Medicare quality improvement: bad apples or bad systems?

Abstract

THE QUALITY IMPROVEMENT GROUP AT THE CENTERS for Medicare and Medicaid Services leads the quality improvement organizations (QIOs, formerly the PROs [peer review organizations], PSROs [professional standards review organizations], EMCROs [experimental medical care review organizations], etc), and according to the results of a study by Jencks and colleagues in this issue of THE JOURNAL, their leadership is effective. No other US organization measures quality at the hospital level. The QIO program uses 24 quality indicators that have strong evidence to support them. Jencks et al report that between 1999 and 2001, the proportion of Medicare patients receiving appropriate care improved from 70% to 73% on average, although this rate varied widely across states and by indicator. Their analysis is valid, robust, understandable, and correct. For the 1999-2002 QIO contract cycle, Centers for Medicare & Medicaid Services required all QIOs to improve quality in 5 clinical areas (acute myocardial infarction, heart failure, pneumonia, surgical infection, and outpatient diabetes), not just to passively review charts. The QIO quality indicators address some aspects of suboptimal quality, but others remain. As summarized in the Institute of Medicine’s recent reports on medical errors, a diverse literature describes the imperfect state of health care quality. The Institute of Medicine asserts that medical errors kill more people in the United States each year than motor vehicle crashes. For complex reasons, existing systems of quality assessment, review, and improvement function suboptimally. A critical issue is whether these errors represent failures of humans or systems. Peer review, malpractice litigation, medical licensing, medical disciplinary actions, insurer audit, governmental investigation, and most other quality assurance systems rely on retrospective review. Examining patient charts assumes that error derives from failure on the part of an incompetent or careless individual. Adverse events therefore identify bad apples for removal. This inspection model (“name, blame, shame”) seeks to improve quality by cutting off one tail of the bell-shaped curve of human performance. In contrast, Deming’s continuous quality improvement (CQI) model assumes that most adverse events represent system failures and that design of work processes should detect and eliminate the human error that inevitably occurs. Industrial quality control statistically analyzes all outcomes for systems improvement opportunities rather than searching for single events that purportedly demonstrate individual error. The CQI model seeks to improve quality by moving the entire bell curve to the left. Unfortunately, the CQI initiative has not yet attained full acceptance by the general public. The name-blame-shame model produces readily understandable headlines, but it does not methodically eliminate errors to improve statistical outcomes. Yet even if every worker in a health care system could do his or her job perfectly, most events that are considered to be errors would still occur. Although organizations like the Institute for Healthcare Improvement have led the effort to extend the CQI initiative into health care, the recent survey by Blendon et al makes it clear that neither members of the public nor physicians appreciate that poor systems cause most errors. According to the classic Donabedian model, health care quality is organized as structure, process, or outcome. Structure refers largely to the paper qualifications of the practitioner or institution (eg, licensed, board certified, insured, or inspected by the Joint Commission on Accreditation of Healthcare Organizations). Process refers to how the practitioner delivers care (eg, drug X was indicated and prescribed). Outcome refers to what happened subsequently to the patient (eg, felt better, returned to work, died). At present, all organizations use process measures for quality review. The QIOs surpass other organizations by using validated measures and in aggregating at the hospital level. To secure hospitals’ cooperation, the QIOs do not publish their hospital-level results. Rather, these results guide the QIOs in targeting technical assistance to improve quality. Levels of aggregation at the regional or state level lack sufficient detail to identify opportunities for quality reengineering within a hospital. The upcoming Agency for Healthcare Research and Quality (AHRQ) national quality