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Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.

ABSTRACT: Importance:Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. Objective:To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. Design, Setting, and Participants:This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N?=?14?607) who had an outpatient oncology encounter during the study period. Interventions:(1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (?10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance. Main Outcomes and Measures:Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group. Results:The sample consisted of 78 clinicians and 14?607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P?

SUBMITTER: Manz CR

PROVIDER: S-EPMC7563672 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.

Manz Christopher R CR Parikh Ravi B RB Small Dylan S DS Evans Chalanda N CN Chivers Corey C Regli Susan H SH Hanson C William CW Bekelman Justin E JE Rareshide Charles A L CAL O'Connor Nina N Schuchter Lynn M LM Shulman Lawrence N LN Patel Mitesh S MS

JAMA oncology 20201210 12

<h4>Importance</h4>Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes.<h4>Objective</h4>To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-pat ...[more]

PMID: 33057696

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Project description:ImportanceSerious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC.ObjectiveTo test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm.Design, setting, and participantsThis prespecified 40-week analysis of a stepped-wedge randomized clinical trial conducted between June 17, 2019, and April 20, 2020 (including 16 weeks of intervention rollout and 24 weeks of follow-up), included 20 506 patients with cancer representing 41 021 encounters at 9 tertiary or community-based medical oncology clinics in a large academic health system. The current analyses were conducted from June 1, 2021, to May 31, 2022.InterventionHigh-risk patients were identified using a validated electronic health record machine learning algorithm to predict 6-month mortality. The intervention consisted of (1) weekly emails to clinicians comparing their SIC rates for all patients against peers' rates, (2) weekly lists of high-risk patients, and (3) opt-out text messages to prompt SICs before encounters with high-risk patients.Main outcomes and measuresThe primary outcome was SIC rates for all and high-risk patient encounters; secondary end-of-life outcomes among decedents included inpatient death, hospice enrollment and length of stay, and intensive care unit admission and systemic therapy close to death. Intention-to-treat analyses were adjusted for clinic and wedge fixed effects and clustered at the oncologist level.ResultsThe study included 20 506 patients (mean [SD] age, 60.0 [14.0] years) and 41 021 patient encounters: 22 259 (54%) encounters with female patients, 28 907 (70.5%) with non-Hispanic White patients, and 5520 (13.5%) with high-risk patients; 1417 patients (6.9%) died by the end of follow-up. There were no meaningful differences in demographic characteristics in the control and intervention periods. Among high-risk patient encounters, the unadjusted SIC rates were 3.4% (59 of 1754 encounters) in the control period and 13.5% (510 of 3765 encounters) in the intervention period. In adjusted analyses, the intervention was associated with increased SICs for all patients (adjusted odds ratio, 2.09 [95% CI, 1.53-2.87]; P < .001) and decreased end-of-life systemic therapy (7.5% [72 of 957 patients] vs 10.4% [24 of 231 patients]; adjusted odds ratio, 0.25 [95% CI, 0.11-0.57]; P = .001) relative to controls, but there was no effect on hospice enrollment or length of stay, inpatient death, or end-of-life ICU use.Conclusions and relevanceIn this randomized clinical trial, a machine learning-based behavioral intervention and behavioral nudges to clinicans led to an increase in SICs and reduction in end-of-life systemic therapy but no changes in other end-of-life outcomes among outpatients with cancer. These results suggest that machine learning and behavioral nudges can lead to long-lasting improvements in cancer care delivery.Trial registrationClinicalTrials.gov Identifier: NCT03984773.

Project description:BackgroundSelf-stigma is highly prevalent in serious mental illness (SMI) and is associated with poorer clinical and functional outcomes. Narrative enhancement and cognitive therapy (NECT) is a group-based intervention combining psychoeducation, cognitive restructuring and story-telling exercises to reduce self-stigma and its impact on recovery-related outcomes. Despite evidence of its effectiveness on self-stigma in schizophrenia-related disorders, it is unclear whether NECT can impact social functioning.MethodsThis is a 12-centre stepped-wedge cluster randomized controlled trial of NECT effectiveness on social functioning in SMI, compared to treatment as usual. One hundred and twenty participants diagnosed with schizophrenia, bipolar disorder or borderline personality disorder will be recruited across the 12 sites. The 12 centres participating to the study will be randomized into two groups: one group (group 1) receiving the intervention at the beginning of the study (T0) and one group (group 2) being a control group for the first 6?months and receiving the intervention after (T1). Outcomes will be compared in both groups at T0 and T1, and 6-month and 12-month outcomes for groups 1 and 2 will be measured without a control group at T2 (to evaluate the stability of the effects over time). Evaluations will be conducted by assessors blind to treatment allocation. The primary outcome is personal and social performance compared across randomization groups. Secondary outcomes include self-stigma, self-esteem, wellbeing, quality of life, illness severity, depressive symptoms and personal recovery.DiscussionNECT is a promising intervention for reducing self-stigma and improving recovery-related outcomes in SMI. If shown to be effective in this trial, it is likely that NECT will be implemented in psychiatric rehabilitation services with subsequent implications for routine clinical practice.Trial registrationClinicalTrials.gov NCT03972735 . Trial registration date 31 May 2019.

Project description:ImportanceDiscussions about goals of care with patients who are seriously ill typically occur infrequently and late in the illness trajectory, are of low quality, and focus narrowly on the patient's resuscitation preferences (ie, code status), risking provision of care that is inconsistent with patients' values. The Serious Illness Care Program (SICP) is a multifaceted communication intervention that builds capacity for clinicians to have earlier, more frequent, and more person-centered conversations.ObjectiveTo explore clinicians' experiences with the SICP 1 year after implementation.Design, setting, and participantsThis qualitative study was conducted at 2 tertiary care hospitals in Canada. The SICP was implemented at Hamilton General Hospital (Hamilton, Ontario) from March 1, 2017, to January 19, 2018, and at Foothills Medical Centre (Calgary, Alberta) from March 1, 2018, to December 31, 2020. A total of 45 clinicians were invited to participate in the study, and 23 clinicians (51.1%) were enrolled and interviewed. Semistructured interviews of clinicians were conducted between August 2018 and May 2019. Content analysis was used to evaluate information obtained from these interviews between May 2019 and May 2020.ExposuresThe SICP includes clinician training, communication tools, and processes for system change.Main outcomes and measuresClinicians' experiences with and perceptions of the SICP.ResultsAmong 23 clinicians interviewed, 15 (65.2%) were women. The mean (SD) number of years in practice was 14.6 (9.1) at the Hamilton site and 12.0 (6.9) at the Calgary site. Participants included 19 general internists, 3 nurse practitioners, and 1 social worker. The 3 main themes were the ways in which the SICP (1) supported changes in clinician behavior, (2) shifted the focus of goals-of-care conversations beyond discussion of code status, and (3) influenced clinicians personally and professionally. Changes in clinician behavior were supported by having a unit champion, interprofessional engagement, access to copies of the Serious Illness Conversation Guide, and documentation in the electronic medical record. Elements of the program, especially the Serious Illness Conversation Guide, shifted the focus of goals-of-care conversations beyond discussion of code status and influenced clinicians on personal and professional levels. Concerns with the program included finding time to have conversations, building transient relationships, and limiting conversation fluidity.Conclusions and relevanceIn this qualitative study, hospital clinicians described components of the SICP as supporting changes in their behavior and facilitating meaningful patient interactions that shifted the focus of goals-of-care conversations beyond discussion of code status. The perceived benefits of SICP implementation stimulated uptake within the medical units. These findings suggest that the SICP may prompt hospital culture changes in goals-of-care dialogue with patients and the care of hospitalized patients with serious illness.

Project description:BackgroundSerious illness conversations (SICs) are an evidence-based approach to eliciting patients' values, goals, and care preferences that improve patient outcomes. However, most patients with cancer die without a documented SIC. Clinician-directed implementation strategies informed by behavioral economics ("nudges") that identify high-risk patients have shown promise in increasing SIC documentation among clinicians. It is unknown whether patient-directed nudges that normalize and prime patients towards SIC completion-either alone or in combination with clinician nudges that additionally compare performance relative to peers-may improve on this approach. Our objective is to test the effect of clinician- and patient-directed nudges as implementation strategies for increasing SIC completion among patients with cancer.MethodsWe will conduct a 2 × 2 factorial, cluster randomized pragmatic trial to test the effect of nudges to clinicians, patients, or both, compared to usual care, on SIC completion. Participants will include 166 medical and gynecologic oncology clinicians practicing at ten sites within a large academic health system and their approximately 5500 patients at high risk of predicted 6-month mortality based on a validated machine-learning prognostic algorithm. Data will be obtained via the electronic medical record, clinician survey, and semi-structured interviews with clinicians and patients. The primary outcome will be time to SIC documentation among high-risk patients. Secondary outcomes will include time to SIC documentation among all patients (assessing spillover effects), palliative care referral among high-risk patients, and aggressive end-of-life care utilization (composite of chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death) among high-risk decedents. We will assess moderators of the effect of implementation strategies and conduct semi-structured interviews with a subset of clinicians and patients to assess contextual factors that shape the effectiveness of nudges with an eye towards health equity.DiscussionThis will be the first pragmatic trial to evaluate clinician- and patient-directed nudges to promote SIC completion for patients with cancer. We expect the study to yield insights into the effectiveness of clinician and patient nudges as implementation strategies to improve SIC rates, and to uncover multilevel contextual factors that drive response to these strategies.Trial registrationClinicalTrials.gov , NCT04867850 . Registered on April 30, 2021.FundingNational Cancer Institute P50CA244690.

Dataset Information

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.

Publications

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial.

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