Subtyping patients with chronic disease using longitudinal BMI patterns.

Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectional BMI recordings has been heavily studied, the role of BMI trajectories is much less explored. In this study, we use a machine learning approach to subtype individuals' risk of developing 18 major chronic diseases by using their BMI trajectories extracted from a large and geographically diverse EHR dataset capturing the health status of around two million individuals for a period of six years. We define nine new interpretable and evidence-based variables based on the BMI trajectories to cluster the patients into subgroups using the k-means clustering method. We thoroughly review each cluster's characteristics in terms of demographic, socioeconomic, and physiological measurement variables to specify the distinct properties of the patients in the clusters. In our experiments, the direct relationship of obesity with diabetes, hypertension, Alzheimer's, and dementia has been re-established and distinct clusters with specific characteristics for several of the chronic diseases have been found to be conforming or complementary to the existing body of knowledge.

Active living-oriented zoning codes and cardiometabolic conditions across the lifespan.

Environments that make it easier for people to incorporate physical activity into their daily life may help to reduce high rates of cardiometabolic conditions. Local zoning codes are a policy and planning tool to create more walkable and bikeable environments. This study evaluated relationships between active living-oriented zoning code environments and cardiometabolic conditions (body mass index, hyperlipidemia, hypertension). The study used county identifiers to link electronic health record and other administrative data for a sample of patients utilizing primary care services between 2012 and 2016 with county-aggregated zoning code data and built environment data. The analytic sample included 7,441,991 patients living in 292 counties in 44 states. Latent class analysis was used to summarize municipal- and unincorporated county-level data on seven zoning provisions (e.g., sidewalks, trails, street connectivity, mixed land use), resulting in classes that differed in strength of the zoning provisions. Based on the probability of class membership, counties were categorized as one of four classes. Linear and logistic regression models estimated cross-sectional associations with each cardiometabolic condition. Models were fit separately for youth (aged 5-19), adults (aged 20-59), and older adults (aged 60+). Little evidence was found that body mass index in youth, adults, or older adults or the odds of hyperlipidemia or hypertension in adults or older adults differed according to the strength of active living-oriented zoning. More research is needed to identify the health impacts of zoning codes and whether alterations to these codes would improve population health over the long term.

The effect of Medicaid expansion on Medicaid participation, payer mix, and labor supply in primary care.

Evidence of increased health care utilization associated with the Medicaid expansion suggests that clinicians increased capacity to meet demand. However, little is known about the mechanism underlying this response. Using a novel source of all-payer data, we quantified clinicians' response to the Medicaid expansion - examining whether and how they changed their Medicaid participation decisions, payer mix, and overall labor supply. Primary care clinicians in expansion states provided an average of 49 additional appointments per year (a 21% relative increase) for patients insured by Medicaid, compared to clinicians in non-expansion states - with new-patient visits representing half (25 appointments) of this overall increase. Clinicians did not increase their labor supply to accommodate these additional appointments. They instead offset the 1.7 percentage point average increase in Medicaid payer mix with an equivalent reduction in commercial payer mix. However, this reduction in commercial patient share represented only a 2.8% relative decrease, with commercially insured patients still comprising the majority of the average clinician's patient panel. Subsample analyses revealed a larger increase in care for Medicaid patients among clinicians with high Medicaid participation preceding the eligibility expansion.

Evidence Of Respiratory Infection Transmission Within Physician Offices Could Inform Outpatient Infection Control.

Early in the COVID-19 pandemic, outpatient clinics throughout the US shifted toward virtual care to limit viral transmission in the office. However, as health care facilities have reopened, evidence about the risk of acquiring respiratory viral infections in medical office settings remains limited. To inform policy for reopening outpatient care settings, we analyzed rates of potential airborne disease transmission in medical office settings, focusing on influenza-like illness. We quantified whether exposed patients (that is, those seen in a medical office after a patient with influenza-like illness) were more likely to return with a similar illness in the next two weeks compared with nonexposed patients seen earlier in the day. Patients exposed to influenza-like illness in the medical office setting were more likely than nonexposed patients to revisit with a similar illness within two weeks (adjusted absolute difference: 0.7 per 1,000 patients). Similar patterns were not observed for exposure to urinary tract infection and back pain as noncontagious control conditions. These results highlight the potential threat of reopening outpatient clinics during the pandemic and the value of virtual visits for patients with suspected respiratory infections.

Provider Teams Outperform Solo Providers In Managing Chronic Diseases And Could Improve The Value Of Care.

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.

Predicting reoperation after operative treatment of proximal humerus fractures.

PURPOSE: The current understanding of the factors associated with a second surgery or loss of alignment after operative treatment of a proximal humerus fracture has relied on small sample studies with stepwise regression analysis. In this study, we used a powerful regression analysis over a large sample and with many variables to test the null hypothesis that there are no factors associated with a revision surgery or loss of alignment after operative treatment of proximal humerus fractures. METHODS: A retrospective review of all surgically treated proximal humerus fractures from January 1, 2000, to December 31, 2015, was performed at a tertiary level hospital. We extracted longitudinal medical records for all patients, and the data were organized into two categories of predictors: fracture/operative characteristics and patient characteristics. RESULTS: During the study period, 423 patients met the inclusion criteria. Three hundred and fourteen of the fractures underwent Open Reduction Internal Fixation (ORIF) and 109 underwent Hemiarthroplasty. Thirty-three patients underwent revision surgery (8%). Seventy-nine patients treated with ORIF had loss of alignment (25%). Across the entire cohort, the least absolute shrinkage selection operator (LASSO) analysis found that patients between 40 and 60 years of age had a higher odds of revision surgery (OR = 1.6). In patients treated with ORIF, the LASSO regression found an unreduced calcar to be the strongest predictor of loss of alignment (OR = 5.5), followed by osteoporosis (OR = 1.3), prior radiation treatment (OR = 1.3), unreduced greater tuberosity (OR = 1.2) and age over 80 years (OR = 1.2). CONCLUSION: Reoperation after proximal humerus surgery is infrequent even though loss of alignment is common. In our cohort, not all patients who had a loss of alignment underwent revision surgery; consequently, obtaining the best possible reduction at the index surgery is paramount.

Nurse Practitioner Autonomy and Complexity of Care in Rural Primary Care.

The growing ranks of nurse practitioners (NPs) in rural areas of the United States have the potential to help alleviate existing primary care shortages. This study uses a nationwide source of claims- and EHR-data from 2017 to construct measures of NP clinical autonomy and complexity of care. Comparisons between rural and urban primary care practices reveal greater clinical autonomy for rural NPs, who were more likely to have an independent patient panel, to practice with less physician supervision, and to prescribe Schedule II controlled substances. In contrast, rural and urban NPs provided care of similar complexity. These findings provide the first claims- and EHR-based evidence for the commonly held perception that NPs practice more autonomously in rural areas than in urban areas.

Measuring Primary Care Exam Length Using Electronic Health Record Data.

BACKGROUND: Physicians' time with patients is a critical input to care, but is typically measured retrospectively through survey instruments. Data collected through the use of electronic health records (EHRs) offer an alternative way to measure visit length. OBJECTIVE: To measure how much time primary care physicians spend with their patients, during each visit. RESEARCH DESIGN: We used a national source of EHR data for primary care practices, from a large health information technology company. We calculated exam length and schedule deviations based on timestamps recorded by the EHR, after implementing sequential data refinements to account for non-real-time EHR use and clinical multitasking. Observational analyses calculated and plotted the mean, median, and interquartile range of exam length and exam length relative to scheduled visit length. SUBJECTS: A total of 21,010,780 primary care visits in 2017. MEASURES: We identified primary care visits based on physician specialty. For these visits, we extracted timestamps for EHR activity during the exam. We also extracted scheduled visit length from the EHR's practice management functionality. RESULTS: After data refinements, the average primary care exam was 18.0 minutes long (SD=13.5 min). On average, exams ran later than their scheduled duration by 1.2 minutes (SD=13.5 min). Visits scheduled for 10 or 15 minutes were more likely to exceed their allotted time than visits scheduled for 20 or 30 minutes. CONCLUSIONS: Time-stamped EHR data offer researchers and health systems an opportunity to measure exam length and other objects of interest related to time.

Physician Work Hours and the Gender Pay Gap - Evidence from Primary Care.

BACKGROUND: The gender gap in physician pay is often attributed in part to women working fewer hours than men, but evidence to date is limited by self-report and a lack of detail regarding clinical revenue and gender differences in practice style. METHODS: Using national all-payer claims and data from electronic health records, we conducted a cross-sectional analysis of 24.4 million primary care office visits in 2017 and performed comparisons between female and male physicians in the same practices. Our primary independent variable was physician gender; outcomes included visit revenue, visit counts, days worked, and observed visit time (interval between the initiation and the termination of a visit). We created multivariable regression models at the year, day, and visit level after adjustment for characteristics of the primary care physicians (PCPs), patients, and types of visit and for practice fixed effects. RESULTS: In 2017, female PCPs generated 10.9% less revenue from office visits than their male counterparts (-$39,143.2; 95% confidence interval [CI], -53,523.0 to -24,763.4) and conducted 10.8% fewer visits (-330.5 visits; 95% CI, -406.6 to -254.3) over 2.6% fewer clinical days (-5.3 days; 95% CI, -7.7 to -3.0), after adjustment for age, academic degree, specialty, and number of sessions worked per week, yet spent 2.6% more observed time in visits that year than their male counterparts (1201.3 minutes; 95% CI, 184.7 to 2218.0). Per visit, after adjustment for PCP, patient, and visit characteristics, female PCPs generated equal revenue but spent 15.7% more time with a patient (2.4 minutes; 95% CI, 2.1 to 2.6). These results were consistent in subgroup analyses according to the gender and health status of the patients and the type and complexity of the visits. CONCLUSIONS: Female PCPs generated less visit revenue than male colleagues in the same practices owing to a lower volume of visits, yet spent more time in direct patient care per visit, per day, and per year. (Funded in part by the Robert Wood Johnson Foundation.).

Practice Patterns of Physicians and Nurse Practitioners in Primary Care.

BACKGROUND: Primary care practices increasingly include nurse practitioners (NPs), in addition to physicians. Little is known about how the patient mix and clinical activities of colocated physicians and NPs compare. OBJECTIVES: To describe the clinical activities of NPs, compared with physicians. RESEARCH DESIGN: We used claims and electronic health record data from athenahealth Inc., on primary care practices in 2017 and a cross-sectional analysis with practice fixed effects. SUBJECTS: Patients receiving treatment from physicians and NPs within primary care practices. MEASURES: First, we measured patient characteristics (payer, age, sex, race, chronic condition count) and visit characteristics (new patient, scheduled duration, same-day visit, after-hours visit). Second, we measured procedures performed and diagnoses recorded during each visit. Finally, we measured daily quantity (visit volume, minutes scheduled for patient care, total work relative value units billed) of care. RESULTS: Relative to physicians, NPs treated younger and healthier patients. NPs also had a larger share of patients who were female, non-White, and covered by Medicaid, commercial insurance, or no insurance. NPs scheduled longer appointments and treated more patients on a same-day or after-hours basis. On average, "overlapping" services-those performed by NPs and physicians within the same practice-represented 92% of all service volume. The small share of services performed exclusively by physicians reflected greater clinical intensity. On a daily basis, NPs provided fewer and less intense visits than physicians within the same practice. CONCLUSIONS: Our findings suggest considerable overlap between the clinical activities of colocated NPs and physicians, with some differentiation based on intensity of services provided.

Association of Obesity With Prescription Opioids for Painful Conditions in Patients Seeking Primary Care in the US.

Importance: Prior studies have identified an association between obesity and prescription opioid use in the US. However, the pain conditions that are factors in this association remain unestablished. Objective: To investigate the association between obesity and pain diagnoses recorded by primary care clinicians as reasons for prescription of opioids. Design, Setting, and Participants: A cross-sectional study including 565 930 patients aged 35 to 64 years with a body mass index (BMI) measurement recorded in 2016 was conducted. Electronic health records of patients seen by primary care clinicians in the US in the multipayer athenahealth network from January 1, 2015, to December 31, 2017, were reviewed, and data were analyzed from March 1 to September 15, 2019. Main Outcomes and Measures: Any prescription of opioids in the 365 days before or after the first BMI measurement in 2016 were identified. All International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, claims within 7 days before each opioid prescription were captured and classified using a pain diagnosis typologic system. Weight was categorized as underweight (BMI, 18.5-19.9), normal weight (BMI, 20.0-24.9), overweight (BMI, 25.0-29.9), obese I (BMI, 30-34.9), obese II (BMI, 35.0-39.9), obese III (BMI, 40.0-49.9), and obese IV (BMI, 50.0-80.0). Results: Among 565 930 patients, 329 083 (58.1%) were women. A total of 125 093 patients (22.1%) were aged 35 to 44 years, 199 384 patients (35.2%) were 45 to 54 years, and 241 453 patients (42.7%) were 55 to 64 years. A total of 177 631 patients (31.4%) were overweight and 273 135 patients (48.2%) were obese at baseline. Over 2 years, 93 954 patients (16.6%) were prescribed opioids. The risk of receiving prescription opioids increased progressively with BMI (adjusted relative risk for overweight: 1.08; 95% CI, 1.06-1.10; obese I: 1.24; 95% CI, 1.22-1.26; obese II: 1.33; 95% CI, 1.30-1.36; obese III: 1.48; 95% CI, 1.45-1.51; and obese IV, 1.71; 95% CI, 1.65-1.77). The percentage of patients with opioid prescriptions attributable to an overweight or obese BMI was 16.2% (95% CI, 15.0%-17.4%). Prescription opioids for management of osteoarthritis (relative risk for obese vs normal weight, 1.90; 95% CI, 1.77-2.05) and other joint disorders (relative risk, 1.63; 95% CI, 1.55-1.72) both had stronger associations with obesity than the mean for any pain diagnosis (relative risk, 1.33; 95% CI, 1.31-1.36). Osteoarthritis, other joint disorders, and other back disorders comprised a combined 53.4% of the absolute difference in prescription of opioids by obesity. Conclusions and Relevance: Joint and back disorders appear to be the most important diagnoses in explaining the increased receipt of opioid prescriptions among patients with obesity. Addressing the opioid crisis will require attention to underlying sources of demand for prescription opioids, including obesity, through its associations with pain.

Working around hierarchy: Resident and medical assistant teaming.

BACKGROUND: In health care, hierarchy can facilitate getting work done efficiently. It can also hinder performance by suppressing valuable contributions from lower-positioned individuals. Team-based care could mitigate negative effects by creating space for all team members to contribute their unique expertise. PURPOSE: This article sought to understand how resident-medical assistant (MA) dyads interacted before and after primary care clinics transitioned to team-based care. We also studied how they negotiated changes in interpersonal dynamics given the challenge these changes presented to hierarchical norms. METHODOLOGY: We conducted two qualitative interview studies, with 37 residents and 30 MAs at primary care clinics transitioning to team-based care. Interviews were transcribed, coded, and analyzed together using a thematic networks approach and focused coding. RESULTS: An intervention that promoted teamwork prompted resident-MA dyads to change their interactions to counter traditional hierarchy. Residents increasingly asked MAs questions about patient care, and MAs initiated interactions and volunteered ideas more frequently. We also found that MAs and residents expressed some discomfort with the hierarchical ambiguity that their new interactions produced and used alternate scripts to buffer this discomfort and to collaborate as teammates despite formal hierarchy. CONCLUSION: Among resident-MA dyads, a team-based care intervention changed interpersonal dynamics by blurring hierarchical lines and shifting traditional boundaries in ways that were uncomfortable for both groups. They were able to work around discomfort by using new scripts that downplayed the threat to hierarchy. PRACTICE IMPLICATIONS: Organizational structures that encourage greater interprofessional collaboration may neutralize barriers that formal hierarchy in medicine can pose for effective teamwork, but this process can also bring social discomfort. Our findings suggest that health care professionals may use microlevel strategies, such as alternative scripts, to overcome formal hierarchies without openly engaging them. Together, new organizational structures and interaction techniques can help professionals work around hierarchy and improve team performance.

Team-based primary care: The medical assistant perspective.

BACKGROUND: Team-based care has the potential to improve primary care quality and efficiency. In this model, medical assistants (MAs) take a more central role in patient care and population health management. MAs' traditionally low status may give them a unique view on changing organizational dynamics and teamwork. However, little empirical work exists on how team-based organizational designs affect the experiences of low-status health care workers like MAs. PURPOSES: The aim of this study was to describe how team-based primary care affects the experiences of MAs. A secondary aim was to explore variation in these experiences. METHODOLOGY/APPROACH: In late 2014, the authors interviewed 30 MAs from nine primary care practices transitioning to team-based care. Interviews addressed job responsibilities, teamwork, implementation, job satisfaction, and learning. Data were analyzed using a thematic networks approach. Interviews also included closed-ended questions about workload and job satisfaction. RESULTS: Most MAs reported both a higher workload (73%) and a greater job satisfaction (86%) under team-based primary care. Interview data surfaced four mechanisms for these results, which suggested more fulfilling work and greater respect for the MA role: (a) relationships with colleagues, (b) involvement with patients, (c) sense of control, and (d) sense of efficacy. Facilitators and barriers to these positive changes also emerged. CONCLUSION: Team-based care can provide low-status health care workers with more fulfilling work and strengthen relationships across status lines. The extent of this positive impact may depend on supporting factors at the organization, team, and individual worker levels. PRACTICE IMPLICATIONS: To maximize the benefits of team-based care, primary care leaders should recognize the larger role that MAs play under this model and support them as increasingly valuable team members. Contingent on organizational conditions, practices may find MAs who are willing to manage the increased workload that often accompanies team-based care.