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Guided Medication Dosing for Inpatients With Renal Insufficiency
Glenn M. Chertow, MD,MPH;
Joshua Lee, MD;
Gilad J. Kuperman, MD;
Elisabeth Burdick;
Jan Horsky;
Diane L. Seger;
Rita Lee;
Aparna Mekala;
Jean Song;
Anthony L. Komaroff, MD;
David W. Bates, MD,MSc
JAMA. 2001;286:2839-2844.
ABSTRACT
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Context Usual drug-prescribing practices may not consider the effects of renal insufficiency on the disposition of certain drugs. Decision aids may help optimize prescribing behavior and reduce medical error.
Objective To determine if a system application for adjusting drug dose and frequency in patients with renal insufficiency, when merged with a computerized order entry system, improves drug prescribing and patient outcomes.
Design, Setting, and Patients Four consecutive 2-month intervals consisting of control (usual computerized order entry) alternating with intervention (computerized order entry plus decision support system), conducted in September 1997–April 1998 with outcomes assessed among a consecutive sample of 17 828 adults admitted to an urban tertiary care teaching hospital.
Intervention Real-time computerized decision support system for prescribing drugs in patients with renal insufficiency. During intervention periods, the adjusted dose list, default dose amount, and default frequency were displayed to the order-entry user and a notation was provided that adjustments had been made based on renal insufficiency. During control periods, these recommended adjustments were not revealed to the order-entry user, and the unadjusted parameters were displayed.
Main Outcome Measures Rates of appropriate prescription by dose and frequency, length of stay, hospital and pharmacy costs, and changes in renal function, compared among patients with renal insufficiency who were hospitalized during the intervention vs control periods.
Results A total of 7490 patients were found to have some degree of renal insufficiency. In this group, 97 151 orders were written on renally cleared or nephrotoxic medications, of which 14 440 (15%) had at least 1 dosing parameter modified by the computer based on renal function. The fraction of prescriptions deemed appropriate during the intervention vs control periods by dose was 67% vs 54% (P<.001) and by frequency was 59% vs 35% (P<.001). Mean (SD) length of stay was 4.3 (4.5) days vs 4.5 (4.8) days in the intervention vs control periods, respectively (P = .009). There were no significant differences in estimated hospital and pharmacy costs or in the proportion of patients who experienced a decline in renal function during hospitalization.
Conclusions Guided medication dosing for inpatients with renal insufficiency appears to result in improved dose and frequency choices. This intervention demonstrates a way in which computer-based decision support systems can improve care.
INTRODUCTION
Renal insufficiency is relatively common among hospitalized patients, and is associated with an increase in hospitalization-related morbidity and mortality.1-4 Persons with acute and chronic renal insufficiency are hospitalized with increased frequency compared with those nonaffected, due to renal disease per se, and to the effects of renal insufficiency on other medical conditions, including congestive heart failure and chronic liver disease.5-6 Practitioners caring for these patients are faced with the challenges of managing the complex interplay between renal insufficiency and other organ system disease, and of altering diagnostic studies (eg, angiography) and therapeutics to avoid further renal injury. With regard to renal insufficiency and pharmacotherapeutics, the majority of clinicians' attention has been directed at avoiding nephrotoxic drugs in patients at risk for worsening renal failure; comparatively little attention has been paid to the disposition of drugs, nephrotoxic and nonnephrotoxic, in patients with renal insufficiency. In prescribing drugs for patients with renal insufficiency, most practitioners rely on their clinical experience or the advice of consultant physicians or pharmacists to guide dosing regimens. Since few clinicians are expert in this area, and medication orders can rarely be delayed until consultation is obtained, the capacity to provide information on drug disposition in real time might be of great value to the practicing clinician and the patient.
The problem of error in medicine has been found to be important and costly.7 Adverse drug events (ADEs) are common and often associated with errors.8 Even basic computerization of physician ordering with relatively little decision support was associated with a 55% decrease in serious medication errors, and an 84% decrease in near misses or potential ADEs in 1 study by our group.9 However, only a 17% decrease was seen in preventable ADEs. The study suggested that computerized advice regarding the dosing of drugs in the setting of renal insufficiency might be among the most potent additional preventive strategies.10
Thus, we hypothesized that the incorporation of guided dosing algorithms for inpatients with renal insufficiency into an existing computer order entry system would result in a larger proportion of appropriate dose and frequency orders, and would be associated with shorter lengths of stay (LOS), lower costs, and a lower frequency of worsening renal function.
METHODS
Study Setting
The study was carried out at Brigham and Women's Hospital, a 720-bed urban tertiary care academic medical center in Boston, Mass. The Brigham Integrated Computing System (BICS) provides administrative and clinical computing services at BWH. All inpatient orders are entered into BICS, including orders for medications, laboratory and radiology studies, and for nursing interventions. The BICS order entry application provides the physician with a range of possible dose amounts for that medication (dose list) along with 1 dose that is highlighted as the default or recommended dose amount (Figure 1A). The clinician is also offered a highlighted frequency as the recommended dosing interval (Figure 1B). The clinician can also hit an additional key to see the data used for calculation of creatinine clearance. Nearly all laboratory, radiology, and pathology results, admission vital signs (including weight), and demographic information can be accessed.
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Figure 1. Screen Displays of Brigham Integrated Computing System's New Application for Dose List and Frequency
The dose and dose frequency lists in which defaults were chosen by the system were based on a simulated patient's estimated creatinine clearance.
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The BICS system had for some years contained an on-line, noninteractive version that could be accessed separately from the order entry system. In an attempt to enhance the impact of this application within the BICS, its internal logic was integrated with the computerized laboratory results reporting system, and was incorporated into the order entry system. Based on information already in the reporting system, the new application first determined whether a patient had renal insufficiency, defined as an estimated creatinine clearance of less than 80 mL/min (1.34 mL/s), by the Cockroft-Gault equation.11 Next, based on the real-time calculation of the estimated creatinine clearance and the drug being prescribed, the application would modify the above-described dose list, default dose amount, and default frequency (dosing interval) in the BICS (Figure 2).
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Figure 2. Screen Display of Brigham Integrated Computing System's New Application for Actual Calculation
Screen that appears when clinician requests drug in which the dose or frequency may be modified for renal function.
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Knowledge Base
After reviewing the relevant literature, an expert panel including a nephrologist, a pharmacist, and a general internist convened to review all medications in the hospital's drug formulary and selected those medications that were renally cleared and/or nephrotoxic. New dosing suggestions were generated in a subset of approximately 500 medications (approximately 2500 in total). To smooth dose recommendations, renal insufficiency was divided into 3 categories: mild (estimated creatinine clearance, 50-80 mL/min [0.84-1.34 mL/s]), moderate (estimated creatinine clearance, 16-49 mL/min [0.27-0.82 mL/s]), and advanced (estimated creatinine clearance,  15 mL/min [0.25 mL/s]). The expert panel then determined optimal adjustments in dose list, default dose amount, and default frequency for each of the medications in the application in each of the renal insufficiency categories. The nonfixed variables in the estimated creatinine clearance calculation (ie, weight, serum creatinine) were the weight entered by the nurse or physician into the BICS database on admission. The latest serum creatinine level was entered by the laboratory and updated regularly during the hospital stay.
Patient Population
All persons admitted to the medical, surgical (including subspecialty surgical services), neurology, and obstetrics and gynecology services between September 1997 and April 1998, whose admission and discharge were within the boundaries of 4 consecutive 2-month periods were included in the study. Admission periods did not overlap.
Intervention and Evaluation
When renal insufficiency was detected and any medication was ordered, the application potentially modified 1 or more of the dose list, default dose amount, and default frequency. To test the effect of this application, an intervention trial was designed. The study periods consisted of 4 alternating 8-week blocks of intervention and control subperiods. Throughout the intervention and control periods, the application was active, determining whether the dose list, default dose amount, and default frequency needed adjustments. During the intervention periods, the adjusted dose list, default dose amount, and default frequency were displayed to the order-entry user and a notation was provided that adjustments had been made based on renal insufficiency. During the control periods, these recommended adjustments were not revealed to the order-entry user, and the unadjusted parameters were instead displayed.
A log was kept of all instances in which an application medication was ordered and the application adjusted the dose list, default dose amount, and/or default frequency. A log was also kept of the order finally made by the ordering physician. A selection was considered appropriate if the dose amount or frequency interval did not exceed the parameters set forth by the expert panel.
If use of a particular medication was considered potentially harmful, the application would provide feedback to the ordering clinician, accompanied by a recommendation for a suitable substitute when appropriate. For instance, if meperidine hydrochloride were prescribed for a patient with an estimated creatinine clearance of less than 15 mL/min (<0.25 mL/s), a warning regarding its potential for promoting seizures would be issued, with a suggestion that an alternative narcotic analgesic be prescribed. The clinician could then either accept or override such a recommendation.
Patient Outcomes
Patient outcomes were determined during discrete admissions. Length of stay was recorded in days. Hospital and pharmacy costs were estimated from billed charges and institution-specific charge-to-cost ratios.
Statistical Analysis
Continuous data were presented as mean (SD) or median (interquartile range), and compared with the t test or Wilcoxon rank-sum test, as appropriate. Categorical data were presented as proportions and compared using the  2 test. Multivariable linear regression analysis was used to compare LOS and costs (both log-transformed) in the intervention and control periods. Age, sex, and diagnosis related group (DRG) weight12 were used as covariates in these analyses. In addition, we evaluated (using multiplicative interaction terms) whether the effect of the application intervention differed by age, sex, or DRG weight. To determine whether the exclusion of patients whose admission extended across study periods exerted any meaningful effects on the analyses of LOS, costs, and renal function, we repeated the analyses without these exclusions. Each patient was assigned to the group (intervention or control) based on the day of admission. All reported P values were based on 2-tailed tests of statistical significance. Analyses were conducted using SAS statistical software (SAS Institute Inc, Cary, NC).
RESULTS
Patients
There were 19 982 admissions that either began or ended during the 8-month study period; we focused on the 17 828 that were wholly contained within a study subperiod. There were 7887 (39.5%) admissions wholly contained in the 2 intervention periods and 9941 (49.7%) admissions wholly contained in the 2 control periods (corresponding to 58 912 and 70 821 patient-days, respectively). There were 2154 (10.8%) admissions that straddled a study-period boundary and were excluded. In-hospital mortality rates were 1.8% and 1.9% (intervention vs control, P = .61). Mean (SD) age (52.5 [18.4] years vs 52.5 [18.3] years; P = .95) and sex (61.4% vs 61.8% female; P = .78) were not significantly different across periods. The mean DRG weight was higher during the control periods (2.3 vs 2.1 in intervention periods; P = .004). The majority of patients (11 896 [60.1%]) had estimated creatinine clearance values greater than 80 mL/min (>1.34 mL/s). One in 4 patients (4927 [24.9%]) had mild renal insufficiency. Fifteen percent had moderate (2563 [12.9%]) or advanced (414 [2.1%]) renal insufficiency. The mean estimated creatinine clearance at admission was higher during the intervention periods (90.9 vs 84.7 mL/min [1.52 vs 1.41 mL/s] in control periods; P<.001).
Drug Orders
There were a total of 2 278 723 orders during the study period, 773 113 of which were medication orders and 108 537 of which were orders for nephrotoxic and/or renally cleared medications. We excluded 11 386 orders because of missing dose amount (3794 [33.3%]) or frequency interval (5102 [44.8%]), or because of an uninterpretable estimated creatinine clearance value (3696 [32.5%]), usually indicating an aberrant weight measurement, and for a variety of other less common reasons (2588 [22.7%]). These exclusions left 97 151 orders for analysis (orders could be excluded for >1 reason).
Of the 97 151 analyzable orders, the application generated a suggestion for the clinician in 14 440 (15%). Table 1 shows a detailed array of these suggestions. Table 2 shows the proportion of orders deemed appropriate, stratified by whether the the application's suggestion was dose-related, frequency-related, or both. In the intervention vs control periods, the frequency of appropriate orders was 51% vs 30% for all relevant orders, 67% vs 54% for orders involving dose changes, and 59% vs 35% for orders involving frequency changes, respectively (P<.001 for all comparisons).
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Table 1. Catalog of Application Suggestions
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Table 2. Rates of Appropriate and Inappropriate Orders in Intervention vs Control Periods*
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LOS, Costs, and Renal Function
Table 3 shows unadjusted LOS and costs (hospital and pharmacy) during the intervention and control periods. The rightward half of the table shows the effect of including the 2154 hospitalizations that overlapped. Hospitalizations were categorized as intervention or control based on conditions on the day of admission.
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Table 3. Unadjusted Length of Stay and Costs in Intervention and Control Periods*
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The adjusted mean LOS (adjusted for age, sex, and DRG weight) remained significantly shorter during the intervention period, both when overlapping admissions were included (P = .002) and when they were excluded (P<.001). The effect of the application on LOS was attenuated at higher DRG weights (P<.001). In contrast, there were no significant differences in adjusted mean total or pharmacy costs between intervention and control periods.
A 10-mL/min (0.17-mL/s) decrement in estimated creatinine clearance from admission to discharge was considered to be of clinical significance. The percentage of patients whose estimated creatinine clearance declined by more than 10 mL/min (0.17 mL/s) was 11.8% and 11.5% (intervention vs control, P = .43). The mean (SD) changes in estimated creatinine clearance were 1.9 (0.2) mL/min (0.03 [0.003] mL/s) and 2.3 (0.2) mL/min (0.04 [0.003] mL/s) during the corresponding periods (P = .18).
COMMENT
We were successful in designing and implementing a computer order entry-based application that provided real-time drug prescription decision support to physicians. Compared with control periods during which information was readily available on-line but not incorporated into the order-entry process, the application intervention led to a statistically significant and clinically meaningful increase in the proportion of prescriptions considered appropriate for inpatients with renal insufficiency.
The large improvements in appropriateness of dosing and frequency were probably realized in part because the application is largely transparent to the clinician. Its key characteristics are that it remembers a huge amount of data essentially impossible for clinicians to master (and keep updated), and it makes it easy to do the right thing. Nonetheless, despite the overall improved appropriateness of dosing, 49% of orders for the application's drugs were still inappropriate in the intervention group. Some physicians may have been reluctant to reduce drug dosages, particularly among more critically ill patients. Others may have simply disregarded the advice in favor of their own established practice patterns. Future studies with this application and similar applications should investigate the reason(s) for accepting or rejecting on-line advice regarding medication ordering, and it might be worthwhile to consider stronger suggestions in specific situations.
A number of other studies have evaluated the impact of decision support on dosing of medications for patients with renal insufficiency. For example, Rind et al13 developed an application that alerted physicians caring for inpatients when there was an increase in the patient's serum creatinine concentration. An alert was triggered by a 0.5 mg/dL (44.2 µmol/L) increase in serum creatinine if the patient was prescribed a potentially nephrotoxic medication (eg, aminoglycoside), and a 50% increase in serum creatinine, to at least 2.0 mg/dL (176.8 µmol/L), if prescribed a medication that was renally excreted (eg, digoxin). The alert was delivered by e-mail to physicians who had accessed computer-based information on the affected patient in the 3 days preceding and following the increase in serum creatinine. The intervention resulted in a significant decrease in the frequency of more severe renal dysfunction, although fewer than half of the recipients (44%) found the alerts helpful and 28% found them "annoying." It is also noteworthy that Rind et al excluded patients on all services other than medicine, and all patients with preexisting moderate or severe renal insufficiency (serum creatinine >3.0 mg/dL [265.2 µmol/L]).
In another important study, one in a series evaluating the influence of computerized decision support, investigators at LDS Hospital in Salt Lake City, Utah, incorporated renal function assessment into an application that assisted physicians in prescribing antibiotics in an intensive care unit.14 These authors found that the use of their program decreased the frequency of inappropriate antimicrobial prescriptions (ie, orders for drugs to which patients had reported allergies, antibiotic susceptibility mismatches, and excessive drug dosages), and ADEs. Among patients who received recommended regimens, there was a significant decrease in LOS and drug and total hospital costs. More recently, Nightingale et al15 implemented a program in the renal unit of a British teaching hospital. Clinicians cancelled more than half of their orders when they were warned that the drug dosage they had requested was excessive. In the Nightingale et al study, there were no formal comparisons made between presystem and postsystem implementation periods with regard to appropriateness of orders, costs, complications, or hospital LOS.
The application used here differs from prior applications in that it is generalized to all hospitalized patients, provides suggestions for a wide range of drugs, and does so in real time. Feedback is most likely to be successful if it is delivered in real time, and in close temporal proximity to the decisions being made.9 As noted earlier, while we found that computerized physician order entry reduced the frequency of serious medication errors, it had a bigger impact on errors that did not actually cause injury compared with those that did injure patients.9 We believe—although this needs to be validated—that part of the reason for the larger impact on potential ADEs than actual ADEs was that the systems evaluated did not include sophisticated decision support, such as that provided by the application described here. With widespread application of sophisticated decision support, major reductions in ADE frequency as well as improvements in efficiency should be possible.
It is unclear why LOS was reduced by the new application's activity. Typically, LOS is a downstream indicator of quality of care. Because of resource constraints, we were unable to evaluate the more subtle effects of the application. For example, avoidance of overdosing of selected drugs in elderly patients may have led to fewer central nervous system or gastrointestinal tract adverse effects or other complications. Alternatively, LOS may have been reduced by other severity factors, which were not adjusted for by age, sex, and DRG weights.
The application had no effect on costs, but an effect may have been present but obscured since all patients were included in the cost analyses. In other words, restricting the analytic population to individuals prescribed selected nephrotoxic or renally cleared medications might have allowed us to show a difference. Regardless, the application itself is inexpensive to implement within the context of an order-entry system, in contrast to other prescription–quality-improvement programs, which generally have significant labor costs and require ongoing expenditure or the effect wanes.
Our study has several limitations. First, the intervention and control periods were not entirely analogous, since the number of admissions and the hospital census were higher during the control periods. The higher census may have prompted shorter LOS (in an effort to open beds), potentially decreasing the relative effect of the application on LOS. Second, the calculation of creatinine clearance by the Cockcroft-Gault formula may not accurately reflect renal function under nonsteady-state conditions (ie, with increasing or decreasing serum creatinine concentrations). In other words, the Cockcroft-Gault formula may overestimate renal function when the serum creatinine is increasing, and underestimate renal function when the serum creatinine is decreasing. However, this misclassification should have affected individuals equally during the intervention and control periods, and would tend to diminish the effect of any intervention toward the null. Third, we did not consider the degree to which individual orders differed from those considered optimal by the application's definitions. In other words, we would have expected that dose-list modification by the application would have led to a larger fraction of near-miss orders during intervention periods, but due to the immense number of orders and resource constraints, these were not calculated. Fourth, the program did not send notices (pages or e-mails) to clinicians as soon as it had evidence of worsening renal function, as did that of Rind et al,13 but only alerted the clinician at the next occasion when the clinician was ordering a medication. Finally, since the intervention was tested at a teaching hospital where house officers write the majority of medication orders, the results may not be generalizable to other, nonteaching hospital settings.
In summary, a computer order entry-based application to guide medication dose and frequency choices for inpatients with renal insufficiency was tested and resulted in a significant improvement in the appropriateness of drug prescription. Provision of real-time advice in drug prescription may prove to be among the most useful applications of medical informatics technology. Such applications may provide clinicians "a better cockpit" and results in enhanced safety and increased efficiency at minimal cost, with little intrusion into practice.
AUTHOR INFORMATION
Author Contributions: Study concept and design: Chertow, Kuperman, Komaroff, Bates.
Acquisition of data: J. Lee, Kuperman, Burdick, Horsky, Seger, R. Lee, Mekala, Song.
Analysis and interpretation of data: Chertow, J. Lee, Kuperman, Burdick, Horsky, Mekala, Komaroff, Bates.
Drafting of the manuscript: Chertow, J. Lee, Kuperman, Bates.
Critical revision of the manuscript for important intellectual content: Chertow, J. Lee, Kuperman, Burdick, Horsky, Seger, R. Lee, Mekala, Song, Komaroff, Bates.
Statistical expertise: Chertow, Burdick, Horsky, Bates.
Administrative, technical, or material support: Kuperman, Burdick, Seger, R. Lee, Mekala, Song, Komaroff, Bates.
Study supervision: Chertow, Kuperman, Komaroff, Bates.
Financial Disclosures: Dr Bates is a consultant and serves on the advisory board for McKesson MedManagement, a company that assists hospitals in preventing adverse drug events. He has received honoraria for speaking from Automated Healthcare, which makes robots that dispense medications. He is on the clinical advisory board for Becton Dickinson, which develops drug delievery systems, and the advisory board for Zynx, which develops evidence-based algorithms. He is a consultant for Alaris, which makes intravenous drug delivery systems.
Dr Bates also has received honoraria for speaking from the Eclipsys Corp, which has licensed the rights to the Brigham and Women's Hospital Clinical Information System for possible commercial development. Dr Bates is also a coinventor on patent No. 6029138 held by Brigham and Women's Hospital on the use of decision support software for medical management, licensed to the Medicalis Corp. He holds a minority equity position in the privately held company Medicalis, which develops Web-based decision support for radiology test ordering, and serves as a consultant to Medicalis.
Previous Presentation: Presented in abstract form at 31st Annual Meeting of the American Society of Nephrology, Philadelphia, Pa, October 25-28, 1998.
Corresponding Author and Reprints: Glenn M. Chertow, MD, MPH, Department of Medicine Research, UCSF Laurel Heights, 3333 California St, Suite 430, San Francisco, CA 94118 (e-mail: chertowg{at}medicine.ucsf.edu).
Author Affiliations: Division of General Internal Medicine (Drs J. Lee, Komaroff, and Bates and Mss Burdick, Horsky, and Seger), and Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School (Dr Chertow), Department of Information Systems, Partners HealthCare System (Dr Kuperman and Mss R. Lee, Mekala, and Song) Boston, Mass. Dr Chertow is now with the Division of Nephrology, Department of Medicine, University of California, San Francisco.
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J. Am. Med. Inform. Assoc. 2006;13:5-11.
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The predictable effect that renal failure has on H2 receptor antagonists--increasing the half-life along with increasing prescribing errors
Boudville
Nephrol Dial Transplant 2005;20:2315-2317.
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Acute Kidney Injury, Mortality, Length of Stay, and Costs in Hospitalized Patients
Chertow et al.
J. Am. Soc. Nephrol. 2005;16:3365-3370.
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Comprehensive Analysis of a Medication Dosing Error Related to CPOE
Horsky et al.
J. Am. Med. Inform. Assoc. 2005;12:377-382.
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Clinical Decision Support and Electronic Prescribing Systems: A Time for Responsible Thought and Action
Miller et al.
J. Am. Med. Inform. Assoc. 2005;12:403-409.
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Improving the ICU: Part 2
Garland
Chest 2005;127:2165-2179.
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A Trial of Automated Decision Support Alerts for Contraindicated Medications Using Computerized Physician Order Entry
Galanter et al.
J. Am. Med. Inform. Assoc. 2005;12:269-274.
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Guided Prescription of Psychotropic Medications for Geriatric Inpatients
Peterson et al.
Arch Intern Med 2005;165:802-807.
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Missed Hypothyroidism Diagnosis Uncovered by Linking Laboratory and Pharmacy Data
Schiff et al.
Arch Intern Med 2005;165:574-577.
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Assessment for Chronic Kidney Disease Service in High-Risk Patients at Community Health Clinics
Patel et al.
The Annals of Pharmacotherapy 2005;39:22-27.
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Characteristics and Consequences of Drug Allergy Alert Overrides in a Computerized Physician Order Entry System
Hsieh et al.
J. Am. Med. Inform. Assoc. 2004;11:482-491.
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Inappropriate Medication Use Among Frail Elderly Inpatients
Hanlon et al.
The Annals of Pharmacotherapy 2004;38:9-14.
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Computerized Physician Order Entry and Medication Errors in a Pediatric Critical Care Unit
Potts et al.
Pediatrics 2004;113:59-63.
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Conversion From Intravenous to Oral Medications: Assessment of a Computerized Intervention for Hospitalized Patients
Fischer et al.
Arch Intern Med 2003;163:2585-2589.
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Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality
Bates et al.
J. Am. Med. Inform. Assoc. 2003;10:523-530.
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Improving Medication Use for Older Adults: An Integrated Research Agenda
Murray and Callahan
ANN INTERN MED 2003;139:425-429.
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Computer Physician Order Entry: Benefits, Costs, and Issues
Kuperman and Gibson
ANN INTERN MED 2003;139:31-39.
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Effects of Computerized Physician Order Entry and Clinical Decision Support Systems on Medication Safety: A Systematic Review
Kaushal et al.
Arch Intern Med 2003;163:1409-1416.
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Improving Safety with Information Technology
Bates and Gawande
NEJM 2003;348:2526-2534.
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Linking Laboratory and Pharmacy: Opportunities for Reducing Errors and Improving Care
Schiff et al.
Arch Intern Med 2003;163:893-900.
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Incidence and Preventability of Adverse Drug Events Among Older Persons in the Ambulatory Setting
Gurwitz et al.
JAMA 2003;289:1107-1116.
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Electronic Medical Records and Diabetes Care Improvement: Are we waiting for Godot?
O'Connor
Diabetes Care 2003;26:942-943.
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The Use of Computers for Clinical Care: A Case Series of Advanced U.S. Sites
Doolan et al.
J. Am. Med. Inform. Assoc. 2003;10:94-107.
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Medication Errors in Acute Cardiac Care: An American Heart Association Scientific Statement From the Council on Clinical Cardiology Subcommittee on Acute Cardiac Care, Council on Cardiopulmonary and Critical Care, Council on Cardiovascular Nursing, and Council on Stroke
Freedman et al.
Circulation 2002;106:2623-2629.
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What Is an Academic General Internist?: Career Options and Training Pathways
Levinson and Linzer
JAMA 2002;288:2045-2048.
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Information technology and medication safety: what is the benefit?
Kaushal and Bates
Qual Saf Health Care 2002;11:261-265.
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