Understanding the influences and impact of patient-clinician communication in cancer care

BACKGROUND: Patient-clinician communication is thought to be central to care outcomes, but when and how communication affects patient outcomes is not well understood. OBJECTIVE: We propose a conceptual model and classification framework upon which the empirical evidence base for the impact of patient-clinician communication can be summarized and further built. DESIGN: We use the proposed model and framework to summarize findings from two recent systematic reviews, one evaluating the use of shared decision making (SDM) on cancer care outcomes and the other evaluating the role of physician recommendation in cancer screening use. KEY RESULTS: Using this approach, we identified clusters of studies with positive findings, including those relying on the measurement of SDM from the patients' perspective and affective-cognitive outcomes, particularly in the context of surgical treatment decision making. We also identify important gaps in the literature, including the role of SDM in post-surgical treatment and end-of-life care decisions, and those specifying particular physician communication strategies when recommending cancer screening. CONCLUSIONS: Transparent linkages between key conceptual domains and the influence of methodological approaches on observed patient outcomes are needed to advance our understanding of how and when patient-clinician communication influences patient outcomes. The proposed conceptual model and classification framework can be used to facilitate the translation of empirical evidence into practice and to identify critical gaps in knowledge regarding how and when patient-clinician communication impacts care outcomes in the context of cancer and health care more broadly

Study Protocol for Investigating Physician Communication Behaviours that Link Physician Implicit Racial Bias and Patient Outcomes in Black Patients with Type 2 Diabetes Using an Exploratory Sequential Mixed Methods Design

Introduction Patient-physician racial discordance is associated with Black patient reports of dissatisfaction and mistrust, which in turn are associated with poor adherence to treatment recommendations and underutilisation of healthcare. Research further has shown that patient dissatisfaction and mistrust are magnified particularly when physicians hold high levels of implicit racial bias. This suggests that physician implicit racial bias manifests in their communication behaviours during medical interactions. The overall goal of this research is to identify physician communication behaviours that link physician implicit racial bias and Black patient immediate (patient-reported satisfaction and trust) and long-term outcomes (eg, medication adherence, self-management and healthcare utilisation) as well as clinical indicators of diabetes control (eg, blood pressure, HbA1c and history of diabetes complication). Methods and analysis Using an exploratory sequential mixed methods research design, we will collect data from approximately 30 family medicine physicians and 300 Black patients with type 2 diabetes mellitus. The data sources will include one physician survey, three patient surveys, medical interaction videos, video elicitation interviews and medical chart reviews. Physician implicit racial bias will be assessed with the physician survey, and patient outcomes will be assessed with the patient surveys and medical chart reviews. In video elicitation interviews, a subset of patients (approximately 20–40) will watch their own interactions while being monitored physiologically to identify evocative physician behaviours. Information from the interview will determine which physician communication behaviours will be coded from medical interactions videos. Coding will be done independently by two trained coders. A series of statistical analyses (zero-order correlations, partial correlations, regressions) will be conducted to identify physician behaviours that are associated significantly with both physician implicit racial bias and patient outcomes

Primary Care Physicians’ Support of Shared Decision Making for Different Cancer Screening Decisions

Despite widespread advocacy, shared decision making (SDM) is not routinely used for cancer screening. To better understand implementation barriers, we describe primary care physicians’ (PCPs’) support for SDM across diverse cancer screening contexts

Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case

PURPOSE Technology could transform routine decision making by anticipating patients’ information needs, assessing where patients are with decisions and preferences, personalizing educational experiences, facilitating patient-clinician information exchange, and supporting follow-up. This study evaluated whether patients and clinicians will use such a decision module and its impact on care, using 3 cancer screening decisions as test cases. METHODS Twelve practices with 55,453 patients using a patient portal participated in this prospective observational cohort study. Participation was open to patients who might face a cancer screening decision: women aged 40 to 49 who had not had a mammogram in 2 years, men aged 55 to 69 who had not had a prostate-specific antigen test in 2 years, and adults aged 50 to 74 overdue for colorectal cancer screening. Data sources included module responses, electronic health record data, and a postencounter survey. RESULTS In 1 year, one-fifth of the portal users (11,458 patients) faced a potential cancer screening decision. Among these patients, 20.6% started and 7.9% completed the decision module. Fully 47.2% of module completers shared responses with their clinician. After their next office visit, 57.8% of those surveyed thought their clinician had seen their responses, and many reported the module made their appointment more productive (40.7%), helped engage them in the decision (47.7%), broadened their knowledge (48.1%), and improved communication (37.5%). CONCLUSIONS Many patients face decisions that can be anticipated and proactively facilitated through technology. Although use of technology has the potential to make visits more efficient and effective, cultural, workflow, and technical changes are needed before it could be widely disseminated

Factors associated with adherence to chemotherapy guidelines in patients with non-small cell lung cancer

Evidence-based guidelines recommend chemotherapy for medically fit patients with stage II–IV non-small cell lung cancer (NSCLC). Adherence to chemotherapy guidelines has rarely been studied among large populations, mainly because performance status (PS), a key component in assessing chemotherapy appropriateness, is missing from claims-based datasets. Among a large cohort of patients with known PS, we describe first line chemotherapy use relative to guideline recommendations and identify patient factors associated with guideline concordant use

Survival among non-small cell lung cancer patients with poor performance status after first line chemotherapy

Performance status (PS) is a commonly used factor in determining the appropriateness for chemotherapy of patients with non-small cell lung cancer (NSCLC). The prevalence of poor PS and impact of chemotherapy on survival among NSCLC patients has not been studied in community populations

Patient and Physician Race and the Allocation of Time and Patient Engagement Efforts to Mental Health Discussions in Primary Care: An Observational Study of Audiorecorded Periodic Health Examinations

This study investigated racial differences in patient-physician communication around mental health versus biomedical issues. Data were collected from audiorecorded periodic health examinations of adults with mental health needs in the Detroit area (2007-2009). Patients and their primary care physicians conversed for twice as long, and physicians demonstrated greater empathy during mental health topics than during biomedical topics. This increase varied by patient and physician race. Patient race predicted physician empathy, but physician race predicted talk time. Interventions to improve mental health communication could be matched to specific populations based on the separate contributions of patient and physician race

Online Patient Portals: If You Build It, Who Will Come?

Research Objective: Many primary care practices have purchased electronic health records (EHRs) and accompanying patient portals. The role online portals may play in quality and outcome improvement will depend not only on who is using such technology, but how it is used. We evaluate the characteristics of patients using an online portal in comparison to those not using, and examine the portal features and functionalities accessed by users.Study Design: Observational, cohort study for which data were obtained from EHR and health system administrative data. Patient-level data (including demographic information, service use, and portal activation and use) were joined with information characterizing clinics in which patients received care (e.g., medical teaching on site, size, and urban/suburban location). The primary study outcome, portal use, was defined by the initiation of at least one online session. Among users, user-initiated clicks were used to determine specific features accessed. Logistic regression models with random effects were fit using the PROC GLIMMIX procedure (SAS software, Version 9.4) to test the role of clinic- and patient-level variables on patient portal activation. Subjects were blocked by physician, nested within clinic, and the Laplace method was used for likelihood approximation.Population Studied: Study eligible patients were aged 18 years and older with an office visit between 4/1/2013 and 3/31/2014 to a primary care physician practicing in one of the 26 primary care clinics of an integrated delivery system serving Detroit, Michigan and the surrounding suburban areas (N=20,282 patients).Principal Findings: As implemented in December 2012, the online portal enabled users to securely schedule appointments, receive appointment reminders, pay bills online, view lab and other test results, manage information about their health, and communicate with care teams via a secure messaging system. Cohort patients were on average 68.7 years of age (SD=14.7), predominately white (65%) or black (30%) race, and 60% female. Within 18 months of implementation, 33% had an activated account, with African Americans (OR=0.50, 95% CI 0.46-0.56), Hispanics (OR=0.63, 95% CI 0.47-0.84), those over aged 70 years (OR=0.48, 95% CI 0.44-0.52), and those preferring a language other than English (OR=0.43, 95% CI 0.31-0.59) less likely to be a portal user. Patients who were married (OR=0.55, 95% CI 1.44-1.67) and more connected with the clinic, as measured by visit frequency and health maintenance visit use, were more likely to be portal users (OR=1.08, 95% CI 1.05-1.10 and OR=1.39, 95% CI 1.27-1.52, respectively). Among users, the medical record access and management feature (95.9%) was most commonly accessed, most often to obtain laboratory testing results (91.7%). The majority of users also accessed appointment management (76.6%) and messaging (59.1%) functionalitiesConclusions: While the diversity of functions accessed by those with a portal account bodes well for the ability of portals to engage patients, without purposeful intervention enhancements to care delivery afforded by portals may be inaccessible to many, including racial/ethnic minorities and those less connected to traditional care services.Implications for Policy or Practice: Online portals have the potential to extend care beyond the confines of traditional office visits, but inattention to who uses portals may exacerbate known disparities in health care access and outcomes

Automated rating of patient and physician emotion in primary care visits

OBJECTIVE: Train machine learning models that automatically predict emotional valence of patient and physician in primary care visits. METHODS: Using transcripts from 353 primary care office visits with 350 patients and 84 physicians (Cook, 2002 [1], Tai-Seale et al., 2015 [2]), we developed two machine learning models (a recurrent neural network with a hierarchical structure and a logistic regression classifier) to recognize the emotional valence (positive, negative, neutral) (Posner et al., 2005 [3]) of each utterance. We examined the agreement of human-generated ratings of emotional valence with machine learning model ratings of emotion. RESULTS: The agreement of emotion ratings from the recurrent neural network model with human ratings was comparable to that of human-human inter-rater agreement. The weighted-average of the correlation coefficients for the recurrent neural network model with human raters was 0.60, and the human rater agreement was also 0.60. CONCLUSIONS: The recurrent neural network model predicted the emotional valence of patients and physicians in primary care visits with similar reliability as human raters. PRACTICE IMPLICATIONS: As the first machine learning-based evaluation of emotion recognition in primary care visit conversations, our work provides valuable baselines for future applications that might help monitor patient emotional signals, supporting physicians in empathic communication, or examining the role of emotion in patient-centered care