Predicting Provider Workload Using Predicted Patient Risk Score and Social Determinants of Health in Primary Care Setting.

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.

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.

Diagnosis and Management of Sodium Disorders: Hyponatremia and Hypernatremia.

Hyponatremia and hypernatremia are electrolyte disorders that can be associated with poor outcomes. Hyponatremia is considered mild when the sodium concentration is 130 to 134 mEq per L, moderate when 125 to 129 mEq per L, and severe when less than 125 mEq per L. Mild symptoms include nausea, vomiting, weakness, headache, and mild neurocognitive deficits. Severe symptoms of hyponatremia include delirium, confusion, impaired consciousness, ataxia, seizures, and, rarely, brain herniation and death. Patients with a sodium concentration of less than 125 mEq per L and severe symptoms require emergency infusions with 3% hypertonic saline. Using calculators to guide fluid replacement helps avoid overly rapid correction of sodium concentration, which can cause osmotic demyelination syndrome. Physicians should identify the cause of a patient's hyponatremia, if possible; however, treatment should not be delayed while a diagnosis is pursued. Common causes include certain medications, excessive alcohol consumption, very low-salt diets, and excessive free water intake during exercise. Management to correct sodium concentration is based on whether the patient is hypovolemic, euvolemic, or hypervolemic. Hypovolemic hyponatremia is treated with normal saline infusions. Treating euvolemic hyponatremia includes restricting free water consumption or using salt tablets or intravenous vaptans. Hypervolemic hyponatremia is treated primarily by managing the underlying cause (e.g., heart failure, cirrhosis) and free water restriction. Hypernatremia is less common than hyponatremia. Mild hypernatremia is often caused by dehydration resulting from an impaired thirst mechanism or lack of access to water; however, other causes, such as diabetes insipidus, are possible. Treatment starts with addressing the underlying etiology and correcting the fluid deficit. When sodium is severely elevated, patients are symptomatic, or intravenous fluids are required, hypotonic fluid replacement is necessary.

Stool-Based Colorectal Cancer Screening Test Performance Characteristics in Those With and Without Hemorrhoids.

Objective: To evaluate the effect of hemorrhoids on noninvasive stool test performance for colorectal cancer (CRC) screening. Patients and Methods: We conducted a retrospective cohort study of test characteristics for the fecal immunochemical test (FIT) and the multitarget stool DNA (mt-sDNA) test, on the basis of hemorrhoid status, recorded at the time of colonoscopy, among patients enrolled in the pivotal prospective study for mt-sDNA that was conducted from June 2011, to May 2013. Test characteristics (sensitivity, specificity, positive, and negative predictive values) for FIT and mt-sDNA (performed < 90 days before colonoscopy) were stratified by the presence of hemorrhoids and compared. Results: Hemorrhoids were found in 51.7% (5163 of 9989) of the study cohort. Across all test characteristics, there were no statistically significant differences for FIT or mt-sDNA when stratified by hemorrhoid status. Analysis revealed mt-sDNA sensitivity of 44% and 41% for advanced precancerous lesions in nonhemorrhoidal and hemorrhoid patients, respectively (P=.41). The FIT sensitivity among the same lesion category was 24.9% in patients without hemorrhoids and 22.8% in those with hemorrhoids (P=.48). The mt-sDNA specificity was 86.4% in patients without hemorrhoids vs 87.7% in those with hemorrhoids (P=.67), although FIT specificity was 95.0% among patients without hemorrhoids vs 94.7% in those with hemorrhoids (P=.44). Conclusion: The presence of asymptomatic hemorrhoids did not adversely affect test performance in this large clinical study. These findings suggest that in the absence of overt gastrointestinal bleeding, FIT and mt-sDNA are options for CRC screening, irrespective of hemorrhoid status. Trial Registration: clinicaltrials.gov Identifier: NCT01397747.

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.

Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study.

BACKGROUND: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. OBJECTIVE: This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. METHODS: A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. RESULTS: Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool's promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. CONCLUSIONS: The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087.

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices.

Importance: Diabetes management operates under a complex interrelationship between behavioral, social, and economic factors that affect a patient's ability to self-manage and access care. Objective: To examine the association between 2 complementary area-based metrics, area deprivation index (ADI) score and rurality, and optimal diabetes care. Design, Setting, and Participants: This cross-sectional study analyzed the electronic health records of patients who were receiving care at any of the 75 Mayo Clinic or Mayo Clinic Health System primary care practices in Minnesota, Iowa, and Wisconsin in 2019. Participants were adults with diabetes aged 18 to 75 years. All data were abstracted and analyzed between June 1 and November 30, 2020. Main Outcomes and Measures: The primary outcome was the attainment of all 5 components of the D5 metric of optimal diabetes care: glycemic control (hemoglobin A1c <8.0%), blood pressure (BP) control (systolic BP <140 mm Hg and diastolic BP <90 mm Hg), lipid control (use of statin therapy according to recommended guidelines), aspirin use (for patients with ischemic vascular disease), and no tobacco use. The proportion of patients receiving optimal diabetes care was calculated as a function of block group-level ADI score (a composite measure of 17 US Census indicators) and zip code-level rurality (calculated using Rural-Urban Commuting Area codes). Odds of achieving the D5 metric and its components were assessed using logistic regression that was adjusted for demographic characteristics, coronary artery disease history, and primary care team specialty. Results: Among the 31 934 patients included in the study (mean [SD] age, 59 [11.7] years; 17 645 men [55.3%]), 13 138 (41.1%) achieved the D5 metric of optimal diabetes care. Overall, 4090 patients (12.8%) resided in the least deprived quintile (quintile 1) of block groups and 1614 (5.1%) lived in the most deprived quintile (quintile 5), while 9193 patients (28.8%) lived in rural areas and 2299 (7.2%) in highly rural areas. The odds of meeting the D5 metric were lower for individuals residing in quintile 5 vs quintile 1 block groups (odds ratio [OR], 0.72; 95% CI, 0.67-0.78). Patients residing in rural (OR, 0.84; 95% CI, 0.73-0.97) and highly rural (OR, 0.81; 95% CI, 0.72-0.91) zip codes were also less likely to attain the D5 metric compared with those in urban areas. Conclusions and Relevance: This cross-sectional study found that patients living in more deprived and rural areas were significantly less likely to attain high-quality diabetes care compared with those living in less deprived and urban areas. The results call for geographically targeted population health management efforts by health systems, public health agencies, and payers.

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.

Association of Neighborhood Measures of Social Determinants of Health With Breast, Cervical, and Colorectal Cancer Screening Rates in the US Midwest.

Importance: Despite advances in cancer treatment and cancer-related outcomes, disparities in cancer mortality remain. Lower rates of cancer prevention screening and consequent delays in diagnosis may exacerbate these disparities. Better understanding of the association between area-level social determinants of health and cancer screening may be helpful to increase screening rates. Objective: To examine the association between area deprivation, rurality, and screening for breast, cervical, and colorectal cancer in patients from an integrated health care delivery system in 3 US Midwest states (Minnesota, Iowa, and Wisconsin). Design, Setting, and Participants: In this cross-sectional study of adults receiving primary care at 75 primary care practices in Minnesota, Iowa, and Wisconsin, rates of recommended breast, cervical, and colorectal cancer screening completion were ascertained using electronic health records between July 1, 2016, and June 30, 2017. The area deprivation index (ADI) is a composite measure of social determinants of health composed of 17 US Census indicators and was calculated for all census block groups in Minnesota, Iowa, and Wisconsin (11 230 census block groups). Rurality was defined at the zip code level. Using multivariable logistic regression, this study examined the association between the ADI, rurality, and completion of cancer screening after adjusting for age, Charlson Comorbidity Index, race, and sex (for colorectal cancer only). Main Outcomes and Measures: Completion of recommended breast, cervical, and colorectal cancer screening. Results: The study cohorts were composed of 78 302 patients eligible for breast cancer screening (mean [SD] age, 61.8 [7.1] years), 126 731 patients eligible for cervical cancer screening (mean [SD] age, 42.6 [13.2] years), and 145 550 patients eligible for colorectal cancer screening (mean [SD] age, 62.4 [7.0] years; 52.9% [77 048 of 145 550] female). The odds of completing recommended screening were decreased for individuals living in the most deprived (highest ADI) census block group quintile compared with the least deprived (lowest ADI) quintile: the odds ratios were 0.51 (95% CI, 0.46-0.57) for breast cancer, 0.58 (95% CI, 0.54-0.62) for cervical cancer, and 0.57 (95% CI, 0.53-0.61) for colorectal cancer. Individuals living in rural areas compared with urban areas also had lower rates of cancer screening: the odds ratios were 0.76 (95% CI, 0.72-0.79) for breast cancer, 0.81 (95% CI, 0.79-0.83) for cervical cancer, and 0.93 (95% CI, 0.91-0.96) for colorectal cancer. Conclusions and Relevance: Individuals living in areas of greater deprivation and rurality had lower rates of recommended cancer screening, signaling the need for effective intervention strategies that may include improved community partnerships and patient engagement to enhance access to screening in highest-risk populations.

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.