PubMed · 10294165
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1989. Executive report: data bases for decision analysis.. https://pubmed.ncbi.nlm.nih.gov/10294165/
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Routine statistics provide only limited support to doctors and managers responsible for the management of surgical waiting lists. Therefore, a study developed a PC-based model that could assist medium-term planning activities that, for example, aim to reduce excessive waiting times. The model allows the user to create scenarios that describe possible future activity levels and calculates how these will affect waiting list behaviour. A key feature of the model is that it recognizes that a waiting list consists of different categories of patients that move through the list at different speeds. The model can be used with routinely available data. The model was tested at three sites where it proved capable of providing valuable insights and assisting doctors and managers to better understand waiting list dynamics. However, although the model quantified the changes required to reduce waiting times, the sites could not always implement these changes owing to resource constraints.
Their personal demeanor is decidedly modest, their corporate culture nurturing and family-oriented. But don't let that charming Midwestern manner fool you: as many health care organizations know, Bill and Richard Lawson are major players in the world of ERP software, and they are poised for even bigger things. Next year they intend to take Minneapolis-based Lawson Software public, a move they hope will give them the capital and clout to go head to head with the giants of the industry and eventually become one of the top two or three players in the world.
Financing of mental health care has changed radically, especially with managed care. Shrinking revenues have forced providers to look for creative ways in which to provide quality services at less expense. Delivery of quality services depends largely on the productive use of the provider's prime resource--the clinicians. Productivity was the focus of the PC-based decision support system developed for mental health providers in New York State. It enables administrators to track key indicators of productivity such as face-to-face time and non-face-to-face time against goals. Unmet goals can be pinpointed quickly, and clinicians' caseloads can be reviewed to determine the underlying causes. A key feature of the system is the conversion of raw data into actionable information to help in problem finding and problem solving. The system has been implemented in Ulster County, the pilot site for the project. The software can be customized easily to suit the data of other providers.