Current Cardiology Reports

, Volume 14, Issue 1, pp 97–105

Provider and Systems Factors in Diabetes Quality of Care

Authors

  • Kimia Ghaznavi
    • Division of Cardiology, Department of MedicineUniversity of California, Irvine
    • Division of Cardiology, Department of MedicineUniversity of California, Irvine
Diabetes and Cardiovascular Disease (ND Wong, Section Editor)

DOI: 10.1007/s11886-011-0234-x

Cite this article as:
Ghaznavi, K. & Malik, S. Curr Cardiol Rep (2012) 14: 97. doi:10.1007/s11886-011-0234-x

Abstract

A gap exists in knowledge and the observed frequency with which patients with diabetes actually receive treatment for optimal cardiovascular risk reduction. Many interventions to improve quality of care have been targeted at the health systems level and provider organizations. Changes in several domains of care and investment in quality by organizational leaders are needed to make long-lasting improvements. In the studies reviewed, the most effective strategies often have multiple components, whereas the use of one single strategy, such as reminders only or an educational intervention, is less effective. More studies are needed to examine the effect of several care management strategies simultaneously, such as use of clinical information systems, provider financial incentives, and organizational model on processes of care and outcomes.

Keywords

Diabetes quality of careProvider factorsSystem factorsOrganization structure

Clinical Trial Acronyms

COMPETE II

A Randomized Trial of Electronic Integration of Care for Better Diabetes Outcomes

DCCT

Diabetes Control and Complications Trial

TRIAD

Translating Research Into Action for Diabetes

UKPDS

United Kingdom Prospective Diabetes Study

Introduction

Diabetes affects 25.8 million people or 8.3% of the US population [1]. Diabetes is responsible for 450,000 deaths annually, of which approximately 65% are due to cardiovascular disease (CVD) causes [2]. In addition, people with diabetes have two to four times the risk of CVD. They also have disproportionately high levels of cardiovascular risk factors, including hypertension, lipid abnormalities, and obesity. In 2001, HEDIS (Healthcare Effectiveness Data and Information Set) data showed that among patients with commercial health insurance, 45% had a blood pressure of over 140/90 mm Hg and 50% had low-density lipoprotein (LDL) cholesterol of over 130 mg/dL [3].

Treatment of Risk Factors

The efficacy of several treatments in reducing morbidity and mortality in people with diabetes is well established. The National Committee for Quality Assurance estimates that improved glycemic control could prevent 13,600 deaths annually in the United States alone [4]. In general, every percentage point drop in hemoglobin A1c (HbA1c) blood test results (eg, from 8.0% to 7.0%) can reduce the risk of microvascular complications (eye, kidney, and nerve diseases) by 40% [1]. Moreover, in patients with type 1 diabetes, intensive insulin therapy has long-term beneficial effects on the risk of CVD [1]. CVD preventive practices have been shown to impact the rate of cardiovascular events as well as mortality. Improved blood pressure control can reduce diabetes-related complications by 24% and total mortality by one-third [5, 6]. In general, for every 10-mm Hg reduction in systolic blood pressure, the risk for any complication related to diabetes is reduced by 12% [1]. In addition, appropriate lipid management with lipid-lowering drugs such as statins can reduce coronary events by as much as 45% and total mortality by 43% [7]. Aspirin therapy for persons with diabetes at high risk of CVD can reduce the risk of myocardial infarction by 28% and total CVD risk by 18% [8].

The Quality Gap in Diabetes Care

Given the overwhelming evidence for use of cardiovascular preventive care in persons with diabetes, a surprising gap exists in knowledge and the observed frequency with which patients with diabetes actually receive treatment for optimal cardiovascular risk reduction [9, 10]. Nearly 1 in 5 diabetic patients has poor glycemic control (HbA1c level >9.5%), more than one-third have elevated blood pressure (>140/90 mm Hg), and more than half of diabetes patients have elevated LDL cholesterol levels (>130 mg/dL) [11]. In addition, process of care measures also show similar quality gaps; only 28% receive the recommended HbA1c measurements whereas 63% obtain recommended annual dilated eye examination [11]. Although these data were collected before publication and widespread distribution of the DCCT and UKPDS results, more recent data do not indicate significant improvement [12]. Racial and ethnic disparities persist as well. African American patients have age-adjusted diabetes mortality rates that are approximately twice those of Caucasians [13], whereas Native Americans and other vulnerable populations suffer a disproportionate burden of diabetes and diabetes-related morbidity and mortality [14].

The Institute of Medicine (IOM) attributes this quality of care gap to rapid increases in chronic care disease prevalence and the inability of our health care system to meet demands due to poor organization and constraints in using modern information technology [15]. Earlier efforts to improve quality of care in persons with chronic disease, such as diabetes, focused on helping providers change practice patterns and subsequently improve patient outcomes by interventions such as guideline development, and implementation. The American Diabetes Association and American Heart Association have formal guidelines for the treatment of hyperlipidemia and hypertension in persons with diabetes [1618]. However, despite the attention drawn to appropriate treatment as dictated by guidelines, several studies show that implementation and adherence to these guidelines is not only lacking, and varies considerably, but also rarely improves outcomes [19, 20].

Many previous and current efforts to improve quality of care have been targeted at the health systems level and provider organizations [21]. Changes in several domains of care and investment in quality by organizational leaders are needed to make improvements in quality. Managed care strategies to improve quality of care and control costs include dissemination of practice guidelines, use of disease management programs, and use of provider financial incentives. Most of these strategies attempt to alter the behavior of physicians because their decisions guide nearly 80% of all medical spending and most directly impact quality at the point care is delivered. Our article examines the most recent organizational- and provider-level strategies to affect not only processes of care, but intermediate and long-term clinical outcomes in persons with diabetes.

Targeting Provider and System Factors in Quality Improvement

Several factors affect the quality and outcome of health care in diabetic patients, which could be considered in three different levels: patient level, provider level, and health care system level (Fig. 1). Some factors are immutable such as biological traits; or difficult to change such as income and education. Many interventions target patient factors; however we focus on reviewing the literature on provider-level and system-level interventions to improve quality of care in diabetes. Interventions at these levels has been effective in narrowing the “quality gap” that is in large part responsible for suboptimal health care practices and outcomes. We describe key features of the interventions and its effects on improving process and outcomes in diabetes care.
https://static-content.springer.com/image/art%3A10.1007%2Fs11886-011-0234-x/MediaObjects/11886_2011_234_Fig1_HTML.gif
Fig. 1

Conceptual model for the relationship of patient/provider/system level factors with the process and outcome of care. ESRD end-stage renal disease; HbA1c hemoglobin A1c; LDL-C low-density lipoprotein cholesterol

Provider Factors

Several provider factors have been studied in relationship to quality of care. Certain provider factors are inherent characteristics of physicians such as gender, race, and specialty. Other mutable factors are specific interventions directed at providers such as use of physician facilitators and financial incentives. In this section we examine the effect of mutable provider factors on diabetes quality of care.

Specialty

Although primary care physicians (PCPs) proved the majority of diabetes care (90% of physician visits are to PCPs) [22]; they are at times challenged in their efforts to fully meet the needs of their patients with diabetes as the disease progresses or when self-management demands become complex [23]. Previous studies have shown that lower use of preventive services and poorer glycemic control were found in patients who receive their diabetes care from primary care providers compared with specialists [2426]. Those who received care from a diabetes specialist (endocrinologist, diabetologist, or attendance at a diabetes clinic) had higher rates of self-monitoring of blood glucose (93.8% vs 79.5%), were more likely to have received diabetes education (23.0% vs 13.3%), had more diabetes knowledge (52.2% vs 35.2%), and had better glycemic control (9.7% vs 10.3%) [27]. Interestingly, when associations with glycemic control were examined by level of education and income, those with higher incomes and lower levels of education appeared to receive the most benefit from specialist care.

However, given the growing demand for diabetes services, developing strategies that will assist with the timely, appropriate, and supported transition of responsibility for diabetes care from specialists back to primary care may be one way of ensuring future access to specialist services for those with complex care needs [28].

A recent study showed implementation of strategies that enhance PCP competencies and confidence related to diabetes management such as continuing medical education can increase PCP capacity to provide complex diabetes care. They suggested that specialist centers can support the PCP with establishing interactive, applied learning opportunities such as clinic-based learning, more formalized mentorship, and easy phone access for “just in time” specialist advice [28].

Provider Financial Incentives

Over the past two decades funders and policy makers worldwide have experimented with initiatives to change physicians’ behavior and improve the quality and efficiency of medical care [29]. Success has been mixed, and attention has recently turned to payment mechanism reform, in particular offering direct financial incentives to providers for delivering high quality care [3032]. However, financial incentives have several potential unintended consequences. For example, they might result in diminished provider professionalism, neglect of patients for whom quality targets are perceived to be more difficult to achieve, and widening of health inequalities [33, 34].

Doran et al. [35•] examined trends in quality of care for 42 activities (23 incentivized under the UK Quality and Outcomes Framework incentive scheme and 19 not incentivized) selected from 428 identified quality of care indicators. Their study showed the quality of care initially improved for incentivized activities but quickly reached a plateau. Incentives had little impact on non-incentivized activities in the short term, but after 3 years quality of care for some fell significantly below levels predicted from pre-incentive trends [35•].

An observational study by Keating et al. [36] surveyed 399 physicians in three health plans in Minnesota and examined processes and outcomes for patients with diabetes using medical chart review. They found that quality scores were lower for patients whose providers were paid according to fee-for-service (FFS) compared with salary. Scores were also lower for patients whose physicians served as gatekeepers for greater than 50% of their patients (P = 0.06). This study was limited because it examined a limited number of plans in one state. Also, it did not control for possible confounders such as organizational model.

System Factors

Health care systems/organizations have played a central role in the public health response to the growing problem of diabetes [37] and its complications. During the 1990s, managed care organizations (MCOs) began seeking system-level approaches to improve chronic disease care, including diabetes. Performance-reporting initiatives, such as the National Committee on Quality Assurance’s Diabetes Quality Improvement Program [38], led MCOs to develop disease management programs that used diabetes registries, internal performance monitoring and feedback, physician and patient reminder systems, case management, and provider incentives to improve quality [39].

Health Systems Structure

Most studies of the health systems attribute measures of quality in a handful of ways. These methods include HEDIS scores; assessment of the application of specific evidence-based practices; and the presence of care management protocols or guidelines, electronic medical records or information technology, and other quality improvement activities [40]. Interventions on the organizational level have yielded improvements in both processes of care as well in intermediate outcomes.

Kim et al. [41•], using TRIAD data recently found that among for-profit plans, those classified by the researchers as being group/network plans provided 10% more recommended diabetes processes of care than those classified as being independent practice association (IPA)/direct contracting plans. They found no differences among provider groups who contracted with nonprofit health plans. However, this study did not examine intermediate outcomes to see if the observed differences in processes translated into better control of intermediate outcomes.

Gillies et al. [42] examined the quality of care by comparing composite quality scores (based on HEDIS measures) for staff and group model health maintenance organizations (HMOs) to those of HMOs built on loosely affiliated physician networks. The greater the extent to which an HMO’s physician network was characterized as either a group or staff model, the higher the plan’s performance on 4 out of 5 composite quality measures: women’s health screening, immunization rates, heart disease screening, and diabetes screening.

Himmelstein et al. [43] examined 14 HEDIS quality of care indicators to assess differences between investor-owned versus not-for-profit HMOs. The authors found that investor ownership was associated with lower quality of care, including lower rates of annual eye examinations in patients with diabetes. When analyzed by type of organizational model, staff and group-model HMOs had higher scores on almost all quality of care indicators compared with IPA or network-model HMOs. However, this study focused on processes of care and did not measure any differences in intermediate or long-term clinical outcomes or costs.

Alternately, Safran et al. [44] reported that network model HMOs performed more favorably than staff/group model HMOs on 9 out of 11 quality of care indicators. These indicators assessed characteristics of primary care such as access, continuity, comprehensiveness, and clinical interactions. However, the authors failed to measure any clinical outcomes or costs associated with these indicators.

Li et al. [45] surveyed 1104 physician organizations and found that physician organizations owned by HMOs or hospital systems used more diabetes care management processes (CMPs) than physician-owned physician organizations. They described that the result may be explained by the fact that HMO- and hospital-owned physician organizations have more resources available to implement CMPs [45].

Diabetes Disease Management Strategies

In addition to organizational structure, other interventions have focused on the following management strategies: 1) performance feedback to physicians; 2) physician reminders; 3) use of clinical guidelines; 4) patient reminders; 5) formal care/case management by non-physician providers; and 6) patient education [41•, 46].

Mangione et al. [46] in the TRIAD study found the latter four were highly correlated and were combined into a structured care management score. Greater intensity of performance feedback, physician reminders, and structured care management were each strongly associated with better care processes [41•, 46].

We have reviewed more recent studies in the area of disease management in diabetes care that also show improvement in processes and outcomes:
  • Patient reminders: In an academic outpatient practice, Parikh et al. [47] studied patient acceptance and no-show rates among 10,546 patients receiving a clinic staff reminder or an automated appointment reminder compared with no reminders. They concluded that clinic staff reminder was significantly more effective in lowering the no-show rate compared with an automated appointment reminder system [47]. Another study tested the feasibility of using an automated system to deliver reminder messages to 253 adults with diabetes enrolled in a randomized controlled trial. At the end of the study, the number of received reminder calls was not associated with a change in the number of physician visits or diabetes-related laboratory tests during follow-up [48].

  • Patient education: Efforts to improve patients’ self-management are thought to be central to improving diabetes management [49, 50]. The most innovative and effective self-management interventions may be those that target the main provider and the patient as a unit, ideally producing a motivated patient who is willing to take charge of his or her care, with the primary care provider as a resource coach and triage to consultation services as needed [50, 51].

  • Guideline use: Francke et al. [52], in a review of 12 studies on guideline use, found that: 1) Characteristics of the guidelines themselves affect actual use: guidelines that are easy to understand, can easily be tried out, do not require specific resources, and have a greater chance of implementation. 2) Characteristics of professionals (eg, awareness of the existence of the guideline and familiarity with its content) likewise affect implementation. 3) Patient characteristics appear to exert influence (eg, comorbidity reduces the chance that guidelines are followed). 4) Environmental characteristics may influence guideline implementation (eg, a lack of support from peers or superiors, as well as insufficient staff and time, appear to be the main impediments) [52]. It also has been suggested that end-user involvement in the development and adaptation of national guidelines can result in an increased uptake [53]. The effects of implementation of guidelines in patients with diabetes using a computer-based clinical decision support system were assessed in 1034 patients in Norway [54]. The use of this system resulted in lower diastolic blood pressure; however, no differences were found in HbA1c or body mass index.

  • Formal case management by non-physician providers: The use of non-physician providers in chronic disease management has been shown to be clinically effective and cost-efficient. Nurses, pharmacists, and other non-physician health care professionals using detailed algorithms working under the supervision of physicians have demonstrated effectiveness in reducing HbA1c and blood pressure [55, 56]. Nurse practitioners may help ease physician workload through taking over some duties usually performed by a physician. They may also affect performance through involvement in delivering care to the patients and teaching them directly on self-management techniques.

In a cross-sectional survey in 137 primary care practices in Ontario, Canada, in which the nurse practitioners were an important part of many of community health clinics, with their activities ranging from consultation-based primary care to organization of chronic disease clinics, Russell et al. [57] found among organizational factors independently associated with chronic disease management scores that the presence of a nurse practitioner in the clinic was associated with a 10% absolute increase in disease management scores.

Kinmonth et al. [58] assessed the effect of training nurses and general practitioners on self-management strategies on quality of life as well as metabolic outcomes in 250 patients with diabetes in England. They found that although patients in the intervention group reported greater treatment satisfaction and well-being, their body mass index and triglyceride concentrations were higher and knowledge scores were lower. There were no differences in glycemic control. However, this study did not assess any process of care measures or long-term clinical outcomes.

Data Systems and Health Information Technology

The expanded use of electronic medical records has been shown to improve the quality and cost-effectiveness of patient care [59, 60]. Health information technology (HIT) is defined as a broad array of technologies involved in managing and sharing patient information electronically rather than through paper records. These information technologies include the application of health information systems designed primarily to support the management of patient’s records such as electronic medical record system, and to assist medical and health care delivery such as clinical decision support system and computerized provider order entry system [61].

In a study done by Li et al. [62] they showed that information technology facilitates better chronic illness care by giving physicians access to patient information, enabling identification of at-risk populations, and providing decision support at the point of care. Without information technology infrastructure, it is difficult to generate a registry of diabetic patients and to provide tools for patient tracking and follow-up [62].

Computerized decision-support systems integrated with the electronic medical record may improve prescribing and quality of care through the provision of patient-specific summaries and recommendations that are seamlessly integrated into the practice workflow [63]. The limited amount of high-quality research available suggests that computerized decision-support systems can change provider behavior. However, there have been too few randomized trials to confirm that they can reliably improve patient outcomes [64].

In the COMPETE II randomized trial, Holbrook et al. [50] evaluated the efficacy of an advanced, diabetes-specific computerized decision-support system. Their intervention involved shared access by primary care providers and patients to a Web-based, color-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets, and brief, prioritized messages of advice. They found statistically significant improvements in actual blood pressure and HbA1c among the intervention group. Also by the end of study survey, provider’s knowledge of diabetes targets, patients’ adherence with appointments, and patients’ access to high-quality diabetes care were assessed to be improved. In this study, they demonstrated that the care of a complex chronic disease can be improved with electronic tracking and decision support shared by providers and patients [50].

Montori et al. [65] assessed the role of implementing a planned care program, a diabetes electronic management system (DEMS), involving use of guideline implementation, clinical information system, and a chronic disease management flow sheet in 400 patients with diabetes. The use of DEMS-directed care was associated with improvements in measurement of HbA1c and microalbuminuria, as well as provision of smoking cessation counseling from baseline. DEMS use was also associated with improvements in microalbuminuria, retinal examination, foot examinations, and self-management support.

Telecommunication Systems

As the number of patients with diabetes is increasing sharply in recent years, novel systems are introduced to achieve more effective diabetes management. The interventions aimed at improving diabetes care for patients have focused on using a telecommunication system to assist in outpatient management of these patients. Clinical information collected directly from patients is relayed to the provider in situations where the data are not generally collected during a patient visit, or when collected using a means other than the existing local medical record system (eg, transmission of a patient’s home glucose level).

In a study in Korea, Cho et al. [66] showed that interactive communication systems using information technology such as Internet or telecommunications devices were effective in diabetes management. They showed that the Internet-based glucose monitoring system could significantly reduce the development of most diabetic complications relative to outpatient management systems [66].

In 2010, Rossi et al. [67•] introduced a new telemedicine system based on an interactive diabetes diary, which helped patients to follow a flexible diet and insulin therapy using a carbohydrate program. The interactive diabetes diary was at least as effective as traditional carbohydrate counting education, allowing dietary freedom for patients with type 1diabetes. This reduced the time required for education and was associated with lower levels of weight gain [67•].

Conclusions

The original IOM report on the chasm in quality of care recommended among other strategies use of information technologies to improve access to clinical information and support clinical decision making as well as alignment of other strategies with quality improvement such as provider payment incentives based on quality of care delivered. Since the report was published, many studies examining interventions that improve quality of care in diabetes support the conclusion that utilizing multiple quality improvement strategies appears to exert stronger effects than single intervention studies.

There is little disagreement that the health care system must be redesigned to reward the drivers of quality and efficiency. Payment reform might be highly effective intervention in diabetes care. Many experts agree that the predominant FFS model discourages the organized, integrated care that is the hallmark of systems. Under FFS, physicians and hospitals are rewarded for taking actions—doing procedures, prescribing drugs, performing tests, etc. This payment system may not encourage quality when the best evidence calls for doing more. FFS may also stand in the way of cooperation and collaboration across the delivery system, as each provider has an economic interest in providing more services for the patient, rather than in collectively determining how much and what mix of care is ideal. Changing payment to reward quality and efficiency requires action on two fronts; first, payments should reward better care, and second, the unit of payment should be large enough to encourage providers to seek efficient combinations of care resources. A bundled payment for a complete episode of care or a specific condition, for example, might encourage coordination of inpatient and post-acute care and better prevention.

Studies on implementation of data systems for disease management universally show improved outcomes. These systems need to be user-friendly to allow wider acceptance and use. Providing educational electronic/computer lessons and workshops for providers along with simplification of health information technology programs will assist health professionals to become more efficient in taking advantage of these systems.

Finally, a substantial proportion of the reviews indicate that effective strategies often have multiple components and that the use of one single strategy, such as reminders only or an educational intervention, is less effective. We believe more studies are necessary to examine the effect of several care management strategies simultaneously, such as use of clinical information systems, provider financial incentives, and organizational model on processes of care and outcomes using different levels of data such as patient/provider/system levels. Studying these factors simultaneously is important in understanding previously reported associations, which may have been confounded by omitted variables. This may explain the lack of consistent findings among various studies in these three domains and it is important to link these data to patient processes and outcomes, across a large and diverse geographic area. In general, multifaceted interventions targeting different barriers to change are more likely to be effective than single intervention and better outcome may follow an improved process of care over time.

Disclosure

No potential conflicts of interest relevant to this article were reported.

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© Springer Science+Business Media, LLC 2011