While hospital electronic medical records (EMR) are intended to reduce medical errors, several aspects of the EMR may actually increase the incidence of certain types of errors or produce new safety risks that result in harm (Ammenwerth and Shaw 2004; Bates et al. 2001; Horsky, Zhang, and Patel 2005; Koppel et al. 2005). Threats to patient safety can be introduced during any phase of the EMR lifecycle, such as planning, design, development, testing, implementation, operations, and maintenance. Within each of these processes, technology, people, and the work environment can individually or collectively generate errors (Ash, Berg, and Coiera 2004).
The EMR is a very complicated technology, consisting of millions of lines of code typically authored by multiple programmers (Ash, Berg, and Coiera 2004). More important than its technical reliability is that many functions may be designed by people who do not know or fully appreciate the complex interaction of the human-computer interface and the consequences of designs that may, in hindsight, have impaired patient safety. As far back as 1995, the Food and Drug Administration acknowledged that insufficient design and testing of software-driven products could result in errors, increased healthcare costs, and patient harm (Burlington 1996).
End users can serve as sources to identify safety issues during multiple processes. While end users are sometimes consulted in design or designenhancement processes, many end users lack knowledge of standardized dictionaries, design principles, human-computer interaction, and the impact of poor design on work and patient safety. During operation of the EMR, multiple end users can introduce errors into the EMR through multiple points of data entry (Hogan and Wagner 1997).
The EMR and the end user come together in a work environment that is also very complex (Ash, Berg, and Coiera 2004). Healthcare work environments are characterized by excessive noise, high workloads, complex tasks that require rapid user responses to information, multitasking, and serious consequences when errors occur (Salvemini 1998). Tasks carried out by healthcare professionals in these environments are often context-dependent, nonlinear, interrupted, and dependent on clear and timely communication (Horsky, Zhang, and Patel 2005). The convergence of the complexities of the EMR and the need for changes in associated work flow create a large sociotechnical system where new behaviors emerge, some leading to unintended consequences that cause harm (Ash, Berg, and Coiera 2004).
Healthcare leaders must appreciate the complexity surrounding EMRs and understand the safety issues in order to mandate sound EMR design, development, implementation, and use. This article seeks to highlight what has been learned through research on the safety of these systems from 2000 to 2009. Three aspects of the EMR were selected for examination. These include computerized physician order entry (CPOE), clinical decision support systems (CDSS), and bar-coded medication administration (BCMA). The intended purpose is to prevent errors through effective design, development, and implementation and, as a result, reduce safety risks. However, all new systems generate unintended adverse consequences to patient safety that may relate to design or implementation problems. What follows is what has been learned related to beneficial effectiveness and unintended consequences associated with three major functions of the EMR.
In 2000, the Institute of Medicine's (IOM) Committee on Quality of Health Care in America released a seminal report titled To Err is Human: Building a Safer Health System that estimated that more than a million injuries and nearly 100,000 deaths each year in the United States are attributable to medical errors. In this report, the authors differentiated between active and latent errors. Active errors occur on the front line where the effects of these errors are felt almost immediately. Latent errors result from system failures and tend to be removed from the direct control of frontline people (Reason 1990). Latent errors include things such as poor design, incorrect installation, faulty maintenance, bad management decisions, and poorly structured organizations that create an environment that may fail to prevent or even may promote a human failure that may result in patient risk of injury. The IOM report asserted that latent errors, such as those hidden in complex health information technology applications, pose the greatest threat to safety in a complex system because they are difficult for end users to see and can lead to multiple types of active errors.
A second IOM report, Crossing the Quality Chasm, identified challenges associated with use of information technology in healthcare to improve quality of care (Committee on Quality of Health Care in America 2001). The authors called healthcare the most complex sector of the economy because of numerous and complicated transactions that require many behavioral changes by patients, clinicians, and provider organizations. Underinvestment in clinical information systems by provider organizations is compounded by difficulties in demonstrating the benefit of clinical information systems. Healthcare providers are also challenged in securely maintaining patient health information and creating an infrastructure that enables exchange of data and information across diverse settings.
Another landmark article described anecdotal evidence that while electronic medical records and associated clinical information systems can reduce errors, they can also cause errors (Bates et al. 2001). Examples provided included the wrong selection from two medications similarly spelled appearing in close proximity on the computer screen and physicians writing orders in the wrong electronic record. The authors recommended that adverse consequences resulting from the use of information technology be continuously monitored, measured, and evaluated.
By 2007 sufficient evidence in the literature prompted Weiner and colleagues to coin the term “e-iatrogenesis” to denote patient harm resulting at least in part from health information technology. The authors referred to e-iatrogenesis as the most critical unintended consequence of health information technologies and said they coined the term to draw attention this critical issue. An e-iatrogenic event may involve errors of commission or omission and can be associated with any aspect of a health information system such as the EMR, CPOE, or CDSS. E-iatrogenic errors fall into technical, human-machine interface, or organizational domains and may represent an electronic version of a “traditional” error, such as a medication error, or new errors never seen before, such as a CDSS recommendation for a wrong diagnosis.
Also in 2007, Palmieri, Peterson, and Ford coined the term “technological iatrogenesis” to describe errors caused by the addition of technological innovations into complex healthcare systems. The authors acknowledged the contribution of health information technology to make healthcare delivery safer and the new varieties of iatrogenic errors stemming from this technology. They also advocated for the use of risk management solutions such as failure mode effect and root cause analyses. Additionally, the authors encouraged healthcare leaders to avoid quick fixes to issues surrounding technology, such as a focus on human error, and move to a broader system perspective.
By 2008 The Joint Commission released a sentinel event alert titled “Safely Implementing Health Information and Converging Technologies” focusing on technology-related adverse events and encouraging healthcare providers to be alert to the associated safety risks and preventable adverse events. The Joint Commission cited Weiner and colleagues (2007) stating that unintended adverse events typically arise from two areas, human-machine interfaces and organization or system design. Recommendations in the sentinel event alert were to design technology to be safe and to use technology safely.
A comprehensive review of the literature was conducted using CINAHL, PubMed, and MEDLINE databases to identify relevant research published between and including the years of 2000 and 2009. Other databases were added when the original searches yielded fewer than 20 articles. These additional databases included Academic Search Premier, Academic OneFile, Business Source Complete, JSTOR, and Google Scholar. Manual searches of reference lists in published articles on safety issues in the EMR were also conducted.
Search terms included “safety,” “errors,” “electronic medical record,” “electronic health record,” “clinical decision support,” “computerized physician order entry,” and “bar-coded/barcode/barcoding medication administration.” Additional terms were identified during the search and included terms such as “unintended consequences,” “e-iatrogenesis,” “work flow,” “work processes,” “workarounds,” and “computerized provider order entry.”
Inclusion criteria were predetermined to be (1) English language publications; (2) research studies identifying errors related to EMR, including CPOE, CDSS, and BCMA; and (3) the hospital setting. Titles and abstracts identified in the database and reference list search meeting these inclusion criteria were initially screened to exclude those clearly not meeting the inclusion criteria. Publications focused solely on the surveillance or prevention of errors or in settings outside of the hospital were excluded. Similarly, studies published only as abstracts with insufficient information were also excluded.
A spreadsheet was created that contained key elements to be extracted from the publications. Extracted data included author(s), title, journal, publication year, sample, design/methods, findings, conclusions, types of errors, and causes of errors. Data from the published studies meeting the inclusion criteria were then abstracted and entered into the spreadsheet.
Summary of Search Findings
A total of 24 studies matching the inclusion criteria were identified through a comprehensive search of databases and references lists as previously described. These studies are outlined in an exhibit on www.ache.org/pubs/jhmsub.cfm.
Analysis of Literature Review Findings
As can be seen in the online exhibit, studies were found in 13 different journals. Thirty-three percent of the research was presented through the journal or conference proceedings of the American Medical Informatics Association (AMIA). Five studies (21 percent) were published in the Journal of the American Medical Informatics Association and three (12 percent) in the AMIA Annual Symposium Proceedings. Few studies were published in clinical journals alerting clinicians to the possibilities of system-induced errors. No publications on EMR-related errors were found in healthcare management journals thereby educating management about the potential issues. Lastly, few articles on safety issues related to the EMR were found in safety or quality journals in the last decade.
Exhibit 1 illustrates a temporal trend of articles related to EMR safety issues in CPOE, CDSS, and BCMA during the last decade. While the adoption of EMRs by hospitals is increasing steadily, the published research involving the safety of the EMR seems to have peaked in 2005, and only three papers per year have been published for the last four years (see Exhibit 2).
The design of the 24 studies is also worth noting. There was one randomized controlled trial, one interventional study, one quantitative and qualitative study, two qualitative studies, and three case studies. The remaining 16 studies used descriptive or comparison designs.
Exhibit 2 illustrates differences related to the focus of published research on errors related to CPOE, CDSS, and BCMA during the last decade. The dominance of articles related to CPOE is notable in 71 percent of the publications, followed by clinical decision support systems (17 percent) and bar-coded medication administration (12 percent).
By extracting information from the online exhibit, patterns related to the three areas of focus can be identified (Exhibit 3). These patterns are grouped as process, people, technology, organization, and environment related.
Exhibit 4 presents a picture of EMR-related safety issues reported in the research literature from 2000 to 2009, one that should be compelling to healthcare leaders, clinicians, and technology professionals. Thirty-five safety issues were reported in the literature on BCMA, 16 on CDSS, and 83 on CPOE. The larger number reported for CPOE is a result of the significantly larger number of studies evaluating CPOE as demonstrated earlier in Exhibit 3. These findings indicate that the promise of the EMR to reduce errors in healthcare must be tempered by the need to identify, evaluate, and improve safety issues resulting from the EMR.
EXHIBIT 1 EMR Safety Articles Published over the Last Decade, 2000-2009
CPOE has been touted as reducing errors in patient care, in large part by eliminating illegible orders and transcription errors. However, CPOE can actually increase the number of adverse events (Berger and Kichak 2004). Research studies in the last decade demonstrated safety issues with CPOE ranging from communication to medication errors to mortality and other unintended consequences (Dykstra 2003; King et al. 2003; Han et al. 2005; Campbell et al. 2006).
Researchers also found that CPOE can increase the coordination load among clinicians resulting in new opportunities for new sources of error (Cheng et al. 2003). For example, nurses may be unaware of new patient orders when physicians enter new orders remotely using CPOE. Prior to CPOE, nurses would see physicians making rounds, talk with them, and thus know when to expect orders. Using a descriptive design, Cheng and colleagues (2003) observed clinicians in an intensive care unit following implementation of CPOE. They concluded that the increased coordination load and the resulting new sources of errors were related to the assumption by designers that physician ordering is a linear process.
Ash and colleagues (2007) studied the extent and importance of unintended consequences related to CPOE in 176 hospitals drawn from 448 acute care hospitals listed in the HIMSS Analytics Database, plus 113 US Veterans Affairs Hospitals. Using telephone interviews, the researchers found that unintended consequences of CPOE considered most important by subjects included new work or more work, work flow, system demands, communication, emotions, and dependence on the technology. They found no correlation between the types of unintended consequences and number of years using CPOE.
EXHIBIT 2 EMR Aspects Studied in Published Articles over the Last Decade, 2000-2009
CDSS is intended to support the real-time clinical decisions of healthcare professionals in providing optimal patient care. CDSS is thought to be the key differentiator between paper-based and electronic documentation by providing necessary data and/or algorithmbased alerts or reminders to clinicians during their daily work. Tsai, Fridsma, and Gatti (2003) discovered that CDSS can be reduced in its usefulness when incorrect information is provided. Some healthcare professionals may trust the computer more than is warranted, resulting in errors in decisions.
Similarly, the effectiveness of CDSS is reduced when clinicians are subjected to alerts or other information perceived to lack benefit. Van der Sijs and colleagues (2009) studied the alert overrides for time-dependent drug-drug interactions (TDDI). The researchers found that incorrect overrides of TDDI alerts were an important cause of medication administration errors. Overrides were found to be a result of alert fatigue along with low alert specificity and unclear alert information content.
Garg et al. (2005) published a systematic review of randomized and nonrandomized controlled trials to assess the effects of CDSS and to identify study characteristics predicting benefits. The review included 100 studies over six years (1998 through 2004). The researchers concluded that while CDSS has been reported to improve practitioner performance, the effects on patient outcomes are understudied, and findings from the few published studies are inconsistent.
EXHIBIT 3 Types of EMR-Related Safety Issues
BCMA is intended to ensure safe medication administration. As Koppel and colleagues (2008) point out, BCMA can create workarounds that result in errors. The authors found 15 types of workarounds surrounding omission of process steps, steps performed out of sequence, and unauthorized BCMA process steps.
BCMA is also dependent on the correct performance of other processes. McDonald (2006) describes a case where a patient was mistakenly given the wrong bar-coded identification wristband. A subsequent laboratory test demonstrated severe hyperglycemia and the wrong patient was almost administered what could have been a fatal dose of insulin. Verification of patient identity based solely on an armband is insufficient; it does not guarantee that the armband and the person are the same.
Patterson, Cook, and Render (2002) used a qualitative research design to describe the experience of nurses during implementation of BCMA. Using ethnographic observation the researchers found five negative side effects associated with BCMA implementation:
- Nurses experienced “automation surprise” by automated removal of medications.
- Breakdowns occurred in the coordination of care between nurses and physicians.
- Nurses dropped activities during busy periods to gain efficiencies and reduce workload.
- Monitored activities such as medication administration were prioritized.
- There was a decreased ability to deviate from routine linear sequences.
The authors concluded that these side effects of BCMA could create new paths for adverse drug events and require preemptive evaluation and intervention.
Research on safety issues related to the EMR, specifically CPOE, CDSS, and BCMA, has only begun to uncover the unique unintended opportunities for error and harm that derive from clinical information systems deployment. The complexity of these systems, individually and collectively, is notable, as is the effect they have on current practice, work flow, and work environment. While these systems can undoubtedly improve patient care, safety issues left unidentified or unaddressed can undermine their benefits to patient safety. Studies on the benefits of these systems in relation to the potential harm must be considered.
Healthcare leaders must have knowledge of errors resulting from EMRs and access to discussion relative to the accountability for such errors. In 2009, Koppel and Kreda wrote about EMR vendors being liability-free when their products are involved in adverse events. The “hold harmless” contractual and legal device puts liability for technology-induced errors squarely on provider organizations and healthcare professionals referred to as “learned intermediaries.” Under this legal doctrine, physicians, nurses, pharmacists, and other clinicians are held accountable for identifying and correcting any errors generated by software defaults.
The pressure on hospitals to implement EMR has never been greater. A large part of the driving force relates to demonstrated and presumed improvements to patient safety. The findings of this review reveal that the literature demonstrates unintended consequences of EMR deployment that must be considered. Several aspects of the EMR can increase the incidence of certain types of errors or produce new safety risks that result in harm. The existence of only 24 published articles during the past decade suggests that greater focus upon the nature and mechanisms of e-iatrogenesis would be beneficial in the near term. The role of healthcare leaders in the safety of EMR cannot be understated. Leaders must be aware that thorough EMR deployment is not a panacea. Effective planning, design, and development are essential to prevent the time, cost, and safety consequences of poorly created and implemented EMRs. Additionally, leaders on the provider side should be involved in the maturation of products; their leadership is essential to realize the potential of these technologies.
Ammenwerth, E., and N. T. Shaw. 2004. “Bad Informatics Can Kill: Is Evaluation the Answer?” Methods of Information Medicine
44 (1): 1-3.
Ash, J. S., M. Berg, and E. Coiera. 2004. “Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-Related Errors.” Journal of the American Medical Informatics Association
11 (2): 104-12.
Ash, J. S., D. F. Sittig, E. G. Poon, K. Guappone, E. Campbell, and R. H. Dykstra. 2007. “The Extent and Importance of Unintended Consequences Related to Computerized Provider Order Entry.” Journal of the American Medical Informatics Association
14 (4): 415-23.
Bates, D. W., M. Cohen, L. L. Leape, J. M. Overhage, M. M. Shabot, and T. Sheridan. 2001. “Reducing the Frequency of Errors in Medicine Using Information Technology.” Journal of the American Medical Informatics Association
8 (4): 299-308.
Berger, R. G., and J. P. Kichak. 2004. “Computerized Physician Order Entry: Helpful or Harmful?” Journal of the American Medical Informatics Association
11 (2): 100-103.
Burlington, D. B. 1996. “Human Factors and the FDA's Goals: Improved Medical Device Design.” Biomedical Instrumentation & Technology
30 (2): 107-109.
Campbell, E. M., D. F. Sittig, J. S. Ash, K. P. Guappone, and R. H. Dykstra. 2006. “Types of Unintended Consequences Related to Computerized Provider Order Entry.” Journal of the American Medical Informatics Association
13 (5): 547-56.
Cheng, C. H., M. K. Goldstein, E. Geller, and R. E. Levitt. 2003. “The Effects of CPOE on ICU Workflow: An Observational Study.” AMIA Symposium Proceedings
Chuo, J., and R. Hicks. 2008. “Computer-Related Medication Errors in Neonatal Intensive Care Units. Clinics in Perinatology
35 (1): 119-39.
Cochran, G. L., K. J. Jones, J. Brockman, A. Skinner, and R. W. Hicks. 2007. “Errors Prevented By and Associated with Bar-Code Medication.” The Joint Commission Journal on Quality and Patient Safety
33 (3): 293-301.
Committee on Quality of Health Care in America, Institute of Medicine. 2001. Crossing the Quality Chasm: A New Health System for the 21st Century
. Washington, DC: National Academies Press.
Committee on Quality of Health Care in America, Institute of Medicine. 2000. To Err is Human: Building a Safer Health System,
edited by L. T. Kohn, J. M. Corrigan, and M. S. Donaldson. Washington, DC: National Academies Press.
Donyai, P., K. O'Grady, A. Jacklin, N. Barber, and B. D. Franklin. 2007. “The Effects of Electronic Prescribing on the Quality of Prescribing.” British Journal of Clinical Pharmacology
65 (2): 230-37.
Dykstra, R. 2003. “Computerized Physician Order Entry and Communication: Reciprocal Impacts.” AMIA Annual Symposium Proceedings
Eslami, S., A. Abu-Hanna, N. F. de Keizer, and E. de Jonge. 2006. “Errors Associated with Applying Decision Support by Suggesting Default Doses for Aminoglycosides.” Drug Safety
29 (9): 803-809.
Garg, A. X., N. K. Adhikari, H. McDonald, M. P. Rosas-Arellano, P. J. Devereaus, J. Beyene, J. Sam, and R. B. Haynes. 2005. “Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes.” Journal of the American Medical Association
293 (10): 1223-38.
Goldstein, M. K., B. B. Hoffman, R. W. Coleman, S. W. Tu, R. D. Shankar, M. O'Connor, S. Martins, A. Advani, and M. A. Musen. 2001. “Patient Safety in Guideline-Based Decision Support for Hypertension Management: ATHENA DSS.” AMIA Annual Symposium Proceedings
Han, Y. Y., J. A. Carcillo, S. T. Venkataraman, R. S. B. Clark, R. S. Watson, T. C. Nguyen, H. Bayir, and R. A. Orr. 2005. “Unexpected Increased Mortality after Implementation of a Commercially Sold Computerized Physician Order Entry System.” Pediatrics
116 (6): 1506-12.
Hogan, H. R., and M. M. Wagner. 1997. “Accuracy of Data in Computer-Based Patient Records.” Journal of the American Informatics Association
4 (5): 342-55.
Horsky, J., J. Zhang, and V. L. Patel. 2005. “To Err Is Not Entirely Human: Complex Technology and User Cognition.” Journal of Biomedical Informatics
38 (2005): 264-66.
The Joint Commission. 2008. “Safely Implementing Health Information and Converging Technologies.” Sentinel Event Alert
42. [Online information; retrieved 5/31/10.] www.jointcommission.org/SentinelEvents/SentinelEventAlert/sea_42.htm
King, W. J., N. Paice, J. Rangrej, G. J. Forestell, and R. Swartz. 2003. “The Effect of Computerized Physician Order Entry on Medication Errors and Adverse Drug Events in Pediatric Patients.” Pediatrics
112 (3): 506-509.
Koppel, R., and D. Kreda. 2009. “Health Care Information Technology Vendors' ‘Hold Harmless’ Clause.” Journal of the American Medical Association
301 (12): 1276-78.
Koppel, R., J. P. Metlay, A. Cohen, B. Abalock, A. R. Localio, S.E. Kimmel, and B. L. Strom. 2005. “Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors.” Journal of the American Medical Association
293 (10): 1197-203.
Koppel, R., T. Wetterneck, J. L. Telles, and B. T. Karsh. 2008. “Workarounds to Barcode Administration Systems: Their Occurrences, Causes and Threats to Patient Safety.” Journal of the American Medical Informatics Association
15 (4): 408-23.
Kushniruk, A., B. Triola, E. Borycki, B. Stein, and J. Kannry. 2005. “Technology Induced Error and Usability: The Relationship Between Usability Problems and Prescription Errors When Using a Handheld Application.” International Journal of Medical Informatics
74 (7-8): 519-26.
McDonald, C. J. 2006. “Computerization Can Create Safety Hazards: A Bar-Coding Near Miss.” Annals of Internal Medicine
144 (7): 510-16.
Nebeker, J. R., J. M. Hoffman, C. R. Weir, C. L. Bennett, and J. F. Hurdle. 2005. “High Rates of Adverse Drug Events in a Highly Computerized Hospital.” Archives of Internal Medicine
165 (10): 1111-16.
Palmieri, P. A., L. T. Peterson, and E. W. Ford. 2007. “Technological Iatrogenesis: New Risks Necessitate Heightened Management Awareness.” Journal of Healthcare Risk Management
27 (4): 19-24.
Patterson, E. S., R. I. Cook, and M. L. Render. 2002. “Improving Patient Safety by Identifying Effects from Introducing Bar Coding in Medication Administration.” Journal of the American Medical Informatics Association
9 (5): 540-53.
Reason, J. 1990. Human Error
. Cambridge, UK: Cambridge University Press.
Salvemini, A. V. 1998. “Improving the Human-Computer Interface: A Human Factors Engineering Approach.” M.D. Computing 15 (5): 311-15.
Santell, J. P., J. G. Kowiatek, R. J. Weber, R. W. Hicks, and C. A. Sirio. 2009. “Medication Errors Resulting from Computer Entry by Nonprescribers.” American Journal of Health-System Pharmacists
66 (9): 843-53.
Shulman, R., M. Singer, J. Goldstone, and G. Bellingan. 2005. “Medication Errors: A Prospective Cohort Study of Hand-Written and Computerised Physician Order Entry in the Intensive Care Unit.” Critical Care
9 (5): R516-21.
Singh, H., S. Mani, D. Espadas, N. Petersen, V. Franklin, and L. A. Petersen. 2009. “Prescription Errors and Outcomes Related to Inconsistent Information Transmitted Through Computerized Order Entry.” Archives of Internal Medicine
169 (10): 982-89.
Spencer, D. C., A. Leininger, R. Daniels, R. P. Granko, and R. R. Coeytaux. 2005. “Effect of a Computerized Prescriber-Order-Entry System on Reported Medication Errors.” American Journal of Health-System Pharmacists
62 (4): 416-19.
Tsai, T. L., D. B. Fridsma, and G. Gatti. 2003. “Computer Decision Support as a Source of Interpretation Error: The Case of Electrocardiograms.” Journal of the American Medical Informatics Association
10 (5): 478-83.
Thompson, D. A., L. Duling, Holzmueller, C. G., Dorman, T., L. H. Lubomski, F. Dickman, M. Fahey, L. L. Morlock, A. W. Wu, and P. J. Pronovost. 2005. “Computerized Physician Order Entry, a Factor in Medication Errors: Descriptive Analysis of Events in the Intensive Care Unit Safety Reporting System.” Journal of Clinical Outcomes Management
van der Sijs, H., L. Lammers, A. van den Tweel, J. Aarts, M. Berg, A. Vulto, and T. Van Gelder. 2009. “Time-Dependent Drug-Drug Interaction Alerts in Care Provider Order Entry: Software May Inhibit Medication Error Reductions.” Journal of the American Medical Informatics Association
16 (6): 864-68.
Walsh, K. E., W. G. Adams, H. Bauchner, R. J. Vinci, J. B. Chessare, M. R. Cooper, P. M. Hebert, E. G. Schainker, and C. P. Landrigan. 2006. “Medication Errors Related to Computerized Order Entry for Children.” Pediatrics
118 (5): 1872-79.
Weiner, J. P., R. Kfuri, K. Chan, and J. B. Fowles. 2007. “‘e-Iatrogenesis’: The Most Critical Unintended Consequence of CPOE and Other HIT.” Journal of the American Medical Informatics Association
14 (3): 387-88.
Zhan, C., R. W. Hicks, C. M. Blanchette, M. A. Keyes, and D. D. Cousins. 2006. “Potential Benefits and Problems with Computerized Prescriber Order Entry: Analysis of a Voluntary Medication Error-Reporting Database.” American Journal of Health-System Pharmacists
63 (4): 353-58.