Assessing Brief Intervention for Unhealthy Alcohol Use: A Comparison of Electronic Health Record Documentation and Patient Self-Report

Diabetes Life Expectancy Prediction Model Inputs and Results From Patient Surveys Compared With Electronic Health Record Abstraction: Survey Study

Background

Prediction models are being increasingly used in clinical practice, with some requiring patient-reported outcomes (PROs). The optimal approach to collecting the needed inputs is unknown.

Objective

Our objective was to compare mortality prediction model inputs and scores based on electronic health record (EHR) abstraction versus patient survey.

Methods

Older patients aged ≥65 years with type 2 diabetes at an urban primary care practice in Chicago were recruited to participate in a care management trial. All participants completed a survey via an electronic portal that included items on the presence of comorbid conditions and functional status, which are needed to complete a mortality prediction model. We compared the individual data inputs and the overall model performance based on the data gathered from the survey compared to the chart review.

Results

For individual data inputs, we found the largest differences in questions regarding functional status such as pushing/pulling, where 41.4% (31/75) of participants reported difficulties that were not captured in the chart with smaller differences for comorbid conditions. For the overall mortality score, we saw nonsignificant differences (P=.82) when comparing survey and chart-abstracted data. When allocating participants to life expectancy subgroups (<5 years, 5-10 years, >10 years), differences in survey and chart review data resulted in 20% having different subgroup assignments and, therefore, discordant glucose control recommendations.

Conclusions

In this small exploratory study, we found that, despite differences in data inputs regarding functional status, the overall performance of a mortality prediction model was similar when using survey and chart-abstracted data. Larger studies comparing patient survey and chart data are needed to assess whether these findings are reproduceable and clinically important.

Racial/ethnic and gender differences in receipt of brief intervention among patients with unhealthy alcohol use in the U.S. Veterans Health Administration

OBJECTIVE

Brief intervention (BI) for unhealthy alcohol use is a top prevention priority for adults in the U.S, but rates of BI receipt vary across patients. We examine BI receipt across race/ethnicity and gender in a national cohort of patients from the Department of Veterans Affairs (VA)-the largest U.S. integrated healthcare system and a leader in implementing preventive care for unhealthy alcohol use.

METHODS

Among 779,041 VA patients with documented race/ethnicity and gender who screened positive for unhealthy alcohol use (AUDIT-C score ≥ 5) between 10/1/09 and 5/30/13, we fit Poisson regression models to estimate the predicted prevalence of BI (EHR-documented advice to reduce or abstain from drinking) across race/ethnicity and gender.

RESULTS

Rates of BI were lowest among Black women (67%), Black men (68%), and Asian/Pacific Islander women (68%), and highest among white men (75%), Hispanic men (75%), and Asian/Pacific Islander men (75%). A significant race/ethnicity by gender interaction indicated that the associations between race/ethnicity and gender with BI depended on the other factor. Gender differences were largest among Asian/Pacific Islander patients and were nonsignificant among American Indian/Alaska Native patients. Adjustment for covariates not expected to be on the causal pathway (e.g., age, year of AUDIT-C screen) slightly attenuated but did not change the direction of results.

CONCLUSIONS

Receipt of BI for unhealthy alcohol use varied by race/ethnicity and gender, and the impact of one factor depended on the other. Black women, Black men, and Asian/Pacific Islander women had the lowest rates of receiving recommended alcohol-related care. We found these disparities in a healthcare system that has implemented universal alcohol screening and incentivized BI for all patients with unhealthy alcohol use, suggesting that reducing disparities in alcohol-related care may require targeted interventions.