Predictors of online accountability practices in US hospitals: An exploratory investigation

Summary The purpose of this research paper is to explore variations in online accountability practices in US hospitals and determine the factors that are associated with higher levels of online accountability practices. This project employed a quantitative content analysis of 240 US hospital website...

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Veröffentlicht in:The International journal of health planning and management 2020-01, Vol.35 (1), p.e178-e195
1. Verfasser: Cooley, Asya
Format: Artikel
Sprache:eng
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Zusammenfassung:Summary The purpose of this research paper is to explore variations in online accountability practices in US hospitals and determine the factors that are associated with higher levels of online accountability practices. This project employed a quantitative content analysis of 240 US hospital websites. Additionally, secondary data were obtained from the American Hospital Association and the American Hospital Directory. The results show that the external environment somewhat impacted hospitals' online accountability practices, with hospital volume (measured through the number of annual admissions) as an unquestionable predictor. Another key finding is that some of the governance forms impacted online accountability practices. Particularly, hospitals with private ownership structures tended to disclose less accountability information in an online environment, compared with their public and nonprofit counterparts. The financial situation of hospitals did not have any significant impact on overall online accountability practices but was influencing performance disclosure practices. Online accountability studies have not been conducted in a health care setting. This research theoretically relates online accountability practices to organizational characteristics (such as size, volume, financial performance, system affiliation, ownership, and rurality). Knowledge of the online accountability landscape might benefit future policy decisions on accountability models.
ISSN:0749-6753
1099-1751
DOI:10.1002/hpm.2958