Measurement of provider adherence to a guideline-based decision support system (DSS) presents a number of important challenges. Establishing a causal relationship between the DSS and change in concordance requires consideration of both the primary intention of the guideline and different ways providers attempt to satisfy the guideline. During our work with a guideline-based decision support system for hypertension, ATHENA DSS, we document a number of subtle deviations from the strict hypertension guideline recommendations that ultimately demonstrate provider adherence. We believe that understanding these complexities is crucial to any valid evaluation of provider adherence. We also describe the development of an advisory evaluation engine that automates the interpretation of clinician adherence with the DSS on multiple levels, facilitating the high volume of complex data analysis that is created in a clinical trial of a guideline-based DSS.

Objective:

Care remains suboptimal for a substantial proportion of the more than 17 million

patients in the United States with diabetes. This review examines strategies for improving the quality of care for adult type 2 diabetic patients, through changes in provider behavior and modifications to the organization of care.

Search Strategy and Inclusion Criteria: The researchers searched the MEDLINE® database, the Cochrane Collaboration's Effective Practice and Organisation of Care (EPOC) registry, article bibliographies, and relevant journals for experimental evaluations of quality improvement (QI) interventions involving outpatient care for adults with type 2 diabetes mellitus. The investigators included randomized or quasi-randomized controlled trials, controlled before/after studies, and interrupted time series in which at least one reported outcome involved changes in serum hemoglobin A1c or a measure of provider adherence to a recommended process of care.

Data Collection and Analysis:

Two reviewers independently abstracted relevant data, including classifying the components of each QI intervention as provider education, provider reminders, facilitated relay of clinical information, patient education, promotion of self management,patient reminders, audit and feedback, organizational change, or financial incentives. Certain categories were further subdivided into major subtypes (e.g., professional meetings for provider education and disease management for organizational change). The investigators also assessed the impact of clinical information systems as a mediator for interventions of all types. They compared different QI strategies in terms of the median effects achieved for glycemic control and for a generalized measure of clinician adherence. In addition, linear regression analyses were performed using methodologic features and QI types as predictors, taking into account baseline groups differences and study size.

Main Results:

Fifty-eight articles reporting a total of 66 trials met the established inclusion criteria. The most common interventions employed were organizational change in 40 trials, patient education in 28 trials, and provider education in 24 trials. Fifty-two trials involved interventions employing more than one QI strategy, with a median of 2 strategies per trial and a maximum of 5. The included trials reported a median absolute reduction in HbA1c of 0.48% interquartile range: 0.20%, 1.38%), and a median improvement in clinician adherence of 4.9% (interquartile range: 3.8%, 15.0%). Trials in the lower 2 quartiles of sample size reported substantially larger effect sizes, as did non-randomized trials, strongly suggesting the presence of publication bias, with publication of smaller non-randomized trials occurring more often when reported improvements are large. Multifaceted trials reported a median reduction in HbA1c of 0.60% (interquartile range: 0.30%, 1.40%), compared to a median reduction of 0.0% (interquartile range: -0.08%, 0.16%) for trials of a single intervention (p=0.01). The benefit of employing more than one QI strategy appeared to persist among larger, randomized trials, but the small numbers of studies limits the reliability of this impression. The investigators did not find any specific type of QI strategy to confer unambiguous benefit. Provider education and disease management were the only strategies to approach statistical significance, compared with interventions absent these strategies.

Conclusion:

The authors' analysis of quality improvement strategies for diabetes care showed no particular type of QI to have an advantage over others, but suggested that employing at least two strategies provides a greater chance of success than single-faceted interventions, in terms of improving glycemic control or provider adherence. These conclusions are limited by probable publication bias favoring smaller trials and non-randomized trials, and the confounding presence of multiple QI strategies in a given intervention, as well as important patient and provider factors, and organizational characteristics.

Objective: To quantify the effects of informal caregiver availability and public funding on formal long-term care (LTC) expenditures in developed countries.

Data Source/Study Setting: Secondary data were acquired for 15 Organization for Economic Cooperation and Development (OECD) countries from 1970 to 2000. Study Design. Secondary data analysis, applying fixed- and random-effects models to time-series cross-sectional data. Outcome variables are inpatient or home heath LTC expenditures. Key explanatory variables are measures of the availability of informal caregivers, generosity in public funding for formal LTC, and the proportion of the elderly population in the total population.

Data Collection/Extraction Method: Aggregated macro data were obtained from OECD Health Data, United Nations Demographic Yearbooks, and U.S. Census Bureau International Data Base.

Principal Findings: Most of the 15 OECD countries experienced growth in LTC expenditures over the study period. The availability of a spouse caregiver, measured by male-to-female ratio among the elderly, is associated with a $28,840 (1995 U.S. dollars) annual reduction in formal LTC expenditure per additional elderly male. Availability of an adult child caregiver, measured by female labor force participation and full-time/part-time status shift, is associated with a reduction of $310 to $3,830 in LTC expenditures. These impacts on LTC expenditure vary across countries and across time within a country.

Conclusions: The availability of an informal caregiver, particularly a spouse caregiver, is among the most important factors explaining variation in LTC expenditure growth. Long-term care policies should take into account behavioral responses: decreased public funding in LTC may lead working women to leave the labor force to provide more informal care.

OBJECTIVES: To determine (1) whether commercial health plans' coverage criteria for a costly technology-based medical intervention are consistent with recent clinical effectiveness evidence, (2) whether medical directors adhere to planwide coverage criteria when making coverage determinations for individual patients, and (3) if any organizational characteristics are associated with having more stringent coverage criteria or making more frequent coverage denials.

STUDY DESIGN: Case-based survey of medical directors of US commercial health plans.

METHODS: A close-ended survey was mailed to 346 medical directors meeting eligibility criteria, asking about the criteria specified in their plans' coverage policies for electrical bone growth stimulation (EBGS) and whether they would cover this intervention for a hypothetical patient with abnormal union of long-bone fracture.

RESULTS: Responses from 228 (66%) of the 346 directors indicated that approximately 72% of plans have a formal coverage policy for EBGS for long-bone fractures. More than 30% of plans specify that longer than 4 months must elapse before EBGS is attempted, although clinical studies do not support absolute waiting times. Directors of approximately 61% of plans with policies requiring extended waiting periods would nevertheless authorize EBGS for patients who did not meet this criterion.

CONCLUSIONS: Health plans apply varied criteria in coverage policies for technology-based treatments such as EBGS, but do not always adhere to stated criteria when determining coverage for individual patients. For-profit status, accreditation status, geographic location, and size of plan are not associated with being more or less likely to authorize EBGS.

We all have a stake in the size of the physician workforce. With too few physicians, access to care will be compromised; with too many, there will be strong pressures to overconsume health services. Increasing the production of U.S.-trained physicians by expanding physical resources of medical schools and creating new residency and fellowship positions will be costly and will have delayed, long-lasting effects on the supply of physicians' services. According to those who believe that physicians increase the demand for their own services, every additional physician would generate added health care costs for the length of a career, which now averages about 30 years. These increased expenditures would dwarf the short-term costs of expanding our capacity to train physicians.

Because new graduates are a small fraction of the total physician workforce, the supply of physicians would change little in the short run, even if it were possible to expand the number of training positions instantly. In an article in this issue (1), Richard Cooper forcefully argues that this delay is an important reason to take immediate action to increase the production of physicians. He projects that the United States will have 200 000 fewer physicians than we need in 2020. We agree that demographic and economic trends could increase the demand for physician services in the coming years, but we also believe that his forecast contains far too many uncertainties to serve as the basis for taking immediate action. We think that Cooper's analysis does not take account of important factors that could change the need for large increases in physician supply. In this commentary, we discuss the potential roles of a healthier aging population, changes in government policy, new technology, physician-induced demand for health care, and changes in the price of health care.

BACKGROUND/OBJECTIVES: The importance of teams for improving quality of care has received increased attention. We examine both the correlates of self-assessed or perceived team effectiveness and its consequences for actually making changes to improve care for people with chronic illness. STUDY SETTING AND METHODS: Data were obtained from 40 teams participating in the national evaluation of the Improving Chronic Illness Care Program. Based on current theory and literature, measures were derived of organizational culture, a focus on patient satisfaction, presence of a team champion, team composition, perceived team effectiveness, and the actual number and depth of changes made to improve chronic illness care. RESULTS: A focus on patient satisfaction, the presence of a team champion, and the involvement of the physicians on the team were each consistently and positively associated with greater perceived team effectiveness. Maintaining a balance among culture values of participation, achievement, openness to innovation, and adherence to rules and accountability also appeared to be important. Perceived team effectiveness, in turn, was consistently associated with both a greater number and depth of changes made to improve chronic illness care. The variables examined explain between 24 and 40% of the variance in different dimensions of perceived team effectiveness; between 13% and 26% in number of changes made; and between 20% and 42% in depth of changes made. Conclusions: The data suggest the importance of developing effective teams for improving the quality of care for patients with chronic illness.

We conducted a cross-sectional survey of 210 patients who came to a free medical clinic for health care over an 8-month period. We (1) measured their satisfaction with care, (2) determined the frequency of missed opportunities for providing health education and social work consultation, and (3) assessed whether patient-specific factors drive the frequency of these missed opportunities. Of the 210 patients surveyed, a total of 168 (80.0%) completed the entire survey. The mean satisfaction rating was high (4.6 on a scale of 1 to 5). A significant number of missed opportunities occurred, with only 28% of patients receiving patient education material, and 32% of patients visiting the social worker. No particular patient groups emerged as most susceptible to these missed opportunities. This study shows both the high degree of patient satisfaction at this free clinic and the many opportunities for improving patient education and social services. Adding health education and social work consultation to the patient encounter could improve the health of these patients.

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

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Income, education, occupation, age, sex, marital status, and ethnicity are all correlated with health in one context or another. This paper reflects on the difficulties encountered in deriving robust scientific conclusions from these correlations or drawing reliable policy applications. Interactions among the variables, nonlinearities, casual inference, and possible mechanisms of action are discussed. Strategies for future work are suggested, and researchers are urged to pay special attention to possible interactions among health, genes, and socio-economic variables.