Improving diabetes care in a large health care system: an enhanced primary care approach

Outpatient diabetes clinical decision support: current status and future directions

Outpatient clinical decision support systems have had an inconsistent impact on key aspects of diabetes care. A principal barrier to success has been low use rates in many settings. Here, we identify key aspects of clinical decision support system design, content and implementation that are related to sustained high use rates and positive impacts on glucose, blood pressure and lipid management. Current diabetes clinical decision support systems may be improved by prioritizing care recommendations, improving communication of treatment-relevant information to patients, using such systems for care coordination and case management and integrating patient-reported information and data from remote devices into clinical decision algorithms and interfaces.

Systematic care to reduce ethnic disparities in diabetes care

AIMS

We sought to determine whether systematic care can reduce the gap in diabetes control between Maori and non-Maori.

METHODS

A Primary Health Organisation implemented a chronic care management programme for diabetes in 2005. The data constitute an open, prospective cohort followed for approximately two years. Data describing process were also collected.

RESULTS

There were 1311 people with diabetes (354 Maori, 957 non-Maori). Maori started with higher HbA(1c) (mean 8.1%, SD 1.9) than non-Maori (7.1%, SD 1.4) but over about 2 years HbA(1c) for Maori improved to that of non-Maori. LDL and systolic blood pressure decreased for both groups. Improved glucose in Maori was not due to starting insulin or metformin, and rates of sulphonylurea prescription increased in both groups. Urinary albumin:creatinine ratio remained higher for Maori throughout. Smoking rates and Body Mass Index (both higher in Maori) did not change. There is no evidence of selective retention in the cohort.

CONCLUSION

Likely essential components of the programme were that governance was equally shared between Maori and non-Maori; prolonged nurse consultations were free to the patient; nurses used a formal written wellness plan; nurses were formally trained to support patient self-management; and a computer template supported structured care.

Sustained hyperglycemia among patients with diabetes: what matters when action is needed?

OBJECTIVE To estimate prevalence of, and factors associated with, sustained periods of hyperglycemia among patients with diabetes and factors associated with receipt of appropriate care once A1C values are persistently elevated. RESEARCH DESIGN AND METHODS Among patients initiating oral monotherapy (n = 5,070), Kaplan-Meier and Cox proportional hazards methods were used to estimate time to, and factors associated with, sustained hyperglycemia (defined by two A1cs >8% and no recent medication intensification), and among those experiencing sustained hyperglycemia, time to, and factors associated with, appropriate receipt of care (i.e., medication intensification or achieving A1C < or =7%). RESULTS Within 1 year, 8% experienced sustained hyperglycemia, with the proportion rising to 38% within 5 years. Patients using sulfonylurea had greater risk of hyperglycemia (hazard ratio [HR] 1.47 [95% CI 1.30-1.66]) compared with those initiating metformin. Risk increased with age (1.89 [1.27-2.83]), was greater for African Americans (1.19 [1.05-1.36]), and increased with A1C levels >7%. Among individuals with sustained hyperglycemia (n = 1,386), mean time to appropriate care was 9.7 months, with 25% not receiving appropriate care within 1 year. Shorter delays to appropriate care receipt were associated with increasing income (1.03 [1.00-1.07]), A1C >9% (1.38 [1.06-1.79]) and >11% (1.65 [1.25-2.18]), increasing medication adherence (1.03 [1.01-1.04]), and visits to primary care (4.22 [3.65-4.88]) or endocrinology (3.89 [2.26-6.70]). Longer delays were associated with increasing drug copayments (0.96 [0.93-0.98]). CONCLUSIONS Patients incurring sustained hyperglycemia are at risk of further delays in appropriate management. Barriers to appropriate care include prescription drug copayments, few physician contacts, and other factors that are likely amenable to intervention.

Customized feedback to patients and providers failed to improve safety or quality of diabetes care: a randomized trial

OBJECTIVE

To assess whether providing customized clinical information to patients and physicians improves safety or quality of diabetes care.

RESEARCH DESIGN AND METHODS

Study subjects included 123 primary care physicians and 3,703 eligible adult diabetic patients with elevated A1C or LDL cholesterol, who were randomly assigned to receive customized feedback of clinical information as follows: 1) patient only, 2) physician only, 3) both the patient and physician, or 4) neither patient nor physician. In the intervention groups, patients received customized mailed information or physicians received printed, prioritized lists of patients with recommended clinical actions and performance feedback. Hierarchical models were used to accommodate group random assignment.

RESULTS

Study interventions did not improve A1C test ordering (P = 0.35) and negatively affected LDL cholesterol test ordering (P < 0.001) in the 12 months postintervention. Interventions had no effect on LDL cholesterol values (P = 0.64), which improved in all groups over time. Interventions had a borderline unfavorable effect on A1C values among those with baseline A1C >or=7% (P = 0.10) and an unfavorable effect on A1C values among those with baseline A1C >or=8% (P < 0.01). Interventions did not reduce risky prescribing events or increase treatment intensification. Time to next visit was longer in all intervention groups compared with that for the control group (P < 0.05).

CONCLUSIONS

Providing customized decision support to physicians and/or patients did not improve quality or safety of diabetes care and worsened A1C control in patients with baseline A1C >or=8%. Future researchers should consider providing point-of-care decision support with redesign of office systems and/or incentives to increase appropriate actions in response to decision-support information.

A group randomized trial of a complexity-based organizational intervention to improve risk factors for diabetes complications in primary care settings: study protocol

Background

Most patients with type 2 diabetes have suboptimal control of their glucose, blood pressure (BP), and lipids – three risk factors for diabetes complications. Although the chronic care model (CCM) provides a roadmap for improving these outcomes, developing theoretically sound implementation strategies that will work across diverse primary care settings has been challenging. One explanation for this difficulty may be that most strategies do not account for the complex adaptive system (CAS) characteristics of the primary care setting. A CAS is comprised of individuals who can learn, interconnect, self-organize, and interact with their environment in a way that demonstrates non-linear dynamic behavior. One implementation strategy that may be used to leverage these properties is practice facilitation (PF). PF creates time for learning and reflection by members of the team in each clinic, improves their communication, and promotes an individualized approach to implement a strategy to improve patient outcomes.

Specific objectives

The specific objectives of this protocol are to: evaluate the effectiveness and sustainability of PF to improve risk factor control in patients with type 2 diabetes across a variety of primary care settings; assess the implementation of the CCM in response to the intervention; examine the relationship between communication within the practice team and the implementation of the CCM; and determine the cost of the intervention both from the perspective of the organization conducting the PF intervention and from the perspective of the primary care practice.

Intervention

The study will be a group randomized trial conducted in 40 primary care clinics. Data will be collected on all clinics, with 60 patients in each clinic, using a multi-method assessment process at baseline, 12, and 24 months. The intervention, PF, will consist of a series of practice improvement team meetings led by trained facilitators over 12 months. Primary hypotheses will be tested with 12-month outcome data. Sustainability of the intervention will be tested using 24 month data. Insights gained will be included in a delayed intervention conducted in control practices and evaluated in a pre-post design.

Primary and secondary outcomes

To test hypotheses, the unit of randomization will be the clinic. The unit of analysis will be the repeated measure of each risk factor for each patient, nested within the clinic. The repeated measure of glycosylated hemoglobin A1c will be the primary outcome, with BP and Low Density Lipoprotein (LDL) cholesterol as secondary outcomes. To study change in risk factor level, a hierarchical or random effect model will be used to account for the nesting of repeated measurement of risk factor within patients and patients within clinics.

This protocol follows the CONSORT guidelines and is registered per ICMJE guidelines:

Clinical Trial Registration Number

NCT00482768

Telemedicine and diabetes management: current challenges and future research directions

Telemedicine is lying between fading and future. Several clinical studies and critical reviews have been published recently, but the results are inconclusive and the adoption of telemedicine interventions in clinical practice is slow. This article discusses some of the current problems related to the adoption of telemedicine systems and focuses on the information technology solutions that appear to be most promising for diabetes management in the near future. Context awareness, user modeling, intelligent dialogues, and integrated information systems are presented. Some potential future scenarios for the adoption of telemedicine, which combine novel technologies and new organizational models, are also discussed. Within those scenarios, telemedicine may prove to be a good instrument to support health care providers in the effective management and prevention of diabetes mellitus.

Rural communities improving quality through collaboration: the MaineHealth story

Small and rural communities face unique challenges in improving healthcare quality. To address these challenges, MaineHealth, an integrated health system serving small and rural communities, leveraged knowledge, resources, and data through collaboration to help providers improve care and outcomes for asthma, heart failure, diabetes, and depression. The programs emphasized patient self-care, used uniform clinical standards, and supported population-based data collection. This collaborative approach provided an effective way to achieve improved outcomes across a geographically and structurally diverse system, and it can help influence improvement efforts in other small and medium-sized rural communities.

Does ambulatory process of care predict health-related quality of life outcomes for patients with chronic disease?

OBJECTIVE

The validity of quality of care measurement has important implications for practicing clinicians, their patients, and all involved with health care delivery. We used empirical data from managed care patients enrolled in west coast physician organizations to test the hypothesis that observed changes in health-related quality of life across a 2.5-year window reflecting process of care.

DATA SOURCES/STUDY SETTING

Patient self-report data as well as clinically detailed medical record review regarding 963 patients with chronic disease associated with managed care from three west coast states.

STUDY DESIGN

Prospective cohort study of change in health-related quality of life scores across 30 months as measured by change in SF-12 physical component scores.

DATA COLLECTION/EXTRACTION METHODS

Patient self-report and medical record abstraction.

PRINCIPAL FINDINGS

We found a positive relationship between better process scores and higher burden of illness (p<.05). After adjustment for burden of illness, using an instrumental variables approach revealed better process is associated with smaller declines in SF-12 scores across a 30-month observation window (p=.014). The application of the best quartile of process of care to patients currently receiving poor process is associated with a 4.24 increment in delta SF-12-physical component summary scores.

CONCLUSIONS

The use of instrumental variables allowed us to demonstrate a significant relationship between better ambulatory process of care and better health-related quality of life. This finding underscores the importance of efforts to improve the process of care.

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.

The implementation of managed care for diabetes using medical informatics in a large Preferred Provider Organization

BACKGROUND

It has been demonstrated by meta analysis that if a regular review of patients is guaranteed, the standard of primary care can be as good or better than hospital outpatient care, however, empirical data suggests that compliance with diabetes clinical practice recommendations is inadequate in primary care. This study describes the reorganization of diabetes care using disease management principles in a Preferred Provider Organization (PPO) operating on a country-wide basis in which each diabetes clinic became responsible for the overall care of all patients with diabetes.

METHODS

This descriptive pre and post change study was undertaken in a large public-funded PPO insuring over one and half million individuals. The study was possible due the use of a centralized electronic disease registry which enabled the collection of all patient data. Several markers, such as HbA1C and LDC-cholesterol levels, were used to assess the quality of care for the diabetic patients.

RESULTS

Mean HbA1C results of the cohort showed a continuous reduction from 8.1% (S.D. = 1.55) in 1999 to 7.68% (S.D. = 1.47) in 2002 and to 7.79 (S.D. = 1.54) in 2004. Improved results were also recorded for LDL-C 126.37 (S.D. = 35.16) in 1999 to 114.74 (S.D. = 34.49) in 2002, and to 113.39 (S.D. = 33.8) in 2004. The number of diabetic patients seen by the diabetologist increased by 62% over this period, despite an increase in diabetologist work hours of only 23%.

CONCLUSION

The reorganization of health delivery for diabetic patients within a country-wide PPO, based on the principles of disease management and supported by medical informatics improves quality of care.

Diabetes: how are we diagnosing and initially managing it?

PURPOSE

We undertook this study to examine the symptoms, clinical events, and types of health care encounters that preceded the diagnosis of diabetes mellitus in adults, and to examine changes in glycemic control and cardiovascular risk factors in the first year after a diabetes diagnosis.

METHODS

We conducted a historical cohort study of patients in a large multispecialty medical group in Minnesota. Among 55,121 adults who were continuously enrolled in the health plan and receiving care at the study medical group from January 1, 1993, to December 31, 1996, we identified 504 who received a new diagnosis of diabetes in 1995 or 1996. Our main outcome measures were the type of symptoms at diagnosis; the clinical circumstances and type of encounter that led to diabetes diagnosis; and changes in glycemic control (assessed by hemoglobin A1c [HbA1c] value), low-density lipoprotein cholesterol level, blood pressure (BP), aspirin use, and body weight in the first year after diagnosis, ascertained from a detailed review of medical records.

RESULTS

Almost one third (32.3%) of adults with newly diagnosed diabetes had symptoms of hyperglycemia at initial diagnosis. Compared with patients who did not have hyperglycemia symptoms at diagnosis, those who did were younger and more often male, and had lower comorbidity scores and higher HbA1c values (9.9% vs 8.1%) at diagnosis (P <.01 for each comparison). In the 12 months after diagnosis, the group as a whole had significant improvements (P <.001) in HbA1c values (from 8.8% to 7.1%), systolic blood pressure (137.5 to 133.2 mm Hg), diastolic blood pressure (80.7 to 77.3 mm Hg), weight (207.7 to 201.1 lb), and aspirin use (15.3% to 26.1%). Improvements were seen in all patient subgroups, including those defined by symptoms at diagnosis and by visit type at diagnosis.

CONCLUSIONS

Primary care practices may improve detection of undiagnosed diabetes in primary care and improve 1-year outcomes by being vigilant for symptoms of diabetes, by evaluating those at high risk for this disorder, and by instituting appropriate treatments at the time of diagnosis.

Randomized trial of quality improvement intervention to improve diabetes care in primary care settings

OBJECTIVE

To assess the impact of a quality improvement (QI) intervention on the quality of diabetes care at primary care clinics.

RESEARCH DESIGN AND METHODS

Twelve primary care medical practices were matched by size and location and randomized to intervention or control conditions. Intervention clinic staff were trained in a seven-step QI change process to improve diabetes care. Surveys and medical record reviews of 754 patients, surveys of 329 clinic staff, interviews with clinic leaders, and analysis of training session videotapes evaluated compliance with and impact of the intervention. Mixed-model nested analyses compared differences in the quality of diabetes care before and after intervention.

RESULTS

All intervention clinics completed at least six steps of the seven-step QI change process in an 18-month period and, compared with control clinics, had broader staff participation in QI activities (P = 0.04), used patient registries more often (P = 0.03), and had better test rates for HbA(1c) (A1C), LDL, and blood pressure (P = 0.02). Other processes of diabetes care were unchanged. The intervention did not improve A1C (P = 0.54), LDL (P = 0.46), or blood pressure (P = 0.69) levels or a composite of these outcomes (P = 0.35).

CONCLUSIONS

This QI change process was successfully implemented but failed to improve A1C, LDL, or blood pressure levels. Data suggest that to be successful, such a QI change process should direct more attention to specific clinical actions, such as drug intensification and patient activation.

A Cohort Study of the Impact of a National Disease Management Program on HEDIS Diabetes Outcomes

Diabetes disease management programs (DDMP) are proliferating, but their overall impact in improving quality of care using Health Employer Data and Information Set (HEDIS) quality metrics has not been well studied. Furthermore, DDMPs are usually ongoing, but the incremental benefits of continuing the program beyond the initial patient educational intervention have not been rigorously tested. This study evaluates the impact of length of DDMP participation on diabetes-related HEDIS 2002 quality indicators across 20 health plans. Results are stratified by duration of DDMP participation into three levels, "full participants" (6-12 months duration), "partial participants" (<6 months duration) and "non-participants" (0 months duration). The overall national compliance rate across all six combined HEDIS quality measures was 65.6% among full-participants (FP), 58.4% among partial-participants (PP) and 57.0% among non-participants (NP). This study demonstrates that participants in a comprehensive DDMP fair better than non-participants and that those with sustained participation (>6 months) benefit the most.

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.

Is patient readiness to change a predictor of improved glycemic control?

OBJECTIVE

To test the hypothesis that patient readiness to change (RTC) predicts future changes in glycemic control in adults with diabetes.

RESEARCH DESIGN AND METHODS

We linked survey data with HbA1c data for a stratified random sample of consenting adults with diabetes. Change in HbA1c from baseline to the 1-year follow-up was computed and used as a dependent variable. Linear regression models assessed RTC and other patient variables as predictors of HbA1c change.

RESULTS

Among 617 patients with baseline HbA1c > or = 7% and complete data for analysis, RTC predicted subsequent improvement in HbA1c for those with higher physical functioning (interaction t = -2.45, P < 0.05). Other factors that predicted HbA1c improvement in multivariate linear regression models included higher self-reported medication adherence (t = -4.41, P < 0.01), higher baseline HbA1c (t = -15.08, P < 0.01), and older age (t = -2.61, P < 0.01).

CONCLUSIONS

Diabetes RTC independently predicts change in HbA1c for patients with high but not for patients with low functional health status. Customized use of RTC assessment may have potential to improve care.

Case management for patients with poorly controlled diabetes: a randomized trial

PURPOSE

To evaluate the effects of a collaborative case management intervention for patients with poorly controlled type 2 diabetes on glycemic control, intermediate cardiovascular outcomes, satisfaction with care, and resource utilization.

METHODS

We conducted a randomized controlled trial at two Department of Veterans Affairs Medical Centers involving 246 veterans with diabetes and baseline hemoglobin A(1C) (HbA(1C)) levels >or=7.5%. Two nurse practitioner case managers worked with patients and their primary care providers, monitoring and coordinating care for the intervention group for 18 months through the use of telephone contacts, collaborative goal setting, and treatment algorithms. Control patients received educational materials and usual care from their primary care providers.

RESULTS

At the conclusion of the study, both case management and control patients remained under poor glycemic control and there was little difference between groups in mean exit HbA(1C) level (9.3% vs. 9.2%; difference = 0.1%; 95% confidence interval: -0.4% to 0.7%; P = 0.65). There was also no evidence that the intervention resulted in improvements in low-density lipoprotein cholesterol level or blood pressure control or greater intensification in medication therapy. However, intervention patients were substantially more satisfied with their diabetes care, with 82% rating their providers as better than average compared with 64% of patients in the control group (P = 0.04).

CONCLUSION

An intervention of collaborative case management did not improve key physiologic outcomes for high-risk patients with type 2 diabetes. The type of patients targeted for intervention, organizational factors, and program structure are likely critical determinants of the effectiveness of case management. Health systems must understand the potential limitations before expending substantial resources on case management, as the expected improvements in outcomes and downstream cost savings may not always be realized.

The Vermont Diabetes Information System (VDIS): study design and subject recruitment for a cluster randomized trial of a decision support system in a regional sample of primary care practices

BACKGROUND

Despite evidence that optimal care for diabetes can result in reduced complications and improved economic outcomes, such care is often not achieved. The Vermont Diabetes Information System (VDIS) is a registry-based decision support and reminder system based on the Chronic Care Model and targeted to primary care physicians and their patients with diabetes.

PURPOSE

To develop and evaluate a regional decision support system for patients with diabetes.

METHODS

Randomized trial of an information system with clustering at the practice level. Ten percent random subsample of patients selected for a home interview.

SUBJECT

and setting includes 10 hospitals, 121 primary care providers, and 7348 patients in 55 Vermont and New York primary care practices.

RESULTS

We report on the study design and baseline characteristics of the population. Patients have a mean age of 63 years and a mean glycosolated hemoglobin A1C of 7.1 %. Sixty percent of the population has excellent glycemic control (A1 C < 7%); 45% have excellent lipid control (serum LDL-cholesterol <100 mg /dL and serum triglycerides <400 mg/dL). Twenty-five percent have excellent blood pressure control (<130/80mmHg). These results compare favorably to recent national reports. However, only 8% are in optimal control for all three of hyperglycemia, lipids and blood pressure.

CONCLUSIONS

Our experience to date indicates that a low cost decision support and information system based on the Chronic Care Model is feasible in primary care practices that lack sophisticated electronic information systems. VDIS is well accepted by patients, providers and laboratory staff. If proven beneficial in a rigorous, randomized, controlled evaluation, the intervention could be widely disseminated to practices across America and the world with a substantial impact on the outcomes and costs of diabetes. It could also be adapted to other chronic conditions. We anticipate the results of the study will be available in 2006.

Variation in diabetes care by age: opportunities for customization of care

Background

The quality of diabetes care provided to older adults has usually been judged to be poor, but few data provide direct comparison to other age groups. In this study, we hypothesized that adults age 65 and over receive lower quality diabetes care than adults age 45–64 years old.

Methods

We conducted a cohort study of members of a health plan cared for by multiple medical groups in Minnesota. Study subjects were a random sample of 1109 adults age 45 and over with an established diagnosis of diabetes using a diabetes identification method with estimated sensitivity 0.91 and positive predictive value 0.94. Survey data (response rate 86.2%) and administrative databases were used to assess diabetes severity, glycemic control, quality of life, microvascular and macrovascular risks and complications, preventive care, utilization, and perceptions of diabetes.

Results

Compared to those aged 45–64 years (N = 627), those 65 and older (N = 482) had better glycemic control, better health-related behaviors, and perceived less adverse impacts of diabetes on their quality of life despite longer duration of diabetes and a prevalence of cardiovascular disease twice that of younger patients. Older patients did not ascribe heart disease to their diabetes. Younger adults often had explanatory models of diabetes that interfere with effective and aggressive care, and accessed care less frequently. Overall, only 37% of patients were simultaneously up-to-date on eye exams, foot exams, and glycated hemoglobin (A1c) tests within one year.

Conclusion

These data demonstrate the need for further improvement in diabetes care for all patients, and suggest that customisation of care based on age and explanatory models of diabetes may be an improvement strategy that merits further evaluation.

Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement

OBJECTIVE

To describe initial testing of the Assessment of Chronic Illness Care (ACIC), a practical quality-improvement tool to help organizations evaluate the strengths and weaknesses of their delivery of care for chronic illness in six areas: community linkages, self-management support, decision support, delivery system design, information systems, and organization of care.

DATA SOURCES

(1) Pre-post, self-report ACIC data from organizational teams enrolled in 13-month quality-improvement collaboratives focused on care for chronic illness; (2) independent faculty ratings of team progress at the end of collaborative.

STUDY DESIGN

Teams completed the ACIC at the beginning and end of the collaborative using a consensus format that produced average ratings of their system's approach to delivering care for the targeted chronic condition. Average ACIC subscale scores (ranging from 0 to 11, with 11 representing optimal care) for teams across all four collaboratives were obtained to indicate how teams rated their care for chronic illness before beginning improvement work. Paired t-tests were used to evaluate the sensitivity. of the ACIC to detect system improvements for teams in two (of four) collaboratives focused on care for diabetes and congestive heart failure (CHF). Pearson correlations between the ACIC subscale scores and a faculty rating of team performance were also obtained.

RESULTS

Average baseline scores across all teams enrolled at the beginning of the collaboratives ranged from 4.36 (information systems) to 6.42 (organization of care), indicating basic to good care for chronic illness. All six ACIC subscale scores were responsive to system improvements diabetes and CHF teams made over the course of the collaboratives. The most substantial improvements were seen in decision support, delivery system design, and information systems. CHF teams had particularly high scores in self-management support at the completion of the collaborative. Pearson correlations between the ACIC subscales and the faculty rating ranged from .28 to .52.

CONCLUSION

These results and feedback from teams suggest that the ACIC is responsive to health care quality-improvement efforts and may be a useful tool to guide quality improvement in chronic illness care and to track progress over time.