efficiency and the waste of minimizing medical resource become the tendency of the day, seize the opportunity, use for reference advanced experience, Criterion pattern, will occupy first-strike advantage, leads the reform tide of industry.
Clinical medical multidisciplinary and complicacy that is disease adds data depth analysis and purifies as the difficulty of management decision-making support foundation, compared with the data of other industry, medical data has nonadditivity (as financial data) and non-immediate comparability (as size of data) feature, due to each hospital admissions patient crowd and disease degree difference, be irrational by directly adopting the data such as mortality ratio, length of stay and cost to disease kind, doctor, performance comparative assessment between section office and hospital.For example owing to accepting transfer from one hospital to another patient in a large number and accept the more serious patient crowd of the state of an illness for medical treatment, Sichuan West China Hospital just directly can not carry out simple performance evaluation with certain County Hospital.
In order to effectively solve the predicament of clinical data injustice, the disease group inductive method that it is standard that one of evaluation profile that hospital adopts usually has with resource use, as all kinds of DRG and DCG etc., then the medical treatment cost will used in treatment, by analysis, the case complexity index method (CMI) of disease group is obtained.To be retrodicted out by medical resource cost service condition the severity extent of Hospital Disease group, thus realize the assessment in same system of hospital and section office.But in evaluation quality of medical care, operation efficiency and Rational drugs use etc., having its congenital deficiency with the method that CMI calculates, first this pattern also reckons without the characteristic of disease itself and other clinical correlation influence factors, does not meet medical rule; Secondly excessive imaging and treatment and the virtual height cost treatment itself that causes also can increase model instability, thus cause the deviation of judged result.
Existing hospital management system cannot realize relying on the large data analysis of professional medical to assist Decision-making of Hospital Management support, more can not to realize in lean hospital management between different dimensions or medical control Data Comparison between dimension of the same race, the rank in hospital management and location cannot be specified, comprise disease plant between, comparison object present position between doctor, between section office, between hospital and rank etc., cannot the strengths and weaknesses of comparison object between industry, be unfavorable for giving priority to Priority Department, making up short slab subject.In addition, for hospital management regulator, cannot to self-defined hospital, the clinical speciality in region, section office of leaving hospital, sick plant DRG, time period of leaving hospital, primary and secondary will be diagnosed or perform the operation, patient age, sex, classification and aggregation patient satisfaction etc. carry out dynamic queries, cannot by the compound comparison query combined across the different condition of hospital, hospital management supervision is difficult to science, effectively carries out.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of novel hospital's disease control intellectual analysis and evaluating system are provided, be adjusted to basis with inpatient's disease risks and realize the assessment of full disease hospital quality management, model is by the historical data of all In-patients of certain hospital or a certain area, by complication/complication adjoint during patient admission, individual patient speciality is (as sex, age, survival condition etc.), and state source etc. of being admitted to hospital is integrated into the variation factor of disease treatment, by the treatment information that disease associated group (DRG) classification is final with these patients, set up mortality ratio respectively, the statistics correlativity regression model of length of stay and cost of being in hospital.And then by the algorithm that these models draw, the existing patient of hospital is precisely predicted, calculate each patient in mortality ratio, length of stay and the desired value of cost of being in hospital.
The object of the invention is to be achieved through the following technical solutions: hospital's disease control intellectual analysis and evaluating system, comprise patient terminal, evaluating server and user's inquiry terminal, patient terminal is connected with evaluating server respectively by communication network with user's inquiry terminal;
Described patient terminal is provided with collection disease and follows up a case by regular visits to APP, the satisfaction information of patient after leaving hospital for dynamic acquisition;
Described evaluating server comprises clinical data introducting interface module, quality of hospital management evaluation module and user's query interface module:
Clinical data introducting interface module, for connecting hospital clinical data center, imports clinical data carries out medical control quality assessment for quality of hospital management evaluation module;
Quality of hospital management evaluation module is used for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost according to hospital clinical data, finding out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, inferring the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
User's query interface module is used for providing user interface to user's inquiry terminal;
Described user's inquiry terminal is used for the assessment result according to quality of hospital management evaluation module, realizes the multiple screening between different dimensions, independent assortment and comprehensive contrast, realizes the multiple screening between dimension of the same race, combination and comprehensive contrast simultaneously; User's inquiry terminal also can inquire about the satisfaction assessment of discharged patient's feedback by evaluating server, and realizes analysis and the contrast of the patient satisfaction in same standard.
Described quality of hospital management evaluation module comprises a historical data screening and screens and pre-value computing unit with modeling unit, a current data:
Historical data screening and modeling unit comprise historical data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, statistics of variable is checked and screen module, statistical models sets up module, model quality authentication module.Historical data imports module and is used for from hospital database, import historic discharged patient's data; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and extreme value data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; The statistical test of variable and screening module are for realizing the screening to the variable with statistically significant meaning; Statistical models sets up module for completing the foundation of statistical models, patient death rate data acquisition Logic Regression Models, and the data of length of stay and cost then adopt multiple linear regression model, is formed the quantitative formula of predicted value by modeling; Model quality authentication module calculates in sample population and non-sample crowd model for adopting the inspection of the C-Index in statistics and the R-square method of inspection, evaluates according to corresponding result.
Current data screening and pre-value computing unit comprise current data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, risk profile value of being admitted to hospital computing module.Current data imports module and is used for from hospital database, import current discharged patient's data; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; Risk profile value of being admitted to hospital computing module is for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost; The risk profile of disease refers to find out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, infers the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
The algorithmic formula of predicted value is as follows:
Expected mortality
wherein, b
irepresent significant correlation property coefficient, b
0represent model intercept, n represents the significant correlation variable number of patient;
Length of stay and medical treatment cost
wherein, b
0represent model intercept, MSE represents the square error of model, b
irepresent significant correlation property coefficient, 0.5 is statistic bias modified value.
Quality of hospital management inquiry dimension comprises the degree of depth, time, type, patient's information and hospital management: the degree of depth comprises rank, 10 percentiles, 25 percentiles, median; Time comprise the past with now, year, season, monthly; Type comprises international statistical classification ICD, the DRG of diseases and related health problems, clinical sub-subject, clinical speciality, hospital, area, domestic, international; Patient's information comprises people information, way of paying, admission information, information of leaving hospital; Hospital management comprises quality of medical care, medical efficiency, Medical Benefit, patient satisfaction.
The user of described user's inquiry terminal comprises clinical users, hospital management regulator user:
For clinical users: clinical users presses hospital's classification Risk Adjusted model to hospital, the clinical speciality that DMIAES formulates, to leave hospital section office, all kinds of doctor, sick kind DRG, leave hospital the time period, primary and secondary will be diagnosed or perform the operation, patient enters to leave hospital situation, patient age, sex, the Query Result of patient satisfaction of all categories carries out anonymity with other hospitals regional and compares, realize the compound query of various different condition combination and actual occurrence value, predicted value and O/E index (i.e. actual occurrence value/desired value, O/E index <1: illustrate that disease risks is high, but lapsing to for the treatment of, as case fatality rate, length of stay or cost control are lower than expection, O/E index >1: illustrate that disease risks is low, but treatment lapse to, as case fatality rate, length of stay or cost control higher than expection) result show,
For hospital management regulator user: the inquiry of hospital management regulator user is superior to clinical users, hospital's real name can be used to realize comparison, the clinical speciality that user formulates the self-defined hospital in region, DMIAES according to hospital's classification Risk Adjusted model, section office of leaving hospital, sick plant DRG, time period of leaving hospital, primary and secondary will be diagnosed or perform the operation, patient age, sex, classification and aggregation patient satisfaction are inquired about, by the compound comparison query combined across the different condition of hospital, Query Result arranges according to user-defined order.
Hospital's disease control intellectual analysis and appraisal procedure: system adopts large data analysis and the advanced medical data of modeling method to hospital to realize Risk Adjusted and the information integration of disease, take data transformations as the pattern of decision support foundation, for disease treatment management, Decision-making of Hospital Management provide effective assessment and analysis approach.
Advanced medical control decision model: the Decision Support Platform advanced based on external medical treatment fully takes into account various variable again, as under classification of diseases model, disease risks index, complication and crisis sign etc., effective supplementary means is provided to administrators of the hospital by data analysis and mathematical model result, the fortuitous event occurred in timely screening, prediction and process possibility therapeutic process, ensures quality of medical care.The management decision-making support platform built of foreign hospital advanced management experience then helps hospital to solve the structural and coupling problem of each operation system of hospital by a series of core index system (KPIs) of design to models such as quality of medical care, flow path efficiency, patient safety, patient satisfaction analyses, improve quality of medical care and efficiency of operation, increase patient satisfaction, reduce the wasting of resources.In addition for the requirement of quality of medical care, efficiency and patient safety, the evaluation criteria of whole world hospital is the same; But the experience of core index (KPIs) the Ze Hui foreign hospital for hospital financial and performance management, adopts the feasibility model of the administrative standard being applicable to domestic hospitals, completes the localization of product.
The solution that the full course of disease is data integrated: adopt cell phone end data drainage pattern, the feedback information such as symptomatic reaction, sign, satisfaction of patient after doctor is left hospital by App application dynamic acquisition, the data integrating the excavation in institute form complete full course of disease treatment and rehabilitation Data-Link, the clinical research of very big raising objective hospital, clinical testing and chronic disease management level, the data mining of this integration, collection and integration mode are in the leading level in the world.
The invention has the beneficial effects as follows:
(1) Introduced From Abroad advanced management experience and localization are integrated, reformed and improved, define specialty, science, practical management assessment method, this assessment and analysis system based on the large data analysis foundation of hospital's this medical treatment of bulk sample can take into full account the influence degree of disease risks etc., achieve quality of medical care, hospital efficiency, the comprehensive assessment of medical treatment cost control and patient satisfaction, medical quality in hospital management level can be promoted in Comprehensive ground, promote medical lean development and for comprehensive hospital assess, science is located, Priority Department is set up, performance evaluation and comprehensive hospital general management provide scientific basis and reference.
Hospital's disease control intellectual analysis and evaluating system use for reference international advanced medical control experience, with informationization as carrying, science, precisely, comprehensive assessment medical quality managent, efficiency of operation, cost control and patient satisfaction.As the quantification tool of quality of medical care lean management, medical control will be promoted to lean management future development, lead hospital's lean management new direction.
DMIAES efficiently solves the not comparable difficult problem of quality of medical care in hospital management, for administration office of the hospital provides medical quality managent evaluation criteria and decision-making foundation, for hospital's scientific management, performance evaluation provide standard and judgment.
Cost and cost model in hospital's disease control intellectual analysis and evaluating system, full disease disease cost accounting and fee calculating are realized, and take into full account the risk factors of various disease, scientific forecasting cost of illness and expense, effective control medical resource waste, applying of paying for DRG disease kind of dividing into groups by disease is medical provides reference frame.
(2) be adjusted to basis with inpatient's disease risks and achieve the assessment of full disease control, model is by the historical data of all In-patients of certain hospital or a certain area, by complication/complication adjoint during patient admission, individual patient speciality is (as sex, age, survival condition etc.), and state source etc. of being admitted to hospital is integrated into the variation factor of disease treatment, by the treatment information that disease associated group (DRG) classification is final with these patients, set up mortality ratio respectively, the statistics correlativity regression model of length of stay and cost of being in hospital.And then by the algorithm that these models draw, the existing patient of hospital is precisely predicted, calculate each patient in mortality ratio, the desired value of length of stay and cost of being in hospital is adjusted to basis with inpatient's disease risks and realizes the assessment of full disease control, model is by the historical data of all In-patients of certain hospital or a certain area, by complication/complication adjoint during patient admission, individual patient speciality is (as sex, age, survival condition etc.), and state source etc. of being admitted to hospital is integrated into the variation factor of disease treatment, by the treatment information that disease associated group (DRG) classification is final with these patients, set up mortality ratio respectively, the statistics correlativity regression model of length of stay and cost of being in hospital.And then by the algorithm that these models draw, the existing patient of hospital is precisely predicted, each patient can be calculated in mortality ratio, length of stay and the desired value of cost of being in hospital.Achieve the effective conversion of medical data from data to solution by methods such as large data analysis, mathematical statistics and machine learning, achieve data value.
(3) concept of the conjunction complication variable of disease is added, according to the natural law of clinical treatment, the patient previously state of an illness necessarily has very large influence to the treatment results implemented, and meets clinical medicine rule by the disease risks adjustment of closing based on complication.
(4) features such as the identical ill speciality of same ethnic population, and the normal process of medical treatment can both meet the requirement of statistically homoplasy sample, use historical data to carry out modeling and forecasting and also meet mathematical law.Successfully solve the bottleneck of medical data noncomparabilities with the disease risks adjustment being modeled as basis, in the Deng developed country of USA and Europe, progressively achieve extensive promotion and application.
(5) O/E index manner of comparison is adopted, solve an incomparable difficult problem between medical data, the assess medical quality between disease kind can not only be realized, also can realize the performance Rationality Assessment in inpatient's disease treatment management between doctor, between hospital department, between hospital, be that health authorities is to one of effective regulatory measure of subordinate hospital.
(6) except statistical test, the Risk Adjusted model of the application's model and United States Hospital alliance hospital medical quality managent evaluation system is compared, simultaneously carry out the comparison of O/E index with actual occurrence value, the conclusion of com-parison and analysis be the application's model in the precision of prediction and grade of fit in the sample all higher than same class model.
(7) the multiple screening between different dimensions, independent assortment and 360 degree of comprehensive contrasts are not only realized, also achieve the multiple screening between dimension of the same race, combination and comprehensive contrast, by in data analysis and different O&M comparison, rank in clear and definite hospital management and location, comprise the comparison object present position between disease kind, between doctor, between section office, between hospital and rank, can be perfectly clear the strengths and weaknesses of comparison object between industry, maximize favourable factors and minimize unfavourable ones, give priority to Priority Department, make up short slab subject, improve the synthesized competitiveness of hospital fast.
As IT system instrument, can realize faster, comprehensive comprehensively, the medical quality in hospital comparative evaluation of various dimensions and various visual angles.
Accompanying drawing explanation
Fig. 1 is present system structural schematic block diagram;
Fig. 2 is the comprehensive contrast schematic diagram of DMIAES system 360 degree;
Fig. 3 is disease risks adjustment model process flow diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail, but protection scope of the present invention is not limited to the following stated.
As described in Figure 1, hospital's disease control intellectual analysis and evaluating system (DMIAES system), comprise patient terminal, evaluating server and user's inquiry terminal, patient terminal is connected with evaluating server respectively by communication network with user's inquiry terminal;
Described patient terminal is provided with collection disease and follows up a case by regular visits to APP, the satisfaction information of patient after leaving hospital for dynamic acquisition;
Described evaluating server comprises clinical data introducting interface module, quality of hospital management evaluation module and user's query interface module:
Clinical data introducting interface module, for connecting hospital clinical data center, imports clinical data carries out medical control quality assessment for quality of hospital management evaluation module;
Quality of hospital management evaluation module is used for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost according to hospital clinical data, finding out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, inferring the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
User's query interface module is used for providing user interface to user's inquiry terminal;
Described user's inquiry terminal is used for the assessment result according to quality of hospital management evaluation module, realizes the multiple screening between different dimensions, independent assortment and comprehensive contrast, realizes the multiple screening between dimension of the same race, combination and comprehensive contrast simultaneously; User's inquiry terminal also can inquire about the satisfaction assessment of discharged patient's feedback by evaluating server, and realizes analysis and the contrast of the patient satisfaction in same standard.
Described quality of hospital management evaluation module comprises a historical data screening and screens and pre-value computing unit with modeling unit, a current data:
Historical data screening and modeling unit comprise historical data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, statistics of variable is checked and screen module, statistical models sets up module, model quality authentication module: historical data imports module for importing historic discharged patient's data from hospital database; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and extreme value data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; Statistics of variable inspection and screening module are screened for the variable realized having statistically significant meaning; Statistical models sets up module for completing the foundation of statistical models, patient death rate data acquisition Logic Regression Models, and the data of length of stay and cost then adopt multiple linear regression model, are finally formed the quantitative formula of prediction by modeling; Model quality authentication module calculates in sample population and non-sample crowd model for adopting the inspection of the C-Index in statistics and the R-square method of inspection, evaluates according to corresponding result;
Current data screening and pre-value computing unit comprise current data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, risk profile value of being admitted to hospital computing module: current data imports module for importing current discharged patient's data from hospital database; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; Risk profile value of being admitted to hospital computing module is for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost; The risk profile of disease refers to find out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, infers the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
The algorithmic formula of predicted value is as follows:
Expected mortality
wherein, b
irepresent significant correlation property coefficient, b
0represent model intercept, n represents the significant correlation variable number of patient;
Length of stay and medical treatment cost
wherein, b
0represent model intercept, MSE represents the square error of model, b
irepresent significant correlation property coefficient, 0.5 is statistic bias modified value.
As shown in Figure 2, quality of hospital management inquiry dimension comprises the degree of depth, time, type, patient's information and hospital management: the degree of depth comprises rank, 10 percentiles, 25 percentiles, median; Time comprise the past with now, year, season, monthly; Type comprises ICD, DRG, clinical sub-subject, clinical speciality, hospital, area, domestic, international; Patient's information comprises people information, way of paying, admission information, information of leaving hospital; Hospital management comprises quality of medical care, medical efficiency, Medical Benefit, patient satisfaction.
The user of described user's inquiry terminal comprises clinical users, hospital management regulator user:
For clinical users: clinical users by hospital's classification Risk Adjusted model, hospital, DMIAES are formulated clinical speciality, section office of leaving hospital, all kinds of doctor, sick plant DRG, time period of leaving hospital, primary and secondary will be diagnosed or perform the operation, patient enters to leave hospital situation, the Query Result of patient age, sex, patient satisfaction of all categories carries out anonymity with other hospitals regional and compares, and realizes compound query and the displaying of actual occurrence value, predicted value and O/E index results of the combination of various different condition; O/E index and actual occurrence value/desired value, O/E index <1: illustrate that disease risks is high, but case fatality rate, length of stay or cost control are lower than expection; O/E index >1: illustrate that disease risks is low, but case fatality rate, length of stay or cost control are higher than expection.
For hospital management regulator user: the inquiry of hospital management regulator user is superior to clinical users, hospital's real name can be used to realize comparison, the clinical speciality that user formulates the self-defined hospital in region, DMIAES according to hospital's classification Risk Adjusted model, section office of leaving hospital, sick plant DRG, time period of leaving hospital, primary and secondary will be diagnosed or perform the operation, patient age, sex, classification and aggregation patient satisfaction are inquired about, by the compound comparison query combined across the different condition of hospital, Query Result arranges according to user-defined order.
Below for the Query Result of hospital A in 1/1/2014-12/31/2014 time Nei Ge section office quality of medical care rank, operation efficiency rank, cost control rank, satisfaction rank, 360 degree of this patent comprehensive comparison query functions are described:
By clinical speciality (note: the classification of clinical speciality divides by relevant disease diagnosis group DRG, and the section office of non-hospital reality, the rationality being conducive to realizing under same medical diagnosis on disease compares) quality of medical care rank: the O/E index of sick mortality ratio is from small to large
Medical efficiency rank by clinical speciality: the O/E index of sick mortality ratio from small to large
Medical Benefit rank by clinical speciality: the O/E index of sick kind accounting expense from small to large
Patient satisfaction rank by clinical speciality: the value of the Satisfaction Survey of Patients that hospital is total from big to small
Below for the Query Result of certain ten hospitals quality of medical care rank within the 1/1/2014-12/31/2014 time, operation efficiency rank, cost control rank in certain region, 360 degree of this patent comprehensive comparison query functions are described:
Quality of medical care rank by hospital: the O/E index of sick mortality ratio from small to large
Medical efficiency rank by hospital: the O/E index of sick mortality ratio from small to large
Medical Benefit rank by hospital: the O/E index of sick kind accounting expense from small to large
The implementation method of hospital's disease control intellectual analysis and evaluating system, comprises a historical data screening and screens and pre-value calculation procedure with modeling procedure and a current data:
S1: import historic discharged patient's data from hospital database;
S2: data are differentiated and cleaning, filter out bad data and extreme value data and are deleted; Computer programming is adopted to complete the cleaning of bad data and extreme value data.
The definition of bad data: the space data (international statistical classification (ICD coding) etc. as without patient's essential information, the information that enters to leave hospital, diseases and related health problems) 1. in data line; 2. the patient data repeated.
The definition of extreme value data: 1. length of stay is the extreme value patient data of 0 day; 2. the extreme value patient data of length of stay outside the number percent of 99; 3. the extreme value patient data that direct cost of being in hospital is less than 900 yuan; 4. after death remains donations patient data.
S3: the classification of medical diagnosis on disease associated packets DRG and model, realizes the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; The commercial DRG code machine of 3M is adopted to realize sorting out set.
The definition of DRG: according to the sorting and grouping of patient disease's diagnosis, operation kind, complication and complication, leave hospital situation, sex and age etc., each patient has a DRG; Be conducive to by DRG the classification and the assessment that realize correlativity diagnosis, reduce diagnosis quantity simultaneously, improve the relevance grade of model prediction;
The definition of category of model: according to DRG, sorted out by the DRG be associated, each DRG is incorporated into a pattern number, is also DMIAES basis DRG, by pattern number/DMIAES basis DRG, is conducive to the classification and the assessment that realize correlativity DRG;
S4: when being admitted to hospital, the international statistical classification ICD of diseases and related health problems closes the classification set of complication and dependent variable thereof, realizes the classification to inpatient's complication and complication and dependent variable thereof; Adopt the international medical diagnosis on disease criteria for classification of closing complication, adopt computer programming to complete the classification set of variable.
Close the definition of complication variable: the international Disease Diagnosis Standard classification according to human organ and system carries out classification process (see example one) to the complication of the past during patient admission and complication; By classification process, be conducive to the quantity reducing medical diagnosis on disease/operation variable, improve the degree of stability of model prediction performance;
The definition of its dependent variable: its dependent variable comprises age, sex, social economic environment, the information such as situation and source of being admitted to hospital; Be conducive to comprehensive to patient and admission information etc. factor to take into account in Risk Adjusted model, the assessment of risk of being admitted to hospital is more complete.
Adopt international disease to close the collective standard of complication, cluster set is carried out to the ICD diagnosis of patient in same relevant disease group DRG or Operation encoding, is formed and close complication class variable, as shown in example one;
Example one: the classification variable of disease code and people information
Bacterial endocarditis closes complication group (ICD9 coding)
Patient basic population information is as follows with state variable (part) of being admitted to hospital:
S5: in same DRG group, has the conjunction complication group of statistically significant meaning by statistical test method to patient death rate, length of stay and cost and other class variables carry out pre-service.
S6: the described step setting up statistical models carries out by model the influence degree that regretional analysis quantizes variable;
Utilize independent variable to carry out the description of quantification to predictive variable, in disease risks adjustment, predictive variable be patient death rate, length of stay and cost of being in hospital, independent variable is for closing complication variable and its dependent variable; The influence degree (see example two) that regretional analysis quantizes variable is carried out by model.
Patient death rate data acquisition Logic Regression Models modeling method, length of stay and medical treatment cost data acquisition multiple linear regression model modeling method.Statistical model is set up and is utilized LASSO homing method, and automatically carries out parameter optimization on the training data, sets up optimal model.In the linear regression model (LRM) that length of stay and cost are set up, Log conversion is carried out to predictive variable value, made the predictive variable value after converting closer to normal distribution, more meet the hypothesis of linear regression model (LRM);
The regression coefficient of model to independent variable provides as follows: close complication variation coefficient > 0 (increasing mortality ratio, the risk of length of stay and cost); Its dependent variable (as the age etc.) coefficient can just can be born.In addition, variable symbol must be consistent with just choosing the symbol obtained in statistical test.
Sick mortality model #22:(patient age >=18 of example two: DMIAES) acute ischemic stroke and use thrombolytic agent companion seriously to close complication (MSDRG 61), close complication (MSDRG 62), without closing complication (MSDRG 63).
Data Source: texas,U.S medical center Herman memorial hospital
Patient's number in modeling sample: 996 sample time 7/1/2004-6/30/2014
Model classification: Logic Regression Models
| Close the explanatory variable of complication | Related coefficient |
| Intercept | -4.159 |
| Large brain compression | 2.272 |
| Women, age 75-80 year | 1.547 |
| Tracheae built-in pipe | 1.488 |
| Upper lung ventilator in 48 hours when being admitted to hospital | 1.392 |
| Abandon rescuing | 1.388 |
| Epilepsy | 1.344 |
| Acidosis | 1.285 |
| Women, more than 85 years old age | 1.092 |
| Encephaledema | 0.943 |
| Cardiac arrhythmia | 0.906 |
| Acute respiratory failure | 0.322 |
| Atrial fibrillation | 0.057 |
S7: described model quality verification step comprises:
(1) basic comparative analysis: adopt and the compare of analysis with class model, after the data of same test sample book being inputted two models, classification is realized to result and compare; Although the selection of variable is different with the mode of modeling, final result still has (see example six chart) of comparability
(2) adopt statistical testing of business cycles method, comprising:
1. the inspection of Logic Regression Models: the coefficient C-Index that is harmonious of computation model prediction and actual value; Wherein C-Index value is more close to 1, and the prediction effect of model is better.The C-Index > 0.7 of model is required in external comparison model inspection.(the C test value of example 3 1)
2. the inspection of linear regression model (LRM): the fitting coefficient R-square of computation model prediction and actual value; R-square value is more close to 1, and the prediction effect of model is better.The R-square > 0.05 of model is required in external comparison model inspection.(example 32, the R test value of 3)
3. test in test data, test data is independent a data, or the data (example 61, the reality/predicted value of 2,3) of the current patient under the prerequisite not having essence to change in other conditions (as therapy approach, means etc.);
4. carry out C and R by same test sample book data and other with class model to check, compare of analysis model quality (example 61, the test value of 2,3).
Example three: the quality verification result of model and compare of analysis
1. sick mortality ratio
Model #22: acute ischemic stroke DRG 61,62,63
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book patient number: 66 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree C-Index checks: DMIAES model in modeling data: 0.890, DMIAES model in test data: 0.964, U model: 0.933.
Model #328: multiple surgery wound DRG 957,958,959
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book number: 212 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree C-Index checks: DMIAES model in modeling data: 0.955, DMIAES model in test data: 0.987, U model: 0.982.
2. length of stay
Model #22: acute ischemic stroke DRG 61,62,63
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book patient number: 66 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree R-square checks: DMIAES model in modeling data: 0.244, DMIAES model in test data: 0.219, U model: 0.211.
Model #76: cardiac valves and other class cardiothoracic surgery DRG 219,220,221
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book patient number: 199 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree R-square checks: DMIAES model in modeling data: 0.256, DMIAES model in test data: 0.261, U model: 0.269.
3. to be in hospital Direct medical cost
Model #22: acute ischemic stroke DRG 61,62,63
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book patient number: 66 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree R-square checks: DMIAES model in modeling data: 0.366, DMIAES model in test data: 0.217, U model: 0.149.
Model #205: diabetes DRG 637,638,639
Data Source: texas,U.S medical center Herman memorial hospital
Test sample book patient number: 119 leave hospital time 7/1/2004-6/30/2014
Comparison model: the same class model of our DMIAES model and the U.S. (being called for short U model)
Model-fitting degree R-square checks: DMIAES model in modeling data: 0.383, DMIAES model in test data: 0.227, U model: 0.295.
Described current data screening comprises the following steps with pre-value calculation procedure:
SS1: import current discharged patient's data from hospital database;
SS2: data are differentiated and cleaning, filter out bad data and are deleted;
SS3: the classification of medical diagnosis on disease associated packets DRG and model, realizes the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set;
Each patient has a medical diagnosis on disease associated packets DRG, is realized classification and the assessment of correlativity diagnosis by DRG;
According to DRG, sorted out by the DRG be associated, each DRG is incorporated into a pattern number, is realized classification and the assessment of correlativity DRG by pattern number;
SS4: when being admitted to hospital, the international statistical classification ICD of diseases and related health problems closes the classification set of complication and dependent variable thereof, realizes the classification to inpatient's complication and complication and dependent variable thereof;
International Disease Diagnosis Standard classification according to human organ and system carries out classification process to the complication of the past during patient admission and complication;
Its dependent variable comprises age, sex, social economic environment, situation of being admitted to hospital and source-information;
SS5: the predicted value calculating patient admission risk, realizes the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost;
The definition of risk profile value and establishment condition:
Definition: the risk profile of disease refers to find out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, utilizes the methods such as large data analysis, mathematical statistics and machine learning precisely to infer the prediction occurrence value of the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
Establishment condition: essence change does not occur in modeling and current slot for therapy approach and means and the Medical Treatment Price etc. of diagnosis coding, diagnosis classifying method, disease.
Concrete grammar goes out predicted value (see example four) according to all kinds of formulae discovery:
Sick mortality model #22:(patient age >=18 of example four: DMIAES) acute ischemic stroke and use thrombolytic agent companion seriously to close complication (MSDRG 61), close complication (MSDRG 62), without closing complication (MSDRG 63).
Data Source: texas,U.S medical center Herman memorial hospital
Patient's number in modeling sample: 996 sample time 7/1/2004-6/30/2014
Model classification: Logic Regression Models
Degree of fitting in modeling sample: C-Index=0.890
Patient death average expectancy rate in model: 68.4%
Be 1.54% without the pre-value of dying of illness of patient during disease variable, have pre-value during multiple disease variable to be raised to 64.14%.
Example one is shown and is adopted disease risks adjustment model to risk profile during two different acute ischemic stroke patient admissions, because the differences such as age of patient, sex, conjunction complication and disease degree cause the different disease risks coefficient of dying of dying of illness.
The algorithmic formula of predicted value is as follows:
Expected mortality
wherein, b
irepresent significant correlation property coefficient, b
0represent model intercept, n represents the significant correlation variable number of patient;
Length of stay and medical treatment cost
wherein, b
0represent model intercept, MSE represents the square error of model, b
irepresent significant correlation property coefficient, 0.5 is statistic bias modified value;
Final employing reality occurs and expection relative value is assessed inpatient's medical control quality.
The above is only the preferred embodiment of the present invention, be to be understood that the present invention is not limited to the form disclosed by this paper, should not regard the eliminating to other embodiments as, and can be used for other combinations various, amendment and environment, and can in contemplated scope described herein, changed by the technology of above-mentioned instruction or association area or knowledge.And the change that those skilled in the art carry out and change do not depart from the spirit and scope of the present invention, then all should in the protection domain of claims of the present invention.
Claims (4)
1. hospital's disease control intellectual analysis and evaluating system, is characterized in that, comprise patient terminal, evaluating server and user's inquiry terminal, patient terminal is connected with evaluating server respectively by communication network with user's inquiry terminal;
Described patient terminal is provided with collection disease and follows up a case by regular visits to APP, the satisfaction information of patient after leaving hospital for dynamic acquisition;
Described evaluating server comprises clinical data introducting interface module, quality of hospital management evaluation module and user's query interface module:
Clinical data introducting interface module, for connecting hospital clinical data center, imports clinical data carries out medical control quality assessment for quality of hospital management evaluation module;
Quality of hospital management evaluation module is used for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost according to hospital clinical data, finding out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, inferring the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
User's query interface module is used for providing user interface to user's inquiry terminal;
Described user's inquiry terminal is used for the assessment result according to quality of hospital management evaluation module, realizes the multiple screening between different dimensions, independent assortment and comprehensive contrast, realizes the multiple screening between dimension of the same race, combination and comprehensive contrast simultaneously; User's inquiry terminal also can inquire about the satisfaction assessment of discharged patient's feedback by evaluating server, and realizes analysis and the contrast of the patient satisfaction in same standard.
2. hospital according to claim 1 disease control intellectual analysis and evaluating system, is characterized in that, described quality of hospital management evaluation module comprises a historical data screening and screens and pre-value computing unit with modeling unit, a current data:
Historical data screening and modeling unit comprise historical data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, statistics of variable is checked and screen module, statistical models sets up module, model quality authentication module: historical data imports module for importing historic discharged patient's data from hospital database; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and extreme value data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; Statistics of variable inspection and screening module are screened for the variable realized having statistically significant meaning; Statistical models sets up module for completing the foundation of statistical models, patient death rate data acquisition Logic Regression Models, and the data of length of stay and cost then adopt multiple linear regression model, are finally formed the quantitative formula of prediction by modeling; Model quality authentication module calculates in sample population and non-sample crowd model for adopting the inspection of the C-Index in statistics and the R-square method of inspection, evaluates according to corresponding result;
Current data screening and pre-value computing unit comprise current data and import module, data scrubbing module, medical diagnosis on disease associated packets DRG and model classifications module, close complication and dependent variable sorts out collection modules, risk profile value of being admitted to hospital computing module: current data imports module for importing current discharged patient's data from hospital database; Data scrubbing module has been used for data and has differentiated and cleaning, filters out bad data and is deleted; Medical diagnosis on disease associated packets DRG and model classifications module, for completing the classification of medical diagnosis on disease associated packets DRG and model, realize the classification set to medical diagnosis on disease associated packets, category of model, numbering classification set; Conjunction complication and dependent variable thereof are sorted out the international statistical classification ICD of diseases and related health problems when collection modules is admitted to hospital for completing and are closed the classification set of complication and dependent variable thereof, realize the classification to inpatient's complication and complication and dependent variable thereof; Risk profile value of being admitted to hospital computing module is for realizing the be admitted to hospital risk profile of each inpatient in mortality ratio, length of stay and medical treatment cost; The risk profile of disease refers to find out the universal law of the final treatment results of impact and can quantization factor by planting in the historical data of management in each disease of hospital, infers the prediction occurrence value the current mortality ratio, length of stay and the medical treatment cost that have a patient of similar disease degree and similar features;
The algorithmic formula of predicted value is as follows:
Expected mortality
wherein, b
irepresent significant correlation property coefficient, b
0represent model intercept, n represents the significant correlation variable number of patient;
Length of stay and medical treatment cost
wherein, b
0represent model intercept, MSE represents the square error of model, b
irepresent significant correlation property coefficient, 0.5 is statistic bias modified value.
3. hospital according to claim 1 disease control intellectual analysis and evaluating system, it is characterized in that, quality of hospital management inquiry dimension comprises the degree of depth, time, type, patient's information and hospital management: the degree of depth comprises rank, 10 percentiles, 25 percentiles, median; Time comprise the past with now, year, season, monthly; Type comprises ICD, DRG, clinical sub-subject, clinical speciality, hospital, area, domestic, international; Patient's information comprises people information, way of paying, admission information, information of leaving hospital; Hospital management comprises quality of medical care, medical efficiency, Medical Benefit, patient satisfaction.
4. hospital according to claim 1 disease control intellectual analysis and evaluating system, is characterized in that, the user of described user's inquiry terminal comprises clinical users, hospital management regulator user:
For clinical users: clinical users presses hospital's classification Risk Adjusted model to hospital, the clinical speciality that DMIAES formulates, to leave hospital section office, all kinds of doctor, sick kind DRG, leave hospital the time period, primary and secondary will be diagnosed or perform the operation, patient enters to leave hospital situation, patient age, sex, the Query Result of patient satisfaction of all categories carries out anonymity with other hospitals regional and compares, realize the compound query of various different condition combination and actual occurrence value, predicted value and O/E index results are shown, the wherein actual occurrence value/desired value of O/E exponential representation, O/E index <1: illustrate that disease risks is high, but case fatality rate, length of stay or cost control are lower than expection, O/E index >1: illustrate that disease risks is low, but case fatality rate, length of stay or cost control are higher than expection,
For hospital management regulator user: the inquiry of hospital management regulator user is superior to clinical users, hospital's real name can be used to realize comparison, the clinical speciality that user formulates the self-defined hospital in region, DMIAES according to hospital's classification Risk Adjusted model, section office of leaving hospital, sick plant DRG, time period of leaving hospital, primary and secondary will be diagnosed or perform the operation, patient age, sex, classification and aggregation patient satisfaction are inquired about, by the compound comparison query combined across the different condition of hospital, Query Result arranges according to user-defined order.
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