Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial

Abstract

Purpose: Serious Illness Conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Although behavioral interventions may prompt earlier or more frequent SICs, their impact on the quality of SICs is unclear.

Methods: This was a secondary analysis of a randomized clinical trial (NCT03984773) among 78 clinicians and 14,607 patients with cancer testing the impact of an automated mortality prediction with behavioral nudges to clinicians to prompt more SICs. We analyzed 318 randomly selected SICs matched 1:1 by clinicians (159 control and 159 intervention) to compare the quality of intervention vs. control conversations using a validated codebook. Comprehensiveness of SIC documentation was used as a measure of quality, with higher integer numbers of documented conversation domains corresponding to higher quality conversations. A conversation was classified as high-quality if its score was ≥ 8 of a maximum of 10. Using a noninferiority design, mixed effects regression models with clinician-level random effects were used to assess SIC quality in intervention vs. control groups, concluding noninferiority if the adjusted odds ratio (aOR) was not significantly < 0.9.

Results: Baseline characteristics of the control and intervention groups were similar. Intervention SICs were noninferior to control conversations (aOR 0.99; 95% CI, 0.91 to 1.09). The intervention increased the likelihood of addressing patient-clinician relationship (aOR = 1.99; 95% CI, 1.23 to 3.27; P < .01) and decreased the likelihood of addressing family involvement (aOR = 0.56; 95% CI, 0.34 to 0.90; P < .05).

Conclusion: A behavioral intervention that increased SIC frequency did not decrease their quality. Behavioral prompts may increase SIC frequency without sacrificing quality.

Conflict of interest statement

William FerrellResearch Funding: Humana Pallavi KumarConsulting or Advisory Role: Acceleron Pharma (I), Amylyx (I)Research Funding: Amicus Therapeutics (I), Amylyx (I), Acceleron Pharma (I)Travel, Accommodations, Expenses: Acceleron Pharma (I), Amylyx (I) Lynn M. SchuchterConsulting or Advisory Role: IncyteResearch Funding: GlaxoSmithKline (Inst), Merck (Inst), Bristol Myers Squibb (Inst)Expert Testimony: PfizerTravel, Accommodations, Expenses: Stand U 2 Cancer Mitesh S. PatelStock and Other Ownership Interests: Catalyst Health NetworkConsulting or Advisory Role: Life.io, HealthMine, Holistic IndustriesResearch Funding: Deloitte Christopher R. ManzResearch Funding: Genentech Ravi B. ParikhStock and Other Ownership Interests: Merck, Google, GNS HealthcareConsulting or Advisory Role: GNS Healthcare, Cancer Study Group, Onc.AITravel, Accommodations, Expenses: Conquer Cancer Foundation, Flatiron HealthNo other potential conflicts of interest were reported.

Figures

FIG 1.

Distributions of quality scores in control and intervention SICs. Histograms comparing the distribution of SIC quality scores for intervention (n = 159, red) and control (n = 159, blue) SICs. The quality score is an integer score ranging from 0 to 10, which corresponds to the number of validated quality domains present in the SIC. The 10 quality domains include (1) prognostic understanding, (2) information preferences, (3) goals, (4) fears and worries, (5) strength, (6) critical abilities, (7) trade-offs, (8) family involvement, (9) patient-clinician relationship, and (10) practical issues. Quality domains were abstracted by chart review of electronic health record documentation of the SICs by a trained blinded researcher (E.H.L.). Two of the elements (patient-clinician relationship and practical issues) were assessed from free-text elements within the SIC; the other eight elements were assessed from structured data elements within the SIC. SIC, serious illness conversation.

FIG 2.

Effect of intervention on individual SIC quality domains. Mixed effects logistic regression models were fit where the sole predictor is the intervention variable and the outcome is each of the 10 validated quality domains, and a random intercept accounts for the influence of the clinician. An odds ratio above one indicates that SICs in the intervention group are more likely to include documentation of the corresponding SIC quality domain compared with SICs in the control group. OR, odds ratio. SIC, serious illness conversation.