» Articles » PMID: 37848976

Screening for the High-need Population Using Single Institution Versus State-wide Admissions Discharge Transfer Feed

Overview
Publisher Biomed Central
Specialty Health Services
Date 2023 Oct 18
PMID 37848976
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data.

Lee S, French B, Balucan F, McCann M, Vasilevskis E Health Aff Sch. 2024; 1(6):qxad077.

PMID: 38756367 PMC: 10986247. DOI: 10.1093/haschl/qxad077.


Emergency department visits and hospital readmissions after a deprescribing intervention among hospitalized older adults.

Lee J, Hollingsworth E, Shah A, Szanton S, Perrin N, Mixon A J Am Geriatr Soc. 2024; 72(7):2038-2047.

PMID: 38725307 PMC: 11226369. DOI: 10.1111/jgs.18945.

References
1.
Turbow S, Hollberg J, Ali M . Electronic Health Record Interoperability: How Did We Get Here and How Do We Move Forward?. JAMA Health Forum. 2022; 2(3):e210253. DOI: 10.1001/jamahealthforum.2021.0253. View

2.
Lynch C, Wajnberg A, Jervis R, Basso-Lipani M, Bernstein S, Colgan C . Implementation Science Workshop: a Novel Multidisciplinary Primary Care Program to Improve Care and Outcomes for Super-Utilizers. J Gen Intern Med. 2016; 31(7):797-802. PMC: 4907941. DOI: 10.1007/s11606-016-3598-1. View

3.
Kruse C, Kindred B, Brar S, Gutierrez G, Cormier K . Health Information Technology and Doctor Shopping: A Systematic Review. Healthcare (Basel). 2020; 8(3). PMC: 7551569. DOI: 10.3390/healthcare8030306. View

4.
Athey S, Stern S . The impact of information technology on emergency health care outcomes. Rand J Econ. 2003; 33(3):399-432. View

5.
Curka P, Pepe P, Ginger V, Sherrard R, Ivy M, Zachariah B . Emergency medical services priority dispatch. Ann Emerg Med. 1993; 22(11):1688-95. DOI: 10.1016/s0196-0644(05)81307-1. View