Assessment of Value of Neighborhood Socioeconomic Status in Models That Use Electronic Health Record Data to Predict Health Care Use Rates and Mortality.

Importance: Prediction models are widely used in health care as a way of risk stratifying populations for targeted intervention. Most risk stratification has been done using a small number of predictors from insurance claims. However, the utility of diverse nonclinical predictors, such as neighborhood socioeconomic contexts, remains unknown. Objective: To assess the value of using neighborhood socioeconomic predictors in the context of 1-year risk prediction for mortality and 6 different health care use outcomes in a large integrated care system. Design, Setting, and Participants: Diagnostic study using data from all adults age 18 years or older who had Kaiser Foundation Health Plan membership and/or use in the Kaiser Permantente Northern California: a multisite, integrated health care delivery system between January 1, 2013, and June 30, 2014. Data were recorded before the index date for each patient to predict their use and mortality in a 1-year post period using a test-train split for model training and evaluation. Analyses were conducted in fall of 2019. Main Outcomes and Measures: One-year encounter counts (doctor office, virtual, emergency department, elective hospitalizations, and nonelective), total costs, and mortality. Results: A total of 2 951 588 patients met inclusion criteria (mean [SD] age, 47.2 [17.4] years; 47.8% were female). The mean (SD) Neighborhood Deprivation Index was -0.32 (0.84). The areas under the receiver operator curve ranged from 0.71 for emergency department use (using the LASSO method and electronic health record predictors) to 0.94 for mortality (using the random forest method and electronic health record predictors). Neighborhood socioeconomic status predictors did not meaningfully increase the predictive performance of the models for any outcome. Conclusions and Relevance: In this study, neighborhood socioeconomic predictors did not improve risk estimates compared with what is obtainable using standard claims data regardless of model used.

The Impact of Pharmacy-specific Predictors on the Performance of 30-Day Readmission Risk Prediction Models.

RESEARCH OBJECTIVE: Pharmacists are an expensive and limited resource in the hospital and outpatient setting. A pharmacist can spend up to 25% of their day planning. Time spent planning is time not spent delivering an intervention. A readmission risk adjustment model has potential to be used as a universal outcome-based prioritization tool to help pharmacists plan their interventions more efficiently. Pharmacy-specific predictors have not been used in the constructs of current readmission risk models. We assessed the impact of adding pharmacy-specific predictors on performance of readmission risk prediction models. STUDY DESIGN: We used an observational retrospective cohort study design to assess whether pharmacy-specific predictors such as an aggregate pharmacy score and drug classes would improve the prediction of 30-day readmission. A model of age, sex, length of stay, and admission category predictors was used as the reference model. We added predictor variables in sequential models to evaluate the incremental effect of additional predictors on the performance of the reference. We used logistic regression to regress the outcomes on predictors in our derivation dataset. We derived and internally validated our models through a 50:50 split validation of our dataset. POPULATION STUDIED: Our study population (n=350,810) was of adult admissions at hospitals in a large integrated health care delivery system. PRINCIPAL FINDINGS: Individually, the aggregate pharmacy score and drug classes caused a nearly identical but moderate increase in model performance over the reference. As a single predictor, the comorbidity burden score caused the greatest increase in model performance when added to the reference. Adding the severity of illness score, comorbidity burden score and the aggregate pharmacy score to the reference caused a cumulative increase in model performance with good discrimination (c statistic, 0.712; Nagelkerke R, 0.112). The best performing model included all predictors: severity of illness score, comorbidity burden score, aggregate pharmacy score, diagnosis groupings, and drug subgroups. CONCLUSIONS: Adding the aggregate pharmacy score to the reference model significantly increased the c statistic but was out-performed by the comorbidity burden score model in predicting readmission. The need for a universal prioritization tool for pharmacists may therefore be potentially met with the comorbidity burden score model. However, the aggregate pharmacy score and drug class models still out-performed current Medicare readmission risk adjustment models. IMPLICATIONS FOR POLICY OR PRACTICE: Pharmacists have a great role in preventing readmission, and therefore can potentially use one of our models: comorbidity burden score model, aggregate pharmacy score model, drug class model or complex model (a combination of all 5 major predictors) to prioritize their interventions while exceeding Medicare performance measures on readmission. The choice of model to use should be based on the availability of these predictors in the health care system.