Defining Team Effort Involved in Patient Care from the Primary Care Physician’s Perspective

Abstract

BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/ care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.KEY WORDS: primary care redesign; psychosocial; health services research; resource allocation; risk adjustment.

The SUMMIT ambulatory-ICU primary care model for medically and socially complex patients in an urban federally qualified health center: study design and rationale

“…While 6-months pre- and post-enrollment analyses showed a 25% reduction in combined hospitalization and ED utilization, the evaluation lacked a control group. Other gaps in the literature include few interventions targeting HCHN patients in urban community health centers, and those with substance use disorders and co-occurring severe mental illness, independent risk factors for utilization of healthcare services [15–17].…”

Health Complexity Assessment in Primary Care: a validity and feasibility study of the INTERMED tool

“…Such knowledge can help to target those in need for advanced care planning, and effectively develop, coordinate and monitor those plans (1,7,8). Considerable attention has been devoted to identifying the epidemiology of patient complexity, predominantly focusing on medical diagnoses (number of chronic disease and severities) (9). However, comparatively less attention has been paid to how to effect the identification of ‘complex patients’, incorporating not only the physical condition but the mental health and social needs (10), who need care planning in the clinical routine of PHC services (1,4,5).…”

Complexity in pediatric primary care

“…Reports suggest that generalists encountering patients with multimorbidity experience greater workloads, time pressure and feelings of frustration (Sondergaard et al ., 2015; Foster et al ., 2017). It is also increasingly clear that the association of chronic disease and psychosocial issues represent a major barrier to the successful management of patients in the primary care setting, and create additional burdens for providers (Hwang et al ., 2017).…”

“…In primary care, billing codes or scoring systems are often used as a proxy for the time and effort expended on patient care. However, coding data and commonly used scoring or risk adjustment systems designed for single illness paradigms may not accurately capture the complexity of health delivery in this setting (Horner et al ., 1991; Woodward et al ., 1998; Grant et al ., 2011; Cederna-Meko et al ., 2016; Hwang et al ., 2017). The variability in disease definitions and populations is reflected in the wide range of estimates of chronic disease reported in a recent meta-analysis, which reported rates of chronic disease of 0.5–44% in various studies (van der Lee et al ., 2007).…”