Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk

Yarborough, Bobbi Jo H.; Stumbo, Scott P.

doi:10.1016/j.genhosppsych.2021.02.008

Cited by 21 publications

(19 citation statements)

References 15 publications

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“…Given the increasing momentum of the OpenNotes movement [ 40 ], including within the mental health domain [ 41 ], it seems likely that over the long term, we can expect more EHR information to be available to service users. Thus, partnering with patient stakeholders [ 31 ] to develop protocols for transparent, sensitive communications about suicide risk, including key contextual information (eg, how to interpret being in a high-risk category) and language, may also be strategic.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, before widespread clinical deployment, it is critical to partner with stakeholders (eg, frontline clinicians, patients, administrators, and payers) who can help guide such efforts. Recent work in this area has involved collecting self-report survey data from mental health professionals [ 30 ] and patients [ 31 ] on clinical and operational issues pertaining to automated suicide risk models. A recent study used self-report surveys (n=35) and interviews (n=12) to collect qualitative data from Veterans Affairs (VA) clinicians involved in the recently implemented VA program that uses predictive analytics to identify and provide outreach to veterans at high risk for suicide [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers

Bentley¹,

Zuromski²,

Fortgang³

et al. 2022

JMIR Form Res

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Background Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. Objective The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk–prediction models in clinical practice. The specific goals are to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk–prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. Methods We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. Results Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk–prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. Conclusions Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning–based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers

Bentley¹,

Zuromski²,

Fortgang³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…There is increasing recognition that these confidentiality concerns should be addressed before the implementation of suicide predictive analytic tools by making it clear to patients how their data will be used and by providing them with the choice of opting out (Chan et al, 2016; Tucker et al, 2019; Yarborough & Stumbo, 2021). Specifically, while patients may agree to allowing use of their aggregated data to improve system‐level care, they may object to being identified personally (Riordan et al, 2015).…”

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

confidence: 99%

“…However, the specific use of patient electronic health record data for suicide prediction may exceed the scope of existing informed consent (Glenn & Monteith, 2014). The analysis of these data within a healthcare system may require additional informed consent that clearly outlines the benefits and risks of suicide predictive analytic tools (Yarborough & Stumbo, 2021).…”

Section: Introductionmentioning

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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“…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 ]. Privacy was a universal concern, there was a preference that only trusted clinicians should have access to suicide risk information derived from risk models, and patients were worried about the potential for negative consequences including strain on the clinician-patient relationship, risk conversations causing anxiety for patients, and stigma [ 13 ].…”

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 perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk

Cited by 21 publications

References 15 publications

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers

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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