Clinical engineering professionals need to continually review and improve their management strategies in order to keep up with improvements in equipment technology, as well as with increasing expectations of health care organizations. In the last 20 years, management strategies have evolved from the initial obsession with electrical safety to flexible criteria that fit the individual institution's needs. Few hospitals, however, are taking full advantage of the paradigm shift offered by the evolution of Joint Commission standards. The focus should be on risks caused by equipment failure, rather than on equipment with highest maintenance demands. Furthermore, it is not enough to consider risks posed by individual pieces of equipment to individual patients. It is critical to anticipate the impact of an equipment failure on larger groups of patients, especially when dealing with one of a kind, sophisticated pieces of equipment that are required to provide timely and accurate diagnoses for immediate therapeutic decisions or surgical interventions. A strategy for incorporating multiple criteria to formulate appropriate management strategies is provided in this article.

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While debated for over 30 years, productivity and staffing continue to be a challenging topic for the clinical engineering (CE) community. At the core of this challenge is the lack of reliable indicators substantiated by actual data. This article reports an attempt to evaluate some traditional and newer indicators using data collected from 2 distinct sources. Results confirm early concerns that worked hours self-entered by CE staff are subject to misuse and thus should be avoided. In contrast, good statistical correlation was found for staffing data with several hospital indicators that are consistently collected and widely available. Good correlation with CE department indicators was more difficult to find, apparently because of the lack of reliable records and consistent accounting of all CE resources and expenditures. Although no single, easy-to-measure and easy-to-understand indicator emerged as a replacement for the worked-to-paid-hours ratio, it is shown that a multidimensional model can be built to benchmark productivity and staffing. Calculations from such a model are accurate, but not precise, so the results need to be interpreted carefully. With proper precautions, such comparisons can be used as a good starting point for a more detailed analysis of the differences that could reveal substantive causes such as service scope and strategy, organizational characteristics, and geographical challenges as well as opportunities for major productivity improvements.

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