Building Capacity for Pragmatic Trials of Digital Technology in Primary Care.

Frontline primary care teams face important challenges in seeking to transform the quality of care delivered to patients and to reduce clerical burden for clinicians. Digital technologies using artificial intelligence hold substantial promise to aid in this transformation. Both pragmatic clinical trials and implementation science are key tools to successfully introduce, evaluate, and sustain innovations in real-world primary care practices. Previous articles in this thematic series have provided an in-depth overview of pragmatic trials and implementation science. This paper demonstrates and provides a framework for how these concepts, together with digital transformation, can be used to solve many of the challenges facing primary care. This framework is conceived as the collaboration of frontline primary care teams with innovators in academic institutions and industry through pragmatic trials and implementation science.

Modeling Family Medicine Provider Care Team Design to Improve Patient Care Continuity.

BACKGROUND AND OBJECTIVES: Continuity of care is an integral aspect of high-quality patient care in primary care settings. In the Department of Family Medicine at Mayo Clinic, providers have multiple responsibilities in addition to clinical duties or panel management time (PMT). These competing time demands limit providers' clinical availability. One way to mitigate the impact on patient access and care continuity is to create provider care teams to collectively share the responsibility of meeting patients' needs. METHODS: This study presents a descriptive characterization of patient care continuity based on provider types and PMT. Care continuity was measured by the percentage of patient a ppointments s een by a provider in their o wn c are t eam (ASOCT) with the aim of reducing the variability of provider care team continuity. The prediction method is iteratively developed to illustrate the importance of the individual independent components. An optimization model is then used to determine optimal provider mix in a team. RESULTS: The ASOCT percentage in current practice among care teams ranges from 46% to 68% and the per team number of MDs varies from 1 to 5 while the number of nurse practitioners and physician assistants (NP/PAs) ranges from 0 to 6. The proposed methods result in the optimal provider assignment, which has an ASOCT percentage consistently at 62% for all care teams and 3 or 4 physicians (MDs) and NP/PAs in each care team. CONCLUSIONS: The predictive model combined with assignment optimization generates a more consistent ASOCT percentage, provider mix, and provider count for each care team.

Balancing Clinician Workload Through Strategic Patient Panel Designs.

BACKGROUND AND OBJECTIVES: Clinician workload is a key contributor to burnout and well-being as well as overtime and staff shortages, particularly in the primary care setting. Appointment volume is primarily driven by the size of patient panels assigned to clinicians. Thus, finding the most appropriate panel size for each clinician is essential to optimization of patient care. METHODS: One year of appointment and panel data from the Department of Family Medicine were used to model the optimal panel size. The data consisted of 82 881 patients and 105 clinicians. This optimization-based modeling approach determines the panel size that maximizes clinician capacity while distributing heterogeneous appointment types among clinician groups with respect to their panel management time (PMT), which is the percent of clinic work. RESULTS: The differences between consecutive PMT physician groups in total annual appointment volumes per clinician for the current practice range from 176 to 348. The optimization-based approach for the same PMT physician group results in having a range from 211 to 232 appointments, a relative reduction in variability of 88%. Similar workload balance gains are also observed for advanced practice clinicians and resident groups. These results show that the proposed approach significantly improves both patient and appointment workloads distributed among clinician groups. CONCLUSION: Appropriate panel size has valuable implications for clinician well-being, patients' timely access to care, clinic and health system productivity, and the quality of care delivered. Results demonstrate substantial improvements with respect to balancing appointment workload across clinician types through strategic use of an optimization-based approach.

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS: Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS: A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION: Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04000087.

The effect of primary care clinician type and care team characteristics on health care costs.

OBJECTIVE: To evaluate health care costs as a function of assigned primary care clinician type and care team characteristics. METHODS: Administrative 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. RESULTS: In 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). CONCLUSIONS: The primary drivers of greater PMPM cost were patient complexity, ED visits and patient satisfaction.

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

Clinician Care Team Composition and Health Care Utilization.

OBJECTIVE: To test the hypothesis that a greater proportion of physician time on primary care teams are associated with decreased emergency department (ED) visits, hospital admissions, and readmissions, and to determine clinician and care team characteristics associated with greater utilization. PATIENTS AND METHODS: We retrospectively analyzed administrative data collected from January 1 to December 31, 2017, of 420 family medicine clinicians (253 physicians, 167 nurse practitioners/physician assistants [NP/PAs]) with patient panels in an integrated health system in 59 Midwestern communities serving rural and urban areas in Minnesota, Wisconsin, and Iowa. These clinicians cared for 419,581 patients through 110 care teams, with varying numbers of physicians and NP/PAs. Primary outcome measures were rates of ED visits, hospitalizations, and readmissions. RESULTS: The proportion of physician full-time equivalents on the team was unrelated to rates of ED visits (rate ratio [RR] = 0.826; 95% confidence interval [CI], 0.624 to 1.063), hospitalizations (RR = 0.894; 95% CI, 0.746 to 1.072), or readmissions (RR = -0.026; 95% CI, 0.364 to 0.312). In separate multivariable models adjusted for clinician and practice-level characteristics, the rate of ED visits was positively associated with mean panel hierarchical condition category (HCC) score, urban vs rural setting, NP/PA vs physician, and lower years in practice. The rate of inpatient admissions was associated with HCC score, and 30-day hospital readmissions were positively associated with HCC score, lower years in practice, and male clinicians. CONCLUSION: Care team physician and NP/PA composition was not independently related to utilization. More complex panels had higher rates of ED visits, hospitalization, and readmissions. Statistically significant differences between physician and NP/PA panels were only evident for ED visits.

Screening and management of atrial fibrillation in primary care.

Atrial fibrillation is a common chronic disease seen in primary care offices, emergency departments, inpatient hospital services, and many subspecialty practices. Atrial fibrillation care is complicated and multifaceted, and, at various points, clinicians may see it as a consequence and cause of multi-morbidity, as a silent driver of stroke risk, as a bellwether of an acute medical illness, or as a primary rhythm disturbance that requires targeted treatment. Primary care physicians in particular must navigate these priorities, perspectives, and resources to meet the needs of individual patients. This includes judicious use of diagnostic testing, thoughtful use of novel therapeutic agents and procedures, and providing access to subspecialty expertise. This review explores the epidemiology, screening, and risk assessment of atrial fibrillation, as well as management of its symptoms (rate and various rhythm control options) and stroke risk (anticoagulation and other treatments), and offers a model for the integration of the components of atrial fibrillation care.

Optimizing Huddle Engagement Through Leadership and Problem Solving Within Primary Care: Results from a Cluster-Randomized Trial.

BACKGROUND: Leaders play a crucial role in implementing and sustaining changes in clinical practice, yet there is limited evidence on the strategies to engage them in team problem solving and communication. OBJECTIVE: Examine the impact of an intervention focused on facilitating leadership during daily huddles on optimizing team-based care and improving outcomes. DESIGN: Cluster-randomized trial using intention-to-treat analysis to measure the effects of the intervention (n = 13 teams) compared with routine practice (n = 16 teams). PARTICIPANTS: Twenty-nine primary care clinics affiliated with a large integrated health system in the upper Midwest; representing differing practice types and geographic settings. INTERVENTION: Full-day leadership training retreat for team leaders to facilitate of care team huddles. Biweekly coaching calls and two site visits with an assigned coach. MAIN MEASURES: Primary outcomes of team development and function were collected, pre- and post-intervention using surveys. Patient satisfaction and quality outcomes were compared pre- and post-intervention as secondary outcomes. Leadership engagement and adherence to the intervention were also assessed. KEY RESULTS: A total of 279 pre-intervention and 272 post-intervention surveys were completed. We found no impact on team development (- 0.98, 95% CI (- 3.18, 1.22)), improved team credibility (0.18, 95% CI (0.00, 0.35)), but worse psychological safety (- 0.19, 95% CI (- 0.38, 0.00)). No differences were observed in patient satisfaction; however, results were mixed among quality outcomes. Post hoc analysis within the intervention group showed higher adherence to the intervention was associated with improvement in team coordination (0.47, 95% CI (0.18, 0.76)), credibility (0.28, 95% CI (0.02, 0.53)), team learning (0.42, 95% CI (0.10, 0.74)), and knowledge creation (0.74, 95% CI (0.35, 1.13)) compared to teams that were less engaged. CONCLUSIONS: Results of this evaluation showed that leadership training and facilitation were not associated with better team functioning. Additional components to the intervention tested may be necessary to enhance team functioning. TRIAL REGISTRATION: Clinicaltrials.gov Identifier NCT03062670. Registration Date: February 23, 2017. URL: https://clinicaltrials.gov/ct2/show/NCT03062670.

Relationship of Clinician Care Team Composition and Diabetes Quality Outcomes.

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.

Optimizing huddle engagement through leadership and problem-solving within primary care: A study protocol for a cluster randomized trial.

BACKGROUND: Team-based care has been identified as a key component in transforming primary care. An important factor in implementing team-based care is the requirement for teams to have daily huddles. During huddles, the care team, comprising physicians, nurses, and administrative staff, come together to discuss their daily schedules, track problems, and develop countermeasures to fix these problems. However, the impact of these huddles on staff burnout over time and patient outcomes are not clear. Further, there are challenges to implementing huddles in fast-paced primary care clinics. We will test whether the impact of a behavioral intervention of leadership training and problem-solving during the daily huddling process will result in higher consistent huddling in the intervention arm and result in higher team morale, reduced burnout, and improved patient outcomes. METHODS/DESIGN: We will conduct a care-team-level cluster randomized trial within primary care practices in two Midwestern states. The intervention will comprise a 1-day training retreat for leaders of primary care teams, biweekly sessions between huddle optimization coaches and members of the primary care teams, as well as coaching site visits at 30 and 100 days post intervention. This behavioral intervention will be compared to standard care, in which care teams have huddles without any support or training. Surveys of primary care team members will be administered at baseline (prior to training), 100 days (for the intervention arm only), and 180 days to assess team dynamics. The primary outcome of this trial will be team morale. Secondary outcomes will assess the impact of this intervention on clinician burnout, patient satisfaction, and quality of care. DISCUSSION: This trial will provide evidence on the impact of a behavioral intervention to implement huddles as a key component of team-based care models. Knowledge gained from this trial will be critical to broader deployment and successful implementation of team-based care models. TRIAL REGISTRATION: Clinicaltrials.gov , NCT03062670 . Registered on 23 February 2017.

Postfixed brachial plexus radiculopathy due to thoracic disc herniation in a collegiate wrestler: a case report.

OBJECTIVE: To present the unique case of a collegiate wrestler with C7 neurologic symptoms due to T1-T2 disc herniation. BACKGROUND: A 23-year-old male collegiate wrestler injured his neck in a wrestling tournament match and experienced pain, weakness, and numbness in his left upper extremity. He completed that match and 1 additional match that day with mild symptoms. Evaluation by a certified athletic trainer 6 days postinjury showed radiculopathy in the C7 distribution of his left upper extremity. He was evaluated further by the team physician, a primary care physician, and a neurosurgeon. DIFFERENTIAL DIAGNOSIS: Cervical spine injury, stinger/burner, peripheral nerve injury, spinal cord injury, thoracic outlet syndrome, brachial plexus radiculopathy. TREATMENT: The patient initially underwent nonoperative management with ice, heat, massage, electrical stimulation, shortwave diathermy, and nonsteroidal anti-inflammatory drugs without symptom resolution. Cervical spine radiographs were negative for bony pathologic conditions. Magnetic resonance imaging showed evidence of T1-T2 disc herniation. The patient underwent surgery to resolve the symptoms and enable him to participate for the remainder of the wrestling season. UNIQUENESS: Whereas brachial plexus radiculopathy commonly is seen in collision sports, a postfixed brachial plexus in which the T2 nerve root has substantial contribution to the innervation of the upper extremity is a rare anatomic variation with which many health care providers are unfamiliar. CONCLUSIONS: The injury sustained by the wrestler appeared to be C7 radiculopathy due to a brachial plexus traction injury. However, it ultimately was diagnosed as radiculopathy due to a T1-T2 thoracic intervertebral disc herniation causing impingement of a postfixed brachial plexus and required surgical intervention. Athletic trainers and physicians need to be aware of the anatomic variations of the brachial plexus when evaluating and caring for patients with suspected brachial plexus radiculopathies.