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.

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.

Facilitated Stories for Change: Digital Storytelling as a Tool for Engagement in Facilitated Discussion for Reduction of Diabetes-Related Health Disparities Among Rural Latino Patients With Diabetes.

INTRODUCTION: Latino populations, particularly those living in rural areas, experience a disproportionately high prevalence and poorer outcomes of type 2 diabetes mellitus (T2DM). The purpose of this study was to test the acceptability and perceived effectiveness of a group-based, facilitated digital storytelling intervention for T2DM self-management among rural Latino patients. METHOD: Twenty Latino adults with T2DM participated in facilitated storytelling discussions at two primary clinics. Participants viewed a 12-minute T2DM self-management digital storytelling intervention, followed by a facilitated group discussion. Surveys, observations, and focus groups were used to assess for acceptability and perceived effectiveness of the intervention through descriptive and qualitative analysis, informed by narrative and social cognitive theory. RESULTS: All participants found the intervention interesting and useful and reported improvement in confidence, motivation, and behavioral intentions for T2DM self-management. Themes mapped closely with narrative theory models, further suggestive of the behavior change potential. DISCUSSION: Facilitated discussions may add value to viewing of digital stories and represent a scalable approach to provide culturally congruent health care for Latino patients with diabetes in rural settings. Within the paradigm of group-based diabetes educational programs, this lends itself well to critical transcultural nursing 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.

Family Medicine Panel Size with Care Teams: Impact on Quality.

PURPOSE: The demand for comprehensive primary health care continues to expand. The development of team-based practice allows for improved capacity within a collective, collaborative environment. Our hypothesis was to determine the relationship between panel size and access, quality, patient satisfaction, and cost in a large family medicine group practice using a team-based care model. METHODS: Data were retrospectively collected from 36 family physicians and included total panel size of patients, percentage of time spent on patient care, cost of care, access metrics, diabetic quality metrics, patient satisfaction surveys, and patient care complexity scores. We used linear regression analysis to assess the relationship between adjusted physician panel size, panel complexity, and outcomes. RESULTS: The third available appointments (P < .01) and diabetic quality (P = .03) were negatively affected by increased panel size. Patient satisfaction, cost, and percentage fill rate were not affected by panel size. A physician-adjusted panel size larger than the current mean (2959 patients) was associated with a greater likelihood of poor-quality rankings (≤25th percentile) compared with those with a less than average panel size (odds ratio [OR], 7.61; 95% confidence interval [CI], 1.13-51.46). Increased panel size was associated with a longer time to the third available appointment (OR, 10.9; 95% CI, 1.36-87.26) compared with physicians with panel sizes smaller than the mean. CONCLUSIONS: We demonstrated a negative impact of larger panel size on diabetic quality results and available appointment access. Evaluation of a family medicine practice parameters while controlling for panel size and patient complexity may help determine the optimal panel size for a practice.

Haemoglobin levels correlates with the presence of coronary artery disease.

RATIONALE, AIMS AND OBJECTIVES: Although the condition of low haemoglobin (Hb) levels has been established as a risk factor in the development of coronary artery disease (CAD), it is still a debate particularly in patients with angiographically documented disease. In the present study, we sought to identify the relationship between Hb levels and the presence of CAD. METHODS: The study consisted of 356 consecutive patients referred for elective coronary angiography (CAG). Exclusion criteria included a history of prior MI within last 3 months, presence of neoplastic disorders or any inflammatory diseases or overt diabetes mellitus. Blood samples for haematologic and biochemical measurements were collected on admission following at least 12 hours of overnight fasting. Patients were divided into four groups based on the quartiles of Hb (quartile I < 13.50 g/dL, quartile II 13.50-14.70 g/dL, quartile III 14.71-15.74 g/dL, quartile IV > 15.74 g/dL). Additionally, patients filled out a questionnaire of asking their brief medical histories and baseline characteristics. RESULTS: Lower Hb quartiles were independently related to the presence of CAD in subjects who were referred to elective CAG. The patients with older age [P = 0.008, odds ratio (OR) = 1.042], male gender (P = 0.007, OR = 3.408), in quartile I (P = 0.003, OR = 5.697), in quartile II (P < 0.001, OR = 8.767), in quartile III (P = 0.011, P = 3.076), higher white blood cells count (P = 0.037, OR = 1.208), lower platelet count (P = 0.049, OR = 0.995), condition of current smoker (P = 0.030, OR = 2.548), higher value of fasting glucose (P = 0.014, OR = 1.038), estimated glomerular filtration rate < 60 (mL/min/1.73 m(2) ; P = 0.004, OR = 3.269) were more likely associated with the risk of the presence of CAD. CONCLUSIONS: The present study revealed that lower quartiles of Hb levels were independently related to the presence of CAD in subjects who were referred to elective CAG. Hb levels, which can be measured easily in almost all medical centres, may be considered as a potential predictor for the presence of CAD in patients at high risk for CAD.

Levothyroxine dosage is associated with stability of thyroid-stimulating hormone values.

OBJECTIVE: In patients treated for hypothyroidism, the usual practice is to monitor thyroid-stimulating hormone values yearly once a therapeutic dosage of levothyroxine is determined. This study investigates whether there are any clinical predictors that could identify a subset of patients who might be monitored safely on a less frequent basis. METHODS: With the use of a retrospective study design, 715 patients treated for hypothyroidism who had a normal (ie, therapeutic) thyroid-stimulating hormone value in 2006 while taking levothyroxine were identified. All thyroid-stimulating hormone values were then obtained through December 31, 2012. By using a Cox proportional hazard model, gender, age, body mass index, history of chronic autoimmune thyroiditis, initial thyroid-stimulating hormone level, and levothyroxine dose were analyzed for time to first abnormal thyroid-stimulating hormone value. RESULTS: Age, gender, history of chronic autoimmune thyroiditis, and body mass index at the time of initial normal thyroid-stimulating hormone were not associated significantly with time to abnormal thyroid-stimulating hormone value. Levothyroxine dose >125 µg/day had an increased hazard ratio of 2.4 (95% confidence interval, 1.7-3.4; P < .0001) for time to first follow-up abnormal thyroid-stimulating hormone value, but dosages less than that did not increase the hazard ratio. One year after the initial normal thyroid-stimulating hormone value, 91.1% of patients taking ≤ 125 µg/day had a continued normal thyroid-stimulating hormone, whereas only 73.3% of patients taking >125 µg/day did. Transformed thyroid-stimulating hormone value (which represents a measure of how far the initial thyroid-stimulating hormone was from the midpoint of the normal range) also had an increased hazard ratio of 1.14 (95% confidence interval, 1.1-1.2; P < .0001) for time to first abnormal thyroid-stimulating hormone value. CONCLUSIONS: For patients receiving ≤ 125 µg/day of levothyroxine, we propose that a testing interval up to 2 years may be acceptable if their thyroid-stimulating hormone is well within the normal range.

Risk-stratification methods for identifying patients for care coordination.

BACKGROUND: Care coordination is a key component of the patient-centered medical home. However, the mechanism for identifying primary care patients who may benefit the most from this model of care is unclear. OBJECTIVES: To evaluate the performance of several risk-adjustment/stratification instruments in predicting healthcare utilization. STUDY DESIGN: Retrospective cohort analysis. METHODS: All adults empaneled in 2009 and 2010 (n = 83,187) in a primary care practice were studied. We evaluated 6 models: Adjusted Clinical Groups (ACGs), Hierarchical Condition Categories (HCCs), Elder Risk Assessment, Chronic Comorbidity Count, Charlson Comorbidity Index, and Minnesota Health Care Home Tiering. A seventh model combining Minnesota Tiering with ERA score was also assessed. Logistic regression models using demographic characteristics and diagnoses from 2009 were used to predict healthcare utilization and costs for 2010 with binary outcomes (emergency department [ED] visits, hospitalizations, 30-day readmissions, and highcost users in the top 10%), using the C statistic and goodness of fit among the top decile. RESULTS: The ACG model outperformed the others in predicting hospitalizations with a C statistic range of 0.67 (CMS-HCC) to 0.73. In predicting ED visits, the C statistic ranged from 0.58 (CMSHCC) to 0.67 (ACG). When predicting the top 10% highest cost users, the performance of the ACG model was good (area under the curve = 0.81) and superior to the others. CONCLUSIONS: Although ACG models generally performed better in predicting utilization, use of any of these models will help practices implement care coordination more efficiently.

Prolonged care management for depression: a case-controlled study of those enrolled for more than one year.

INTRODUCTION: Collaborative care management (CCM) for the treatment of depression has been shown to be an effective therapy. CCM can be seen as a resource intensive treatment. Early identification of patients who would not be effectively treated with CCM could allow for alteration of therapy or change in modality. METHODS: A retrospective case-controlled study used 132 patients with prolonged enrollment (>1 year) in CCM (cases) and 396 randomized CCM patients who achieved remission within 6 months (controls). The hypothesis was that by studying the epidemiology of patients in prolonged care management (PCM), characteristics could be determined to help define this group. RESULTS: With regression modeling, the odds of a patient having PCM at 1 year was highly significant for those unmarried patients (odds ratio [OR] = 1.736, confidence interval [CI] = 1.115-2.703, P = .015) with dysthymia (OR = 2.362, CI = 1.104-5.052, P = .027) and severe depression (OR = 2.856, CI = 1.551-5.260, P = .001). The adjusted baseline Patient Health Questionnaire-9 (PHQ-9) score showed a difference of 16.0 for the cases versus 14.8 for the controls (P < .001). By 10 weeks, the difference is much larger at (10.7 vs 4.9, P < .001). At 26 weeks, the control group had an adjusted average PHQ-9 score of 2.0, whereas the case group was still elevated at 10.2 (P < .001). CONCLUSIONS: Case-controlled analysis of PCM patients demonstrated independent predictors (such as unmarried status, diagnosis of dysthymia or severe depression), however, no baseline data was of sufficient clarity to suggest changes in clinical practice. The trend of the patient's PHQ-9 over time was strongly suggestive of allowing differentiation between the groups.

Association of HbA1c with emotion regulation, intolerance of uncertainty, and purpose in life in type 2 diabetes mellitus.

BACKGROUND: The extant literature lacks breadth on psychological variables associated with health outcome for type 2 diabetes mellitus (T2DM). This investigation extends the scope of psychological information by reporting on previously unpublished factors. OBJECTIVE: To investigate if intolerance of uncertainty, emotion regulation, or purpose in life differentiate T2DM adults with sustained high HbA(1c) (HH) vs. sustained acceptable HbA(1c) (AH). SUBJECTS AND METHODS: Cross-sectional observational study. Adult patients with diagnosed T2DM meeting inclusionary criteria for AH, HH, or a nondiabetic reference group (NDR) were randomly selected and invited to participate. Patients who consented and participated resulted in a final sample of 312 subgrouped as follows: HH (n = 108); AH (n = 98); and NDR (n = 106). Data sources included a survey, self-report questionnaires, and electronic medical record (EMR). RESULTS: HH individuals with T2DM reported lower purpose in life satisfaction (p = 0.005) compared to the NDR group. The effect size for this finding is in the small-to-medium range using Cohen's guidelines for estimating clinical relevance. The HH-AH comparison on purpose in life was nonsignificant. The emotion regulation and intolerance of uncertainty comparisons across the three groups were not significant. CONCLUSIONS: The present study determined that lower purpose in life satisfaction is associated with higher HbA(1c). In a T2DM patient with sustained high HbA(1c), the primary care clinician is encouraged to consider screening for purpose in life satisfaction by asking a single question such as "Do the things you do in your life seem important and worthwhile?" The patient's response will assist the clinician in determining if meaning or purpose in life distress may be interferring with diabetes self-care. If this is the case, the clinician can shift the conversation to the value of behavioral and emotional health counseling.

Repeat retail clinic visits: impact of insurance coverage and age of patient.

As retail clinics provide a less costly alternative for health care, it would be reasonable to expect an increase in multiple (repeat) retail visits by those patients who may have expenses for receiving primary care. If costs were not a significant factor, then repeat visits should not be significantly different between these patients and those with coverage for primary care visits. The hypothesis for this study was that patients with the potential for out-of-pocket expenses would have a higher frequency of repeat retail clinic visits within 180 days compared to those with primary care coverage. A retrospective chart review was conducted of 5703 patients utilizing a retail clinic in Rochester, Minnesota from January 1, 2009 through June 30, 2009. The first visit to the retail clinic was considered the index visit and the chart was reviewed for repeat retail clinic visits within the next 180 days. Using a multiple logistic regression model, the odds of a pediatric patient (N=2344) having a repeat retail visit within 180 days of the index visit were not significantly impacted by insurance coverage (P=0.4209). Of the 3359 adult patients, those with unknown coverage had a 25.6% higher odds ratio of repeat retail clinic visits than those with insurance coverage (odds ratio 1.2557, confidence interval 1.0421-1.5131). This study suggested that when cost is an issue, the adult patient may favor retail clinics for episodic, low-acuity health care. In contrast, the pediatric population did not, suggesting that other factors, such as convenience, may play more of a role in the choice of episodic health care for this age group.

Use of a clinical decision support system to increase osteoporosis screening.

BACKGROUND: In 2002, the US Preventive Services Task Force recommended routine osteoporosis screening for women aged 65 years or older. However, studies have indicated that osteoporosis remains underdiagnosed, and various methods such as the use of health information technology have been tried to increase screening rates. We investigated whether we could boost the low rates of bone mineral density testing with implementation of a point-of-care clinical decision support system in our primary care practice. METHODS: We retrospectively reviewed the medical records of female patients eligible for osteoporosis screening who had no prior bone mineral density test who were seen at our primary care practice sites in 2007 or 2008 (before and after implementation of a point-of-care clinical decision support system). RESULTS: Overall, screening rates were 80.1% in 2007 and 84.1% in 2008 (P < 0.001). Of patients who did not have osteoporosis screening before the visit, 5.87% completed the screening after the visit in 2007, compared with 9.79% in 2008 (when the clinical support system was implemented), a 66.7% improvement (P = 0.025). CONCLUSION: Clinical decision support for primary care doctors significantly improved osteoporosis screening rates among eligible women. Carefully designed clinical decision support systems can optimize care delivery, ensuring that important preventive services such as osteoporosis screening for patients at risk for fracture are performed while unnecessary testing is avoided.

Predictors of osteoporosis screening completion rates in a primary care practice.

The United States Preventive Services Task Force and the National Osteoporosis Foundation recommend routine osteoporosis screening for women aged 65 years or older. Previous studies have shown that the use of a clinical decision-support tool significantly improves screening rates. In a recently published study, a statistically significant improvement was found in the screening rates for eligible women with use of the tool. To evaluate whether a clinical decision-support tool independently predicts completion of osteoporosis screening tests and to identify predictors of screening completion, we examined the records of 2462 female patients who were eligible for osteoporosis screening but had no prior baseline screening and who were seen in our primary care practices in 2007 and 2008. Patient and provider characteristics and clinic visit type were identified, and their association with screening test completion was statistically analyzed using both univariate and multivariate models. Screening completion rates increased significantly from 2007 to 2008. Factors associated with increased likelihood of screening completion included race, marital status, residence, presence of comorbidity (cancer, rheumatologic disease), and the year and type of visit. Screening was less likely for women aged 80 years or older. The use of a point-of-care decision-support tool not only improved osteoporosis screening rates significantly but appeared to be an independent predictor of screening completion. It potentially can facilitate the systematic and effective delivery of preventive health services to patients in the primary care setting.

Impact of online primary care visits on standard costs: a pilot study.

As medical providers seek new ways to control costs, online visits have begun to receive serious consideration. The purpose of this study was to compare the odds of being a cost outlier during a 6-month period after either an online visit or a standard drop-in visit in a conventional medical office setting. Medical records of primary care patients (both adults and children) seen in a large group practice in Minnesota in 2008 were analyzed for this study. Two groups of patients were studied: those who had an online visit (N = 390) and a comparison group who had regular office care for same-day, acute visits (N = 376). Case types were classified as either complex or common, with common being defined as treatment for pinkeye, sore throat, viral illness, bronchitis, or cough. Outliers were defined as patients for whom standard costs exceeded the 75(th) percentile during a 6-month period after the index visit. Multiple logistic regression analysis was used to adjust for differences between groups. The percentage of online visitors who were cost outliers was 21.2 (versus 28.5 in the standard visit group). Median standard costs were $161 for online visits and $219 for same-day acute visits. The adjusted odds of being a cost outlier was lower for the online visit group than for the standard visit group (odds ratio [OR] 0.52, 95% confidence interval [95% CI] 0.35-0.77) after adjusting for number of visits in the previous 6 months, age, sex, and case type. Outpatient visits in the previous 6 months were positively related to outlier status (OR 1.23, 95% CI 1.17-1.29). Online visits appeared to reduce medical costs for patients during a 6-month period after the visit.

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.