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Effect of Computerized Provider Order Entry with ClinicalDecision Support on Adverse Drug Events in the Long-TermCare Setting Jerry H. Gurwitz, MD,Ã Terry S. Field, DSc,ÃPaula Rochon, MD, MPH, James Judge, MD,z Leslie R. Harrold, MD, MPH,Ã Chaim M. Bell, MD, PhD,§ Monica Lee, RPh, Kathleen White, RPh,z Jane LaPrino, BA,z Janet Erramuspe-Mainard, Martin DeFlorio, RPh,z Linda Gavendo, BScPhm,Joann L. Baril, BS,Ã George Reed, PhD,Ã and David W. Bates, MD, MSck OBJECTIVES: To evaluate the efficacy of computerized (CI) 5 0.92–1.23) for all adverse drug events and 1.02 provider order entry with clinical decision support for pre- (95% CI 5 0.81–1.30) for preventable adverse drug events.
venting adverse drug events in long-term care.
CONCLUSION: Computerized provider order entry with DESIGN: Cluster-randomized controlled trial.
decision support did not reduce the adverse drug event rate or SETTING: Two large long-term care facilities.
preventable adverse drug event rate in the long-term care PATIENTS: One thousand one hundred eighteen long- setting. Alert burden, limited scope of the alerts, and a need to term care residents of 29 resident care units.
more fully integrate clinical and laboratory information mayhave affected efficacy. J Am Geriatr Soc 56:2225–2233, 2008.
INTERVENTION: The 29 resident care units, each withcomputerized provider order entry, were randomized tohaving a clinical decision support system (intervention Key words: patient safety; clinical decision support; com- puterized provider order entry; long-term care MEASUREMENTS: The number of adverse drug events,severity of events, and whether the events were preventable.
RESULTS: Within intervention units, 411 adverse drugevents occurred over 3,803 resident-months of observationtime; 152 (37.0%) were deemed preventable. Within con-trol units, there were 340 adverse drug events over 3,257 There are nearly 1.5 million residents of long-term care resident-months of observation time; 126 (37.1%) were facilities in the United States.1 The intensity of med- characterized as preventable. There were 10.8 adverse drug ication use is high in these settings, adverse drug events events per 100 resident-months and 4.0 preventable events occur commonly, and many of these are preventable. One per 100 resident-months on intervention units. There were study conducted in a sample of community-based Massa- 10.4 adverse drug events per 100 resident-months and 3.9 chusetts nursing homes found that adverse drug events oc- preventable events per 100 resident-months on control curred at a rate of 1.9 per 100 resident-months, with at least units. Comparing intervention and control units, the ad- half being preventable.2 A more recent study, employing justed rate ratios were 1.06 (95% confidence interval enhanced ascertainment methods, reported substantiallyhigher rates: 9.8 per 100 resident-months, with a rate of 4.1preventable events per 100 resident-months.3 If findings From the ÃMeyers Primary Care Institute, University of Massachusetts from the more recent study are extrapolated to all U.S.
Medical School, Fallon Clinic, and Fallon Community Health Plan, nursing homes, then nearly 1.8 million adverse drug events Worcester, Massachusetts; Kunin-Lunenfeld Applied Research Unit,Baycrest Centre, Toronto, Ontario, Canada; zMasonicare, Wallingford, may occur each year in U.S. nursing homes, approximately Connecticut; §Department of Medicine, St. Michael’s Hospital and the Uni- versity of Toronto, Toronto, Ontario, Canada; and kDepartment of Medicine, Computerized provider order entry with clinical deci- Brigham and Women’s Hospital and Harvard Medical School, Boston, sion support has been promoted as a promising tool for reducing medication error and adverse drug event rates in Address correspondence to Jerry H. Gurwitz, MD, Meyers Primary Care the long-term care setting,4,5 but few long-term care facil- Institute, University of Massachusetts Medical School, Fallon Clinic, andFallon Community Health Plan, 630 Plantation Street, Worcester, MA 01605.
ities have implemented such systems because of cost, com- plexity, and logistical challenges, as well as uncertainty about how effective these systems are for reducing r 2008, Copyright the AuthorsJournal compilation r 2008, The American Geriatrics Society drug-related injuries.6 Although the benefits of reducing that messages should be evidence-based, practitioners medication error rates in other clinical settings have been should perceive the messages to be useful and informative, established,7,8 few studies in any clinical setting have as- and the system should have only a modest effect on the time sessed the effect of this technology on rates of adverse drug required for the practitioner to complete an order. The team events.9 The purpose of the present study was to evaluate reviewed the types of preventable adverse drug events based the efficacy of computerized provider order entry with clin- on previous research2,3 and widely accepted published cri- ical decision support for preventing adverse drug events in teria for suboptimal prescribing in elderly people available at the time of this study. All serious drug–drug interactionsfrom a standard pharmaceutical drug interaction database were also reviewed, and alerts were included for a limitednumber of more than 600 potentially serious interactions that were reviewed. A summary of the alerts is provided in This study was conducted in two large, academic long-term care facilities located in Connecticut and Ontario, Canada.
The computerized provider order entry system in place The two facilities have a combined total of 1,229 beds.
at the time of clinical decision support system implemen- Patients residing in areas of the facilities related to short- tation was a commercially available application capable of term care (e.g., subacute care, hospital-level care, and re- linking some laboratory test results with current drug or- habilitation) were not included in the study population.
ders in real time, but the system had several important lim- Each of the facilities had an existing computerized itations, as described previously.10 At the time of the study, provider order entry system without a computer-based clin- it could not combine dose and strength information to de- ical decision support system. Contracted staff prescribed all termine the total daily dose associated with a drug order; medications; in one of the study facilities, this included 27 therefore, some alerts were displayed when they may not physicians, nurse practitioners, and physicians’ assistants.
have been necessary (e.g., the medication order was already In the other facility, 10 physicians prescribed medications.
within the recommended dose range). The underlying soft- At the time of the study, providers entered approximately ware was not capable of distinguishing multiple orders for 90% of new medication orders using the system. The in- the same drug in different forms or strengths or orders that stitutional review board of the University of Massachusetts had been cancelled and reordered within the same pre- Medical School, and the institutional review boards of the scribing session. These orders were interpreted as multiple participating facilities approved this study. This study has orders for drugs in the same category and triggered a num- ber of inappropriate alerts about drug interactions. Despitethe fact that some triggers were likely to produce a sub- stantial number of these unnecessary alerts, it was decidedto include them in the system if the potential effect of the The study was conducted over 1 year in one of the study type of drug interaction in question was considered clini- facilities and 6 months in the other. Across the two long- term care facilities, 29 resident care units, each with existing For residents on the intervention units, the alerts were computer provider order entry, were randomized to having displayed in a pop-up box to prescribers in real time when a a clinical decision support system (intervention units) or not drug order was entered. The pop-up boxes were informa- (control units). Bed size of the resident care units ranged tional; they did not require specific actions from the pre- from 20 to 60. An effort was made to match the units ac- scriber and did not produce or revise orders automatically.
cording to bed size and general characteristics of the res- On the control units, the alerts were not displayed to the idents on the units. Block randomization was undertaken within categories, including dementia units, units wherecare was provided to residents with mental health and be-havioral problems, units where the residents had complex Case-Finding Definitions and Classification of Events medical needs, and units where the residents had profound Drug-related incidents were identified through review of medical records in monthly segments performed by trained On intervention units, prescribers ordering drugs were pharmacist investigators for each eligible long-term care presented with alerts in the form of warning messages; these facility resident. These investigators, who were not aware alerts were not displayed to prescribers when ordering med- of whether the resident was located on an intervention or a ications for residents of control units. Although efforts were control unit, examined the records for possible drug-related initially made to limit crossover of prescribers between in- incidents, such as new symptoms or events that might rep- tervention and control units, over the duration of the study, resent an adverse drug event, changes in medication regi- some providers worked simultaneously on both types of mens (including acute discontinuations or initiations of units on a temporary (coverage) basis and permanently.
medications that might be used to treat a drug-inducedevent), abnormal laboratory values, and all emergency de- Design of the Clinical Decision Support System partment transfers and hospitalizations. In addition to pe- A team of geriatricians, pharmacists, health services re- riodic reviews, medical records were specially targeted for searchers, and information system specialists designed the review based on information derived from selected com- clinical decision support system; the process of developing puter-generated signals including abnormal serum drug the clinical decision support system and its components has concentrations, abnormal laboratory results, and the use of been described previously.4,5 The design principles were medications considered to be antidotes for adverse drug CLINICAL DECISION SUPPORT IN LONG-TERM CARE effects. Administrative incident reports generated within care resident-months in the intervention and control units each participating facility were also reviewed for any indi- of the two facilities. Resident-months were estimated from census data for all residents on eligible units and were ob-tained monthly throughout the course of the project; ab- sences from the facilities (e.g., for hospitalization) were alsoaccounted for when they occurred.
The primary outcome of the study was an adverse drug To assess the effect of the intervention, rate ratios com- event, defined as an injury resulting from the use of a drug.
paring rates of all adverse drug events and preventable ad- This definition is consistent with definitions used in previ- verse drug events in the intervention versus control units ous studies.2,3,11–14 Adverse drug events may have resulted were estimated using Poisson regression models, adjusting from medication errors (e.g., errors in ordering, dispensing, for unit and facility. Additional models were used to esti- administration, and monitoring) or from adverse drug re- mate adjusted rate ratios for more- and less-severe adverse actions in which there was no error.
drug events and preventable more- and less-severe adverse A pharmacist investigator presented the possible drug- drug events. The study was designed with power of 0.90 to related incidents to pairs of physician reviewers (JHG, JJ, identify a reduction of 20% in the rate of adverse drug PR, LRH, and CMB). These physician reviewers indepen- events. These power calculations were conservative, be- dently classified incidents using structured implicit review cause they were based on adverse drug event rates deter- according to the following criteria: whether an adverse drug mined in a study of community nursing homes.2 Subsequent event was present, the severity of the event, and whether the research, employing better methods for event ascertain- event was preventable. In determining whether an adverse ment, has indicated that actual adverse drug event rates in drug event had occurred, the physician reviewers consid- the long-term care setting are substantially higher.3 ered the temporal relation between the drug exposure and One of the investigators (JHG) re-reviewed all of the the event, as well as whether the event reflected a known adverse drug events that had been deemed probably pre- effect of the drug. This structured implicit review process ventable or definitely preventable to determine whether it has been used in numerous prior studies relating to adverse might have been possible for any of the alerts included in the drug events across various clinical settings.2,3,11–17 Physi- clinical decision support system to lead to the prevention of cian reviewers were not aware of whether a drug-related these adverse drug events. This assessment was performed incident being reviewed had occurred in a resident of an for events identified on the intervention and control units, although the reviewer (JHG) was unaware of which type of The severity of adverse events was categorized as less unit the event had occurred on. In a post hoc analysis, which serious, serious, life threatening, or fatal. Adverse drug considered only events for which it might have been pos- events categorized as less serious included a nonurticarial sible for the alerts to have an effect, the rate ratio comparing skin rash, a fall without associated fracture, hemorrhage the rate of preventable adverse drug events in the interven- not requiring transfusion or hospitalization, and overseda- tion versus control units was estimated through a Poisson tion. Examples of events categorized as serious included regression model, adjusting for unit and facility, as was urticaria, falls with associated fracture, hemorrhage requir- done in the main analysis detailed above.
ing transfusion or hospitalization but without hypotension,and delirium. Examples of life-threatening events includedhemorrhage with associated hypotension, hypoglycemicencephalopathy, and acute renal failure. Adverse drug events were considered to be preventable if they were Across the two study sites and the 29 randomized resident judged to be due to an error and were preventable by any care units, 1,118 long-term care residents had an average means available and not just in relation to the clinical de- age of 87.2, and 71.3% were female. The residents con- cision support system. For the purpose of the analysis of the tributed 7,060 months of observation time; there were effect of the intervention, any event characterized as serious 3,803 resident-months of observation on the intervention or greater in severity, was categorized as more severe. All units and 3,257 resident-months of observation on the con- other events were considered less severe.
Preventability was categorized as preventable, proba- Within the intervention units (Table 1), 411 adverse bly preventable, probably not preventable, or definitely not drug events occurred over 3,803 resident-months of obser- preventable; results were collapsed into preventable (pre- vation time. Of the 411 events, 152 (37.0%) were deemed ventable and probably preventable) and nonpreventable preventable. Within the control units, there were 340 adverse (probably not preventable and definitely not preventable) drug events over 3,257 resident-months of observation time.
Of the 340 events, 126 (37.1%) were characterized as When the physician reviewers disagreed on the classi- preventable. There were 10.8 adverse drug events per 100 fication of an incident regarding the presence of an adverse resident-months and 4.0 preventable events per 100 resident- drug event, its severity, or its preventability, they met and months on the intervention units. There were 10.4 adverse reached consensus; consensus was reached in all instances drug events per 100 resident-months and 3.9 preventable in which there was initial disagreement.
events per 100 resident-months on the control units. The rateratio estimated using Poisson regression models was 1.06 (95% confidence interval (CI) 5 0.92–1.23) for all adverse Crude rates of events were determined, dividing the number drug events and 1.02 (95% CI 5 0.81–1.30) for preventable of adverse drug events by the total number of long-term Table 1. Comparison of Rates of Adverse Drug Events (ADEs) Between Control and Intervention Units à Adjusted for unit and facility using Poisson regression models.
w More-severe ADEs include those deemed serious, life-threatening, or fatal.
per 100 resident-months on the intervention units. There In the intervention units, 123 adverse drug events with a were 3.0 of these more-severe adverse drug events per 100 severity rating of serious, life-threatening, or fatal occurred resident-months and 1.8 preventable events per 100 resi- over the 3,803 resident-months of observation time (Table dent-months on the control units. The rate ratio estimated 1). Of these events, 79 (64%) were deemed preventable.
through Poisson regression models was 1.07 (95% Within the control units, there were 97 of these more-se- CI 5 0.82–1.40) for all more-serious adverse drug events rious adverse drug events over the 3,257 resident-months of and 1.15 (95% CI 5 0.82–1.61) for preventable more-seri- observation time. Of these events, 58 (60%) were charac- terized as preventable. There were 3.2 of these more-severe Within the intervention units, there were 288 less-se- events per 100 resident-months and 2.1 preventable events vere adverse drug events. Of these events, 73 (25%) were Table 2. Frequency of Types of Adverse Drug Events (ADEs) Note: ADEs could manifest as more than one type.
à Neuropsychiatric events include oversedation, confusion, hallucinations, and delirium.
w Anticholinergic effects include dry mouth, dry eyes, urinary retention, and constipation.
z ADE manifested only as decline in activities of daily living without any other more-specific type of event. Other types of events may have been associated withfunctional decline.
CLINICAL DECISION SUPPORT IN LONG-TERM CARE Types of adverse drug events were generally similar in the Table 3. Frequency of Adverse Drug Events According to intervention and control units. Neuropsychiatric events (e.g., oversedation, confusion, hallucinations, and delirium) constituted the most common type of preventable and thesecond most common type of nonpreventable events in the intervention and control units. Other frequently identified types of preventable adverse drug events were hemorrhagic (bleeding events), renal or electrolyte (e.g., azotemia, de-hydration, hyperkalemia, hypokalemia, and renal failure), gastrointestinal (e.g., abdominal pain, diarrhea, constipa- tion, and impaction), and metabolic or endocrine (e.g., hy- poglycemic events, thyroid abnormalities).
Table 3 lists medication categories most frequently as- sociated with adverse drug events in order of overall fre- quency across the intervention and control units.
Antipsychotic agents constituted the most common medi- cation category associated with preventable events in theintervention and control units. Other medication categories frequently associated with preventable adverse drug events were anticoagulants, diuretics, antiplatelet agents, cardio- vascular drugs, hypoglycemic agents, and antidepressants.
Atypical antipsychotic agents, warfarin, and loop diuretics were the specific drug types most commonly implicated in preventable adverse drug events across the intervention and Overall, there were 152 preventable adverse drug events on the intervention units and 126 such events on the control units. Each of these events was subsequently re-evaluated to determine whether it might have been possible for any of the alerts included in the clinical decision support system to have led to the prevention of the adverse drug event. Of the 152 preventable events on the intervention units, 59 (38.8%) might have been prevented as a result of one or more of the alerts. Of the 126 preventable events identified on the control units, 56 (44.4%) might have been preventedas a result of one or more of the alerts.
In a post hoc analysis limited to events that might have Note: Drugs in more than one category were associated with some events.
been prevented as a result of one or more of the alerts, the Frequencies in each column sum to greater than the total number of events.
rate was 1.55 preventable adverse drug events per 100 res-ident-months on the intervention units and 1.72 prevent-able events per 100 resident-months on the control units, deemed preventable. Within the control units, there were for an adjusted rate ratio of 0.89 (95% CI 5 0.61–1.28).
243 less-severe adverse drug events. Of these events, 68(28%) were characterized as preventable. There were 7.6of these less-severe events per 100 resident-months and 1.9 preventable events per 100 resident-months on the Information technology–based interventions, including intervention units. There were 7.5 of these less-severe computerized provider order entry with clinical decision adverse drug events per 100 resident-months and 2.1 pre- support, have been widely promoted as the most promising ventable events per 100 resident-months on the control approaches for improving medication safety across all clin- units. The rate ratio estimated through Poisson regression ical settings,18 but much of the previously published re- models was 1.06 (95% CI 5 0.89–1.26) for all less-severe search relating to this particular technology has focused on adverse drug events and 0.92 (95% CI 5 0.66–1.28) for costs, organizational efficiency, appropriateness of alerts, adherence to guidelines, effect on time for the prescriber,satisfaction, usability, and usage.19 No previously pub-lished study has assessed the effect of computerized pro- Results According to Adverse Drug Event Type and Drug vider order entry with clinical decision support on adverse drug events in the long-term care setting.
Table 2 lists the types of adverse drug events in order of Previous studies examining the epidemiology of ad- overall frequency across the intervention and control units.
verse drug events in the long-term care setting have indi- cated that errors in prescribing and ordering are most com- long-term care setting. In addition to improving the effi- monly associated with adverse drug events2,3 and that these ciencies of these systems with regard to reducing alert bur- types of errors may be amenable to computerized provider den and offering alternative orders within the alerts, there is order entry with clinical decision support, but this study a need to increase their scope to address a broader range of found no effect on the overall adverse drug event rate or the drug safety issues. Efforts are also required to further in- preventable adverse drug event rate.
tegrate additional clinical and laboratory information into There are a number of factors specifically related to the the system. This would include linking newly recognized clinical decision support system evaluated in this study that and documented symptoms (e.g., daytime somnolence, probably diminished its effect on adverse drug event rates.
bleeding, edema, cough, dizziness, loose stools) to the use of The clinical decision support system directly addressed only a minority of the adverse drug events identified in the study.
These findings should not dampen enthusiasm for de- Furthermore, the system must be considered first genera- veloping and testing health information technology inter- tion, because it did not offer several important advantages ventions that may enhance patient safety in the long-term recommended for optimal clinical decision support such care setting. Such systems are costly and complex to im- as providing alternative orders within alerts that prescribers plement, and stakeholders, including payers, providers, fa- could directly accept.20,21 Additionally, it has previously cilities, and policy makers, require a clear understanding been reported that, on average, there were 2.5 alerts gen- about their benefits to make decisions about the substantial erated per resident-month, and more than half of the alerts investments that are required.6 Formal, rigorous evalua- displayed to providers were determined to be unneces- tions of these systems are absolutely essential so that they sary.10 This was primarily related to the inability of the can be improved upon and promoted with confidence for system to assess the total 24-hour dose of a drug that was already in use and relate it to the recommended dose rangeand to recognize prior medication orders, leading to un-necessary warnings about drug interactions as well as rec- ommendations for therapies (e.g., laxatives in the setting of We thank Mary Ellen Stansky and Jackie Cernieux, MPH, opioid use) when they had already been ordered. High sig- for their assistance with technical aspects of this study and nal-to-noise ratios may produce alert fatigue and lead pre- Bessie Petropoulos for assistance with manuscript prepara- scribers to click past alerts without considering or even reading them.22 Finally, the alerts were addressed only to Conflict of Interest: Dr. Bates is a coinventor on Patent the prescriber and did not consider the efforts of the entire No. 6029138 held by Brigham and Women’s Hospital on healthcare team, who are particularly important in moni- the use of decision support software for medical manage- toring the resident for beneficial and adverse effects of drug ment, licensed to the Medicalis Corporation. He holds a therapy. Despite these limitations, the study findings remain minority equity position in the privately held company relevant, because the features of the clinical decision sup- Medicalis, which develops Web-based decision support for port system are comparable with or exceed those of most radiology test ordering, and serves as a consultant to Med- commercially available products that long-term care facil- icalis. He is on the clinical advisory board for Zynx, Inc., ities might conceivably implement at the current time. Ad- which develops evidence-based algorithms, and IntelliDot, vanced clinical decision support systems have rarely been which makes barcoding applications for hospitals. He disseminated beyond the institutions (mainly hospitals) serves as a consultant to Healthgate, which makes tools that allow collaboration on development of decision support.
This study had a number of additional limitations. It He serves on the board of Care Management International, focused solely on adverse drug events and did not assess the which is involved in chronic disease management. He is a effect of the intervention on medication errors that did not consultant for Cardinal Health, which makes intravenous lead to adverse drug events. There was also potential for drug delivery systems. Supported by grants from the Agency contamination by cross-over between intervention and con- for Healthcare Research and Quality (HS010481 and trol units, because clinicians exchanged duties and covered HS15430). Dr. Bell is the recipient of a Canadian Institutes for each on many occasions. To assess the possibility that of Health Research, Institute of Aging New Investigator this may have led to changes in prescribing and orders for corollary laboratory tests in the control units, the rate of Author Contributions: Dr. Gurwitz had full access to responses to ‘‘unseen’’ alerts in the control units during the all the data in the study and takes responsibility for the first versus the last quarter of the study year was assessed at integrity of the data and the accuracy of the analysis. Study one of the study sites.10 The rate of response was lower in concept and design: Gurwitz, Field, Rochon, Judge. Acqui- the last quarter, suggesting that prescribers did not adopt sition of data: Gurwitz, Rochon, Judge, Harrold, Bell, Lee, new habits due to seeing alerts while caring for residents on White. Analysis and interpretation of data: Gurwitz, Field, the intervention units. This is consistent with a previous Reed. Drafting of the manuscript: Gurwitz. Critical revision study that found that physicians who had received alerts of the manuscript for important intellectual content: Gur- had no better knowledge of the issues highlighted in the witz, Field, Rochon, Judge, Harrold, Bell, Lee, White, La- alerts at the end of a 1-year period than they had at the Prino, Erramuspe-Mainard, DeFlorio, Gavendo, Baril, Reed, Bates. Statistical analysis: Field, Reed. Administrative, tech- Over the coming years, it is expected that computerized nical, or material support: LaPrino, Erramuspe-Mainard, provider order entry with clinical decision support will play DeFlorio, Gavendo, Baril. Study supervision: Gurwitz.
an important role in improving medication safety in the Other (intervention implementation): Gurwitz, Field, Ro- CLINICAL DECISION SUPPORT IN LONG-TERM CARE chon, Judge, Harrold, Bell, Lee, White, LaPrino, Er- Table A1. List of Warning Messages Targeting Prescrib- ramuspe-Mainard, DeFlorio, Gavendo, Baril, Reed, Bates.
ing Decisions Associated with the Development of Ad- Sponsor’s Role: The funding agencies did not contrib- ute to the study design; data collection, analysis, or inter-pretation; or the decision to submit the manuscript for INR is _____. Current INR is high.
Reduce WARFARIN dose and/or 1. National Nursing Home Survey (NNHS) [on-line]. Available at 2. Gurwitz JH, Field TS, Avorn J et al. Incidence and preventability of adverse drug events in nursing homes. Am J Med 2000;109:87–94.
3. Gurwitz JH, Field TS, Judge J et al. The incidence of adverse drug events in two large academic long-term care facilities. Am J Med 2005;118: 4. Rochon PA, Field TS, Bates DW et al. Computerized physician order entry with clinical decision support in the long-term care setting: Insights fromthe Baycrest Centre for Geriatric Care. J Am Geriatr Soc 2005;53:1780– 5. Rochon PA, Field TS, Bates DW et al. Clinical application of a computerized system for physician order entry with clinical decision support to prevent adverse drug events in long-term care. Can Med Assoc J 2006;174:52–54.
6. Subramanian S, Hoover S, Gilman B et al. Computerized physician order entry with clinical decision support in long-term care facilities: Costs and benefits to stakeholders. J Am Geriatr Soc 2007;55:1451–1457.
7. Kuperman GJ, Gibson RF. Computer physician order entry: Benefits, costs, and issues. Ann Intern Med 2003;139:31–39.
8. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decisions support systems on medication safety. Arch InternMed 2003;163:1409–1416.
9. Wolfstadt JI, Gurwitz JH, Field TS et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: A systematic review. J Gen Intern Med 2008;23:451–458.
10. Judge J, Field TS, DeFlorio M et al. Prescribers’ responses to alerts during medication ordering in the long term care setting. J Am Med Inform Assoc 11. Gurwitz JH, Field TS, Harrold LR et al. Incidence and preventability of ad- verse drug events among older persons in the ambulatory setting. JAMA 12. Bates DW, Cullen DJ, Laird et al. Incidence of adverse drug events and po- tential adverse drug events implications for prevention. ADE Prevention Study 13. Leape LL, Bates DW, Cullen DJ et al. Systems analysis of adverse drug events.
14. Leape LL, Cullen DJ, Clapp MD et al. Pharmacist participation on physician rounds and adverse drug events in the intensive care unit. JAMA 15. Kaushal R, Bates DW, Landrigan C et al. Medication errors and adverse drug events in pediatric in-patients. JAMA 2001;285:2114–2120.
16. Bates DW, Leape LL, Cullen DJ et al. Effect of computerized physician order entry and a team intervention on prevention on serious medication errors.
17. Bates DW, Spell N, Cullen DJ et al. The costs of adverse drug events in hos- pitalized patients. JAMA 1997;277:307–311.
18. Bates DW, Gawande AA. Improving safety with information technology.
19. Eslami S, Abu-Hanna A, deKeizer NF. Evaluation of outpatient computerized physician medication order entry systems: A systematic review. J Am Med inhibitors, angiotensin receptor blockers 20. Bates DW, Kuperman GJ, Wang S et al. Ten commandments for effective (e.g., losartan), diuretics, and NSAIDs.
clinical decision support: Making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10:523–530.
21. Kuperman GJ, Bobb A, Payne TH et al. Medication-related clinical decision support in computerized provider order entry systems: A review. J Am Med 22. van der Sijs H, Aarts J, Vulto A et al. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006;13: 23. Glassman PA, Belperio P, Simon B et al. Exposure to automated drug alerts over time: Effects on clinicians’ knowledge and perceptions. Med Care Risk of hyperglycemiaFconcomitant WARNINGFRISK OF DigoxinFany order and any order in WARNINGFRISK OF DRUG oversedation, confusion, delirium,falls, and injury. Evaluate the need for bromazepam have a very long half-lifeincreasing risk for CNS side effects.
medicationsFespecially high risk for ANTICHOLINERGIC EFFECTS concentration 5–7 days after initiation Monitor closely and preventconstipation. Choose a laxative other CLINICAL DECISION SUPPORT IN LONG-TERM CARE INR 5 international normalized ratio; NSAIDs 5 nonsteroidal anti-inflam-matory drugs; ACE 5 angiotensin-converting enzyme; BUN 5 blood urea ni- trogen; SSRIs 5 selective serotonin reuptake inhibitors; CNS 5 central nervous system; TSH 5 thyroid-stimulating hormone; ECG 5 electrocardio- allopurinol and thiazide diuretics (e.g.,hydrochlorothiazide)


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Curriculum vitae, 1998

John Douglas Hudson, MD Sleep Medicine Consultants M.D. - University of Texas Medical Branch, Galveston, Texas MBA– Northwestern University, Chicago BA Chemistry –Texas Tech University, Lubbock, Texas INTERNSHIPS & RESIDENCIES: Neurology Residency – University of Iowa, Iowa City, Iowa Medical Internship – Methodist Hospital, Dallas, Texas Administrative Residency – Br

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