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Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study.
Parikh, Ravi B; Manz, Christopher R; Nelson, Maria N; Ferrell, William; Belardo, Zoe; Temel, Jennifer S; Patel, Mitesh S; Shea, Judy A.
Afiliación
  • Parikh RB; Perelman School of Medicine and University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Manz CR; Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Nelson MN; Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA.
  • Ferrell W; Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
  • Belardo Z; Harvard Medical School, Boston, Massachusetts, USA.
  • Temel JS; Perelman School of Medicine and University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Patel MS; Perelman School of Medicine and University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Shea JA; Perelman School of Medicine and University of Pennsylvania, Philadelphia, Pennsylvania, USA.
J Palliat Med ; 25(11): 1702-1707, 2022 11.
Article en En | MEDLINE | ID: mdl-35984992
ABSTRACT

Background:

Early serious illness conversations (SICs) about goals of care and prognosis improve mood, quality of life, and end-of-life care quality. Algorithm-based behavioral nudges to oncologists increase the frequency and timeliness of such conversations. However, clinicians' perspectives on such nudges are unknown.

Design:

Qualitative study consisting of semistructured interviews among medical oncology clinicians who participated in a stepped-wedge cluster randomized trial of Conversation Connect, an algorithm-based intervention consisting of behavioral nudges to promote early SICs in the outpatient oncology setting.

Results:

Of 79 eligible oncology clinicians, 56 (71%) were approached to participate in interviews and 25 (45%) accepted. Key facilitators to algorithm-based nudges included prompting documentation of conversations, peer comparisons, performance reports, and validating norms around early conversations. Barriers included cancer-specific heterogeneity in algorithm performance and the frequency and tone of text messages. Areas of improvement included utilizing different information channels, identifying patients earlier in the disease trajectory, and incorporating patient-targeted messaging that emphasizes the value of early conversations.

Conclusions:

Oncology clinicians identified key facilitators and barriers to Conversation Connect. These insights inform future algorithm-based supportive care interventions in oncology. Controlled trial (NCT03984773).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Planificación Anticipada de Atención / Oncólogos Tipo de estudio: Clinical_trials / Prognostic_studies / Qualitative_research Aspecto: Patient_preference Límite: Humans Idioma: En Revista: J Palliat Med Asunto de la revista: SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Planificación Anticipada de Atención / Oncólogos Tipo de estudio: Clinical_trials / Prognostic_studies / Qualitative_research Aspecto: Patient_preference Límite: Humans Idioma: En Revista: J Palliat Med Asunto de la revista: SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
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