Coordinated rule following is often important for ensuring consistent performance by execution-oriented teams in high-reliability industries. In health care, clinical practice guidelines codify best practices as rules for providers to follow. We examine primary care teams that manage new-onset type II diabetes, where diagnosis is based on an abnormally elevated hemoglobin A1c lab value. Using nationwide electronic health record data that include more than 12 million visits by over 1 million patients from 2013 to 2018, we apply a regression discontinuity design—with hemoglobin A1c as the running variable—and a difference-in-discontinuity strategy to compare providers’ rule-following responses to the diabetes diagnosis threshold across different team characteristics, including size, concentration, predominant provider type, and familiarity. We measure team performance on rule following and clinical outcomes, including diabetes diagnosis, prescribing of medications, diabetes monitoring, and guideline-concordant disease control. We find that teams that are larger and those with effort more diffusely distributed across members are more responsive to the diabetes diagnosis threshold than are solo providers. We find no meaningful differences in team performance based on whether the predominant provider is a physician or a nurse practitioner, nor do we see an impact of team familiarity. Health care managers can consider organizing large or diffuse teams—where each member contributes meaningfully but not exhaustively—to promote rule following and manage chronic diseases more effectively.
Given the known detrimental impact of cancer treatment on fertility, fertility preservation (FP) is recommended for reproductive age patients who are newly diagnosed with cancer. However, the rate of referral to fertility specialists remains suboptimal. The objective of this study was to determine the impact of a dedicated Nurse Navigator Program (NNP) on the rate of referrals and utilization of FP services. A retrospective cohort study of all women ≥ 18 years old referred for FP consultation with a known cancer diagnosis from 2007 to 2021 at a single, large academic center was conducted. FP referrals for non-cancer indications were excluded. Descriptive statistics were performed including comparing referrals received per 30 days and FP utilization rates pre-NNP (October 2007-September 2013) to post-NNP (October 2013-December 2021). A total of 176 patients were included pre-NNP and 990 patients post-NNP. Overall, the mean age at the time of referral was 31.5 ± 6.9 years. The referral rates post-NNP were higher among those without prior exposure to chemotherapy/radiation (0.33 pre-NNP vs. 2.75 post-NNP per 30 days, p < 0.01) and lower among those with prior exposure to chemotherapy/radiation (1.26 pre-NNP vs. 0.70 post-NNP per 30 days, p < 0.01). After the launch of a dedicated fertility preservation nurse navigation program at our institution, we observed a higher number of referrals for FP as well as greater use of FP overall. While not the only variable that changed during this period, this program has optimized patient care and clinical workflow at our institution and serves as a model for such improvement.
Scope-of-practice regulations, including prescribing limits and supervision requirements, may influence the propensity of providers to form care teams. Therefore, policy makers need to understand the effect of both team-based care and provider type on clinical outcomes. We examined how care management and biomarker outcomes after the onset of three chronic diseases differed both by team-based versus solo care and by physician versus nonphysician (that is, nurse practitioner and physician assistant) care. Using 2013-18 deidentified electronic health record data from US primary care practices, we found that provider teams outperformed solo providers, irrespective of team composition. Among solo providers, physicians and nonphysicians exhibited little meaningful difference in performance. As policy makers contemplate scope-of-practice changes, they should consider the effects of not only provider type but also team-based care on outcomes. Interventions that may encourage provider team formation, including scope-of-practice reforms, may improve the value of care.
Physicians' knowledge about each other's quality is central to clinical decision-making, but such information is not well understood and is rarely harnessed to identify exemplars for disseminating best practices or quality improvement. One exception is chief medical resident selection, which is typically based on interpersonal, teaching, and clinical skills.
The Medicaid continuous enrollment provision mandated by the Families First Coronavirus Response Act of 2020 effectively prohibited the termination of enrollees from Medicaid during the COVID-19 public health emergency, including people enrolled in Medicaid during pregnancy. Using data from the Transformed Medicaid Statistical Information System, we found that the rate of continuous Medicaid enrollment during the twelve months postpartum increased from 59.3 percent for births during March–December 2018 to 90.7 percent for births during March–December 2020, when the public health emergency was in effect. This corresponds to approximately 430,000 fewer people losing Medicaid coverage after pregnancy and an average of more than 2.5 months of additional postpartum enrollment. These findings indicate that states that have extended or that plan to extend pregnancy-related Medicaid eligibility in the postpartum year are likely to experience significant gains in continuity of coverage.
Research Objective The quality of chronic disease management is highly variable and low on average. As chronic care is increasingly delivered by teams instead of solo providers, an important yet unresolved question concerns the structure of teams that tend to perform best. Optimal team structure likely depends on the mechanisms by which team‐based care may improve outcomes. Mechanisms that may favor larger, more diverse teams include more frequent patient touchpoints, complementarities between team member skill, or a higher chance of developing a strong patient‐provider relationship. Conversely, coordination costs or diffusion of responsibility may cap the optimal team size. We examine the relationship between team structure and chronic disease outcomes. Study Design Analysis of patient outcomes by team structure (i.e., number and types of providers seen) using rich, de‐identified EHR data. We classify teams by constructing a measure analogous to the Herfindahl–Hirschman Index (HHI) to assess provider concentration across a patient's primary care visits. Our chronic disease outcomes include biomarkers that often determine clinical decision‐making, including hemoglobin A1c, blood pressure, and LDL cholesterol, as well as process measures. First, we compare outcomes for patients who receive care from solo providers (HHI 10,000), concentrated teams (5000 < HHI < 10,000), balanced teams (HHI 5000), and diffuse teams (HHI < 5000), adjusting for organization fixed effects and baseline disease biomarkers. Second, we identify the effect of team‐based care by studying practices that switch from solo to team‐based care, using a difference‐in‐differences design. Population Studied >10 million primary care visits of >1.2 million patients cared for by ~2000 providers at >250 practices part of the athenahealth network from 2013–2018. Principal Findings Taking diabetes as an example, we find that 75.7% of patients with new‐onset disease receive care from a solo provider, 12.3% from a concentrated team, 8.5% from a balanced team, and 3.5% from a diffuse team. We find that process measures differ by team structure, with solo providers ordering fewer A1c tests and prescribing fewer anti‐diabetics, compared to concentrated and diffuse teams, but not compared to balanced teams. Finally, we find that solo providers are less likely to bring their patients' diabetes under control compared to diffuse teams, but not compared to balanced or concentrated teams. Conclusions Performance differed by team concentration, suggesting that optimal team structure may trade off team size and frequency of visits per team member. Our study is the first, to our knowledge, to investigate the effect of different team structures on individual biomarker‐level outcomes. Implications for Policy or Practice Our results may provide insights about the most effective team structures for increasingly consolidated primary care practices (e.g., PCMHs and ACOs). For practices seeking to adopt a team‐based care model, team structure is a potentially important factor to consider when deciding on staffing models. For payers, our results suggest that novel care coordination incentives, such as the CMS Chronic Care Management Services CPT code introduced in 2015, can be designed and further improved upon to encourage team structures that optimize the value of team‐based care.
