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.

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.

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.

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.

COVID Care Clinic: A Unique Way for Family Medicine to Care for the Community During the SARS-CoV-2 (COVID-19) Pandemic.

The COVID-19 pandemic has presented new challenges in how Primary Care clinicians care for community patients. Our organization quickly allocated 1 of our community clinic sites into a dedicated COVID Clinic caring for the COVID positive or any patient with COVID like symptoms to minimize contact with the well patients. A prerequisite for all patients to be seen in the COVID Care Clinic was a virtual visit staffed with Advanced Practice Providers that would further determine if the patient needed to seek emergency medical care or be seen in the COVID Clinic. From March 23, 2020 through May 15, 2020, 852 patients with COVID symptoms were seen in this clinic rather than the emergency department. This article describes a collaborative effort to care for a community during the COVID-19 pandemic. This unique setting allowed us to focus an appropriate level of care to a high risk population in a safe and effective manner in the ongoing effort to flatten the epidemiological curve.

Uses of Mobile Device Digital Photography of Dermatologic Conditions in Primary Care.

BACKGROUND: PhotoExam is a mobile app that incorporates digital photographs into the electronic health record (EHR) using iPhone operating system (iOS, Apple Inc)-based mobile devices. OBJECTIVE: The aim of this study was to describe usage patterns of PhotoExam in primary care and to assess clinician-level factors that influence the use of the PhotoExam app for teledermatology (TD) purposes. METHODS: Retrospective record review of primary care patients who had one or more photos taken with the PhotoExam app between February 16, 2015 to February 29, 2016 were reviewed for 30-day outcomes for rates of dermatology consult request, mode of dermatology consultation (curbside phone consult, eConsult, and in-person consult), specialty and training level of clinician using the app, performance of skin biopsy, and final pathological diagnosis (benign vs malignant). RESULTS: During the study period, there were 1139 photo sessions on 1059 unique patients. Of the 1139 sessions, 395 (34.68%) sessions documented dermatologist input in the EHR via dermatology curbside consultation, eConsult, and in-person dermatology consult. Clinicians utilized curbside phone consults preferentially over eConsults for TD. By clinician type, nurse practitioners (NPs) and physician assistants (PAs) were more likely to utilize the PhotoExam for TD as compared with physicians. By specialty type, pediatric clinicians were more likely to utilize the PhotoExam for TD as compared with family medicine and internal medicine clinicians. A total of 108 (9.5%) photo sessions had a biopsy performed of the photographed site. Of these, 46 biopsies (42.6%) were performed by a primary care clinician, and 27 (25.0%) biopsies were interpreted as a malignancy. Of the 27 biopsies that revealed malignant findings, 6 (22%) had a TD consultation before biopsy, and 10 (37%) of these biopsies were obtained by primary care clinicians. CONCLUSIONS: Clinicians primarily used the PhotoExam for non-TD purposes. Nurse practitioners and PAs utilized the app for TD purposes more than physicians. Primary care clinicians requested curbside dermatology consults more frequently than dermatology eConsults.

Factors influencing patients' choice of primary medical doctors.

This study investigated factors that influence adult patients' choice of primary care physicians and aimed to determine whether patients know the difference between internal medicine and family medicine. One thousand patients who had seen their primary care physician in either the family medicine or internal medicine department at Mayo Clinic in 2001 were sent a questionnaire to determine 1) the factors that influenced their choice of physician and 2) their knowledge of the characteristics of both specialties. Forty-six percent of the patients responded. Patients most often cited the doctor's "Approach to patient care" and "Interpersonal skills/communication" as affecting their choice of physician. Results also showed patient knowledge of the difference between family medicine and internal medicine was poor. The authors conclude that patients must be educated about the differences in the training of and care provided by different types of primary care physicians.

Improving risk assessment in family medicine through the family history.

The family history is used as a screening tool to identify persons who may be at risk for a heritable disorder. Primary care providers sometimes do not thoroughly gather and document the family history. This pilot study was undertaken to determine whether having a genetic counselor on site at our family medicine clinic 2 days a week for 3 months would improve the quality of the family history field in patient records. We compared 7 elements in the family history field for patients seen before and after the genetic counselor was on site. Documentation of 1 of the 7 elements (major disease) improved significantly after the intervention period (P = .02). Changing provider behavior with regard to gathering and documenting family history of major disease may be facilitated by tools to help collect the family history and by using the increasing number of available genetic tests.

Coping style in primary care adult patients with abridged somatoform disorders.

Background and objective Empirical data are scarce regarding the adaptive response to stress for patients with somatoform disorders. Our objective was to identify the preferred coping strategies of patients with abridged somatisation, a common condition in primary care. Because of the functional impairment associated with somatisation, we predicted a preference for less effective, emotion-focused coping strategies over more effective, problem-focused adaptations.Design We conducted a cross-sectional, observational study of physician referred primary care patients who presented with persistent, medically unexplained, physical symptoms. Patients were classified into two abridged somatisation groups by symptom frequency and duration, as determined by the Diagnostic Interview Schedule. The groups were compared with each other and with a non-clinical reference group; outcome variables were eight emotion- and problem-focused strategies, as measured by the Ways of Coping Questionnaire.Results Of the 72 eligible individuals, 48 participated in the study. Median age was 48 years and 75% of patients were women; 26 had somatic syndrome and 22 had a subthreshold somatising level. Patients with abridged somatisation disorders preferred emotion-focused coping strategies - typically detachment and impact minimisation, wishful thinking and problem avoidance.Conclusions Patients with abridged somatising disorder responded to stress with predominantly emotion-focused strategies, which may be associated with a lower level of positive adaptive outcome. Our findings suggest that patients with abridged somatising disorders might benefit from emphasis on problem-focused coping strategies, delivered through primary care, to improve quality of life and decrease healthcare utilisation costs.