Predicting Hospital Safety Measures using Patient Experience of Care Responses

Michael A. Pratt, Henry Chu

Abstract

To make healthcare more cost effective, the current trend in the U.S. is towards a hospital value-based purchasing program. In this program, a hospital’s performance is measured in the safety, patient experience of care, clinical care, and efficiency and cost reduction domains. We investigate the efficacy of predicting the safety measures using the patient experience of care measures. We compare four classifiers in the prediction tasks and concluded that random forest and support vector machine provided the best performance.

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Paper Citation


in Harvard Style

Pratt M. and Chu H. (2018). Predicting Hospital Safety Measures using Patient Experience of Care Responses.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 371-378. DOI: 10.5220/0006588403710378


in Bibtex Style

@conference{icpram18,
author={Michael A. Pratt and Henry Chu},
title={Predicting Hospital Safety Measures using Patient Experience of Care Responses},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006588403710378},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Predicting Hospital Safety Measures using Patient Experience of Care Responses
SN - 978-989-758-276-9
AU - Pratt M.
AU - Chu H.
PY - 2018
SP - 371
EP - 378
DO - 10.5220/0006588403710378