To the Editor: In their Commentary, Drs Nolan and Berwick1 advocate the widespread adoption of all-or-none measures in the assessment of health care quality. The all-or-none measure works in a multiplicative sense, compared with composite measures that are additive. For example, for diabetes care they note 5 activities that could be measured by a present/absent indicator and recorded as 1 (present) and 0 (absent). The all-or-none score for 3 possible patients would be calculated as: patient A: 1 × 1 × 1 × 1 × 1 = 1; patient B: 1 × 0 × 1 × 1 × 1 = 0; and patient C: 0 × 0 × 0 × 0 × 0 = 0.
The composite scores, which are summed, would yield scores of 5 (patient A), 4 (patient B), and 0 (patient C).
The all-or-none measurement hides the differences in care received by patients B and C, while the composite measurement reveals these differences. If the 3 patients above were scored as a unit, the all-or-none measurement would yield 33% (1/3), while the composite measurement would yield 60% (9/15) for group scores.
An example such as sterile technique would strongly support the all-or-none measurement; diabetes is less compelling. Consumers should be aware of such differences.
In addition, Nolan and Berwick1 argue that a change in the metric, particularly from item-level measurement, would remove the challenges involved with telling a clinician or unit at 95% compliance that it must get better. Changing to the all-or-none measurement would immediately lower scores but with time even the new measures may increase to the 90s and the issue will return. An alternate approach might follow that used by a number of industries. When concerned about complacency due to a 98% success rate, Firestone Tire & Rubber Co changed the metric to parts per million “defective” (ppm); within months, one plant improved from 20 000 ppm to 60 ppm.2 Similar motivations influenced Baldrige Award winner Corning to convert most measurements from percentage to parts per million.3
Applying this to a medical example, one study of errors found that patients in a medical intensive care unit experienced an average of 1.7 errors from 178 “activities” per day, indicating that the intensive care unit was functioning at a level of 99% proficiency.4 But when measured on a parts per million metric, the score is 10 000 and shows a clear improvement opportunity. For the 3 patients above, parts per million = [(1 − 9/15)] × 1 million = 400 000.
Motorola and General Electric, among others pursuing “Six Sigma”5 quality, aim for 3.4 ppm.
Financial Disclosures: None reported.
Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature
Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal
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