Garrison, Gregory M.

Introduction: In the traditional model of care, an annual visit was dedicated to the task of assessing and delivering preventive care. With the move away from annual physicals, primary care physicians are addressing preventive services at every clinic visit often aided by electronic clinical decision support (CDS) systems. Methods: We conducted a case study of a visit-driven CDS system in use at a primary care clinic. Steady-state performance was assessed via control charts of quality metrics, data regarding completion of recommendations, and an analysis of screening intervals achieved with patient visits. Results: Under this visit-driven CDS system, quality metric performance was poor and declining. Almost half of the patients were not screened (46.5%), and the other half were overscreened the majority of the time (83.3%). Recommended preventive services were ordered less than half the time (42.6%), despite CDS reminders. Discussion: Various barriers and systematic inefficiencies combined to produce ineffective screening in this visit-driven CDS preventive service delivery system. As a result, we conclude a visit-driven system cannot produce optimal results. In order to improve performance, preventive services should be delivered separately from clinical visits, perhaps by a “preventive service ranger” (PSR) utilizing the CDS system to review each patient once annually. Under such a system, patients would receive preventive services in an organized and efficient fashion, potentially leading to better continuity, higher quality metrics that are mathematically predictable, and improved satisfaction.

The number and timing of unplanned admissions to inpatient teaching services vary. Recent changes to resident duty hours make it essential to maximize learning experiences and balance workload on these services. Queueing theory provides a mechanism for understanding and planning for the variations in admissions and daily census. Daily admissions, length of stay, and daily census were modeled for a teaching inpatient family medicine service over 46 months using an M/G/∞ queueing model. Q–Q plots and a Kolmogorov–Smirnov test were used to check the fit of actual data to the model. Admissions and daily census followed a Poisson distribution (λ = 3.28 and λ = 8.28, respectively), while length-of-stay followed a lognormal distribution (µ = 0.49, σ2 = 0.83). The M/G/∞ queueing model proved useful for predicting overflow admission frequency, defining expected resident workload in terms of patient-days, and determining hospital unit size requirements.

Controlling the overall cost of medical care requires controlling the number of physician visits. Nurse practitioners and physician assistants (NPs/PAs) may function as lower-cost substitutes for physicians or they may complement physician services. The association between NP/PA and physician visits when NPs/PAs are not working as primary care providers (PCPs) has not been thoroughly studied. A sample of 400 family medicine patients drawn from 1 large multisite practice was studied using multiple logistic regression analysis. NPs/PAs did not function as PCPs during the study period. Patients were defined as outliers if they visited physicians more than 5 times in a year. Patients who visited NPs/PAs in non-retail clinics were significantly more likely to be physician visit outliers. Visits to NPs/PAs in retail clinics were not related to physician visits. NP/PA visits in standard medical office settings complement physician visits when the NPs/PAs were not working as PCPs in this large multisite practice. Health care reform proposals relying on increased use of NPs/PAs may be more cost-efficient if NPs/PAs are located in retail settings or function as PCPs. ( Population Health Management 2013;16:242–245)

Background: Polypharmacy has been identified as a quality indicator, but no studies have been reported about the epidemiology of polypharmacy among hospital patients at discharge. Methods: Records of 142 family medicine patients aged ≥65 years who were discharged from the hospital during the period November 2008 to October 2009 were extracted. Forty-six of these patients were readmitted within 30 days and the remaining 96 not readmitted within 30 days. Polypharmacy was measured as >16 medications at dismissal. Independent variables related to person (use of medical care in the 12 months prior to hospitalization, number of high-risk diagnoses, and demographic characteristics), place (living situation at admission and disposition location), and time (month of admission). Chronic obstructive pulmonary disease, cancer, diabetes mellitus, congestive heart failure, and coronary artery disease were diagnoses determined to be high-risk. Results: Mean number of medications at dismissal was 13.5 and 23.2% of patients were prescribed more than 16 medications. No interactions were found between readmission status and any of the independent variables. Use of medical services in the previous year was not related to polypharmacy and no seasonal pattern was detected. Two or more high-risk diagnoses were independently related to polypharmacy (odds ratio [OR] = 4.75, confidence interval [CI] = 1.0-11.2, P = .00). Being discharged to a location with personal health services such as home care or a skilled nursing facility was also related to polypharmacy (OR = 3.07, CI = 1.3-7.2, P = .01). Conclusion: Drug reviews intended to reduce the rate of polypharmacy among discharged persons aged ≥65 years can be targeted at patients who have 2 or more high-risk diagnoses and at those discharged to receive personal health services either at home or in a convalescence facility.

Purpose: The purpose of this study was to compare continuity of care for family medicine patients using retail medicine clinics to continuity for patients not using retail clinics. Retail medicine clinics have become popular in some markets. However, their impact on continuity of care has not been studied. Methods: Electronic medical records of adult primary care patients seen in a large group practice in Minnesota in 2011 were analyzed for this study. Two randomly chosen groups of patients were selected (N = 400): those using 1 of 3 retail walk-in clinics staffed by nurse practitioners in addition to standard office care and a comparison group that only used standard office care. Continuity was measured as the percentage of visits that involved the primary care provider. We also compared patients who made zero visits to their primary care providers with those who made some visits to their primary care providers. Results: Continuity of care was lower for patients who used retail clinics than for patients who did not use retail clinics (0.17 vs 0.44, mean difference 0.27). The percentage of patients who made zero visits to their primary care providers was 54.5 for users of retail clinics versus 31.0 for those who did not use retail clinics. Conclusions: Continuity of care should be monitored as retail medicine continues to expand.

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. ( Population Health Management 2012;15:358–361)

The purpose of this study was to compare return visits made by patients within 2 weeks after using retail nurse practitioner clinics to return visits made by similar patients after using standard medical office clinics. Retail medicine clinics have become widely available. However, their impact on return visit rates compared to standard medical office visits for similar patients has not been extensively studied. Electronic medical records of adult primary care patients seen in a large group practice in Minnesota in 2009 were analyzed for this study. Patients who were treated for sinusitis were selected. Two groups of patients were studied: those who used one of 2 retail walk-in clinics staffed by nurse practitioners and a comparison group who used one of 4 regular office clinics. The dependent variable was a return office visit to any site within 2 weeks. Multiple logistic regression analysis was used to adjust for case-mix differences between groups. Unadjusted odds of return visits were lower for retail clinic patients than for standard office care patients. After adjustment for case mix, patients with more outpatient visits in the previous 6 months had higher odds of return visits within 2 weeks (2–6 prior visits: odds ratio [OR]=1.99, P =0.00; 6 or more prior visits: OR=6.80, P =0.00). The odds of a return visit within 2 weeks were not different by clinic type after adjusting for propensity to use services (OR=1.17, P =0.28). After adjusting for case mix differences, return visit rates did not differ by clinic type. ( Population Health Management 2012;15:216–219)