Patient Feedback on the Use of Predictive Analytics for Suicide Prevention

Reger, Mark A.; Ammerman, Brooke A.; Carter, Sarah P.; Gebhardt, Heather M.; Rojas, Sasha M.; Lee, Jacob M; Buchholz, Jonathan

doi:10.1176/appi.ps.202000092

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…Another example of the promise and challenges of AI implementation in psychiatric population health is the REACH VET program at the US Veterans Affairs (111,112). The VA has leveraged its immense and harmonized EHR system to investigate a new ML-based program that identifies high suicide risk individuals.…”

Section: Clinical Challengesmentioning

confidence: 99%

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Lee

Torous

Choudhury

et al. 2021

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

153

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Clinical Challengesmentioning

confidence: 99%

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Lee

Torous

Choudhury

et al. 2021

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

153

View full text Add to dashboard Cite

show abstract

“…Considerable debate exists regarding the potential benefits of suicide predictive analytic tools. For instance, the implementation of the REACH VET program within the Veterans Affairs suggests that this program is both acceptable and feasible among Veterans (Reger et al, 2021). Others, however, have argued that properly validated machine‐learning models had no clear advantage over linear models in predicting suicide (Jacobucci et al, 2021).…”

Section: Clinical and Ethical Considerations Within A Risk‐benefit Co...mentioning

confidence: 99%

From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools

et al. 2021

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Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high‐risk suicidal patients. Such application will require the aggregation of large‐scale, sensitive patient data to help inform complex and potentially stigmatizing health care decisions. This paper provides a description of how suicide prediction is uniquely difficult by comparing it to nonmedical (weather and traffic forecasting) and medical predictions (cancer and human immunodeficiency virus risk), followed by clinical and ethical challenges presented within a risk‐benefit conceptual framework. Because the misidentification of suicide risk may be associated with unintended negative consequences, clinicians and policymakers need to carefully weigh the risks and benefits of using suicide predictive analytics across health care populations. Practical recommendations are provided to strengthen the protection of patient rights and enhance the clinical utility of suicide predictive analytics tools.

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“…Far fewer have investigated patient perspectives on whether and how this technology ought to be used. When patients receiving inpatient psychiatric treatment ( n = 102) completed anonymous questionnaires and reacted to three hypothetical vignettes exploring different approaches to introducing a predictive model-driven suicide prevention program, negative reactions and privacy concerns were rare [ 12 ]. However, focus groups and a survey of 1,357 members of a large integrated health system revealed that although patients hypothetically supported this use of their health data, they had reservations about how risk models might be implemented [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [ 12 , 13 ] measured participants’ responses to hypothetical implementation of suicide risk models. Conducted in three health systems, the present study afforded the opportunity to extend that work by further exploring patients’ perceptions of suicide risk model technology, concerns, implementation preferences, and, among a subset, actual experiences of suicide risk prediction models being implemented during a pilot study.…”

Section: Introductionmentioning

confidence: 99%

Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice

et al. 2022

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Background Suicide risk prediction models derived from electronic health records (EHR) and insurance claims are a novel innovation in suicide prevention but patient perspectives on their use have been understudied. Methods In this qualitative study, between March and November 2020, 62 patients were interviewed from three health systems: one anticipating implementation of an EHR-derived suicide risk prediction model and two others piloting different implementation approaches. Site-tailored interview guides focused on patients’ perceptions of this technology, concerns, and preferences for and experiences with suicide risk prediction model implementation in clinical practice. A constant comparative analytic approach was used to derive themes. Results Interview participants were generally supportive of suicide risk prediction models derived from EHR data. Concerns included apprehension about inducing anxiety and suicidal thoughts, or triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care. Participants who were engaged in mental health care or case management expected to be asked about their suicide risk and largely appreciated suicide risk conversations, particularly by clinicians comfortable discussing suicidality. Conclusion Most patients approved of suicide risk models that use EHR data to identify patients at-risk for suicide. As health systems proceed to implement such models, patient-centered care would involve dialogue initiated by clinicians experienced with assessing suicide risk during virtual or in person care encounters. Health systems should proactively monitor for negative consequences that result from risk model implementation to protect patient trust.

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Patient Feedback on the Use of Predictive Analytics for Suicide Prevention

Cited by 13 publications

References 16 publications

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools

Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice

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