Role of Computerized Physician Order Entry Systems in Facilitating Medical Errors

Department of Sociology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia 19104 USA.
JAMA The Journal of the American Medical Association (Impact Factor: 35.29). 04/2005; 293(10):1197-203. DOI: 10.1001/jama.293.10.1197
Source: PubMed


Hospital computerized physician order entry (CPOE) systems are widely regarded as the technical solution to medication ordering errors, the largest identified source of preventable hospital medical error. Published studies report that CPOE reduces medication errors up to 81%. Few researchers, however, have focused on the existence or types of medication errors facilitated by CPOE.
To identify and quantify the role of CPOE in facilitating prescription error risks.
We performed a qualitative and quantitative study of house staff interaction with a CPOE system at a tertiary-care teaching hospital (2002-2004). We surveyed house staff (N = 261; 88% of CPOE users); conducted 5 focus groups and 32 intensive one-on-one interviews with house staff, information technology leaders, pharmacy leaders, attending physicians, and nurses; shadowed house staff and nurses; and observed them using CPOE. Participants included house staff, nurses, and hospital leaders.
Examples of medication errors caused or exacerbated by the CPOE system.
We found that a widely used CPOE system facilitated 22 types of medication error risks. Examples include fragmented CPOE displays that prevent a coherent view of patients' medications, pharmacy inventory displays mistaken for dosage guidelines, ignored antibiotic renewal notices placed on paper charts rather than in the CPOE system, separation of functions that facilitate double dosing and incompatible orders, and inflexible ordering formats generating wrong orders. Three quarters of the house staff reported observing each of these error risks, indicating that they occur weekly or more often. Use of multiple qualitative and survey methods identified and quantified error risks not previously considered, offering many opportunities for error reduction.
In this study, we found that a leading CPOE system often facilitated medication error risks, with many reported to occur frequently. As CPOE systems are implemented, clinicians and hospitals must attend to errors that these systems cause in addition to errors that they prevent.

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Available from: Abigail Cohen, Oct 05, 2015
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    • "Health IT has the potential to improve healthcare, help patients obtain and renew their medications, assist in retrieving medical information during emergencies, and ensure access to medical histories (Shekelle et al. 2006). It may also play an important role in reducing medical errors (Armstrong 2000; Bates and Gawande 2003; Bates et al. 2001; Brown et al. 2005; Kaushal et al. 2003; Koppel et al. 2005) and improving health care quality (Liu et al. 2010). The adoption of health IT can promote better clinical outcomes, improve medication adherence, lower overall healthcare expenditures, and improve efficiency gains and cost savings (Balfour et al. 2009; Bates 2002; Chaudry et al. 2006; Dixon and Zafar 2009; Kaushal and Bates 2001; Wang et al. 2003; Possant et al. 2005). "
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    ABSTRACT: Applying exploratory qualitative methodology, we examine the role of IT vendors in health IT adoption in nursing homes. We identify three roles pertaining to IT vendors in various adoption stages-Information Sources and Financiers, Strategic Consultants, and Educators in the initiation, implementation, and the institutionalization phases respectively. Thus, vendors could be a critical factor in the health IT adoption, but nursing home management must critically evaluate not only the services, but also the strategic partnerships offered by the vendors because their business interests may not address a nursing home's distinctive need, and may prevent integration and institutionalization of health IT.
    Southwest Academy of Management, Houston, TX; 03/2015
    • "For example, in the study by Vardi et al. [82] systems such as clinical decision support systems (CDSS) and CPOE, reduced errors of medication forms by nearly 100%. Although none of the studies reported increasing medical errors by health information systems in Iran, some studies around the world have shown that these type of systems may introduce new kind of errors [83] [84]. Failure to identify potential errors concerning health information systems in Iranian studies might be due to the fact that these studies have sought staffs' perception towards the effects of these systems rather than studying the real outcome of the activities carried out by the systems. "
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    ABSTRACT: The most important goal of a health information system (HIS) is improvement of quality, effectiveness and efficiency of health services. To achieve this goal, health care systems should be evaluated continuously. The aim of this paper was to study the impacts of HISs in Iran and the methods used for their evaluation. We systematically searched all English and Persian papers evaluating health information systems in Iran that were indexed in SID, Magiran, Iran medex, PubMed and Embase databases until June 2013. A data collection form was designed to extract required data such as types of systems evaluated, evaluation methods and tools. In this study, 53 out of 1103 retrieved articles were selected as relevant and reviewed by the authors. This study indicated that 28 studies used questionnaires to evaluate the system and in 27 studies the study instruments were distributed within a research population. In 26 papers the researchers collected the information by means of interviews, observations, heuristic evaluation and the review of documents and records. The main effects of the evaluated systems in health care settings were improving quality of services, reducing time, increasing accessibility to information, reducing costs and decreasing medical errors. Evaluation of health information systems is central to their development and enhancement, and to understanding their effect on health and health services. Despite numerous evaluation methods available, the reviewed studies used a limited number of methods to evaluate HIS. Additionally, the studies mainly discussed the positive effects of HIS on health care services. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    International Journal of Medical Informatics 02/2015; 84(6). DOI:10.1016/j.ijmedinf.2015.02.002 · 2.00 Impact Factor
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    • "Although on the one hand there are studies published that have evaluated the ePrescription system and reported the positive results [5] [11] [12], on the other hand there are studies revealing CPOE systems possess potential risk for 22 types of medication error [13] Up to 35% of prescribing errors were system-related (selection of an inappropriate (unintentional) drug from the drop-down menu next to a likely drug)[14]. "
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    ABSTRACT: Background The report from the Institute of Medicine, To Err Is Human: Building a Safer Health System in 1999 drew a special attention towards preventable medical errors and patient safety. The American Reinvestment and Recovery Act of 2009 and federal criteria of ‘Meaningful use’ stage 1 mandated e-prescribing to be used by eligible providers in order to access Medicaid and Medicare incentive payments. Inappropriate prescribing has been identified as a preventable cause of at least 20% of drug-related adverse events. A few studies reported system-related errors and have offered targeted recommendations on improving and enhancing e-prescribing system. Objective This study aims to enhance efficiency of the e-prescribing system by shortening the medication list, reducing the risk of inappropriate selection of medication, as well as in reducing the prescribing time of physicians. Method 103.48 million prescriptions from Taiwan's national health insurance claim data were used to compute Diagnosis-Medication association. Furthermore, 100,000 prescriptions were randomly selected to develop a smart medication recommendation model by using association rules of data mining. Results and Conclusion The important contribution of this model is to introduce a new concept called Mean Prescription Rank (MPR) of prescriptions and Coverage Rate (CR) of prescriptions. A proactive medication list (PML) was computed using MPR and CR. With this model the medication drop-down menu is significantly shortened, thereby reducing medication selection errors and prescription times. The physicians will still select relevant medications even in the case of inappropriate (unintentional) selection.
    Computer Methods and Programs in Biomedicine 11/2014; 117(2). DOI:10.1016/j.cmpb.2014.06.019 · 1.90 Impact Factor
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