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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Almost since the beginning of clinical engineering as a profession, the need for scheduled maintenance (mostly safety and performance inspections) and its appropriate frequency have been debated extensively but could not be resolved conclusively because of the lack of comparable data. The combination of regulatory requirements typically based on manufacturers' recommendations and concern for patient safety discouraged experimentation by clinical engineering professionals and thus limited the possibility of comparisons within the same organization. Lateral comparisons among different hospitals have been difficult because of different computerized maintenance management systems, failure classification, and reluctance to share information. Using a small set of standardized failure codes, more than 62,000 work orders were classified by dozens of biomedical technicians at 8 hospitals for almost 2 years. These data were used to compare different maintenance strategies adopted for 7 types of medical equipment commonly encountered in acute-care hospitals. No prominent differences were found among the data collected from hospitals that adopted different maintenance frequencies, statistical sampling, and even run-to-failure strategies. Most of the small differences were comparable to the SDs calculated from the data for each maintenance strategy. These results suggest that it is justifiable to adopt a less resource-demanding maintenance strategy for most equipment types, except for the scheduled replacement of wearable parts that was outside the scope of this study.

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During the early years of clinical engineering (CE), CE professionals in the United States devoted a significant portion of their resources to detect failures through inspections (incoming and scheduled) and prevent failures through periodic parts replacement, lubrication, and other tasks (preventive maintenance), with the goal of reducing patient risks. With the rapid evolution of technology in the last 3 decades that increased medical equipment reliability, it is unclear whether CE professionals should continue to focus their attention on equipment failure detection and prevention or broaden their scope to enhance further patient safety. Using scheduled and unscheduled maintenance data collected for almost 2 years from 8 hospitals and a standardized failure classification method, 22 equipment types were analyzed in terms of actions that CE can undertake to improve safety: directly, indirectly, or in the future. For each of these 3 CE action groups, the risk associated with the use of equipment was estimated from the respective failure probability and severity of harm. The results show that, for most equipment types, CE professionals have reached the saturation point of what they can do to reduce risks, although some redirection of their attention from certain equipment types to others would optimize the use of limited resources. On the other hand, plenty of opportunities exist in helping the users and other allied health professionals to reduce risks significantly through further training, better communication, and better selection in future acquisitions.