Inpatient medical management quality assessment method

A technology for medical management and inpatients, applied in the field of lean hospital quality management, can solve problems such as increased model instability, deviation of judgment results, non-compliance with medical laws, etc., and achieve the effect of improving the level of medical quality management

Inactive Publication Date: 2015-11-11
中科厚立信息技术(成都)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the calculation method based on CMI has its inherent deficiencies in evaluating medical quality, operating efficiency, and rational drug use. First, this model does not take into account the characteristics of the disease itself and oth

Method used

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  • Inpatient medical management quality assessment method
  • Inpatient medical management quality assessment method

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0149] Example 1: Correlation Analysis of Predictor Variables in Renal Failure Mortality Model

[0150] Model #218: DRG682,683,684

[0151] Data source: Memorial Hermann Hospital, Texas Medical Center, USA

[0152] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0153] Number of Significant Variables Filtered: 37

[0154] Number of strongly correlated variables: 4

[0155] Statistical method: VarianceInflationFactor (VIF)

[0156]

[0157] Note: From the 37 variables that were screened out, the strong correlation test was carried out. The value below is the VIF value, and the variables with VIF>5 are marked.

example 2

[0158] Example 2: Importance list of predictor variables in the mortality model for patients with renal failure

[0159] Model #218: DRG682,683,684

[0160] Data source: Memorial Hermann Hospital, Texas Medical Center, USA

[0161] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0162] Total number of model variables: 280

[0163] Number of Significant Variables Filtered: 37

[0164] Statistical method: LASSO

[0165]

[0166] Note: The value below the variable represents the importance of the variable, and the higher the value, the greater the impact of the variable on the model.

example 3

[0167] Example 3: Predictor Variable Selection for a Renal Failure Mortality Model

[0168] Model #218: DRG682,683,684

[0169] Data source: Memorial Hermann Hospital, Texas Medical Center, USA

[0170] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0171] Total number of model variables: 280

[0172] Number of Significant Variables Filtered: 37

[0173] Statistical method: LASSO

[0174]

[0175]

[0176] Note: Combining the model and clinical experience, the variable rhabdomyolysis, chronic liver disease and later variables were deleted; the strongly correlated variable-organ internal tube was also deleted.

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Abstract

The invention discloses an inpatient medical management quality assessment method which screens historical data and builds models, including authenticating and clearing data, DRG(Diagnosis Related Group)and model classifying, classification and collection of ICD complication and other variables during hospital admission, the statistic tests and screening of hospital admission and complication variables, the building of statistic models and quality assessment. The method also screens current data and calculates a predicated value, including predicated value of risk when a patient is sent in hospital, to realize risk predictions on the mortality rate of each patient, the days spent in hospital and the medical cost in hospital as well. According to the invention, the method effectively converts medical data to find solutions so as to achieve the values of data based on the analysis of big data, the mathematic statistical method and machine learning method. The method also makes medical data comparable. The method not only performs assessment to medical quality among diseases but also realizes proper assessment to managements of treatment in a hospital between doctors, between different departments and between different patients.

Description

technical field [0001] The invention relates to the field of lean hospital quality management, medical big data analysis and hospital management decision support, and in particular to a method for evaluating the quality of inpatient medical management. Background technique [0002] In recent years, due to the rapid development of IT technology in domestic hospitals, the accumulation of raw data of patients and diseases has been preliminarily completed. Most of the data can only be stored in the hospital's data warehouse, wasting resources. If we can fully learn from the US government's successful model and excellent methodology of hospital management, and then make localized improvements, it will not only allow domestic medical management agencies to increase effective monitoring methods and means, but also promote hospitals to accelerate the transition from extensive to The pace of refined management model transformation. [0003] Recently, the government actively advocat...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/22
Inventor 李涛杨思坦陶金蓝陈霞
Owner 中科厚立信息技术(成都)有限公司
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