A qualitative study of processes used to implement evidence-based care in a primary care practice.

BACKGROUND: Evidence-based guidelines for care of coronary heart disease patients are not fully implemented. Primary care practices provide most of the care for these patients. OBJECTIVE: To learn how providers and staff in a busy primary care practice implement interventions to provide evidence-based care of coronary heart disease patients. METHODS: We conducted a qualitative analysis of the responses to open-ended questions in nine electronically administered bimonthly surveys of key physicians, clinic staff and managers in the practice. RESULTS: Ten to 16 (mean=12.3) personnel responded to each survey. Nearly 30% were physicians and 40.5% were clinic staff. Four major themes emerged from the qualitative analysis: (i) giving data about not-at-goal patients to providers for care plan development; (ii) developing team roles and defining tasks; (iii) providing patient care and implementing care plans and (iv) providing technology support to generate useful, accurate data. The frequency that the subthemes were mentioned varied from survey to survey, but their mention persisted over the entire time of all nine surveys. CONCLUSIONS: Developing a system for implementing evidence-based care involves considerations of roles and teamwork, technology use to develop a patient registry and obtain needed clinical data, care processes for pre-visit planning, and between-visit care management. A registered nurse care manager is a central figure in implementing and sustaining the process. Implementing evidence-based guidelines is an ongoing process of revision, retraining and reinforcement.

A comparative analysis of recruitment methods used in a randomized trial of diabetes education interventions.

Recruitment methods heavily impact budget and outcomes in clinical trials. We conducted a post-hoc examination of the efficiency and cost of three different recruitment methods used in Journey for Control of Diabetes: the IDEA Study, a randomized controlled trial evaluating outcomes of group and individual diabetes education in New Mexico and Minnesota. Electronic databases were used to identify health plan members with diabetes and then one of the following three methods was used to recruit study participants: 1. Minnesota Method 1--Mail only (first half of recruitment period). Mailed invitations with return-response forms. 2. Minnesota Method 2--Mail and selective phone calls (second half of recruitment period). Mailed invitations with return-response forms and subsequent phone calls to nonresponders. 3. New Mexico Method 3--Mail and non-selective phone calls (full recruitment period): Mailed invitations with subsequent phone calls to all. The combined methods succeeded in meeting the recruitment goal of 623 subjects. There were 147 subjects recruited using Minnesota's Method 1, 190 using Minnesota's Method 2, and 286 using New Mexico's Method 3. Efficiency rates (percentage of invited patients who enrolled) were 4.2% for Method 1, 8.4% for Method 2, and 7.9% for Method 3. Calculated costs per enrolled subject were $71.58 (Method 1), $85.47 (Method 2), and $92.09 (Method 3). A mail-only method to assess study interest was relatively inexpensive but not efficient enough to sustain recruitment targets. Phone call follow-up after mailed invitations added to recruitment efficiency. Use of return-response forms with selective phone follow-up to non-responders was cost effective.

Does diabetes double the risk of depression?

PURPOSE: In this study, we compared the rate of depression diagnoses in adults with and without diabetes mellitus, while carefully controlling for number of primary care visits. METHODS: We matched adults with incident diabetes (n = 2,932) or prevalent diabetes (n = 14,144) to nondiabetic control patients based on (1) age and sex, or (2) age, sex, and number of outpatient primary care visits. Logistic regression analysis was used to assess the association between various predictors and a diagnosis of depression in each diabetes cohort relative to matched nondiabetic control patients. RESULTS: With matching for age and sex alone, patients with prevalent diabetes having few primary care visits were significantly more likely to have a new depression diagnosis than matched control patients (odds ratio [OR] = 1.46, 95% confidence interval [CI], 1.19-1.80), but this relationship diminished when patients made more than 10 primary care visits (OR = 0.95, 95% CI, 0.77-1.17). With additional matching for number of primary care visits, patients with prevalent diabetes mellitus with few primary care visits were more likely to have a new diagnosis of depression than those in control group (OR = 1.32, 95% CI, 1.07-1.63), but this relationship diminished and reversed when patients made more than 4 primary care visits (OR = 0.99, 95% CI, 0.80-1.23). Similar results were observed in the subset of patients with incident diabetes and their matched control patients. CONCLUSIONS: Patients with diabetes have little or no increase in the risk of a new diagnosis of depression relative to nondiabetic patients when analyses carefully control for the number of outpatient visits. Studies showing such an association may have inadequately adjusted for comorbidity or for exposure to the medical care system.

Identification and characteristics of vaccine refusers.

BACKGROUND: This study evaluated the utility of immunization registries in identifying vaccine refusals among children. Among refusers, we studied their socioeconomic characteristics and health care utilization patterns. METHODS: Medical records were reviewed to validate refusal status in the immunization registries of two health plans. Racial, education, and income characteristics of children claiming refusal were collected based on the census tract of each child. Health care utilization was identified using both electronic medical record and insurance claims. Within the immunization registries of two HMOs in the study, some providers use refusal and medical contraindication interchangeably, and some providers tend to always use "ever refusal." Therefore, we combined medical contraindication and refusal together and treated them all as "refusal" in this study. RESULTS: The immunization registry, compared to chart review, had negative predictive values of 85-92% and 90-97% for 2- and 6-year olds, and positive predictive values of only 52-74% and 59-62% to identify vaccine refusals. Refusers were more likely to reside in well-educated, higher income areas than non-refusers. Refusers had not opted out of health care system and continued, although less frequently for the age 2 and under group, to use services. CONCLUSION: Without enhancements to immunization registries, identifying children with immunization refusal would be time consuming. Since communities where refusers live are well educated, interventions should target these communities to communicate vaccine adverse events and consequences of vaccine preventable diseases.

Predictors of health care costs in adults with diabetes.

OBJECTIVE: The purpose of this study was to assess the impact of baseline A1c, cardiovascular disease, and depression on subsequent health care costs among adults with diabetes. RESEARCH DESIGN AND METHODS: A prospective analysis was performed of data from a patient survey and medical record review merged with 3 years of medical claims. Costs were estimated using detailed data on resource use and Medicare payment methodologies. Generalized linear models were used to analyze costs related to clinical predictors after adjusting for demographic and socioeconomic factors. RESULTS: In multivariate analysis of 1,694 adults with diabetes, 3-year costs in those with coronary heart disease (CHD) and hypertension were over 300% of those with diabetes only (46,879 dollars vs. 14,233 dollars; P < 0.05). Depression was associated with a 50% increase in costs (31,967 dollars vs. 21,609 dollars; P < 0.05). Relative to those with a baseline A1c of 6%, those with an A1c of 10% had 3-year costs that were 11% higher (26,408 dollars vs. 23,873 dollars; P < 0.05). Higher A1c predicted higher costs only for those with baseline A1c >7.5% (P = 0.015). CONCLUSIONS: In adults with diabetes, CHD, hypertension, and depression spectrum disorders more strongly predicted future costs than the A1c level. Concurrent with aggressive efforts to control glucose, greater efforts to prevent or control CHD, hypertension, and depression are necessary to control health care costs in adults with diabetes.