Rushlow, David

Background/Objectives: Appointment volume is a primary measure of provider workload and a key contributor to provider burnout in primary care settings. It is highly driven by the size and patient mix of patient panels assigned to providers. To ensure that workload is balanced among providers, an effective patient panel assignment strategy is essential. Methods: A patient panel assignment framework was developed and demonstrated using a year of data from a large family medicine practice to assign patients evenly among providers with the following steps: 1. Predicting patient appointment needs using patient characteristics and Adjusted Clinical Group (ACG) scores; 2. Placing patients in appointment groups based on predictive appointment needs; 3. Determining the patient cap of each appointment group for each provider group; 4. Allocating patients from each appointment group to each provider group without exceeding the patient cap. Results: The results were demonstrated across 100 simulated reassignments to a retrospective dataset. The variations in the number of appointments among providers within a provider group were compared between current practice and the proposed framework. For the largest provider group with 14 providers, the proposed framework with appointment prediction using all features reduced the standard deviation and range by 85% and 84%, respectively. While the proposed framework improved appointment workload balance among providers, 11.7% of patients required reassignment. Conclusions: The proposed framework demonstrates its utility in supporting workload balance. Future work is needed to minimize patient reassignments while accounting for patient care continuity and to incorporate other contributing factors into the framework.

Introduction Video visits in primary care have decreased significantly since their surge early in the COVID-19 pandemic. Methods Clinicians including physicians, nurse practitioners, and physician assistants in the Mayo Clinic Department of Family Medicine were invited to dedicate one to two half-days per month exclusively to video visits. Schedules for these half-day sessions remained restricted to video appointments until the day prior. If at least 80% of slots were not filled by then, remaining openings were converted to in-person visits. If the half-day sessions were at least 80% full with video visits, clinicians had the option of performing these sessions remotely. Results Of 155 eligible clinicians, 34 (22%) participated voluntarily in this scheduling intervention. For the participating clinicians, the average video visit rate increased from 6.3% per month preintervention to 11.5% postintervention ( p < 0.001). On a departmental level, the trend line for the percent of outpatient visits via video visit was decreasing by −0.1% per month preintervention. The postintervention slope of the trend line was 0.3% per month ( p < 0.001). The majority (65%) of the time the half-day video visit schedules filled at least 80% and did not require conversion to allow in-person visits allowing clinicians to work remotely if they chose. Conclusion Allowing family medicine clinicians to perform 1–2 half-day schedules dedicated to only video visits increases the percentage of video visit performed per month both for individual clinicians and on a departmental level.

Abstract Background Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non–visit care interactions. These interactions are highly influenced by patients' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score. Objectives This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non–visit care interactions. Methods Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non–visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non–visit care interactions, integrating demographic data, SDOH, and predicted ACG scores. Results The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non–visit care interactions. The R 2 values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non–visit care interactions. Conclusion The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

AbstractObjectiveTo evaluate health care costs as a function of assigned primary care clinician type and care team characteristics.MethodsAdministrative data were collected for 68 family medicine clinicians (40 physicians and 28 nurse practitioners [NPs]/physician assistant [PAs]), on 11 care teams (variable MD, NP and PA on teams), caring for 77,141 patients. We performed a generalized linear mixed multivariable regression model of standardized per member per month (PMPM) median cost as the outcome, with four practice sites included as random effects.ResultsIn bivariate analysis, cost was higher in physicians than NP/PAs, in more complex patients, and associated with emergency department (ED) visit rate. On multivariate analysis, patient complexity, ED visit rate and higher patient experience ratings were independently associated with greater PMPM cost. More time in practice was associated with lower PMPM cost. In the adjusted multivariate model, physicians had 8.3% lower median PMPM costs than NP/PAs (p = 0.046).ConclusionsThe primary drivers of greater PMPM cost were patient complexity, ED visits and patient satisfaction.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.

The objective was to determine if a greater proportion of physician full-time equivalent (FTE%) relative to nurse practitioners/physician assistants (NPs/PAs) on care teams was associated with improved individual clinician diabetes quality outcomes. The authors conducted a retrospective cross-sectional study of 420 family medicine clinicians in 110 care teams in a Midwest health system, using administrative data from January 1, 2017 to December 31, 2017. Poisson regression was used to examine the relationship between physician FTE% and the number of patients meeting 5 criteria included in a composite metric for diabetes management (D5). Covariates included panel size, clinician type, sex, years in practice, region, patient satisfaction, care team size, rural location, and panel complexity. Of the 420 clinicians, 167 (40%) were NP/PA staff and 253 (60%) were physicians. D5 criteria were achieved in 37.9% of NP/PA panels compared with 44.5% of physician panels ( P < .001). In adjusted analysis, rate of patients achieving D5 was unrelated to physician FTE% on the care team ( P = .78). Physicians had a 1.082 (95% confidence interval 1.007–1.164) times greater rate of patients with diabetes achieving D5 than NPs/PAs. Clinicians at rural locations had a .904 (.852-.959) times lower rate of achieving D5 than those at urban locations. Physicians had a greater rate of patients achieving D5 compared with NPs/PAs, but physician FTE% on the care team was unrelated to D5 outcomes. This suggests that clinician team composition matters less than team roles and the dynamics of collaborative care between members.