Reconciling Statistical and Clinicians' Predictions of Suicide Risk
Authors
Simon, Gregory E.Matarazzo, Bridget B.
Walsh, Colin G.
Smoller, Jordan W.
Boudreaux, Edwin D
Yarborough, Bobbi Jo H.
Shortreed, Susan M.
Coley, R. Yates
Ahmedani, Brian K.
Doshi, Riddhi P.
Harris, Leah I.
Schoenbaum, Michael
UMass Chan Affiliations
Department of Emergency MedicineDocument Type
Journal ArticlePublication Date
2021-05-01Keywords
EpidemiologyMachine learning
Prediction models
Statistical modeling
Suicide and self-destructive behavior
Epidemiology
Psychiatry and Psychology
Statistical Models
Metadata
Show full item recordAbstract
Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.Source
Simon GE, Matarazzo BB, Walsh CG, Smoller JW, Boudreaux ED, Yarborough BJH, Shortreed SM, Coley RY, Ahmedani BK, Doshi RP, Harris LI, Schoenbaum M. Reconciling Statistical and Clinicians' Predictions of Suicide Risk. Psychiatr Serv. 2021 May 1;72(5):555-562. doi: 10.1176/appi.ps.202000214. Epub 2021 Mar 11. PMID: 33691491. Link to article on publisher's site
DOI
10.1176/appi.ps.202000214Permanent Link to this Item
http://hdl.handle.net/20.500.14038/29775PubMed ID
33691491Related Resources
ae974a485f413a2113503eed53cd6c53
10.1176/appi.ps.202000214