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.

BACKGROUND: Patients with end-stage renal disease are known to have decreased survival after myocardial infarction, but the association of less severe renal dysfunction with survival after myocardial infarction is unknown.

OBJECTIVES: To determine how patients with renal insufficiency are treated during hospitalization for myocardial infarction and to determine the association of renal insufficiency with survival after myocardial infarction.

DESIGN: Cohort study.

SETTING: All nongovernment hospitals in the United States.

PATIENTS: 130 099 elderly patients with myocardial infarction hospitalized between April 1994 and July 1995.

MEASUREMENTS: Patients were categorized according to initial serum creatinine level: no renal insufficiency (creatinine level < 1.5 mg/dL [<132 micromol/L]; n = 82 455), mild renal insufficiency (creatinine level, 1.5 to 2.4 mg/dL [132 to 212 micromol/L]; n = 36 756), or moderate renal insufficiency (creatinine level, 2.5 to 3.9 mg/dL [221 to 345 micromol/L]; n = 10 888). Vital status up to 1 year after discharge was obtained from Social Security records.

RESULTS: Compared with patients with no renal insufficiency, patients with moderate renal insufficiency were less likely to receive aspirin, beta-blockers, thrombolytic therapy, angiography, and angioplasty during hospitalization. One-year mortality was 24% in patients with no renal insufficiency, 46% in patients with mild renal insufficiency, and 66% in patients with moderate renal insufficiency (P < 0.001). After adjustment for patient and treatment characteristics, mild (hazard ratio, 1.68 [95% CI, 1.63 to 1.73]) and moderate (hazard ratio, 2.35 [CI, 2.26 to 2.45]) renal insufficiency were associated with substantially elevated risk for death during the first month of follow-up. This increased mortality risk continued until 6 months after myocardial infarction.

CONCLUSIONS: Renal insufficiency was an independent risk factor for death in elderly patients after myocardial infarction. Targeted interventions may be needed to improve treatment for this high-risk population.