Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

BACKGROUND

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.

OBJECTIVE

The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.

METHODS

We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.

RESULTS

We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.

CONCLUSIONS

The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

A validation study of the high acuity readmission risk pediatric screen (HARRPS) tool©: Predicting readmission risk within the pediatric population

BACKGROUND

The initial research study of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool © focused on using retrospective data to apply weighted values to the questions within the tool, identify overall risk score, and attribute risk categories (low, moderate, high risk) to the overall risk score. This study focused on validating the data from the initial study, as well as cross examining the need to include admission diagnosis within the tool.

METHOD

Study was a single-centered, retrospective chart review study using a different subset of patients from the initial study. Pediatric patients with thirty-day readmissions were compared to pediatric patients without a thirty-day readmission over a twelve-month period. Utilized same statistical software and methodology from initial study to identify if readmission risk probability could be replicated with a different population.

RESULTS

The initial study performed in 2018 demonstrated a c-statistic score/ area under the curve (AUC) of 0.68 [95% CI: 0.67, 0.69]. In addition, the initial study demonstrated as risk score increases, the probability of readmission gradually increased until a patient had a risk score of seven or greater, at which point readmission risk plateaued. This resulted in low, moderate, and high readmission risk categories. The current study performed using data from 2019 demonstrated an improved c-statistic score / AUC of 0.83 [95% CI: 0.80, 0.87] with admission diagnosis included, and a c-statistic score / AUC of 0.80 [95% CI: 0.76, 0.83] without the admission diagnosis included. The analysis of overall risk score demonstrated a substantial difference in how to interpret final readmission risk scores. Both the initial study and validation study were consistent in demonstrating a risk score of three or less was associated with low readmission risk. However, in the validation study, there was no substantial difference between moderate or high risk, leading to updating the tool from 3 risk categories into 2 risk categories of low risk and at risk of readmission.

CONCLUSION

Based on the finding from the validation study, the admission diagnosis was removed from the HARRPS Tool© as the difference in c-statistic score was nominal, and the risk categories were changed from three categories (low, moderate, high risk) to two categories of low risk (score 0-2) and at risk of readmission for a score of 3+. The ability of the HARRPS Tool© to predict readmission risk preforms best with a c-statistic = 0.80, outperforming the following tools: LACE (0.65), LACE -SDH (0.67), LACE + (0.61), Epic's readmission risk model (0.69), and SQLAPE ® (0.71) (Ryan, et al., 2021; Hwang, et al., 2021).

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction

Objective

Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance.

Methods

This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance.

Results

Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695–0.7]; ref) to AUC = 0.708 (95% CI [0.705–0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4).

Discussion

We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction.

Conclusion

These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.