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The Johns Hopkins Fall Risk Assessment Tool

A Study of Reliability and Validity

Poe, Stephanie S. DNP, RN-BC; Dawson, Patricia B. MSN, RN; Cvach, Maria DNP, RN, FAAN; Burnett, Margaret MS, RN, CNRN; Kumble, Sowmya PT, NCS; Lewis, Maureen MSN, RN, PMHNP-BC, PMHCNS-BC; Thompson, Carol B. MS, MBA; Hill, Elizabeth E. PhD, RN

doi: 10.1097/NCQ.0000000000000301

Patient falls and fall-related injury remain a safety concern. The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) was developed to facilitate early detection of risk for anticipated physiologic falls in adult inpatients. Psychometric properties in acute care settings have not yet been fully established; this study sought to fill that gap. Results indicate that the JHFRAT is reliable, with high sensitivity and negative predictive validity. Specificity and positive predictive validity were lower than expected.

The Johns Hopkins Hospital, Baltimore, Maryland (Dr Poe and Mss Dawson, Burnett, Kumble, and Lewis); Johns Hopkins Health System, Baltimore, Maryland (Drs Poe and Cvach); The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Ms Thompson); School of Nursing, The Johns Hopkins University, Baltimore, Maryland (Dr Hill); and College of Nursing, The Pennsylvania State University, University Park (Dr Hill).

Correspondence: Stephanie S. Poe, DNP, RN-BC, The Johns Hopkins Hospital, Billings Administration Bldg Room 229B, 600 N. Wolfe St, Baltimore, MD 21287 (

The authors acknowledge the contributions of Tameria Joy, BSN, RN, and Kelly Jarrett, RN, for data collection and Fall Champion services during this study.

This study was funded by the Johns Hopkins University School of Nursing Dorothy Evans Lyne (DEL) grant.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

Accepted for publication: August 28, 2017

Published ahead of print: September 29, 2017

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