The latest medical research on MedicalDirector software

The COVID-19 pandemic disrupted how primary care patients with chronic pain received care. Our study sought to understand how long-term opioid therapy (LtOT) for chronic pain changed over the course of the pandemic overall and for different demographic subgroups.

We used data from electronic health records of 64 primary care clinics across Washington state and Idaho to identify patients who had a chronic pain diagnosis and were receiving long-term opioid therapy. We defined 10-month periods in 2019 to 2021 as prepandemic, early pandemic and late pandemic and used generalized estimating equations analysis to compare across these time periods and demographic characteristics.

We found a proportional decrease in LtOT for chronic pain in the early months of the pandemic (OR = 0.94, P = .007) followed by an increase late pandemic (OR = 1.08, P = .002). Comparing late pandemic to prepandemic, identifying as Asian or Black, having fewer comorbidities, or living in an urban area were associated with higher likelihood of being prescribed LtOT.

The use of LtOT for chronic pain in primary care has increased from before to after the COVID-19 pandemic with racial/ethnic and geographic disparities. Future research is needed to understand these disparities in LtOT and their effect on patient outcomes.

Effective management of hypertension (HTN) is a priority in primary care. With telehealth now considered a staple care delivery method, uninsured and low-income patients without home blood pressure (BP) monitors may need additional attention and resources to achieve successful HTN control.

This prospective study at an underserved community clinic assessed the impact of distributing free BP monitors on patients' HTN control and therapy adherence. Enrollees were randomized into 2 groups, both completing 4 primary care physician (PCP) visits over a 6-month study period. Intervention participants collected home BP readings to report to their PCP and comparison participants completed an equivalent number of visits without having home BP data available for their PCP to review. Both groups completed an initial and final Therapy Adherence Scale (TAS) questionnaire.

263 patients were invited and 200 participants (mean age 50, 60% female, 19% Black, 67% Hispanic) completed the study. Intervention and comparison subjects featured comparable initial BP levels and TAS scores. After adjusting for age, race, ethnicity, sex, presence of diabetes and therapy adherence, intervention participants experienced higher odds of controlled HTN (OR 4.0; 95% Confidence Interval 2.1 to 7.7). A greater proportion of participants achieved BP control in the intervention arm compared with the comparison arm (82% vs 54% of participants, P < .001). TAS scores were higher in the intervention group (Mean = 44.1 vs 41.1; P < .001).

The provision of free home BP monitors to low-income patients may feasibly and effectively improve BP control and therapy adherence.

The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes.

Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts.

ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated.

This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.