Clinicians’ Perspectives After Implementation of the Serious Illness Care Program

Lagrotteria, Andrew; Swinton, Marilyn; Simon, Jessica; King, Seema; Boryski, Gwenn; Yan, Irene Wai; Dunne, Fiona; Singh, Japteg; Bernacki, Rachelle; You, John J.

doi:10.1001/jamanetworkopen.2021.21517

Cited by 33 publications

(42 citation statements)

References 44 publications

(93 reference statements)

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“… Clinician factors, including inaccurate prognostication and perceptions that SICs are appropriate only close to death, contribute to this dearth of SICs. To address these factors, we conducted a stepped-wedge, cluster-randomized clinical trial that evaluated behavioral nudges to clinicians to prompt SICs among patients at high risk for death within 180 days (high-risk patients) identified by a machine learning algorithm. A prior analysis after 16 weeks showed significant increases in SICs among all and high-risk patients.…”

Section: Introductioncontrasting

confidence: 99%

Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer

et al. 2023

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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 &lt; .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

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Section: Introductioncontrasting

confidence: 99%

Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer

et al. 2023

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“…The goal of this program is for every seriously ill patient to have more, better, and earlier conversations with their clinicians about their goals, values, and priorities that will inform their future care [16]. The SICP has been studied and implemented in a variety of settings, including outpatient primary care [17,18], oncology [19][20][21][22][23][24], and hospitalized medical patients [25,26].…”

Section: Why Are Serious Illness Conversations Needed?mentioning

confidence: 99%

The Serious Illness Care Program in Oncology: Evidence, Real-World Implementation and Ongoing Barriers

2022

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The Serious Illness Care Program (SICP), designed by Ariadne Labs, is a multicomponent intervention to improve conversations about values and goals for patients with a life-limiting illness. In oncology, implementation of the SICP achieved more, earlier, and better-quality conversations and reduced anxiety and depression among patients with advanced cancer. In this commentary, we describe the SICP, including results from the cluster-randomized trial, provide examples of real-world implementation of this program, and highlight ongoing challenges and barriers that are preventing widespread adoption of this intervention into routine practice. For the SICP to be successfully embedded into routine patient care, it will require significant effort, including ongoing leadership support and training opportunities, champions from all sectors of the interdisciplinary team, and adaptation of the program to a wider range of patients. Future research should also investigate how early conversations can be translated into personalized care plans for patients.

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“…Between 20 and 30 interviews were estimated to be needed to achieve data saturation based on the homogeneity of work setting among participants and the narrow focus of the study. This estimate was based on recommendations in methodological texts and papers as well as publications of similar studies.in journals with high impact factors (29)(30)(31)(32). Interviews were audio-recorded and professionally transcribed verbatim and field notes were taken during the interviews.…”

Section: Interviewsmentioning

confidence: 99%

A Qualitative Study of Health Workers' Experiences During Early Surges in the COVID-19 Pandemic in the U.S.: Implications for Ongoing Occupational Health Challenges

Goff

Wallace

Putnam

et al. 2022

Front. Public Health

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BackgroundHealth workers (HWs) have faced significant threats to physical and psychological health during the COVID-19 pandemic. The recent surges associated with the spread of the delta variant in the U.S., coupled with political resistance to effective public health mitigation strategies, indicate that the risks experienced early in the pandemic are not likely to abate soon. This study sought to better understand the experiences, thoughts, concerns, and recommendations of HWs during one of the first major surges in the U.S. and to explore how these experiences might inform efforts to mitigate potential ongoing COVID-related negative health and psychological impacts on HWs.MethodsHWs were recruited using a multi-faceted approach tailored to public health mitigation guidelines. Semi-structured interviews were conducted via video conference with front line HWs, support staff, and opioid use disorder service organization providers between April 1 and July 9, 2020 using the Social-Ecological Model as a framework. Interviews were audio-recorded and professionally transcribed; transcripts were analyzed inductively and deductively using thematic analytic methods, generating major themes and subthemes.ResultsA total of 22 HWs participated in the study; 14 were female; 3 identified as a member of a racial or ethnic minority population. Major themes identified included: (1) Institutions, Infrastructure, and the Pandemic; (2) Working Under Fire; (3) The Political Becomes Personal and (4) Hope. Themes and subthemes explicated the ways in which phenomena at personal, interpersonal, community, organizational, and societal levels affected HWs experiences and suggested potential mechanisms through which negative effects on HW mental health and health may be mitigated.ConclusionsPrevious global infectious disease epidemics have had profound negative effects on HWs' health and mental health. This study suggests the potential for similar negative impacts that may be exacerbated by the U.S.'s current sociopolitical milieu. Efforts to systematically describe and quantify these effects and to intervene to mitigate them are warranted.

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Clinicians’ Perspectives After Implementation of the Serious Illness Care Program

Cited by 33 publications

References 44 publications

Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer

Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer

The Serious Illness Care Program in Oncology: Evidence, Real-World Implementation and Ongoing Barriers

A Qualitative Study of Health Workers' Experiences During Early Surges in the COVID-19 Pandemic in the U.S.: Implications for Ongoing Occupational Health Challenges

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