Predicting and Assessing Fall Risk in an Acute Inpatient Rehabilitation Facility
Purpose Unintentional falls account for 70% of all hospital accidents. The objective of this study was to identify risk factors for falls and develop an assessment tool specific for an inpatient rehabilitation facility setting. Design/Method Diagnosis and Functional Independence Measure (FIM) scores...
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Veröffentlicht in: | Rehabilitation nursing 2014-03, Vol.39 (2), p.86-93 |
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creator | Rosario, Emily R. Kaplan, Stephanie E. Khonsari, Sepehr Patterson, David |
description | Purpose
Unintentional falls account for 70% of all hospital accidents. The objective of this study was to identify risk factors for falls and develop an assessment tool specific for an inpatient rehabilitation facility setting.
Design/Method
Diagnosis and Functional Independence Measure (FIM) scores were collected for 174 patients to assess predictors for fall risk. Independent t‐tests, chi‐square, and logistic regression analysis were conducted to examine differences between fallers and nonfallers.
Findings
We identified several risk factors for falls including 4 FIM items: toileting, bed transfer, tub/shower transfer, and stairs; and three diagnoses: right stroke, traumatic brain injury, and amputation. From these findings, we completed initial development of a risk assessment tool.
Conclusions
Evaluation of the tool suggests good specificity with 20%–30% of the patient population identified as high risk and good sensitivity by correctly predicting nearly 90% of patient falls.
Clinical Relevance
Continued evaluation of this assessment tool is needed to identify effectiveness in predicting patients who are at high risk for falling. |
doi_str_mv | 10.1002/rnj.114 |
format | Article |
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Unintentional falls account for 70% of all hospital accidents. The objective of this study was to identify risk factors for falls and develop an assessment tool specific for an inpatient rehabilitation facility setting.
Design/Method
Diagnosis and Functional Independence Measure (FIM) scores were collected for 174 patients to assess predictors for fall risk. Independent t‐tests, chi‐square, and logistic regression analysis were conducted to examine differences between fallers and nonfallers.
Findings
We identified several risk factors for falls including 4 FIM items: toileting, bed transfer, tub/shower transfer, and stairs; and three diagnoses: right stroke, traumatic brain injury, and amputation. From these findings, we completed initial development of a risk assessment tool.
Conclusions
Evaluation of the tool suggests good specificity with 20%–30% of the patient population identified as high risk and good sensitivity by correctly predicting nearly 90% of patient falls.
Clinical Relevance
Continued evaluation of this assessment tool is needed to identify effectiveness in predicting patients who are at high risk for falling.</description><identifier>ISSN: 0278-4807</identifier><identifier>EISSN: 2048-7940</identifier><identifier>DOI: 10.1002/rnj.114</identifier><identifier>PMID: 23813799</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Accidental Falls - prevention & control ; Accidental Falls - statistics & numerical data ; brain injury ; Education, Nursing, Continuing ; fall risk ; Humans ; inpatient rehabilitation facility ; Inpatients - statistics & numerical data ; Nursing ; Older people ; Predictive Value of Tests ; Rehabilitation ; Rehabilitation Centers - statistics & numerical data ; Rehabilitation Nursing - methods ; Risk Factors ; Safety Management - methods ; stroke ; Stroke - nursing ; Stroke Rehabilitation</subject><ispartof>Rehabilitation nursing, 2014-03, Vol.39 (2), p.86-93</ispartof><rights>2013 Association of Rehabilitation Nurses</rights><rights>2013 Association of Rehabilitation Nurses.</rights><rights>2014 Association of Rehabilitation Nurses</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4824-b9ce2dea61082f6a60134fbe1f11c003f67883723db7c86804a7c52f5ac39a533</citedby><cites>FETCH-LOGICAL-c4824-b9ce2dea61082f6a60134fbe1f11c003f67883723db7c86804a7c52f5ac39a533</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Frnj.114$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Frnj.114$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23813799$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rosario, Emily R.</creatorcontrib><creatorcontrib>Kaplan, Stephanie E.</creatorcontrib><creatorcontrib>Khonsari, Sepehr</creatorcontrib><creatorcontrib>Patterson, David</creatorcontrib><title>Predicting and Assessing Fall Risk in an Acute Inpatient Rehabilitation Facility</title><title>Rehabilitation nursing</title><addtitle>Rehabil Nurs</addtitle><description>Purpose
Unintentional falls account for 70% of all hospital accidents. The objective of this study was to identify risk factors for falls and develop an assessment tool specific for an inpatient rehabilitation facility setting.
Design/Method
Diagnosis and Functional Independence Measure (FIM) scores were collected for 174 patients to assess predictors for fall risk. Independent t‐tests, chi‐square, and logistic regression analysis were conducted to examine differences between fallers and nonfallers.
Findings
We identified several risk factors for falls including 4 FIM items: toileting, bed transfer, tub/shower transfer, and stairs; and three diagnoses: right stroke, traumatic brain injury, and amputation. From these findings, we completed initial development of a risk assessment tool.
Conclusions
Evaluation of the tool suggests good specificity with 20%–30% of the patient population identified as high risk and good sensitivity by correctly predicting nearly 90% of patient falls.
Clinical Relevance
Continued evaluation of this assessment tool is needed to identify effectiveness in predicting patients who are at high risk for falling.</description><subject>Accidental Falls - prevention & control</subject><subject>Accidental Falls - statistics & numerical data</subject><subject>brain injury</subject><subject>Education, Nursing, Continuing</subject><subject>fall risk</subject><subject>Humans</subject><subject>inpatient rehabilitation facility</subject><subject>Inpatients - statistics & numerical data</subject><subject>Nursing</subject><subject>Older people</subject><subject>Predictive Value of Tests</subject><subject>Rehabilitation</subject><subject>Rehabilitation Centers - statistics & numerical data</subject><subject>Rehabilitation Nursing - methods</subject><subject>Risk Factors</subject><subject>Safety Management - methods</subject><subject>stroke</subject><subject>Stroke - nursing</subject><subject>Stroke Rehabilitation</subject><issn>0278-4807</issn><issn>2048-7940</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0VtrFDEYBuAgil2r-A9kwIsKMjVfksnhcqn2RLsuSz3chUwmo9nOZtZkBt1_3yxTeyFIr8JLHl74eBF6DfgYMCYfYlgfA7AnaEYwk6VQDD9FM0yELJnE4gC9SGmNMTDF2XN0QKgEKpSaoeUyusbbwYcfhQlNMU_JpbRPp6bripVPt4UP-auY23FwxUXYmsG7MBQr99PUvvNDzn3I3O7D7iV61pouuVf37yH6cvrp5uS8vPp8dnEyvyotk4SVtbKONM5wwJK03HAMlLW1gxbAYkxbLqSkgtCmFlZyiZkRtiJtZSxVpqL0EL2berex_zW6NOiNT9Z1nQmuH5MGoap8rQLyOK0wZwDAq0zf_kPX_RhDPiQrzkSlJFdZHU3Kxj6l6Fq9jX5j4k4D1vs9dN5D5z2yfHPfN9Yb1zy4vwNk8H4Cv33ndv_r0avF5VRXTtqnwf150Cbeai6oqPS3xZm-5NeL5c3Hr_o7vQMxp6Bx</recordid><startdate>201403</startdate><enddate>201403</enddate><creator>Rosario, Emily R.</creator><creator>Kaplan, Stephanie E.</creator><creator>Khonsari, Sepehr</creator><creator>Patterson, David</creator><general>Blackwell Publishing Ltd</general><general>Lippincott Williams & Wilkins Ovid Technologies</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ASE</scope><scope>FPQ</scope><scope>K6X</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope></search><sort><creationdate>201403</creationdate><title>Predicting and Assessing Fall Risk in an Acute Inpatient Rehabilitation Facility</title><author>Rosario, Emily R. ; Kaplan, Stephanie E. ; Khonsari, Sepehr ; Patterson, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4824-b9ce2dea61082f6a60134fbe1f11c003f67883723db7c86804a7c52f5ac39a533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Accidental Falls - prevention & control</topic><topic>Accidental Falls - statistics & numerical data</topic><topic>brain injury</topic><topic>Education, Nursing, Continuing</topic><topic>fall risk</topic><topic>Humans</topic><topic>inpatient rehabilitation facility</topic><topic>Inpatients - statistics & numerical data</topic><topic>Nursing</topic><topic>Older people</topic><topic>Predictive Value of Tests</topic><topic>Rehabilitation</topic><topic>Rehabilitation Centers - statistics & numerical data</topic><topic>Rehabilitation Nursing - methods</topic><topic>Risk Factors</topic><topic>Safety Management - methods</topic><topic>stroke</topic><topic>Stroke - nursing</topic><topic>Stroke Rehabilitation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rosario, Emily R.</creatorcontrib><creatorcontrib>Kaplan, Stephanie E.</creatorcontrib><creatorcontrib>Khonsari, Sepehr</creatorcontrib><creatorcontrib>Patterson, David</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>British Nursing Index</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>British Nursing Index</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Rehabilitation nursing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rosario, Emily R.</au><au>Kaplan, Stephanie E.</au><au>Khonsari, Sepehr</au><au>Patterson, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting and Assessing Fall Risk in an Acute Inpatient Rehabilitation Facility</atitle><jtitle>Rehabilitation nursing</jtitle><addtitle>Rehabil Nurs</addtitle><date>2014-03</date><risdate>2014</risdate><volume>39</volume><issue>2</issue><spage>86</spage><epage>93</epage><pages>86-93</pages><issn>0278-4807</issn><eissn>2048-7940</eissn><abstract>Purpose
Unintentional falls account for 70% of all hospital accidents. The objective of this study was to identify risk factors for falls and develop an assessment tool specific for an inpatient rehabilitation facility setting.
Design/Method
Diagnosis and Functional Independence Measure (FIM) scores were collected for 174 patients to assess predictors for fall risk. Independent t‐tests, chi‐square, and logistic regression analysis were conducted to examine differences between fallers and nonfallers.
Findings
We identified several risk factors for falls including 4 FIM items: toileting, bed transfer, tub/shower transfer, and stairs; and three diagnoses: right stroke, traumatic brain injury, and amputation. From these findings, we completed initial development of a risk assessment tool.
Conclusions
Evaluation of the tool suggests good specificity with 20%–30% of the patient population identified as high risk and good sensitivity by correctly predicting nearly 90% of patient falls.
Clinical Relevance
Continued evaluation of this assessment tool is needed to identify effectiveness in predicting patients who are at high risk for falling.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>23813799</pmid><doi>10.1002/rnj.114</doi><tpages>8</tpages></addata></record> |
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subjects | Accidental Falls - prevention & control Accidental Falls - statistics & numerical data brain injury Education, Nursing, Continuing fall risk Humans inpatient rehabilitation facility Inpatients - statistics & numerical data Nursing Older people Predictive Value of Tests Rehabilitation Rehabilitation Centers - statistics & numerical data Rehabilitation Nursing - methods Risk Factors Safety Management - methods stroke Stroke - nursing Stroke Rehabilitation |
title | Predicting and Assessing Fall Risk in an Acute Inpatient Rehabilitation Facility |
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