This issue of CHP/PCOR's quarterly newsletter covers news and developments from the summer 2004 quarter. It features articles about:

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.

COPYRIGHT RESTRICTION NOTICE:

This material was originally published in the Journal of the American Medical Informatics Association (Volume 11; 368-376). This material may be read on-line or downloaded for personal use only. The material may be referenced by appropriate hyperlinks. However, the text of the material may not be altered without the express permission of the author and AMIA. Care should be taken when excerpting or referencing text to ensure that the views, opinions, and arguments of the author presented in the excerpt accurately reflect those contained in the original work.

This grant does not extend to publication or posting of the paper to any website to which a fee or paid subscription is required to view or otherwise access the work.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.

Diabetes mellitus is a leading cause of morbidity and death in the United States. Type 2 diabetes mellitus accounts for the majority of affected persons (90% to 95%) and affects older adults, particularly those older than 50 years of age. It affects an estimated 16 million Americans, 11 million of whom have both diabetes and hypertension. Most adverse diabetes outcomes are a result of vascular complications. These complications are generally classified as microvascular, such as retinopathy, nephropathy, and neuropathy (although neuropathy may not be entirely a microvascular disease), or macrovascular, such as coronary artery disease, cerebrovascular disease, and peripheral vascular disease.

In order to prevent, or diminish the progression of, microvascular and macrovascular complications, recommended diabetes management necessarily encompasses both metabolic control and cardiovascular risk factor control. The need for good glycemic control is supported by the Diabetes Control and Complications Trial in type 1 diabetes mellitus and, more recently, the United Kingdom Prospective Diabetes Study (UKPDS) in type 2 diabetes mellitus. In these studies, tight blood sugar control reduced microvascular complications, such as nephropathy and retinopathy, but had little effect on macrovascular outcomes. Up to 80% of patients with type 2 diabetes mellitus will develop or die of macrovascular disease, underscoring the importance of preventing macrovascular complications.

In an effort to provide internists and other primary care physicians with effective management strategies for diabetes care, the American College of Physicians decided to develop guidelines on the management of hypertension in people with type 2 diabetes mellitus. The target audience for this guideline is all clinicians who provide care to patients with type 2 diabetes. The target patient population is all persons with type 2 diabetes who have hypertension, defined as systolic blood pressure of at least 140 mm Hg or diastolic blood pressure of at least 90 mm Hg. This target patient population includes those who already have some form of microvascular complication and, of particular importance, premenopausal women with diabetes. We will attempt to answer the following questions: 1) What are the benefits of tight blood pressure control in type 2 diabetes? 2) What should the target levels of systolic blood pressure and diastolic blood pressure be for patients with type 2 diabetes? and 3) Are certain antihypertensive agents more effective or beneficial in patients with diabetes?

When analyzing benefit or effectiveness for this review, we included only studies that measured clinical end points. The four major classes of clinical end points were all-cause mortality, cardiovascular mortality, cardiovascular events (myocardial infarction, stroke, or congestive heart failure), and microvascular complications (photocoagulation, nephropathy, neuropathy, or amputation).

The review was divided into two categories. The first included studies that evaluated the effects of blood pressure control if the comparison examined an antihypertensive drug versus placebo or the effects of different target blood pressure levels. The second category evaluated the effect of different classes of drugs. A discussion of this evidence follows.

Context Low-carbohydrate diets have been popularized without detailed evidence of their efficacy or safety. The literature has no clear consensus as to what amount of carbohydrates per day constitutes a low-carbohydrate diet.

Objective To evaluate changes in weight, serum lipids, fasting serum glucose, and fasting serum insulin levels, and blood pressure among adults using low-carbohydrate diets in the outpatient setting.

Data Sources We performed MEDLINE and bibliographic searches for English-language studies published between January 1, 1966, and February 15, 2003, with key words such as low carbohydrate, ketogenic, and diet.

Study Selection We included articles describing adult, outpatient recipients of low-carbohydrate diets of 4 days or more in duration and 500 kcal/d or more, and which reported both carbohydrate content and total calories consumed. Literature searches identified 2609 potentially relevant articles of low-carbohydrate diets. We included 107 articles describing 94 dietary interventions reporting data for 3268 participants; 663 participants received diets of 60 g/d or less of carbohydrates—of whom only 71 received 20 g/d or less of carbohydrates. Study variables (eg, number of participants, design of dietary evaluation), participant variables (eg, age, sex, baseline weight, fasting serum glucose level), diet variables (eg, carbohydrate content, caloric content, duration) were abstracted from each study.

Data Extraction Two authors independently reviewed articles meeting inclusion criteria and abstracted data onto pretested abstraction forms.

Data Synthesis The included studies were highly heterogeneous with respect to design, carbohydrate content (range, 0-901 g/d), total caloric content (range, 525-4629 kcal/d), diet duration (range, 4-365 days), and participant characteristics (eg, baseline weight range, 57-217 kg). No study evaluated diets of 60 g/d or less of carbohydrates in participants with a mean age older than 53.1 years. Only 5 studies (nonrandomized and no comparison groups) evaluated these diets for more than 90 days. Among obese patients, weight loss was associated with longer diet duration (P = .002), restriction of calorie intake (P = .03), but not with reduced carbohydrate content (P = .90). Low-carbohydrate diets had no significant adverse effect on serum lipid, fasting serum glucose, and fasting serum insulin levels, or blood pressure.

Conclusions There is insufficient evidence to make recommendations for or against the use of low-carbohydrate diets, particularly among participants older than age 50 years, for use longer than 90 days, or for diets of 20 g/d or less of carbohydrates. Among the published studies, participant weight loss while using low-carbohydrate diets was principally associated with decreased caloric intake and increased diet duration but not with reduced carbohydrate content.

OBJECTIVE: The prevalence of type 2 diabetes, especially in developing countries, has grown over the past decades. We performed a controlled clinical study to determine whether a community-based, group-centered public health intervention addressing nutrition and exercise can ameliorate glycemic control and associated cardiovascular risk factors in type 2 diabetic patients in rural Costa Rica.

RESEARCH DESIGN AND METHODS: A total of 75 adults with type 2 diabetes, mean age 59 years, were randomly assigned to the intervention group or the control group. All participants received basic diabetes education. The subjects in the intervention group participated in 11 weekly nutrition classes (90 min each session). Subjects for whom exercise was deemed safe also participated in triweekly walking groups (60 min each session). Glycosylated hemoglobin, fasting plasma glucose, total cholesterol, triglycerides, HDL and LDL cholesterol, height, weight, BMI, and blood pressure were measured at baseline and the end of the study (after 12 weeks).

RESULTS: The intervention group lost 1.0 +/- 2.2 kg compared with a weight gain in the control group of 0.4 +/- 2.3 kg (P = 0.028). Fasting plasma glucose decreased 19 +/- 55 mg/dl in the intervention group and increased 16 +/- 78 mg/dl in the control group (P = 0.048). Glycosylated hemoglobin decreased 1.8 +/- 2.3% in the intervention group and 0.4 +/- 2.3% in the control group (P = 0.028).

CONCLUSIONS: Glycemic control of type 2 diabetic patients can be improved through community-based, group-centered public health interventions addressing nutrition and exercise. This pilot study provides an economically feasible model for programs that aim to improve the health status of people with type 2 diabetes.

The Institute of Medicine recently issued a landmark report on medical error. In light of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.

Also published in the Proceedings of the American Medical Informatics Association's 2001 Symposium.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a unified model representation and algorithm for evidence-adaptive quality assessment scoring that can: (1) use both complex case-specific guidelines and single-step population-wide performance-indicators as quality measures; (2) score adherence consistently with quantitative population-based medical utilities of the quality measures where available; and (3) give worst-case and best-case scores for variations based on (a) uncertain knowledge of the best practice, (b) guideline customization to an individual patient or particular population, (c) physician practice style variation, or (d) imperfect reliability of the quality measure. Our solution uses fuzzy measure-theoretic scoring to handle the uncertain knowledge about best-practices and the ambiguity from practice variation. We show results of applying our method to retrospective data from a guideline project to improve the quality of hypertension care.