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Hospital Readmission after Post-acute Care at Different Settings: Estimation using the Propensity Score Matching Method | OMICS International | Abstract
ISSN: 2161-0711

Journal of Community Medicine & Health Education
Open Access

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Research Article

Hospital Readmission after Post-acute Care at Different Settings: Estimation using the Propensity Score Matching Method

Li J1, Daldalian MC1, Erdmann C1, Hunter KG2, Sutariya B1 and Leung R3*

1Advocate Cerner Collaboration, Cerner Corporation, Kansas City, MO, USA

2Advocate Cerner Collaboration, Advocate Health Care, Downer Groves, IL, USA

3SUNY-Albany, School of Public Health, Rensselaer, NY, USA

*Corresponding Author:
Ricky Leung
SUNY-Albany, School of Public Health
Rensselaer, NY, USA
Tel: 1-518-402-6512
E-mail: [email protected]

Received date: Jan 08, 2016; Accepted date: Mar 21, 2016; Published date: Mar 30, 2016

Citation: Li J, Daldalian MC, Erdmann C, Hunter KG, Sutariya B, et al. (2016) Hospital Readmission after Post-acute Care at Different Settings: Estimation using the Propensity Score Matching Method. J Community Med Health 6:408. doi:10.4172/2161-0711.1000408

Copyright: © 2016 Li J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objective: This study examines the relative risk of hospital readmission for patients receiving post-acute care (PAC) at different health care settings by utilizing an innovative big-data approach.

Study design: The electronic health records of 124, 127 patients from a large-scale health care system are extracted to allow for propensity score (PS) matching. The PS method is able to put patients into matched pairs, which became the unit of analysis in computing the odds ratios (OR) of hospital readmission for patients having received PAC at 5 different settings-home, home health agency (HHA), skilled nursing facility (SNF), inpatient rehabilitation facility (IRF), and long term care hospital (LTCH). The PS matching method controlled the effects of a large number of confounding variables, and computed the odd ratios of hospital readmission for patients at different PAC settings.

Results: We obtained mixed results regarding the odds ratios of hospital readmission for PAC settings in comparison with home care. While PAC patients at IRF and LTCH had a lower OR of hospital readmission than home care (0.77 and 0.76, respectively), PAC patients at HHA and SNF had a higher OR of hospital readmission (1.26 and 1.25, respectively). These results are statistically significant at p<0.05.

Conclusions: This research demonstrates an innovate approach to utilize EHR data for improving population health. Our findings call for rigorous techniques to improve care coordination specifically for PAC patients at institutional settings. Improved PAC coordination is able to reduce health care cost, and improve quality of health care delivery.

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