Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization
Dados Bibliográficos
BACKGROUND: Using electronic health records (EHRs) for population risk stratification has gained attention in recent years. Compared with insurance claims, EHRs offer novel data types (eg, vital signs) that can potentially improve population-based predictive models of cost and utilization. OBJECTIVE: To evaluate whether EHR-extracted body mass index (BMI) improves the performance of diagnosis-based models to predict concurrent and prospective health care costs and utilization. METHODS: We used claims and EHR data over a 2-year period from a cohort of continuously insured patients (aged 20-64 y) within an integrated health system. We examined the addition of BMI to 3 diagnosis-based models of increasing comprehensiveness (ie, demographics, Charlson, and Dx-PM model of the Adjusted Clinical Group system) to predict concurrent and prospective costs and utilization, and compared the performance of models with and without BMI. RESULTS: The study population included 59,849 patients, 57% female, with BMI class I, II, and III comprising 19%, 9%, and 6% of the population. Among demographic models, R improvement from adding BMI ranged from 61% (ie, R increased from 0.56 to 0.90) for prospective pharmacy cost to 29% (1.24-1.60) for concurrent medical cost. Adding BMI to demographic models improved the prediction of all binary service-linked outcomes (ie, hospitalization, emergency department admission, and being in top 5% total costs) with area under the curve increasing from 2% (0.602-0.617) to 7% (0.516-0.554). Adding BMI to Charlson models only improved total and medical cost predictions prospectively (13% and 15%; 4.23-4.79 and 3.30-3.79), and also improved predicting all prospective outcomes with area under the curve increasing from 3% (0.649-0.668) to 4% (0.639-0.665; and, 0.556-0.576). No improvements in prediction were seen in the most comprehensive model (ie, Dx-PM). DISCUSSION: EHR-extracted BMI levels can be used to enhance predictive models of utilization especially if comprehensive diagnostic data are missing
Body mass index · Emergency department · Environmental health · Family medicine · Health care · Medical record · Population · Predictive modelling · Prospective cohort study · Statistics · Chronic Disease Management Strategies · Emergency Medicine · Healthcare Policy and Management · Internal Medicine · Medical Coding and Health Information · Medicine · Pharmacy
Child abuse
Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates
Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age, 2007-2008 to 2015-2016
Cause-Specific Excess Deaths Associated With Underweight, Overweight, and Obesity
A new method of classifying prognostic comorbidity in longitudinal studies
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
A Practical Comparison Between the Predictive Power of Population-based Risk Stratification Models Using Data From Electronic Health Records Versus Administrative Claims
Development and Application of a Population-Oriented Measure of Ambulatory Care Case-Mix
Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records
Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance
Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims
| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 0,2 |
| Intervalo de citações | 2021 - 2021 (1) |
| Velocidade de citação | historical |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |