Implementing Antibiotic Practice Guidelines through Computer-Assisted Decision Support: Clinical and Financial Outcomes

Physicians' decisions control between 70% and 80% of all health care dollars spent [1-3], and many strategies to influence or control physician decision making have been advocated. These strategies include education, peer review with feedback, administrative interventions, financial incentives and penalties, critical pathways, and, most recently, nationally derived guidelines [2, 4]. To date, none of these strategies has been clearly effective [4]. Berwick [5] has outlined the inherent flaws in many of these strategies. He concedes that these methods may lead to predictable care but notes that they cannot lead to continual improvement of care. Nowhere in health care are these strategies to control or influence physicians more prevalent than in the area of drug use, particularly use of antimicrobial agents [6]. The hospital-wide use of drugs and the involvement of various health care providers create a system of diffuse responsibility, enormous variation, and escalating costs [6-9]. The United States currently spends $40 billion annually on pharmaceuticals; this is 8% of the total cost of health care [3, 7-9]. Prescription drugs now constitute between 5% and 20% of an individual hospital's total budget [7]. Antimicrobial agents are one of the costliest categories of drug expenditures in hospitals, accounting for approximately 20% to 50% of total spending on drugs [9-14]. Investigations in various clinical practice settings have indicated that as much as 50% of antibiotic use is inappropriate [14-17]. The consequences of this have been addressed in terms of antimicrobial resistance [18, 19], adverse drug reactions [15, 17], and cost [11-14]. In response to these pressures, professional societies and individual investigators have outlined methods with which to improve antibiotic use [20-29]. Most of these methods (for example, drug formularies) use some form of a control mechanism, and, to date, experience with them has been mixed [11, 16, 25, 27, 28]. Kassirer [30] has challenged the health care system to develop strategies that inform rather than enforce or control medical decisions. For more than a decade, we have been developing and investigating clinical management programs that augment and inform clinical decision making, in addition to focusing on continual quality improvement [31, 32], in antibiotic therapy, infection control surveillance, and the safety of drug use. These programs were designed to provide continuous surveillance and computer-assisted decision support [33, 34] to all clinicians responsible for inpatient care in a general hospital. The hallmark of these computer-assisted decision support programs was local clinician-derived consensus practice guidelines [5, 31, 34, 35] that were programmed into a hospital information system as rules, algorithms, and predictive models. These programs managed antibiotic use at three basic levels: prophylactic use, empiric use, and therapeutic use. We review the clinical and process outcomes and the financial effects of these hospital-wide decision support programs during a 7-year period. Methods LDS Hospital, located in Salt Lake City, Utah, is a 520-bed private, community, acute-care referral hospital that serves as a teaching facility for the University of Utah Schools of Medicine, Nursing, and Pharmacy. The hospital provides most clinical services but not general pediatric care. An integrated, clinically oriented hospital information system has been under development at the institution for more than 20 years [36]. This system routinely collects and stores all patient data from multiple sources throughout the hospital. The system currently serves as the hospital's clinical computing system, providing clinical information management and establishing computer-based patient records. The computer-based patient record contains both clinical and financial data. The financial data are derived from a standard cost-manager microcomputer software system that is linked to the clinical information system [37, 38]. The information system also provides online clinical decision support through its expert system capabilities. Infectious diseases surveillance and therapeutics was the first medical domain to use the expert system features of the hospital information system on a widespread clinical basis [39]. The clinical decision support systems and the implementation methods for this domain were developed, tested, and implemented by clinical investigators in the Division of Infectious Diseases at LDS Hospital [37-52]. The process used to develop the local consensus guidelines for antimicrobial use was similar to the approach described by East and colleagues [34]. Our approach also included thorough evaluations of published reports, use of national guidelines and local expert opinion, and exhaustive analyses of the LDS Hospital patient database; we subsequently developed step-wise logistic regression models [48, 49]. Through various committee representations, we also frequently consulted the medical staff of LDS Hospital; in these consultations, we presented data and interim results. Using the aforementioned formal techniques [34, 35], the staff also helped develop, test, and implement the clinical practice guidelines that were embedded in the decision support programs. The practice guidelines were encoded into the knowledge base of the hospital information system as rules, algorithms, and predictive models. This allowed for decision support at the point of care, with feedback to physicians in real time. Thus, guideline application was patient specific, and recommendations corresponded to actual clinical conditions at a particular point in time. Feedback to physicians was open looped [53], and the physicians ultimately decided whether or not to follow the recommendations. Since 1985, many of these clinical decision support programs and guidelines have been prospectively developed and tested in the patient populations of LDS Hospital, often in randomized studies. Decision support programs have been systematically expanded to include comprehensive, institution-wide antibiotic management programs. These decision support programs were designed to comprehensively manage all antibiotic agents used in the institution throughout the continuum of hospital care: 1) prophylactic [surgical] antibiotic use; 2) empiric antibiotic use [for suspected infection without microbiological data]; and 3) therapeutic antibiotic use (for established infection with microbiological data). These programs continually track and assist physicians in managing each patient treated with an antibiotic at LDS Hospital and in all aspects of antibiotic use; no antibiotic can be prescribed at LDS Hospital without being affected by these decision support programs. The methods used in these programs have been described elsewhere [37-52]. These programs are continually updated as medical knowledge and the health care delivery system change, both locally and nationally. The surgical prophylactic decision support programs were developed with our surgical colleagues and resulted in strategies that ensured appropriate case selection, delivery time, intra-operative dosing, and duration of antibiotic use rather than solely concentrating on the specific antibiotic agent or class of agents for each surgical procedure [41, 42, 45]. The empiric and therapeutic antibiotic decision support programs provide information to the clinician in the form of computer-generated alerts or suggestions on the following: the presence of resistant pathogens; untreated infections; an incorrect dose, route, or interval of an antibiotic; the absence of current renal function data; the need for serum drug levels; population-based probabilities of infections in relation to specific patient variables; and cost-effective alternatives (for example, oral therapy or narrower-spectrum agents) [43, 48, 49]. Furthermore, these management programs monitor patients for excessive or suboptimal antibiotic doses, depending on the patients' current renal function status [46, 47], and they address the prevention, early detection, and archiving of adverse drug events associated with these agents [44, 46, 50]. All but one of the computer-assisted antibiotic decision support programs described were in clinical use throughout the study period; the exception was the adverse drug event program, which has been used since 1989. Beginning in 1985, investigators in the infectious disease division developed database analysis programs that quantify antibiotic use and expenditures, identify prescriber and diagnosis-related groups for patients receiving antibiotics, track antibiotic resistance patterns, and distinguish therapeutic from prophylactic use of antibiotics. The reports generated by these database analysis programs summarize antibiotic use by specific agent and place them in the following categories: numbers of patients treated, total milligrams administered, total doses administered, defined daily doses per 100 occupied bed-days [12, 13, 54], and total amount spent. We used the number of defined daily doses per 100 occupied bed-days because it is a standardized technical unit of measurement that estimates drug use. A defined daily dose is based on the average adult maintenance dose (usually in grams) for the primary indication of the drug and is adjusted per 100 occupied bed-days. The concept of the defined daily dose per 100 occupied bed-days was established by a joint project of the Nordic Council on Medicines and the World Health Organization Center for Drug Collaboration Statistics [12, 13, 54]. Because the defined daily dose per 100 occupied bed-days is independent of cost (which eliminates confounding introduced by the buying practices of group purchasing organizations) and differences in dose forms, it establishes a standardized basis for comparing drug use. The World Health Organization has agreed that the defined daily dose method of analysis can be use