Title

Developing A Hospital-Specific Electronic Inpatient Fall Surveillance Program Phase 1

Keywords

Ehr; Falls; Hospital; Nursing; Research

Abstract

Patient falls in hospitals continue to exist as a serious societal problem. The purpose of this study was to analyze nurses' perceptions of patient fall risk factors that may be used to develop an electronic patient decision support system to prevent patient falls. A survey was distributed to 150 nurses in a moderate-size hospital system in Central Florida (200+ beds). Survey questions were developed to identify 3 fall risk factor categories: patient-centered, operational, and critical. Sixty-five surveys (43.3%) were returned. Descriptive statistics such as frequencies and percentages were calculated on all study variables. All participants indicated they were familiar with the circumstances that have contributed to falls or near-falls of patients. Findings included the majority of nurses perceived both patient-centered and operational factors increased the risks for patient falls, with pertinent results indicating a lack of appropriate ambulatory device (90.8%), low to very low nurse staffing levels (87.7%), and a history of a fall within the past year (738%) increased the risk for falls. The nurses' perceptions define a standard medical terminology that can be recorded in electronic progress notes and programmed to quickly link to additional sources of fall risk data (eg, laboratory work, medications) housed within the hospital's electronic health record. Further research is needed to assess the feasibility of an electronic health record-based system to prevent hospital falls using risk factors identified in this and other studies. Copyright © 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Publication Date

12-1-2013

Publication Title

Health Care Manager

Volume

32

Issue

4

Number of Pages

359-369

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1097/HCM.0b013e3182a9d6ec

Socpus ID

84892712468 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84892712468

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