Digital decision making: computer models and antibiotic prescribing in the twenty-first century.

Received 26 December 2007; accepted 28 December 2007; electronically published 17 March 2008. Reprints or correspondence: Dr. Bruce Y. Lee, Dept. of Medicine and Biomedical Informatics, Section of Decision Sciences and Clinical Systems Modeling, University of Pittsburgh, 200 Meyran Ave., Rm. 217, Pittsburgh, PA 15213 (BYL1@pitt.edu). Clinical Infectious Diseases 2008; 46:1139–41 2008 by the Infectious Diseases Society of America. All rights reserved. 1058-4838/2008/4608-0002$15.00 DOI: 10.1086/529441 Antibiotic selection has become complicated. Gone are the days when you only needed to search a small book, such as the Sanford Guide to Antimicrobial Therapy [1], to find the appropriate antibiotic. Today, the right antibiotic may depend on your patient, other patients in the vicinity of your patient, your patient’s insurance, your hospital, your hospital’s formulary, your city, the time of year, and many other factors [2–6]. Some antimicrobial decisions require one to serve as part physician, part epidemiologist, part economist, part pharmacist, part historian, and part sociologist. With limited time in which to see each patient and to make decisions, juggling all of these roles is challenging for even the most multitalented physicians. When decisions are complicated and are subject to rapidly changing conditions, computer models can help. Many other industries have long used computer models to facilitate decision making. If automobile manufacturing, building construction, air traffic control, financial planning, and meteorological forecasting did not use computer simulation, we would surely see many more accidents, wasted investments, and other mishaps. Why should antimicrobial therapy decisions be different? The stakes are potentially very high. Improper choices can lead to morbidity, mortality, and significant costs and even have the potential to change the long-term infectious disease ecosystem [7, 8]. Why not use all of the techniques at our disposal? Consequently, we need more computer decision models, such as the macrolide model presented by Daneman et al. [9] in this issue of Clinical Infectious Diseases. Their model analyzes how the local prevalence of macrolide resistance affects the choice of using a macrolide to treat community-acquired pneumonia. This represents an important advance, because most antibiotic prescribing mandates, to date, have been based on expert opinion [10– 13]. Without computer models, even the most knowledgeable experts have limitations. Experts often rely on their personal experience and retrospective data review. Depending exclusively on past data and experience to understand the present and to predict the future is dangerous. A decade or two ago, how many could fully predict the economic and epidemiologic conditions that we face today? Although computer models vary in complexity, generalizability, and applicability, general principles guide the construction, interpretation, and use of all antibiotic decision models. Understanding these principles precludes their misinterpretation and misuse. The relative strengths and flaws of the macrolide model help to illustrate the following general principles. Perfection is not the goal. No antibiotic research study is perfect. Even a well-designed, randomized, controlled trial has many shortcomings. Similarly, every computer model simplifies real life situations and incorporates many assumptions. When considering a computer model, the temptation often is to discard the model completely once any flaws are identified. However, a flawed model is usually better than no model, as long as the flaws are understood, and the model still provides useful information. Daneman et al. [9] clearly acknowledge the limitations of their model. Nonetheless, their model remains superior to any other currently available model. Moreover, the model’s imperfections raise pertinent questions that can be used to guide future research and policymaking. For example, the model does not show how the availability of different alternative therapies may affect optimal macrolide use, which is an issue that future studies may explore. The model also exposes the general limitations of the data, which leads us to the next principle. Consider but do not obsess over the