A Computer-Assisted Management Program for Antibiotics and Other Antiinfective Agents
R. Scott Evans, Ph.D., Stanley L. Pestotnik, M.S., R.Ph., David C. Classen, M.D., M.S., Terry P. Clemmer, M.D., Lindell K. Weaver, M.D., James F. Orme, M.D., James F. Lloyd, B.S., and John P. Burke, M.D.
Background and Methods Optimal decisions about the use of antibioticsand other antiinfective agents in critically ill patients requireaccess to a large amount of complex information. We have developeda computerized decision-support program linked to computer-basedpatient records that can assist physicians in the use of antiinfectiveagents and improve the quality of care. This program presentsepidemiologic information, along with detailed recommendationsand warnings. The program recommends antiinfective regimensand courses of therapy for particular patients and providesimmediate feedback. We prospectively studied the use of thecomputerized antiinfectives-management program for one yearin a 12-bed intensive care unit.
Results During the intervention period, all 545 patients admittedwere cared for with the aid of the antiinfectives-managementprogram. Measures of processes and outcomes were compared withthose for the 1136 patients admitted to the same unit duringthe two years before the intervention period. The use of theprogram led to significant reductions in orders for drugs towhich the patients had reported allergies (35, vs. 146 duringthe preintervention period; P<0.01), excess drug dosages(87 vs. 405, P<0.01), and antibiotic-susceptibility mismatches(12 vs. 206, P<0.01). There were also marked reductions inthe mean number of days of excessive drug dosage (2.7 vs. 5.9,P<0.002) and in adverse events caused by antiinfective agents(4 vs. 28, P<0.02). In analyses of patients who receivedantiinfective agents, those treated during the interventionperiod who always received the regimens recommended by the computerprogram (n = 203) had significant reductions, as compared withthose who did not always receive the recommended regimens (n= 195) and those in the preintervention cohort (n = 766), inthe cost of antiinfective agents (adjusted mean, $102 vs. $427and $340, respectively; P<0.001), in total hospital costs(adjusted mean, $26,315 vs. $44,865 and $35,283; P<0.001),and in the length of the hospital stay (adjusted mean, 10.0vs. 16.7 and 12.9 days; P<0.001).
Conclusions A computerized antiinfectives-management programcan improve the quality of patient care and reduce costs.
Faced with an increasing loss of autonomy in the managed caremarketplace, physicians often view the debate about the qualityof care as simply about finding ways to reward them for doingless for patients and to control costs by the use of arbitraryrules for clinical care.1 Skeptics view quality-of-care projectsas a disguised form of marketing; this skepticism will not disappearuntil physicians can see quality-of-care efforts that make difficultdecisions easier and more accurate.2,3 Establishing systemsfor improving care is difficult, at best, for groups of specialistphysicians, but it is next to impossible for physicians workingalone or for those who are employees in large bureaucratic organizations.4Both the provision of care and the monitoring of its qualitydepend on data that are often not available either in papermedical records or in administrative and billing data bases.Elaborate clinical computer systems, which are increasinglyavailable, are vital for health care organizations.
The usefulness of clinical computer systems is beginning tobe recognized. Perhaps their immediate value can best be demonstratedin terms of the most common therapeutic intervention in medicine,the prescription.5 Direct, computer-based physician-order entry— as a means of ensuring quality, preventing errors, makingcost-effective decisions, and integrating clinical-decisionsupport into the order-entry process — has had many champions,yet it has not been widely adopted in clinical practice.6 Moreover,computer systems are unable to incorporate and address localepidemiologic factors, such as antibiotic-resistance patterns.A more promising approach is the design of comprehensive computerizeddisease-management programs that enable clinicians to augmenttheir clinical decision-making skills rather than replace orcontrol them and that use locally derived data to guide theselection of drugs.
For over a decade at LDS Hospital in Salt Lake City, we havebeen developing and testing computerized clinical-decision–supportprograms that provide timely and accurate information relevantto decisions about the treatment of infectious diseases. Theseclinical-decision–support tools are intended to improvethe use of antiinfective drugs for prophylaxis in patients undergoingsurgery,7 for empirical therapy,8 and for the treatment of microbiologicallyconfirmed infections.9 We have used this experience to developa comprehensive computerized antiinfectives-management program.In this report, we evaluate the effect of this program on thequality of patient care.
Methods
Description of the System
The computerized antiinfectives-management program was designedas a tool to provide clinicians with relevant, immediate informationpertaining to the treatment of infections and the use of antiinfectiveagents. The program is linked to the computer-based patientrecords at Intermountain Health Care hospitals and clinics10and makes available as much patient-specific information aspossible. The management program can be accessed on computerterminals available at locations throughout the hospital, includingthe bedside, nursing stations, operating rooms, and emergencyroom. The program also is available for physicians by remoteaccess from their private offices and homes.
The program was designed to fit into the work flow of practitioners.It alerts physicians to the latest pertinent information onthe individual patient at the time therapeutic decisions aremade (Figure 1). Decision-support logic is used by the programto suggest an appropriate antiinfective regimen for the patientor to indicate the lack of a current need for antiinfectivetherapy. The program uses the patient's admission diagnosis,white-cell count, temperature, surgical data, chest radiograph,and information from pathology, serology, and microbiology reportsto identify the need for and recommended type of antiinfectivetherapy. Information that is not available in a given patient'scomputer-based record is derived by matching patient-specificvariables with those of similar patients from the previous fiveyears.8,9,10,11 The program uses computerized "antibiograms"(antimicrobial-susceptibility patterns) and empirical logicfor identified pathogens for which antibiotic-susceptibilitydata are not available.11,12 For example, the program relieson empirical recommendations from infectious-disease specialistswhen a gram-negative bacillus is first identified in a bloodculture, whereas the antibiograms are used once the pathogenis identified but before the results of susceptibility testsare available. The program suggests an antiinfective regimenthat would cover the identified and potential pathogens.
Figure 1. Example of the Type of Information Initially Displayed When the Computerized Antiinfectives-Management Program Is Used.
Dx denotes diagnosis, max maximal, WBC white-cell count, CrCl creatinine clearance, Cr serum creatinine, IBW ideal body weight, Diff differential, arrows direction of change, IV intravenous, Abx antiinfective, Hx history, ID Rnds infectious-disease rounds, Lab laboratory, and D/C discontinue.
In addition to information on the infection, the program usesdata on the patient's allergies, drug–drug interactions,toxicity, and cost in the selection of suggested antiinfectiveagents. Measures of the patient's renal and hepatic functionare used to calculate the dose and dosing interval for eachsuggested antiinfective agent. The first screen shown when theprogram is used was designed to present any important informationthat should be taken into account in the selection of antiinfectiveagents. It also contains a number of options physicians canuse to obtain more detailed information. The computerized managementprogram addresses and displays for physicians a multitude ofpatient-specific and disease-specific issues (Table 1).
Table 1. Patient-Specific and Disease-Specific Issues Addressed by the Computerized Antiinfectives-Management Program.
Antiinfective agents can be ordered, discontinued, or modifiedby using the options provided at the bottom of the screen. Forthe suggested antiinfective agents, the dose, route of administration,dosing interval, and infusion rate, adjusted for the patient'srenal and hepatic function, are shown. The program also suggestsa duration of antiinfective treatment for the patient. Physicianscan order the computer-suggested antiinfective agents simplyby pressing the appropriate key (<*>). The "Explain Logic"option allows physicians to review the rationale for the treatmentplans suggested by the computer. When physicians select theirown treatment plans, the computer automatically checks for allergiesand drug interactions and suggests the dose, route, interval,and infusion rate for any antiinfective agents selected.
Implementation of the System
LDS Hospital is a private, 520-bed, community, acute care referralhospital located in Salt Lake City and is a teaching centerfor the University of Utah School of Medicine. The computerizedmanagement program was tested in the 12-bed shock–trauma–respiratoryintensive care unit (ICU) at the hospital. All patients admittedto the unit from July 1992 through June 1995 were prospectivelyfollowed with respect to relevant measures of processes andoutcomes. From July 1994 through June 1995 (the interventionperiod), the management program was used and evaluated. Duringthe intervention period all patients in the ICU were evaluatedon a daily basis, and their care was managed with use of theprogram. Each day during morning rounds, the program was usedto monitor every patient, as well as around the clock for allantiinfective therapeutic interventions. Physicians could orderany antiinfective agents they wished ("open-looped decisionsupport"13) but were required to explain their reasons accordingto a structured menu if they did not choose the antiinfectiveagents suggested by the computer program.
Measures of Processes
An analysis of the cohorts treated during the preinterventionperiod (July 1992 through June 1994) and the intervention period(July 1994 through June 1995) was used to evaluate the effectof the use of the computerized management program on a varietyof selected measures of processes and outcomes. The measuresof processes included warnings, or "alerts," generated by thecomputer program, alerting the physician to the occurrence ofdrug allergies (when a drug was prescribed to which the patienthad reported an allergy), excessive drug dosages, antibiotic-susceptibilitymismatches, and lack of appropriateness of selected drugs.9,14Moreover, all patients' renal function was calculated on a dailybasis, and each day that a patient received a dose of an antiinfectiveagent that was excessive in relation to his or her correspondingrenal function was counted as a day of excessive antiinfectivedosage.
Measures of Outcome
Outcome variables included measures of the use of antiinfectiveagents and their costs, the cost of hospitalization, the numberof adverse events caused by antiinfective agents, the numberof days of excessive antibiotic dosage, the length of the hospitalstay, and mortality. We compared the overall rate of use ofantiinfective agents during the preintervention and interventionperiods, using the number of defined daily doses per 100 occupiedbed-days as the unit of measure.15
We used the medical care component of the consumer price indexto adjust the costs of hospitalization during the preinterventionperiod to the comparable figures during the intervention period.The costs of acquiring individual antiinfective agents duringthe preintervention period were adjusted to equal those duringthe intervention period. The same methods of prospective surveillancefor adverse drug-related events were used to identify eventscaused by antiinfective agents during all three years.16,17,18Patients hospitalized during the intervention period who alwaysreceived the computer-suggested antiinfective regimens werecompared with patients hospitalized during the preinterventionperiod and with patients hospitalized during the interventionperiod who did not always receive the computer-suggested antiinfectiveregimens.
Statistical Analysis
The chi-square test and the Mann–Whitney U test were usedto identify statistical differences in the measures of processesbetween the intervention period and the preceding two-year preinterventionperiod. Fisher's exact test and the chi-square test were usedto detect statistically significant differences in rates ofadverse events and in mortality. All statistical tests weretwo-tailed.
A linear regression model was used to test the null hypothesisthat there was no difference in selected outcome variables betweenthe preintervention and intervention periods. This test controlledfor age, sex, severity of underlying disease, medical service,and mortality. Adjustment for the severity of disease was accomplishedwith use of the calculated Computer Severity Index (CSI) scoreat the time of admission to the ICU.19,20 Mortality was includedbecause early death could artificially lower costs and lengthsof stay. Unadjusted means and standard deviations for outcomevariables were used to compare patients who always receivedthe computer-suggested antiinfective agents with other patientsin the intervention period and those in the preinterventionperiod. Linear least-squares regression techniques were thenused to compare adjusted outcome measures between the interventionand preintervention periods. Outcome measures were again adjustedfor age, sex, CSI score on admission to the ICU, medical service,and mortality. Results were similar whether or not mortalitywas included in the model. Residual plots indicated the possibilityof a non-normal distribution of data. Regressions were repeatedafter logarithmic transformation of outcome measures, with similarresults. Therefore, the results of ordinary least-squares regressionare displayed. Adjusted means and 95 percent confidence intervalswere determined. Adjusted means are predicted values evaluatedat the means of the adjustment variables from the least-squaresregression model. We have reported actual P values.
Results
During the one-year intervention period, the physicians in theICU used the management program 5222 times (14.3 per day), andthe program was used to order antiinfective-agent regimens 942times. The physicians prescribed the computer-suggested antiinfectiveagents — including the recommended dose, route, and interval— for 46 percent (437) of the orders, but they followedthe computer-suggested dose and interval for 93 percent (872)of the orders.
Of the 545 patients admitted to the 12-bed ICU during the interventionperiod, 398 (73 percent) received antiinfective agents, as comparedwith 766 of 1136 patients admitted to the ICU during the two-yearpreintervention period (67 percent, P<0.03) (Table 2). Althoughthere were no significant differences in the frequency of hospital-acquiredinfections or the distribution of pathogens during the threeyears, there were increases in bacteremia and infections causedby Pseudomonas aeruginosa and enterococcus species and decreasesin infections caused by Staphylococcus aureus and Escherichiacoli during the intervention period.
Table 2. Characteristics of the Study Population and Procedural and Outcome Measures for the Preintervention and Intervention Periods.
Changes in Process Measures
During the intervention period, 12 susceptibility-mismatch alertswere generated, as compared with 206 during the two-year preinterventionperiod (P<0.01) (Table 2). Alerts of allergies to antiinfectiveagents were generated for ICU patients 35 times during the interventionperiod, as compared with 146 orders for drugs to which patientshad reported allergies during the preintervention period (P<0.01), and alerts of excessive dosage of antiinfective agentswere generated 87 times, as compared with 405 times (P<0.01).Automated daily monitoring of patients' renal function revealedthat during the intervention period, there were significantlyfewer days when doses of antiinfective agents were excessivethan during the preintervention period (2.7 days vs. 5.9 daysper patient, respectively; P<0.002).
Outcome Measures
During the preintervention period, there were 28 adverse drugreactions to antiinfective agents, as compared with only 4 duringthe intervention period (a reduction of over 70 percent, P =0.018). Linear regression showed that during the interventionperiod patients received an average of 4.7 fewer doses of antiinfectiveagents (P = 0.042), had an average decrease of $81 in the costof antiinfective agents (P = 0.079), and received excessiveantiinfective doses for an average of 2.9 fewer days (P<0.001)than during the preintervention period.
Table 3 shows the unadjusted means for a number of selectedoutcome variables, including the rate of use of antiinfectiveagents, their costs, hospital costs, and length of stay. Wecompared patients hospitalized during the intervention periodwho always received the computer-suggested antiinfective agents,patients in the preintervention group, and patients in the interventiongroup who did not always receive the antiinfective agents suggestedby the computer. Comparison of means with adjustment for age,sex, CSI score on admission to the unit, medical service, andmortality (Table 4) demonstrated significant differences inoutcome variables, including reductions in the number of dosesof antiinfective agents (11.4 vs. 23.6 and 27.6, respectively;P<0.001), the cost of antiinfective agents ($102 vs. $340and $427, P<0.001), total length of stay (10.0 vs. 12.9 and16.7 days, P<0.001), and the total cost of hospitalization($26,315 vs. $35,283 and $44,865, P<0.001).
Table 4. Adjusted Outcomes of Patients Who Received Antiinfective Agents during the Preintervention and Intervention Periods.
Discussion
Any program designed to measure and improve the quality of carefor hospitalized patients must include decisions about the useof antiinfective agents and the management of infectious diseases,given the importance of these issues in inpatient clinical care.Infectious-disease problems cross all specialty boundaries,involve a multitude of causal agents and hundreds of genericantiinfective compounds, and usually require management by clinicianswho have not received special training in infectious diseases.More than half the patients in general hospitals now receiveantiinfective agents, which, in turn, account for one thirdto one half of the pharmacy budgets of most inpatient facilitiesand are one of the leading classes of drugs causing adversereactions.17
There is at present no suitable definition of acceptable qualityin the prescribing of antiinfective agents. Nonetheless, thereis a consensus that excessive and inappropriate use of antiinfectiveagents is a global problem that not only adds a substantialeconomic burden to the health care system but also contributesto the selective pressures favoring the development of resistanceto antiinfective agents. Our approach proceeds from the assumptionthat the misuse of antiinfective agents more often results frominsufficient information than from inappropriate behavior.21To address this need, the antiinfectives-management programwas designed to make patient-specific and epidemiologic informationavailable at the point of care and at the time when clinicaldecisions are made, to offer educational information about costsand choices and easy on-line feedback, and to be easy to useand to access.
Furthermore, measuring the quality of antiinfective treatmentis more problematic than assessing the acceptance and efficiencyof the computer program itself. The expansion of medical knowledgecontributes to increasing uncertainty in complex decision-makingprocesses, and it is often unclear whether patients are actuallycured as a result of their treatment.22 We believe that assessmentsof the quality of antiinfective therapy cannot be based exclusivelyon the outcomes of patients with defined infections, since thesepatients make up a minority of those who receive antiinfectiveagents. Dose reduction can lead to fewer side effects or lesssevere ones, or both, as well as to lower costs, but it mayalso result in lower levels of response and survival, as hasbeen noted in patients with breast cancer who are receivingchemotherapy.23 Moreover, global measures of outcome, such ascosts and length of stay, are subject to the influence of manyother variables that change over time. It has become increasinglyclear that some adjustment for the severity of illness is necessaryin research when quality assessment is the chief purpose.24
No single measure of quality with respect to antiinfective therapy,therefore, is likely to be sufficient. However, we believe thatactive surveillance can contribute to both measuring and improvingquality. Assessing the effectiveness of other clinical interventionshas been recognized to require a system of intense surveillancethat involves monitoring, analysis of variations, assessmentof interventions, feedback, and education.25 In the presentstudy, the numbers of alerts for antibiotic-susceptibility mismatches,drug allergies, and excessive drug dosages in the differentperiods provided a direct measure of change in response to theuse of the computerized management program and also an effectivemeans to improve therapy further. The number of adverse reactionsto antiinfective agents is a further sensitive indicator ofthe quality of prescribing practice and complements other measures,such as the cost of antiinfective drugs and the total costsof hospitalization.
In our study, physicians could override the suggestions of thecomputer program but were required to specify the reasons fortheir disagreement by way of a structured menu. In some cases,physicians specified that insufficient information had beenavailable for the computer to make a specific recommendation.The fact that the antiinfective agents suggested by the computerwere selected only approximately half the time is evidence thatthe management tool was not being followed blindly but, rather,was used for the intended purpose of decision support. However,in the analysis of patients hospitalized during the interventionperiod for whom the computer-generated suggestions were alwaysaccepted, significant differences were noted in both clinicaland financial outcomes from those in the group of patients whowere not always treated according to the computer's suggestions.
The data from Table 3 may indicate that patients whose physiciansoverrode the suggestions of the computer program were more severelyill than the patients for whom the computer recommendationswere followed. However, we did adjust for the severity of illnessin the final model. The differences between the subgroups mayalso be due in part to cases in which the computer recommendationsshould have been followed but were not. An override with adverseconsequences occurred, for example, when a physician orderedan antibiotic dose much higher than the computer-suggested dosefor a patient with decreased renal function. The patient hada seizure and spent an extra seven days in the hospital. Nonetheless,the physicians' disagreements with the computer's suggestionshave offered a unique learning opportunity that has producednot only improvements in the computer program but also changesin physicians' decision-making practices.
Because this evaluation was conducted in an institution withadvanced computerized medical-information systems that wereoperative even in the preintervention period, our results maynot adequately reflect the benefits of the computerized toolfor ordering antiinfective agents in other health care settings.In a time-and-motion study performed during the interventionperiod, we found that an average of 14 minutes (range, 8 to25) was required for an infectious-disease specialist to retrievethe same patient-specific information that the computerizedantiinfectives-management program retrieved in 3.5 seconds (range,1 to 5). By making more time available for physicians to practiceevidence-based medicine, the use of the computer in the normaldaily work of physicians has the potential to improve the qualityof care further. To date, this program has demonstrated suchdramatic improvements in clinical and financial outcomes, aswell as remarkable acceptance by physicians, that it has beenrequested and installed in additional inpatient and outpatientfacilities in our integrated health care delivery system.
Supported by Intermountain Health Care.
We are indebted to Gabriela S. Kaufman, M.D., for help withthe time-and-motion study, to Karen J. Meyer, M.Stat., and DianaL. Handrahan, B.S., for help in the statistical analysis, andto the Bureau of Economic and Business Research at the Universityof Utah for assistance in adjusting costs for inflation.
Source Information
From the Departments of Clinical Epidemiology (R.S.E., S.L.P., D.C.C., J.F.L., J.P.B.), Critical Care (T.P.C., L.K.W., J.F.O.), and Medical Informatics (R.S.E.), LDS Hospital, Salt Lake City.
Address reprint requests to Dr. Evans at the Department of Clinical Epidemiology, LDS Hospital, 8th Ave. and C St., Salt Lake City, UT 84143.
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Paul, M., Andreassen, S., Tacconelli, E., Nielsen, A. D., Almanasreh, N., Frank, U., Cauda, R., Leibovici, L., on behalf of the TREAT Study Group,
(2006). Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. J Antimicrob Chemother
58: 1238-1245
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Abboud, P. A., Ancheta, R., McKibben, M., Jacobs, B. R., Clinical Informatics Outcomes Research Group,
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[Abstract]
McGregor, J. C., Weekes, E., Forrest, G. N., Standiford, H. C., Perencevich, E. N., Furuno, J. P., Harris, A. D.
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Sidorov, J.
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25: 1079-1085
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Thursky, K. A., Buising, K. L., Bak, N., Macgregor, L., Street, A. C., Macintyre, C. R., Presneill, J. J., Cade, J. F., Brown, G. V.
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Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S. C., Shekelle, P. G.
(2006). Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care. ANN INTERN MED
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Drew, R. H., Kawamoto, K., Adams, M. B.
(2006). Information technology for optimizing the management of infectious diseases. Am J Health Syst Pharm
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Morrison, R. S., Meier, D. E., Fischberg, D., Moore, C., Degenholtz, H., Litke, A., Maroney-Galin, C., Siu, A. L.
(2006). Improving the management of pain in hospitalized adults.. Arch Intern Med
166: 1033-1039
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Kaushal, R., Jha, A. K., Franz, C., Glaser, J., Shetty, K. D., Jaggi, T., Middleton, B., Kuperman, G. J., Khorasani, R., Tanasijevic, M., Bates, D. W., Brigham and Women's Hospital CPOE Working Group,
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Kilbridge, P M, Welebob, E M, Classen, D C
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Feifer, C., Ornstein, S. M., Jenkins, R. G., Wessell, A., Corley, S. T., Nemeth, L. S., Roylance, L., Nietert, P. J., Liszka, H.
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[Abstract]
Ozdas, A., Speroff, T., Waitman, L. R., Ozbolt, J., Butler, J., Miller, R. A.
(2006). Integrating "Best of Care" Protocols into Clinicians' Workflow via Care Provider Order Entry: Impact on Quality-of-Care Indicators for Acute Myocardial Infarction. J. Am. Med. Inform. Assoc.
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Webster, J. L., Cao, C. G. L.
(2006). Lowering Communication Barriers in Operating Room Technology. Human Factors: The Journal of the Human Factors and Ergonomics Society
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Shah, N. R., Seger, A. C., Seger, D. L., Fiskio, J. M., Kuperman, G. J., Blumenfeld, B., Recklet, E. G., Bates, D. W., Gandhi, T. K.
(2006). Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care. J. Am. Med. Inform. Assoc.
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Voit, S. B., Todd, J. K., Nelson, B., Nyquist, A.-C.
(2005). Electronic Surveillance System for Monitoring Surgical Antimicrobial Prophylaxis. Pediatrics
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Stevenson, K. B., Barbera, J., Moore, J. W., Samore, M. H., Houck, P.
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Cutler, D. M., Feldman, N. E., Horwitz, J. R.
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Yeom, J. H., Park, J. S., Oh, O.-H., Shin, H. T., Oh, J. M.
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Hsu, J., Huang, J., Fung, V., Robertson, N., Jimison, H., Frankel, R.
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Cohen, M M, Kimmel, N L, Benage, M K, Cox, M J, Sanders, N, Spence, D, Chen, J
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Wang, C. J., Marken, R. S., Meili, R. C., Straus, J. B., Landman, A. B., Bell, D. S.
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Galanter, W. L., Didomenico, R. J., Polikaitis, A.
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Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., Strom, B. L.
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Nash, I. S., Rojas, M., Hebert, P., Marrone, S. R., Colgan, C., Fisher, L. A., Caliendo, G., Chassin, M. R.
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Bratzler, D. W., Houck, P. M., Richards, C., Steele, L., Dellinger, E. P., Fry, D. E., Wright, C., Ma, A., Carr, K., Red, L.
(2005). Use of Antimicrobial Prophylaxis for Major Surgery: Baseline Results From the National Surgical Infection Prevention Project. Arch Surg
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Ohsfeldt, R. L., Ward, M. M., Schneider, J. E., Jaana, M., Miller, T. R., Lei, Y., Wakefield, D. S.
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SCHLEYER, T. K.L.
(2004). Should dentistry be part of the National Health Information Infrastructure?. Journal of the American Dental Association
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Hsieh, T. C., Kuperman, G. J., Jaggi, T., Hojnowski-Diaz, P., Fiskio, J., Williams, D. H., Bates, D. W., Gandhi, T. K.
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Trivedi, M. H., Kern, J. K., Grannemann, B. D., Altshuler, K. Z., Sunderajan, P.
(2004). A Computerized Clinical Decision Support System as a Means of Implementing Depression Guidelines. Psychiatr. Serv.
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Bogucki, B., Jacobs, B. R., Hingle, J., the Clinical Informatics Outcomes Research Group,
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Frush, K. S., Luo, X., Hutchinson, P., Higgins, J. N.
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Senn, L., Burnand, B., Francioli, P., Zanetti, G.
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Mosen, D., Elliott, C. G., Egger, M. J., Mundorff, M., Hopkins, J., Patterson, R., Gardner, R. M.
(2004). The Effect of a Computerized Reminder System on the Prevention of Postoperative Venous Thromboembolism. Chest
125: 1635-1641
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Kelley, M. A., Angus, D., Chalfin, D. B., Crandall, E. D., Ingbar, D., Johanson, W., Medina, J., Sessler, C. N., Vender, J. S.
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Bravata, D. M., McDonald, K. M., Szeto, H., Smith, W. M., Rydzak, C., Owens, D. K.
(2004). A Conceptual Framework for Evaluating Information Technologies and Decision Support Systems for Bioterrorism Preparedness and Response. Med Decis Making
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Bell, D. S., Cretin, S., Marken, R. S., Landman, A. B.
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Bindels, R., Hasman, A., Derickx, M., van Wersch, J. W. J., Winkens, R. A. G.
(2003). User satisfaction with a real-time automated feedback system for general practitioners: a quantitative and qualitative study. Int J Qual Health Care
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Tamblyn, R., Huang, A., Perreault, R., Jacques, A., Roy, D., Hanley, J., McLeod, P., Laprise, R.
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Brower, R. G., Rubenfeld, G., Thompson, B. T.
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Payne, T. H., Hoey, P. J., Nichol, P., Lovis, C.
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Fraser, H. S. F., Long, W. J., Naimi, S.
(2003). Evaluation of a Cardiac Diagnostic Program in a Typical Clinical Setting. J. Am. Med. Inform. Assoc.
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Kuperman, G. J., Gibson, R. F.
(2003). Computer Physician Order Entry: Benefits, Costs, and Issues. ANN INTERN MED
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Kaushal, R., Shojania, K. G., Bates, D. W.
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Bates, D. W., Gawande, A. A.
(2003). Improving Safety with Information Technology. NEJM
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Fortescue, E. B., Kaushal, R., Landrigan, C. P., McKenna, K. J., Clapp, M. D., Federico, F., Goldmann, D. A., Bates, D. W.
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Classen, D.
(2003). Medication Safety: Moving From Illusion to Reality. JAMA
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Maviglia, S. M., Zielstorff, R. D., Paterno, M., Teich, J. M., Bates, D. W., Kuperman, G. J.
(2003). Automating Complex Guidelines for Chronic Disease: Lessons Learned. J. Am. Med. Inform. Assoc.
10: 154-165
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Shah, A. N., Frush, K., Luo, X., Wears, R. L.
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Burke, J. P.
(2003). Infection Control -- A Problem for Patient Safety. NEJM
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Doolan, D. F., Bates, D. W., James, B. C.
(2003). The Use of Computers for Clinical Care: A Case Series of Advanced U.S. Sites. J. Am. Med. Inform. Assoc.
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Ortiz, E., Meyer, G., Burstin, H.
(2002). Clinical Informatics and Patient Safety at the Agency for Healthcare Research and Quality. J. Am. Med. Inform. Assoc.
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Wester, C. W., Durairaj, L., Evans, A. T., Schwartz, D. N., Husain, S., Martinez, E.
(2002). Antibiotic Resistance: A Survey of Physician Perceptions. Arch Intern Med
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Gerberding, J. L.
(2002). Hospital-Onset Infections: A Patient Safety Issue. ANN INTERN MED
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Kaushal, R, Bates, D W
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Anderson, J. G., Jay, S. J., Anderson, M., Hunt, T. J.
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Patterson, E. S., Cook, R. I., Render, M. L.
(2002). Improving Patient Safety by Identifying Side Effects from Introducing Bar Coding in Medication Administration. J. Am. Med. Inform. Assoc.
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Shojania, K. G., Duncan, B. W., McDonald, K. M., Wachter, R. M.
(2002). Safe but Sound: Patient Safety Meets Evidence-Based Medicine. JAMA
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Safdar, N., Maki, D. G.
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Chastre, J., Fagon, J.-Y.
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Morris, A H
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Mandl, K. D., Lee, T. H.
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Hersh, W. R., Patterson, P. K., Kraemer, D. F., Shea, S.
(2002). Telehealth: The Need for Evaluation Redux. J. Am. Med. Inform. Assoc.
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Chertow, G. M., Lee, J., Kuperman, G. J., Burdick, E., Horsky, J., Seger, D. L., Lee, R., Mekala, A., Song, J., Komaroff, A. L., Bates, D. W.
(2001). Guided Medication Dosing for Inpatients With Renal Insufficiency. JAMA
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Eggimann, P., Pittet, D.
(2001). Infection Control in the ICU. Chest
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Mullett, C. J., Evans, R. S., Christenson, J. C., Dean, J. M.
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James, B. C.
(2001). Making It Easy to Do It Right. NEJM
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Murff, H. J., Kannry, J.
(2001). Physician Satisfaction with Two Order Entry Systems. J. Am. Med. Inform. Assoc.
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Kaushal, R., Barker, K. N., Bates, D. W.
(2001). How Can Information Technology Improve Patient Safety and Reduce Medication Errors in Children's Health Care?. Arch Pediatr Adolesc Med
155: 1002-1007
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Knab, J. H., Wallace, M. S., Wagner, R. L., Tsoukatos, J., Weinger, M. B.
(2001). The Use of a Computer-Based Decision Support System Facilitates Primary Care Physicians' Management of Chronic Pain. Anesth. Analg.
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