Yet if hospital settings are sometimes conducive to making people sick, they also have the potential to heal themselves. The article by Redd and Susser in this issue of the Journal 4 demonstrates the value of hospital-based operational research in addressing current challenges to the health care system. Focusing on the toll that the resurgence of tuberculosis takes on an urban emergency room, the authors use data readily accessible to emergency room clinicians to derive an algorithm that selects patients in need of isolation and spares a substantial portion of others from costly isolation. Faced with infectious diseases emergent and reemergent in an era of increasing economic constraints, hospitals should be well equipped to find creative solutions to new public health challenges. In 1997, hospitals may no longer be able to afford to remain data rich and analysis poor.
Economic factors are a driving force in much recent decision making at the hospital level.
Patient isolation, although expensive, reduces the likelihood of in-hospital transmission of pathogens, so any algorithm that restricts the number of patients qualifying for isolation must lead to transmission rates that remain acceptable by community standards. Redd and Susser provide an example of how analytic methods applied to easily available data allow for respecting economic concerns without compromising the public interest. Redd and Susser point out the importance of disease prevalence in determining the performance characteristics of any screening algorithm. According to Bayes' theorem, the predictive value of a screening test varies according to the prevalence of disease in a population. Although the authors appropriately challenge readers to apply epidemiological methods to similar concerns, emergency care providers and others who take them up on this offer should adhere to basic epidemiological principles in designing their studies.