Factors Affecting Physicians’ Intentions to Communicate Personalized Prognostic Information to Cancer Patients at the End of Life

Abstract

Purpose To explore the effects of personalized numeric prognostic information on physicians’ intentions to communicate prognosis to cancer patients at the end of life, and to identify factors that moderate these effects. Methods A factorial experiment was conducted in which 93 Family Medicine physicians were presented with a hypothetical case vignette depicting an end-stage gastric cancer patient seeking prognostic information. Physicians’ intentions to communicate prognosis were assessed before and after provision of personalized prognostic information, while emotional distress of the patient and ambiguity (manifest by imprecision) of the prognostic estimate were varied between subjects. General linear models were used to test the effects of personalized prognostic information, patient distress, and ambiguity on prognostic communication intentions, and explored potential moderating effects of: 1) perceived patient distress, 2) perceived credibility of prognostic models, 3) physician numeracy (objective and subjective), and 4) physician aversion to risk and ambiguity. Results Provision of personalized prognostic information increased prognostic communication intentions (p<.001, η2=.38), although experimentally-manipulated patient distress and prognostic ambiguity had no effects. Greater change in communication intentions was positively associated with higher perceived credibility of prognostic models (p=.007, η2=.10), higher objective numeracy (p=.01, η2=.09), female sex (p=.01, η2=.08), and lower perceived patient distress (p=.02, η2=.07). Intentions to communicate available personalized prognostic information were positively associated with higher perceived credibility of prognostic models (p=.02, η2=.09), higher subjective numeracy (p=.02, η2=.08), and lower ambiguity aversion (p=.06, η2=.04). Conclusions Provision of personalized prognostic information increases physicians’ prognostic communication intentions to a hypothetical end-stage cancer patient, and situational and physician characteristics moderate this effect. More research is needed to confirm these findings and to elucidate the determinants of prognostic communication at the end of life.

Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer

“…29 Weekly e-mails highlighting high-risk patients resolved ambiguity regarding patient prioritization/discussion timing and eliminated time-intensive patient screening. 18,19,27,29 The intervention defined specific roles for care coaches, providers, and support staff, clarifying uncertainties regarding who should be responsible for which components of ACP. 12…”

“…17 In addition to aiding in patient selection for ACP, sharing ML model results with providers could increase their confidence in their prognosis estimation and willingness to discuss prognosis with patients. 18,19 One trial found that using a ML model to prioritize patients for ACP resulted in more conversations. 20 That trial did not include lay health workers, so did not address how to operationally integrate the ML model with care coaches.…”

Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors

“… 1 , 2 , 3 , 4 , 5 Studies have shown that physicians often overestimate survival or are reticent to discuss prognosis and end-of-life preferences owing to perceived patient distress, rapidly progressive science, and lack of prognostic confidence. 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 This overestimation may result in unwanted care and overuse of health care services near the end of life as evidenced by findings that most patients die outside the home and patient preferences are followed completely only about half of the time. 17 , 18 , 19 Within the oncology population, there remains high use of intensive care and chemotherapy and underuse of hospice care near the end of life, costing billions of dollars to the US health care system.…”

Prognostication in advanced cancer: update and directions for future research

“…Prognostication of survival is a challenging task, however. Clinician intuition (ie, clinician prediction of survival [CPS]) is often inaccurate, and prognostic uncertainty decreases clinician confidence in communicating prognosis with patients [3]. Inaccurate prognostic understanding may also contribute to more aggressive end-of-life care [4,5].…”