The effect of advanced access implementation on quality of diabetes care.

INTRODUCTION: The study analyzes the effect of an advanced access program on quality of diabetes care. METHODS: We conducted this study in a medical group of 240,000 members served by 17 primary care clinics. Seven thousand adult patients older than 18 years of age with diabetes were identified from administrative databases. Two aspects of advanced access - wait time for appointments and continuity of care - were calculated yearly for each patient during 1999 through 2001. We developed three composite measures of glucose and lipid control - process (proportion of patients with appropriate testing rates of hemoglobin A1c [HbA1c] and low-density lipoprotein [LDL]), good control (proportion with HbA1c < 8% and LDL < 130 mg/dL) and excellent control (proportion with HbA1c < 7% and LDL < 100 mg/dL) - and assessed them each year for each patient. We used multilevel logistic regression to predict the measures in 2000 and 2001 (years during and after advanced access implementation) relative to 1999 (year pre-advanced access). RESULTS: After implementation of advanced access, wait time decreased from 21.6 days to 4.2 days, and continuity improved by 6.5% (both P < .01). The percentage of patients with HbA1c < 7% increased from 44.4% to 52.3% and with LDL < 100 mg/dL from 29.8% to 38.7%. Increased continuity predicted improved process (P = .01), good control (P = .033), and excellent control (P < .001). However, wait time did not significantly predict process (P = .62) or quality measures (P = .95). CONCLUSION: Measures of the quality of diabetes control improved in the year after implementation of advanced access, but better care did not correlate with decreased wait time to see a provider. However, improved continuity of care predicted improvements in both process and quality of diabetes care.

Ambulatory care quality measures for the 6 aims from administrative data.

Pressure is building for performance measures that can be collected inexpensively and repeatedly for internal and external accountability and quality improvement. The objective of this study was to develop and test measures obtainable from administrative data covering each of the Institute of Medicine's (IOM) 6 aims. Measure definitions were developed for 3 common chronic conditions and were revised after testing the feasibility of collecting them from claims data. The setting was a large, multispecialty medical group in the Midwest and included all adult patients with diabetes, coronary heart disease, or depression. Problems identified in the original 99 measures led to refinements or elimination. The resulting 46 measures ready for use include 11 measures for 5 aims applicable to most common chronic conditions, plus 10 to 14 effectiveness measures for each condition. They have been successfully used to describe care quality changes for these patients over time. This starter set for the 6 IOM aims should be tested and expanded by others.

Care quality and implementation of the chronic care model: a quantitative study.

PURPOSE: We wanted to test whether improvements in care quality were correlated with changes in the Chronic Care Model (CCM) in a large medical group that attempted to implement the CCM. METHODS: The leaders of 17 primary care clinics in this medical group completed the Assessing Chronic Illness Care (ACIC) survey measure of CCM implementation before and after care system changes were made. We used administrative data to measure care quality changes for yearly samples of patients with diabetes, coronary heart disease, or depression. RESULTS: The total ACIC score for the CCM increased by an overall average of 1.4 points (from 5.8 to 7.2 on a scale of 1 to 11, P = .02) and significant increases occurred for 3 of the 6 components of the CCM. During this time, patients experienced a significant increase in the proportion meeting a composite outcome measure for low-density lipoprotein (LDL) and glycated hemoglobin levels (from 15.7% to 25.5%, P = .001). Heart disease patients meeting a composite measure for LDL values increased from 46.8% to 57.8%, and the percentage of patients with 1 or more cardiac events dropped from 17.2% to 11.4% (P = .001 for each). Persistent use of new antidepressants did not change, but more of these patients had follow-up visits (P = .02). Only the diabetes measure was significantly correlated with 2 CCM elements-clinical information systems and decision support. CONCLUSION: Despite implementation of the CCM and improvements in quality measures for 3 chronic diseases, there were few significant correlations between these changes. Showing such a relationship may require larger changes, a larger number of clinics, changes in other CCM elements, or a more-sensitive measurement tool.

Challenges of change: a qualitative study of chronic care model implementation.

PURPOSE: The Chronic Care Model (CCM) provides a conceptual framework for transforming health care for patients with chronic conditions; however, little is known about how to best design and implement its specifics. One large health care organization that tried to implement the CCM in primary care provided an opportunity to study these issues. METHODS: We conducted a qualitative, comparative case study of 5 of 18 group clinics 18 to 23 months after the implementation began. Built on knowledge of the clinics from a previous study of advanced access implementation, data included in-depth interviews with organizational leaders and varied clinic personnel, observation of clinic care processes, and review of written materials. RESULTS: Relatively small and highly variable care process changes were made during the study period. The change process underwent several marked shifts in strategy when initial efforts failed to achieve much and bore little resemblance to the change process used in the previously successful large-scale implementation of advanced access scheduling. Many barriers were identified, including too many competing priorities, a lack of specificity and agreement about the care process changes desired, and little engagement of physicians. CONCLUSION: These findings highlight specific organizational challenges with health care transformation in the absence of a blueprint more specific than the CCM. Effective models of organizational change and detailed examples of proven, feasible care changes still need to be demonstrated if we are to transform care as called for by the Institute of Medicine.

Are claims data accurate enough to identify patients for performance measures or quality improvement? The case of diabetes, heart disease, and depression.

The objective of this study was to demonstrate a method to accurately identify patients with specific conditions from claims data for care improvement or performance measurement. In an iterative process of trial case definitions followed by review of repeated random samples of 10 to 20 cases for diabetes, heart disease, or newly treated depression, a final identification algorithm was created from claims files of health plan members. A final sample was used to calculate the positive predictive value (PPV). Each condition had unacceptably low PPVs (0.20, 0.60, and 0.65) when cases were identified on the basis of only 1 International Classification of Diseases, ninth revision, code per year. Requiring 2 outpatient codes or 1 inpatient code within 12 months (plus consideration of medication data for diabetes and extra criteria for depression) resulted in PPVs of 0.97, 0.95, and 0.95. This approach is feasible and necessary for those wanting to use administrative data for case identification for performance measurement or quality improvement.

Transforming medical care: case study of an exemplary, small medical group.

PURPOSE: Most published descriptions of organizations providing or improving quality of care concern large medical groups or systems; however, 90% of the medical care in the United States is provided by groups of no more than 20 physicians. We studied one such group to determine the organizational and cultural attributes that seem related to its achievements in care quality. METHODS: A 15-family physician medical group was identified from comparative public performance scores of 27 medical groups providing most of the primary care in our metropolitan area. Semistructured interviews were conducted with diverse personnel in this group, operations were observed, and written documents were reviewed. Four primary care physician researchers and a consultant then reviewed transcriptions, field notes, and materials during semistructured sessions to identify the main attributes of this group and their probable origins. RESULTS: This medical group ranked first in a composite measure of preventive services and fourth and sixth, respectively, in composite scores for coronary artery disease and diabetes care. Our analysis identified 12 attributes of this group that seemed to be associated with its good care quality, with patient-centeredness being the foundational attribute for most of the others. Historical factors important to most of these attributes included small size, physician ownership, and a high value on practice consistency among the clinicians in the group. CONCLUSIONS: The identified 12 attributes of this medical group seem to be associated with its superior care quality, and most of them might be replicable by other small groups if they choose to work toward that end.

Effect of improved primary care access on quality of depression care.

PURPOSE: We wanted to determine whether a major improvement in access to primary care during 2000 was associated with changes in the quality of care for patients with depression. METHODS: Health plan administrative data were analyzed by multilevel regression to compare the quality of care received by patients with depression between 1999 and 2001, a time without major changes in depression care guidelines. Approximately 6,000 patients with depression who received all care in a large multispecialty medical group during any single year were subjects for this study. Thirteen different quality measures assessed process quality under the dimensions of effectiveness, timeliness, safety, and patient-centeredness. RESULTS: The largest change was a reduction in the proportion of depressed patients with no follow-up visit in primary care after starting a new antidepressant medication: from 33.0% before a change in access to care to 15.4% afterward, P =.001. During the same period, continuity of care in primary care improved (>50% of primary care visits to 1 doctor increased from 67.3% to 74.0%, P = or <.001), as did persistence of 6-month antidepressant medication (from 46.2% to 50.8%, P = or <.001). Further analyses found that the latter change was primarily associated with the change in continuity of care. Measures of subspecialty mental health care worsened during this time. CONCLUSION: Marked improvement in access to primary care for 1 year was associated with some improvement in primary care for patients with depression, but the mechanism appeared to be improved continuity. Those planning to implement advanced access to care need to do so in such a way that continuity of care is enhanced rather than harmed by the change.

Do all components of the chronic care model contribute equally to quality improvement?

BACKGROUND: The Chronic Care Model (CCM) is a widely used conceptual model for improving chronic illness care, but there is limited evidence to support the validity of this model. Seventeen clinics in a single medical group used the CCM in a diabetes improvement initiative, and the study tested which CCM components at the clinic level were associated with improved glycohemoglobin (A1C) and low-density (LDL) outcomes for patients with diabetes. DESIGN: Three leaders from each of 17 clinics completed a survey in 2002 that assessed the presence of CCM components in their clinics. Clinic scores from this survey were correlated with change in clinic-level measures of the quality of care for their diabetes patients (A1C and LDL outcomes) during the preceding year. RESULTS: Delivery system design (DSD) scores were positively correlated with improvements in A1C and lipid test rates and levels at these clinics (r = 0.49-0.57, p < .05). Self-management support and clinical information systems demonstrated associations that may have substantive significance but did not reach p < 05. CONCLUSIONS: These data suggest that delivery system design is associated with improved A1C and LDL outcomes for people with diabetes. Additional studies are necessary to determine the association of other CCM components with improved outcomes.

Key issues in transforming health care organizations for quality: the case of advanced access.

BACKGROUND: The 2001 Institute of Medicine (IOM) report highlighted the need for transformation of the U.S. health care system. This rigorous qualitative evaluation of transformational change for patient access in one large multispecialty group practice identifies the major issues facing organizations addressing the IOM challenge. METHODS: Semistructured depth interviews were conducted with the medical and administrative leaders at all levels, physicians, and nurses from 17 primary care clinics in one integrated medical group two years after they began to transform their approach to primary care patient appointment access. RESULTS: The mean time to third-next-available appointment was reduced by 76% during one year, from 17.8 days to 4.2 days. Nine important issues related to the change process were identified from clinic interviews. When combined with issues identified by central leaders, 13 themes stood out as lessons in transformational change. A major issue is the tension between physician autonomy and both effective organizational function and putting patients first. Physician autonomy is also diminished by the need to standardize and systematize care. CONCLUSIONS: Transformational change in care delivery is possible in large and complex group practices. Changes that directly affect care delivery and physician autonomy present particular challenges to physicians that need to be attended to if the changes are to be successful.