Clinical prediction models for hospital falls: a scoping review protocol
IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial...
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Published in | BMJ open Vol. 11; no. 9; p. e051047 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
British Medical Journal Publishing Group
13.09.2021
BMJ Publishing Group LTD BMJ Publishing Group |
Series | Protocol |
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Abstract | IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences. |
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AbstractList | IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences. Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients' fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment. This scoping review will follow the Arksey and O'Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis. Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences. Introduction Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysis This scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and dissemination Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences. |
Author | McPhail, Steven M Parsons, Rex Cramb, Susanna M |
AuthorAffiliation | 2 Jamieson Trauma Institute , Royal Brisbane and Women's Hospital, Metro North Health , Herston , Queensland , Australia 3 Clinical Informatics Directorate , Metro South Health , Woolloongabba , Queensland , Australia 1 Australian Centre for Health Services Innovation and Centre for Healthcare Translation, School of Public Health and Social Work , Queensland University of Technology , Kelvin Grove , Queensland , Australia |
AuthorAffiliation_xml | – name: 2 Jamieson Trauma Institute , Royal Brisbane and Women's Hospital, Metro North Health , Herston , Queensland , Australia – name: 1 Australian Centre for Health Services Innovation and Centre for Healthcare Translation, School of Public Health and Social Work , Queensland University of Technology , Kelvin Grove , Queensland , Australia – name: 3 Clinical Informatics Directorate , Metro South Health , Woolloongabba , Queensland , Australia |
Author_xml | – sequence: 1 givenname: Rex orcidid: 0000-0002-6053-8174 surname: Parsons fullname: Parsons, Rex email: rex.parsons@hdr.qut.edu.au organization: Australian Centre for Health Services Innovation and Centre for Healthcare Translation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia – sequence: 2 givenname: Susanna M orcidid: 0000-0001-9041-9531 surname: Cramb fullname: Cramb, Susanna M organization: Jamieson Trauma Institute, Royal Brisbane and Women's Hospital, Metro North Health, Herston, Queensland, Australia – sequence: 3 givenname: Steven M surname: McPhail fullname: McPhail, Steven M organization: Clinical Informatics Directorate, Metro South Health, Woolloongabba, Queensland, Australia |
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10.1097/PTS.0000000000000163 contributor: fullname: Bettale – volume: 31 start-page: 139 year: 2012 ident: R5 article-title: Estimating the cost of serious injurious falls in a Canadian acute care hospital publication-title: Can J Aging doi: 10.1017/S0714980812000037 contributor: fullname: Zaric – volume: 45 start-page: 761 year: 2016 ident: R19 article-title: Diabetes mellitus and risk of falls in older adults: a systematic review and meta-analysis publication-title: Age Ageing doi: 10.1093/ageing/afw140 contributor: fullname: Zhang – volume: 37 start-page: 81 year: 2011 ident: R4 article-title: The cost of serious fall-related injuries at three midwestern hospitals publication-title: Jt Comm J Qual Patient Saf doi: 10.1016/S1553-7250(11)37010-9 contributor: fullname: Jones – volume: 158 start-page: 390 year: 2013 ident: R9 article-title: Inpatient fall prevention programs as a patient safety strategy: a systematic review publication-title: Ann Intern Med doi: 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question of previous falls publication-title: Spinal Cord doi: 10.1038/s41393-018-0175-y contributor: fullname: Skavberg Roaldsen – volume: 131 start-page: 211 year: 2015 article-title: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD group publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.114.014508 contributor: fullname: Altman – volume: 328 start-page: 676 year: 2004 article-title: Effectiveness of targeted falls prevention programme in subacute hospital setting: randomised controlled trial publication-title: BMJ doi: 10.1136/bmj.328.7441.676 contributor: fullname: Osborne – volume: 9 start-page: 311 year: 2017 article-title: Falls and depression in octogenarians - life and living in advanced age: a cohort study in New Zealand publication-title: J Prim Health Care doi: 10.1071/HC17012 contributor: fullname: Rolleston – volume: 8 start-page: 19 year: 2005 article-title: Scoping studies: towards a methodological framework publication-title: Int J Soc Res Methodol doi: 10.1080/1364557032000119616 contributor: fullname: O'Malley – volume: 158 start-page: 390 year: 2013 article-title: Inpatient fall prevention programs as a patient safety strategy: a systematic review publication-title: Ann Intern Med doi: 10.7326/0003-4819-158-5-201303051-00005 contributor: fullname: Ganz – volume: 30 start-page: 1 year: 2018 article-title: Tools for assessing fall risk in the elderly: a systematic review and meta-analysis publication-title: Aging Clin Exp Res doi: 10.1007/s40520-017-0749-0 contributor: fullname: Park – volume: 8 year: 2020 article-title: Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: case-control study publication-title: JMIR Med Inform doi: 10.2196/16970 contributor: fullname: Uchiyama – volume: 352 start-page: h6781 year: 2016 article-title: 6-PACK programme to decrease fall injuries in acute hospitals: cluster randomised controlled trial publication-title: BMJ doi: 10.1136/bmj.h6781 contributor: fullname: Wolfe – volume: 334 start-page: 82 year: 2007 article-title: Strategies to prevent falls and fractures in hospitals and care homes and effect of cognitive impairment: systematic review and 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claims with electronic medical records to predict heart failure outcomes publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2019.18962 contributor: fullname: Vaduganathan – volume: 45 start-page: 761 year: 2016 article-title: Diabetes mellitus and risk of falls in older adults: a systematic review and meta-analysis publication-title: Age Ageing doi: 10.1093/ageing/afw140 contributor: fullname: Zhang – volume: 37 start-page: 621 year: 2008 article-title: A systematic review and meta-analysis of studies using the stratify tool for prediction of falls in hospital patients: how well does it work? publication-title: Age Ageing doi: 10.1093/ageing/afn203 contributor: fullname: Giangregorio – volume: 31 start-page: 139 year: 2012 article-title: Estimating the cost of serious injurious falls in a Canadian acute care hospital publication-title: Can J Aging doi: 10.1017/S0714980812000037 contributor: fullname: Zaric – volume: 14 start-page: 27 year: 2018 article-title: Medications and patient characteristics associated with falling in the hospital publication-title: J Patient Saf doi: 10.1097/PTS.0000000000000163 contributor: fullname: Bettale – year: 2020 article-title: JBI manual for evidence synthesis publication-title: JBI doi: 10.46658/JBIMES-20-01 contributor: fullname: Munn – volume: 9 start-page: CD005465 year: 2018 article-title: Interventions for preventing falls in older people in care facilities and hospitals publication-title: Cochrane Database Syst Rev doi: 10.1002/14651858.CD005465.pub4 contributor: fullname: Panagoda – volume: 9 year: 2021 article-title: Divesting from a scored Hospital fall risk assessment tool (frat): a cluster randomized non-inferiority trial publication-title: J Am Geriatr Soc doi: 10.1111/jgs.17125 contributor: fullname: Hill – volume: 5 start-page: 371 year: 2014 article-title: A scoping review of scoping reviews: advancing the approach and enhancing the consistency publication-title: Res Synth Methods doi: 10.1002/jrsm.1123 contributor: fullname: Greig – volume: 315 start-page: 1049 year: 1997 article-title: Development and evaluation of evidence based risk assessment tool (stratify) to predict which elderly inpatients will fall: case-control and cohort studies publication-title: BMJ doi: 10.1136/bmj.315.7115.1049 contributor: fullname: Seed – volume: 5 year: 2016 article-title: Rayyan-a web and mobile APP for systematic reviews publication-title: Syst Rev doi: 10.1186/s13643-016-0384-4 contributor: fullname: Fedorowicz – volume: 7 start-page: 38 year: 2001 article-title: Falls risk factors in the hospital setting: a systematic review publication-title: Int J Nurs Pract doi: 10.1046/j.1440-172x.2001.00269.x contributor: fullname: Lambert – volume: 203 start-page: 367 year: 2015 article-title: The extra resource burden of in-hospital falls: a cost of falls study publication-title: Med J Aust doi: 10.5694/mja15.00296 contributor: fullname: Watts – volume: 135 start-page: e826 year: 2017 article-title: The learning healthcare system and cardiovascular care: a scientific statement from the American heart association publication-title: Circulation doi: 10.1161/CIR.0000000000000480 contributor: fullname: Borden – volume: 22 start-page: 333 year: 2015 article-title: Primary care physicians’ perspectives on computer-based health risk assessment tools for chronic diseases: a mixed methods study publication-title: J Innov Health Inform doi: 10.14236/jhi.v22i3.153 contributor: fullname: Straus – volume: 169 start-page: 467 year: 2018 article-title: PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation publication-title: Ann Intern Med doi: 10.7326/M18-0850 contributor: fullname: Zarin – volume: 31 start-page: 471 year: 2007 article-title: Falls in the acute hospital setting--impact on resource utilisation publication-title: Aust Health Rev doi: 10.1071/AH070471 contributor: fullname: Walsh – volume: 39 start-page: e70 year: 2020 article-title: Toileting-related falls at night in hospitalised patients: the role of nocturia publication-title: Australas J Ageing doi: 10.1111/ajag.12696 contributor: fullname: Everaert – ident: 2024102819014894000_11.9.e051047.32 doi: 10.1136/bmj.h6781 – ident: 2024102819014894000_11.9.e051047.7 doi: 10.1071/AH070471 – ident: 2024102819014894000_11.9.e051047.19 doi: 10.1093/ageing/afw140 – ident: 2024102819014894000_11.9.e051047.9 doi: 10.7326/0003-4819-158-5-201303051-00005 – ident: 2024102819014894000_11.9.e051047.21 doi: 10.1046/j.1440-172x.2001.00269.x – ident: 2024102819014894000_11.9.e051047.10 doi: 10.1136/bmj.328.7441.676 – volume: 5 start-page: 371 year: 2014 ident: 2024102819014894000_11.9.e051047.35 article-title: A scoping review of scoping reviews: advancing the approach and enhancing the consistency publication-title: Res Synth Methods doi: 10.1002/jrsm.1123 contributor: fullname: Pham – ident: 2024102819014894000_11.9.e051047.2 – volume: 39 start-page: e70 year: 2020 ident: 2024102819014894000_11.9.e051047.15 article-title: Toileting-related falls at night in hospitalised patients: the role of nocturia publication-title: Australas J Ageing doi: 10.1111/ajag.12696 contributor: fullname: Rose – ident: 2024102819014894000_11.9.e051047.36 doi: 10.1186/s13643-016-0384-4 – ident: 2024102819014894000_11.9.e051047.37 doi: 10.46658/JBIMES-20-01 – ident: 2024102819014894000_11.9.e051047.28 doi: 10.1161/CIRCULATIONAHA.114.014508 – volume: 3 start-page: e1918962 year: 2020 ident: 2024102819014894000_11.9.e051047.24 article-title: Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2019.18962 contributor: fullname: Desai – ident: 2024102819014894000_11.9.e051047.26 doi: 10.1161/CIR.0000000000000480 – ident: 2024102819014894000_11.9.e051047.29 doi: 10.1136/bmj.315.7115.1049 – ident: 2024102819014894000_11.9.e051047.31 doi: 10.1093/ageing/afn203 – ident: 2024102819014894000_11.9.e051047.33 doi: 10.7326/M18-0850 – volume: 8 year: 2020 ident: 2024102819014894000_11.9.e051047.27 article-title: Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: case-control study publication-title: JMIR Med Inform doi: 10.2196/16970 contributor: fullname: Nakatani – volume: 27 start-page: 1011 year: 2019 ident: 2024102819014894000_11.9.e051047.16 article-title: Multilevel factors influencing falls of patients in hospital: the impact of nurse staffing publication-title: J Nurs Manag doi: 10.1111/jonm.12765 contributor: fullname: Kim – ident: 2024102819014894000_11.9.e051047.8 doi: 10.1136/bmj.39049.706493.55 – ident: 2024102819014894000_11.9.e051047.3 – ident: 2024102819014894000_11.9.e051047.34 doi: 10.1080/1364557032000119616 – ident: 2024102819014894000_11.9.e051047.23 – volume: 9 year: 2021 ident: 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contributor: fullname: Wong – volume: 9 start-page: 311 year: 2017 ident: 2024102819014894000_11.9.e051047.14 article-title: Falls and depression in octogenarians - life and living in advanced age: a cohort study in New Zealand publication-title: J Prim Health Care doi: 10.1071/HC17012 contributor: fullname: Atlas – ident: 2024102819014894000_11.9.e051047.6 doi: 10.5694/mja15.00296 – volume: 15 start-page: 3006 year: 2017 ident: 2024102819014894000_11.9.e051047.11 article-title: Effectiveness of patient-centered interventions on falls in the acute care setting compared to usual care: a systematic review publication-title: JBI Database System Rev Implement Rep doi: 10.11124/JBISRIR-2016-003331 contributor: fullname: Avanecean – ident: 2024102819014894000_11.9.e051047.22 doi: 10.1111/jan.12542 – volume: 57 start-page: 91 year: 2019 ident: 2024102819014894000_11.9.e051047.17 article-title: Predictors of falls in persons with spinal cord injury—a prospective study using the downton fall risk index and a single question of previous falls publication-title: Spinal Cord doi: 10.1038/s41393-018-0175-y contributor: fullname: Butler Forslund – ident: 2024102819014894000_11.9.e051047.20 doi: 10.1177/1062860615581199 – ident: 2024102819014894000_11.9.e051047.1 doi: 10.1002/14651858.CD005465.pub4 – volume: 14 start-page: 27 year: 2018 ident: 2024102819014894000_11.9.e051047.18 article-title: Medications and patient characteristics associated with falling in the hospital publication-title: J Patient Saf doi: 10.1097/PTS.0000000000000163 contributor: fullname: OʼNeil – volume: 22 start-page: 333 year: 2015 ident: 2024102819014894000_11.9.e051047.25 article-title: Primary care physicians’ perspectives on computer-based health risk assessment tools for chronic diseases: a mixed methods study publication-title: J Innov Health Inform doi: 10.14236/jhi.v22i3.153 contributor: fullname: Voruganti |
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Snippet | IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs.... Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess... INTRODUCTIONFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs.... Introduction Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs.... |
SourceID | doaj pubmedcentral proquest crossref pubmed bmj |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
StartPage | e051047 |
SubjectTerms | Clinical trials Falls geriatric medicine health & safety Health Informatics Hospitals Humans Models, Statistical Patients Peer Review Prognosis quality in health care Research Design Review Literature as Topic Risk assessment risk management Systematic Reviews as Topic |
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Title | Clinical prediction models for hospital falls: a scoping review protocol |
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