BMC Health Services Research (Oct 2023)

Screening for the high-need population using single institution versus state-wide admissions discharge transfer feed

  • Francis Salvador Balucan,
  • Benjamin French,
  • Yaping Shi,
  • Sunil Kripalani,
  • Eduard E. Vasilevskis

DOI
https://doi.org/10.1186/s12913-023-10017-5
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 6

Abstract

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Abstract Background Access to programs for high-needs patients depending on single-institution electronic health record data (EHR) carries risks of biased sampling. We investigate a statewide admission, discharge, and transfer feed (ADT) in assessing equity in access to these programs. Methods This is a retrospective cross-sectional study. We included high-need patients at Vanderbilt University Medical Center (VUMC) 18 years or older, with at least three emergency visits (ED) or hospitalizations in Tennessee from January 1 to June 30, 2021, including at least one at VUMC. We used the Tennessee ADT database to identify high-need patients with at least one VUMC ED/hospitalization. Then, we compared this population with high-need patients identified using VUMC’s Epic® EHR database. The primary outcome was the sensitivity of VUMC-only criteria for identifying high-need patients compared to the statewide ADT reference standard. Results We identified 2549 patients with at least one ED/hospitalization and assessed them as high-need based on the statewide ADT. Of those, 2100 had VUMC-only visits, and 449 had VUMC and non-VUMC visits. VUMC-only visit screening criteria showed high sensitivity (99.1%, 95% CI: 98.7 − 99.5%), showing that the high-needs patients admitted to VUMC infrequently access alternative systems. Results showed no meaningful difference in sensitivity when stratified by patient’s race or insurance. Conclusions ADT allows examination for potential selection bias when relying upon single-institution utilization. In VUMC’s high-need patients, there’s minimal selection bias when depending on same-site utilization. Further research must understand how biases vary by site and durability over time.

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