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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 < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001)., Conclusions and Relevance: In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life., Trial Registration: ClinicalTrials.gov Identifier: NCT03984773.

Objectives: Voluntary patient uptake and use of electronic health record (EHR) features have been low. It is unknown whether EHRs fully meet needs of providers or patients with chronic diseases., Study Design: To explore in-depth user experiences, we conducted 6 focus groups: 3 of patients followed by 3 of providers discussing 2 key EHR components: the after-visit summary (AVS) and the patient portal (PP). Focus groups were audio-recorded, transcribed, and analyzed by 3 independent coders., Methods: Participants with moderate-to-severe asthma and prevalent comorbidities were recruited from 4 primary care and 2 asthma clinics serving low-income urban neighborhoods. Participants discussed their expectations and experience using the AVS and PP, and responded to prototype formats of these features. Additionally, one-on-one interviews were conducted with 10 patients without PP experience to assess their ability to use the system., Results: The 21 patient and 13 provider perspectives differed regarding AVS features and use. Patients wanted a unified view of their medical issues and health management tools, while providers wanted to focus on recommendations from 1 visit at a time. Both groups advocated improving the AVS format and content. Lack of awareness and knowledge about the PP was patients' largest barrier, and was traced back to providers' lack of PP training., Conclusions: Our results underscore the importance of user-centered design when constructing the content and features of the EHR. As technology evolves, an ongoing understanding of patient and provider experiences will be critical to improve uptake, increase use, and ensure engagement, optimizing the potential of EHRs.