Journal of the American Board of Family Medicine最新文献

Introduction: Unplanned readmissions can be avoided by standardizing and improving the coordination of care after discharge. Telemedicine has been increasingly utilized; however, the quality of this care has not been well studied. Standardized measures can provide an objective comparison of care quality. The purpose of our study was to compare quality performance transitions of care management in the office vs telemedicine.

Methods: The Epic SlicerDicer tool was used to compare the percentage of encounters that were completed via telemedicine (video visits); or via in-person for comparison, Chi-squared tests were used.

Results: A total of 13,891 patients met the inclusion criteria during the study time frame. There were 12,846 patients in the office and 1,048 in the telemedicine cohort. The office readmission rate was 11.9% with 1,533 patients out of 12,846 compared with telemedicine with the rate of readmission at 12.1% with 126 patients out of 1,045 patients. The P-value for the Chi-squared test between the prepandemic and study time frame was 0.15 and 0.95, respectively. Demographic comparability was seen.

Discussion: Our study found a comparable readmission rate between patients seen via in-office and telemedicine for Transitions of Care Management (TCM) encounters. The findings of this study support the growing body of evidence that telemedicine augments quality performance while reducing cost and improving access without negatively impacting HEDIS performance in health care systems.

Conclusion: Telemedicine poses little threat of negatively impacting HEDIS performance and might be as effective as posthospitalization traditional office care transitions of care management.

Background: 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.

Methods: 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.

Results: 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.

Discussion: 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.

Background: Primary care clinicians do not adhere to national and international guidelines recommending pulmonary function testing (PFTs) in patients with suspected asthma. Little is known about why that occurs. Our objective was to assess clinician focused barriers to ordering PFTs.

Methods: An internet-based 11-item survey of primary care clinicians at a large safety-net institution was conducted between August 2021 and November 2021. This survey assessed barriers and possible electronic health record (EHR) solutions to ordering PFTs. One of the survey questions contained an open-ended question about barriers which was analyzed qualitatively.

Results: The survey response rate was 59% (117/200). The top 3 reported barriers included beliefs that testing will not change management, distance to testing site, and the physical effort it takes to complete testing. Clinicians were in favor of an EHR intervention to prompt them to order PFTs. Responses to the open-ended question also conveyed that objective testing does not change management.

Discussion: PFTs improve diagnostic accuracy and reduce inappropriate therapies. Of the barriers we identified, the most modifiable is to educate clinicians about how PFTs can change management. That in conjunction with an EHR prompt, which clinicians approved of, may lead to guideline congruent and improved quality in asthma care.

Purpose: Filipinos have unique social determinants of health, cultural values, and beliefs that contribute to a higher prevalence of cardiovascular comorbidities such as hypertension, diabetes, and dyslipidemia. We aimed to identify Filipino values, practices, and belief systems that influenced health care access and utilization.

Methods: We conducted 1-on-1 semistructured interviews with self-identified Filipino patients. Our qualitative study utilized a constant-comparative approach for data collection, thematic coding, and interpretive analysis.

Results: We interviewed 20 Filipinos in a remote rural community to assess structural and social challenges experienced when interacting with the health care system. Our results suggest that Filipinos regard culture and language as pillars of health access. Filipinos trust clinicians who exhibited positive tone and body language as well as relatable and understandable communication. These traits are features of Pakikisama, a Filipino trait/value of "comfortableness and getting along with others." Relatability and intercultural values familiarity increased Filipino trust in a health care clinician. Filipinos may lack understanding about how to navigate the US Health care system, which can dissuade access to care.

Conclusions: For the Filipino community, culture and language are fundamental components of health access. Health care systems have the opportunity to both improve intercultural clinical training and increase representation among clinicians and support staff to improve care delivery and navigation of health services. Participants reported not routinely relying on health care navigators.

Introduction: Previous research has found an association between low health literacy and poor clinical outcomes in type 2 Diabetes Mellitus (T2DM) patients. We sought to determine if this association can be mitigated by a self-management support (SMS) program provided by trained health workers using a technology assisted menu driven program, called Connection to Health (CTH).

Methods: This study is a secondary analysis from a randomized trial of 2 similar versions of CTH implemented in 12 Northern California community health centers. As part of this, each participant completed a single validated question to assess health literacy. We used unadjusted and adjusted linear regression analyses to determine the extent to which baseline health literacy was predictive of prepost changes in hemoglobin A1c (HbA1c).

Results: Of 365 participants for whom prepost HbA1c data were available, HbA1c concentrations declined by an average of 0.76% (from 9.9% to 9.2%, 95% CI (0.53%-1.0%). Almost 114 (31.2%) of the participants had low health literacy, but there was no significant association between health literacy and the reduction in HbA1c concentrations in either the unadjusted or adjusted models, nor did baseline health literacy predict prepost changes in body mass index, medication adherence, exercise, or diet.

Discussion: The study found that implementing the CTH program in 2 versions via a randomized clinical trial improved HbA1c concentrations without increasing disparities between participants with high and low health literacy. This suggests CTH-like programs can enhance diabetes outcomes in community health centers without exacerbating inequities for those with low health literacy.

Background: 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.

Methods: 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.

Results: 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.

Discussion: 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.

Purpose: Although interventions can increase advance care planning (ACP) engagement, it remains unclear which interventions to choose in primary care settings. This study compares a passive intervention (mailed materials) to an interactive intervention (group visits) on participant ACP engagement and experiences.

Methods: We used mixed methods to examine ACP engagement at baseline and six months following two ACP interventions. Eligible patients were randomized to receive mailed materials or participate in two ACP group visits. We administered the 4-item ACP Engagement survey (n = 110) and conducted interviews (n = 23). We compared mean scores and percent change in ACP engagement, analyzed interviews with directed content analysis to understand participants' ACP experiences, and integrated the findings based on mailed materials or group visits intervention.

Results: All participants demonstrated increased ACP engagement scores. At six months, group visit participants reported higher percent change in mean overall score compared with mailed materials participants (+8% vs +3%, P < .0001). Group visits participants reported that being prompted to think about end-of-life preferences, gaining knowledge about ACP, and understanding the value of completing ACP documentation influenced their ACP readiness. While both interventions encouraged patients to start considering and refining their end-of-life preferences, group visits made patients feel more knowledgeable about ACP, highlighted the importance of completing ACP documentation early, and sparked further ACP discussions with others.

Conclusions: While primary care patients may benefit from mailed ACP materials, patients reported increased readiness after ACP group visits. Group visits emphasized the value of upstream preparation, ongoing conversations, and increased knowledge about ACP.

The singular label of "Asian" obscures socioeconomic differences between Asian ethnic groups that affect matriculation into the field of medicine. Using data from American Board of Family Medicine Examination candidates in 2023, we found that compared to the US population, among Asian-American family physicians, Indians were present at higher rates, while Chinese and Filipinos were underrepresented, suggesting the importance of continued disaggregation of Asian ethnicities in medicine.

Introduction: Does telehealth decrease health disparities by improving connections to care or simply result in new barriers for vulnerable populations who often lack access to technology? This study aims to better understand the role of telehealth and social determinants of health in improving care connections and outcomes for Community Health Center patients with diabetes.

Methods: This retrospective analysis of Electronic Health Record (EHR) data examined the relationship between telehealth utilization and glycemic control and consistency of connection to the health care team ("connectivity"). EHR data were collected from 20 Community Health Centers from July 1, 2019 through December 31, 2021. Descriptive statistics were calculated, and multivariable linear regression was used to assess the associations between telehealth use and engagement in care and glycemic control.

Results: The adjusted analysis found positive, statistically significant associations between telehealth use and each of the 2 primary outcomes. Telehealth use was associated with 0.89 additional months of hemoglobin A1c (HbA1c) control (95% confidence interval [CI], 0.73 to 1.04) and 4.49 additional months of connection to care (95% CI, 4.27 to 4.70).

Discussion: The demonstrated increased engagement in primary care for telehealth users is significant and encouraging as Community Health Center populations are at greater risk of lapses in care and loss to follow up.

Conclusions: Telehealth can be a highly effective, patient-centered form of care for people with diabetes. Telehealth can play a critical role in keeping vulnerable patients with diabetes connected to their care team and involved in care and may be an important tool for reducing health disparities.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.