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Preventing Medication Errors 6 Action Agenda for the Pharmaceutical, Medical Device, and Health Information Technology Industries CHAPTER SUMMARY Pharmaceutical, medical device, and health information technology companies represent the chief drug product-related industry sectors of the medication-use system. If designed well, their products can improve the health and well-being of consumers, advance medical science, and enhance clinical practice. As with other components of the medication-use system, however, certain features of design processes and communication mechanisms warrant significant improvement to better serve the health needs of consumers and the practice needs of providers and, most important, prevent medication errors. This chapter provides an action agenda for the pharmaceutical, medical device, and health information technology industries that, in collaboration with appropriate government agencies, can begin to address key problems that affect the safety and quality of the medication-use system. This chapter presents an action agenda first for the pharmaceutical industry, and then for the medical device and health information technology industries. PHARMACEUTICAL INDUSTRY As discussed in Chapter 2, improving the safety of medication use requires improving the quality of information generated by industry and
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Preventing Medication Errors other researchers regarding drug products and their use in clinical practice. Also required are improvements in the way drug information is presented to providers and consumers through labeling and packaging since such materials have a direct effect on medication errors and adverse drug events (ADEs). This section reviews key problems involved in the generation and presentation of information that should be addressed by the pharmaceutical industry and the Food and Drug Administration (FDA). Generation of Information Current methods for generating information about medications are insufficient to meet the changing medical needs of the population, particularly given expected increases in the numbers of elderly people with multiple chronic conditions (IOM, 2000, 2001). While a comprehensive review of the drug research and development process and recommendations for its redesign are beyond the scope of this report, certain key aspects of information generation germane to medication safety merit discussion here. Clinical Data Determining that a medication error has occurred presumes that the correct dose of a drug for a given patient at a particular time is known, and that the indication for that drug is correct relative to alternative approaches to treatment. Unfortunately, this fundamental presumption is too often unwarranted. The benefits of drugs can be categorized as improvement in longevity, improvement or stabilization of symptoms (improvement in quality of life), prevention of adverse events, or reduction in the costs of other medical interventions. To determine whether the benefits of a drug outweigh its risks, both the benefits and the risks must be measured in the population to whom the drug will be given for a relevant period of time (Yusuf et al., 1984; Prentice, 1989; Fleming and DeMets, 1996). Ideally, after these measurements have been made, individuals should be informed about both the benefits that can be expected and the potential risks. Since the benefits and risks are measured with different metrics, it is important to recognize that in the end, a subjective judgment regarding the balance of benefit and risk is necessary, since a ratio cannot be calculated (CERTS, 2003; Tsintis and La Mache, 2004; Edwards et al., 2005). Over the past several decades, our understanding of therapeutic evaluation has advanced significantly. Nonetheless, the balance of benefit and risk of a drug compared with alternative treatments usually is not known. A variety of examples can be used to illustrate this point. Hormone replacement therapy, for instance, was once the most prevalent drug prescription
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Preventing Medication Errors globally, with its most common indication for use being prevention of cardiovascular disease in postmenopausal women. Many years after the therapy was marketed, however, both the HERS (Heart and Estrogen/ progestin Replacement study) Trial and the Women’s Health Initiative demonstrated its association with an excess of vascular events (Hulley et al., 1998; WHI Steering Committee, 2004). As a second example, the COX-2 (cyclo-oxygenase-2) inhibitors were expected to be a safer alternative to nonsteroidal anti-inflammatory drugs (NSAIDs). Vioxx and Bextra (among others), however, were removed from the market after the FDA Advisory Committee meeting in February 2005. Some experts believed the true balance of benefit and risk was not known for any of the COX-2 inhibitors (Psaty and Furberg, 2005). Perhaps even more startling, it was pointed out at the hearing that the same could be said for the traditional NSAIDs, which had been considered safe enough to sell over the counter. As a final example, a variety of antihypertension drugs have been developed and marketed as superior to the older, generic drugs used for this indication. However, when the National Institutes of Health (NIH) funded a pragamatic clinical trial involving more than 40,000 patients, it was found that the newer drugs provided no greater protection against stroke, heart failure, or death than the generic drug chlorthalidone (ALLHAT, 2002). Given that these examples involve some of the most commonly used and intensively studied drugs, there is uncertainty that drugs receiving less attention are better characterized. Since the only way to be confident about the balance of benefit and risk is empirical measurement, this information is lacking for most prescriptions that are written, especially those for chronically administered drugs. The above issues are magnified in certain populations that bear much of the risk of drug prescription and administration: The majority of prescriptions written for children are off label,1 with no empirical demonstration of safety and efficacy (Roberts et al., 2003). The Best Pharmaceuticals Act for Children has stimulated a major increase in clinical trials in children, but the legacy of sparse evidence remains substantial, and few of these trials have provided definitive information about indications and doses for the drugs involved. Pediatric oncology has been at the forefront in terms of enrolling a significant number of children in trials and could possibly be used as a model for other drug categories. Almost nothing is known about the balance of benefit and risk in the fastest-growing segment of the population—those over age 80. These patients have only recently been enrolled in clinical trials (Alexander and 1 The FDA permits the prescribing of approved medications for other than their intended indications. This practice is known as off-label use.
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Preventing Medication Errors Peterson, 2003). Given the major changes in organ physiology that occur in the elderly, people over age 80 have unique characteristics related to drug metabolism and pharmacodynamics. Patients with renal dysfunction represent a large and growing population requiring more comprehensive studies. Over 10 percent of the population now has a creatinine clearance below 60 milligrams per deciliter (mg/ dl), indicating moderate or worse renal function (Reddan et al., 2003). The fact that many drugs are excreted by the kidneys raises obvious issues about dosing as a function of renal clearance. In addition, however, almost everyone with impaired renal function is either elderly or chronically ill, so that a simple mathematical calculation of clearance will not yield an accurate estimate of the balance of the benefit and risk of a drug at a particular dose. Patients with multiple comorbidities are typically excluded from premarketing clinical trials, yet many of the major problems involving drug toxicity have occurred in those taking multiple medications because of multiple diseases (Gurwitz, 2004). Drug interactions and additive toxic effects are common, and while they can be anticipated based on studies in other populations, the cumulative effects of multiple drugs cannot be predicted accurately without empirical study. Drugs for patients with psychiatric illnesses are particularly controversial. Most studies in these populations have been small and incapable of providing pragmatic, comparative information (March et al., 2005). Recent studies funded by the National Institute of Mental Health (NIMH) have fueled concern about the basic knowledge base for treatment of depression, manic-depressive illness, and schizophrenia. The theory of clinical pharmacology has not been well supported by the academic community or the NIH. In particular, the characteristics of patients that determine the manner in which the pharmacokinetics and pharmacodynamics of drugs will be manifest are poorly understood and often overlooked (Fitzgerald, 2005). As a result of marketing considerations, the industry has tended to attempt to develop drugs that are given once a day and intravenous formulations that have fixed doses for ease of administration. Thus recommended doses are not specifically tailored to the needs of the individual patient. The field of clinical pharmacology needs to be invigorated. Few training programs in this area exist today in the United States, prompting the Institute of Medicine (IOM) to initiate its own national course in drug development. With the anticipated availability of pharmacogenomic data, a cadre of experts will be needed to evaluate the modifiers of drug concentration and activity. A large increase in the number of patients for whom clinical outcomes are measured is needed to elucidate the proper dosing of drugs in individu-
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Preventing Medication Errors als (Califf and DeMets, 2002a,b). The case of anticoagulant drugs illustrates this need. These drugs are characterized by a complex balance of benefit (prevention of blood clots) and risk (bleeding) (Schünemann et al., 2004). Aspirin has been available for over 100 years and heparin for over 50 years. Yet the best dose of each for preventing arterial thrombosis remains controversial. Multiple new drugs, including direct antithrombins, low-molecular-weight heparins, P2Y12 inhibitors, and glycoprotein IIb/IIIa inhibitors, have been developed in the past two decades and have been demonstrated to provide a net balance of benefit on average in patients entered into clinical trials. Yet little is known about the appropriate dose of these agents in children, the very elderly, and patients with renal impairment. The adjusted dosing regimens for heparin and coumadin, each of which has been marketed for more than four decades, were delineated relatively recently after thousands of patients had been entered into clinical trials that included outcome measurement to determine the degree of anticoagulation with each agent that led to prevention of thrombosis without unacceptable bleeding. Once a drug is on the market, the expansion to new indications continues throughout its life cycle. Most postmarket studies funded by industry are intended specifically to expand the market for a drug, and such studies are usually not undertaken unless the calculated probabilities indicate that the study will yield a positive financial return (Tunis et al., 2003). Direct comparisons of a drug with an alternative drug or other treatment rarely meet this financial test because there is too great a risk of finding that there is no difference or that the competing treatment is better. An increasing number of reports over the past several decades have called for a marked increase in pragmatic clinical trials that answer questions relevant to clinical practice (Crowley et al., 2004). A new approach is needed that includes industry participation, but also independent oversight to stimulate more such trials. Lacking the results of such trials, neither prescriber nor patient can know what treatment plan is best. A critical issue is where to draw the line between the premarketing development phase and the point at which the drug is allowed on the market. Scientifically, the gaining of knowledge about a drug should be a continuous process in which new information is used to refine understanding of the drug’s uses, benefits, and risks at particular doses in particular patients. In actuality, however, the development of scientific knowledge about drugs is quite discontinuous, and the process is dependent on clearing a series of hurdles with defined criteria. In particular, tremendous effort and expense go into the New Drug Application (NDA) required to obtain initial approval for marketing (see Chapter 2). Ideally, at the time of initial marketing, the balance of a drug’s benefit and risk would be known so that the label for its use could be clear. In reality, however, the costs of drug
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Preventing Medication Errors mation, a more flexible system is needed to ensure that the demands of the public for complete disclosure of drug benefits and risks are met. The recently released IOM workshop report Clinical Trial Registration: Developing a National Registry to Improve Public Access and Reliability states that the best course of action to build the nation’s repository of information about therapeutics and improve the quality of that information may be a broad expansion of the ClinicalTrials.gov database (IOM, 2006). A single national registry populated with information generated through clinical studies of all drug products would be a critically important resource for all stakeholders in the medication-use system. Each stakeholder group (e.g., patients, providers, researchers, medical journal editors, pharmaceutical companies, health insurers, information technology vendors, and regulators) has different needs and uses for the information contained in such a registry (see Box 6-1) (IOM, 2006). For optimal functioning, the registry should serve several purposes: List and track the status of ongoing clinical trials. Provide information on patient recruitment. Report results of clinical trials, including late Phase II, Phase III, and postmarketing studies; “head-to-head” comparisons of drugs; comparisons of drugs and alternative treatments; and effectiveness studies. Full disclosure of the results of all clinical trials and postmarket studies in a national registry is particularly important to fill the current knowledge gaps that affect clinical practice, patient self-management, and medication safety. The distortion of information that results from the design of post-marketing studies has been described above. Well beyond this distortion, however, positive study results are much more likely to be published than negative results. This publication bias yields an incomplete picture of the drug characteristics that must be known for more accurate medication use and error prevention, and can therefore have a detrimental effect on patients. This has clearly been a major issue with COX-2 inhibitors and NSAIDs (see the discussion above). Thus all clinical trial results must be disseminated in a comprehensive, objective, and unbiased manner (IOM, 2006). Clear communication of risk information (not just benefits) is essential to preventing errors and potential adverse reactions. The same holds true for other study results that should be incorporated into a national registry (i.e., postmarket, comparison, and effectiveness studies). Postmarket studies are especially important in relaying new safety information revealed as a drug is used in clinical practice. Comparative and effectiveness studies contribute further to understanding a drug’s characteristics and therapeutic value. Given the proposed national registry, patients, providers, and others would not have to search multiple database systems for these study results but could easily maneuver within one comprehensive
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Preventing Medication Errors BOX 6-1 Diverse Expectations and Perceived Needs for a National Registry of Clinical Trial Data The public and various entities within the medical community have different expectations and perceived needs regarding a public registry of clinical trial data: Individuals suffering from various diseases—and their family members—want to know that appropriate therapies are being offered and that patient safety is being ensured. Patients today want to be able to search on their own for research results that pertain to their disease and potentially to enroll in a clinical trial if appropriate. Health care professionals need both unbiased summary information derived from all trials conducted on a drug or therapy and the capacity to review the clinical data from any single study. They do not want to confine their review to the approved drug labeling or articles published in medical journals. Researchers may generate new ideas for investigation or look for data trends by accessing all the trials conducted on a drug or therapy. Medical journals have an enormous impact on clinical practice and medical policy. When journal editors receive clinical trial manuscripts for publication, they are concerned that they understand the research fully. They want to know whether clinical trials exist that may conflict with the submitted manuscript. And they want to know whether the authors failed to follow the original research plan, because such discrepancies may reflect serious defects in the research. Indeed, the integrity of the journal is at stake, as is the entire scientific enterprise, when research is published through the peer-review process. Regulators would use the information in a registry to develop policies regarding clinical research. Health insurers want to remain abreast of evidence-based results as the basis for insurance coverage policy. Sponsors of research aimed at developing a new therapy or drug incur great expense. Some of the information involved is highly proprietary and confidential to the sponsor. Companies are concerned that if all such proprietary information were required to be made broadly available to the public at the outset of clinical trials, they could not recoup their investment because competitors in the United States or abroad could copy their innovations. At the same time, industry recognizes its responsibility to do everything possible to ensure patient safety and secure the public trust. system to learn more about a particular medication. The registry would also facilitate more efficient use of clinical data for such purposes as cross-referencing patients’ response to a drug during clinical trials and their response in clinical practice, patients’ response to one drug and their response to another, and patients’ response to a drug and their response to other treatment options. Also, a drug’s overall effectiveness in terms of patient outcomes is becoming a valuable measure of therapeutic success. Further discussion and recommendations concerning such a national
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Preventing Medication Errors registry will be provided in the forthcoming report of the IOM Committee on the Assessment of the U.S. Drug Safety System. Communication of Information Drug information is communicated to providers and consumers through labeling and packaging, marketing practices, and advertisements. Poorly designed materials and inadequate representation of drug benefits and risks has led to errors across the medication-use continuum, such as inappropriate prescribing, confusion among products affecting dispensing and administration, and compromised ability to monitor a drug’s effects adequately. This section addresses these issues. Recommendation 4: Enhancing the safety and quality of the medication-use process and reducing errors requires improved methods for labeling drug products and communicating medication information to providers and consumers. For such improvements to occur, materials should be designed according to designated standards to meet the needs of the end user. Industry, the Agency for Healthcare Research and Quality (AHRQ), the FDA, and others as appropriate (e.g., U.S. Pharmacopeia, Institute for Safe Medication Practices) should work together to undertake the following actions to address labeling, packaging, and the distribution of free samples: The FDA should develop two guidance documents for industry: one for drug naming and another for labeling and packaging. The FDA and industry should collaborate to develop (1) a common drug nomenclature that standardizes abbreviations, acronyms, and terms to the extent possible, and (2) methods of applying failure modes and effects analysis to labeling and packaging. Additional study of optimum designs for all drug labeling and information sheets to reflect human and cognitive factors should be undertaken. Methods for testing and measuring the effects of these materials on providers and consumers should also be established, including methods for field testing of the materials. The FDA, the National Library of Medicine (NLM), and industry should work with consumer and patient safety organizations to improve the nomenclature used in consumer materials. The FDA, the pharmaceutical industry, and other stakeholders should collaborate to develop a strategy for expanding unit-of-use packaging for consumers to new therapeutic areas. Studies should be undertaken to evaluate different unit-of-use packaging
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Preventing Medication Errors and design approaches that will best support various consumer groups in their medication self-management. AHRQ should fund studies to evaluate the impact of free samples on overall patient safety, provider prescribing practices, and consumer behavior (e.g., adherence to the medication regimen), as well as alternative methods of distribution that can improve safety, quality, and effectiveness. Naming, Labeling, and Packaging Drug names that look or sound alike increase the risk of medication errors (Cohen, 2000). Confusion over the similarity of drug names for prescription, generic, and over-the-counter (OTC) products accounts for up to 25 percent of all errors reported to the U.S. Pharmacopeia (USP) (NCC MERP, 2001). Abbreviations, acronyms, certain dose designations, and other symbols used for labeling also have caused a number of errors (FDA, 2005b). Even the layout and presentation of drug information on the drug container or package label can be visually confusing, particularly when designed for the marketplace instead of clinical practice. From January 2000 to March 2004, close to 32,000 reports were submitted to USP’s MedMarx Reporting System that linked errors to look-alike or sound-alike drug names (Santell and Camp, 2004). The Joint Commission on Accreditation of Healthcare Organizations’ (JCAHO) National Patient Safety Goals reference several look-alike/sound-alike generic drug names that have contributed to 9 of 10 serious medication errors in the hospital setting (JCAHO, 2006). And labeling and packaging issues were cited as the cause of 33 percent of errors, including 30 percent of fatalities, reported to the USP–Institute for Safe Medication Practices (ISMP) Medication Error Reporting Program (MERP) database (USP, 1998). Box 6-2 outlines the major problems in drug naming, labeling, and packaging that contribute to medication errors. Addressing these problems requires understanding the processes and requirements involved in naming, labeling, and packaging drug products. Drug naming is a complex process. Each drug has multiple names assigned by different organizations for different purposes (Berman, 2004). The chemical name is assigned by the International Union of Pure and Applied Chemistry and identifies molecular structure. It serves the needs of scientific researchers. The nonproprietary or generic name is assigned by the United States Adopted Name Council (USAN) using a series of guidelines to ensure uniformity and safety.2 These guidelines require that the 2 The World Health Organization (WHO) coordinates international efforts to create a single worldwide standard and has established an International Nonproprietary Name (INN) for every product (Berman, 2004).
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Preventing Medication Errors BOX 6-2 Examples of Major Naming, Labeling, and Packaging Problems Brand names that look alike or sound alike—Celebrex® (celecoxib), Cerebryx® (fosphenytoin), and Celexa® (citalopram) (Zoeller, 1999). Celebrex® is a nonsteroidal anti-inflammatory drug; Cerebryx® is an intravenous antiepileptic; and Celexa® is an antidepressant. Generic names that look alike or sound alike—Amrinone (used to treat heart failure) and Amiodarone (an antiarrythmic). Amrinone was renamed Inamrinone to avoid confusion with Amiodarone (FDA, 2005b). Different formulations with the same brand name—Dulcolax (bisacodyl—a stimulant laxative) and Dulcolax (docusate—a stool softener). Different formulations of a generic drug—Four different versions of amphotericin B products are on the market—conventional amphotericin B (Amphocin®, Fungizone®, and a generic), amphotericin B cholesteryl sulfate complex (Amphotec®), amphotericin B lipid complex (Abelcet®), and amphotericin B liposomal (AmBisome®) (USP, 2005). Multiple abbreviations to represent the same concept—The extended-release version of a drug can use any number of different suffixes (e.g., LA, XL, XR, CC, CD, ER, SA, CR, XT, SR) to indicate long-acting or slow, delayed, or extended release (Berman, 2004). Word derivatives or abbreviations that can be confused—Similar prefixes (e.g., chlor-, clo-) (Aronson, 2004) can be misinterpreted, as can abbreviations used for labeling (e.g., AD [aura dexter or right ear] can be confused with “as directed,” OD [oculus dexter or right eye], QD [once daily], and PO [by mouth]) (ISMP, 2002). Unclear dose concentration/strength designations—The contents of a 20 ml, 40 mg/ml gentamicin vial can be mistaken for a 40 mg/ml vial single dose (Cohen, 2000). Lack of terminology standardization—Use of the term “concentrate” for oral morphine sulfate products is inconsistent among manufacturers. Roxanol Concentrated Oral Solution and Roxanol-T Concentrated Oral Solution (with tinting and flavoring) both contain morphine sulfate 20 mg/ml, but one is expressed as 20 mg/5 ml. A nurse could easily misread the label and think they are the same. Use of symbols that can be confused—Symbols such as the ampersand (&) and the slash mark (/) can be misidentified as numbers (Cohen, 2000; JCAHO, 2003). Cluttered labeling, small font, and serif typeface resulting in poor readability of printed information—Containers with labels that have line after line of small print, identical-looking text, and extraneous, unnecessary commercial information are difficult to read and to differentiate from others that look similar (Cohen, 2000). Serif typeface is more difficult to read correctly than sans serif. At home, older adults also have difficulty reading cluttered labels (Wogalter and Vigilante, 2003). Lack of adequate background contrast—Drug information printed directly on a clear product container (e.g., vial, intravenous bag) is extremely difficult
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Preventing Medication Errors information. Further, radio frequency identification (RFID) technology may replace bar codes on external packaging altogether, particularly in light of the growing problems with counterfeit drug imports entering the U.S. market. However, RFID will not replace the need for standardized bar code systems for patient care. User Interface The ability of clinicians to use a medical device or decision-support system successfully depends on how well the technologies have been designed at the level of the human–machine interaction (i.e., user interface). From the user’s perspective, the interface is the system (Shortliffe et al., 2001). When interacting with technology, clinicians aim to carry out tasks in which information is assessed, manipulated, or created (van Bemmel and Musen, 1997). The quality and style of the interface directly affect this processing of information. Well-organized information that is presented in a logical and meaningful way results in a higher degree of usability, whereas the display of information in a cluttered, illogical, or confusing manner leads to decreases in user performance and satisfaction (van Bemmel and Musen, 1997). Most important, a poorly designed user interface can even contribute to medication errors for all drug-related technologies (Patterson et al., 2002; Ash et al., 2004; Koppel et al., 2005). As noted earlier, several studies have confirmed that many medication errors resulting in patient harm involve intravenous infusion devices, with the most common cause of the errors being incorrect programming (Kaushal et al., 2001; Taxis and Barber, 2003; Tourville, 2003). Several problems with the interface design for these devices in terms of programming keys, display screens, and menu structure have contributed to these high rates of ADEs. In an effort to simplify programming and reduce pump size, a limited number of programming keys are provided on the pumps. Each key serves multiple functions, and clinical protocol is selected through scroll menus. However, menu structures are so complex that even skilled users could easily get confused (Nemeth, 2003). Device programming is often further complicated by small display screens that are difficult to read and follow. As a result, the state of the infusion pump is not always obvious during each step of the process. Even small data entry errors can result in numerous unforeseen medical complications that cause patient harm. Clinicians frequently must power down the pumps and start over to clear programming mistakes. Device manufacturers have been working to improve the user interface by incorporating the principles of human factors engineering into the pumps’ design structure. Standards for human factors design have been established by the Association for the Advancement of Medical Instrumentation (AAMI) and approved by the American National
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Preventing Medication Errors Standards Institute (ANSI), and are a part of the FDA’s Good Manufacturing Practices (GMP) regulatory requirements (IOM, 2004). The standards do not go far enough to address user interface issues, however, and additional work is needed. Medication errors also result from comparable problems in the user interface design for decision-support systems. A recent study of CPOE systems found that human–machine interface flaws facilitated 22 types of medication errors (Koppel et al., 2005). A number of factors affect the ability of clinicians to interact effectively and efficiently with decision-support systems (whether CPOE, electronic health records, or pharmacy database). First, most of the commercial systems on the market were designed according to rigid machine rules that do not correspond appropriately to the clinician’s workflow and behavior (Koppel et al., 2005). The natural chain of clinical events is disrupted while clinicians are forced to accommodate the rigid data requirements of the technology (Han et al., 2005). Often, a second physician devoted solely to entering orders is needed when time-sensitive therapeutic interventions must be administered, such as in emergency or intensive care. Second, many interface designs are highly impractical or outdated. Information is presented in numerous lines of identical-looking text, without a windows-based structure or intuitive graphical navigation aids (Ash et al., 2004). Even when the information is there, it is difficult to find. Clinicians must click on multiple different screens to either retrieve all of a patient’s information or enter new clinical information. Information becomes fragmented, and clinicians lose their ability to develop a more comprehensive overview and conceptual understanding of the case (Ash et al., 2004). For example, in many inpatient CPOE systems, patient names are grouped alphabetically rather than by clinical staff or rooms. Thus similar names, combined with small fonts, hectic workstations, and interruptions, can easily be confused (Koppel et al., 2005). Equally troubling, a patient’s medication information is seldom synthesized on one screen; a clinician may need to access up to 20 screens to view all the medications included in the patient’s regimen. Although decision-support systems use standard computer monitors to display information, a significant amount of work is needed to develop optimal user interface designs that can make data capture and manipulation easier for clinicians and more accurate for patient safety. Data presentation and the user interface affect the usability of bar code medication administration systems as well. Although there are no studies indicating that the design of such systems directly caused medication errors (Johnson et al., 2002), several studies have confirmed that negative unintended consequences resulting from the introduction of these systems may
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Preventing Medication Errors create new paths to ADEs (Patterson et al., 2002; Patterson, 2003). The information display of the systems is much like that of other decision-support systems that rely on computer-based monitors and graphical interfaces. Specific issues with interface design vary depending on the vendor, but generally relate to the incompleteness of the medication information displayed and its effect on clinical coordination (Cipriano, 2003; Patterson, 2003). For example, the more inflexible systems require a long, confusing sequence of programming activities for a simple change to medication administration times. Moreover, important medication information is either not available or not displayed in a timely manner. Key problems identified include (1) pending and discontinued medication orders not displayed; (2) inability to document medications not displayed as administered when they had been administered; (3) automated removal of medications from bar code medication administration systems, resulting in confusion; (4) inability to view changes to medication orders without opening a patient record; (5) difficulty of undoing actions: (6) difficulty of revising database information once entered; and (7) poorly organized data screens, resulting in missed medications (Patterson et al., 2002; Rogers et al., 2005). The fragmentation of patient data also contributes to clinicians’ inability to obtain at a glance a comprehensive overview of patients’ medication information, as well as to degraded coordination between physicians and nurses—one of the more noted negative side effects of bar code medication administration systems (Patterson et al., 2002). Efforts in the United Kingdom have started to address user interface issues through an agreement with Microsoft Corporation. Microsoft will develop a health-specific user interface for clinical systems used by the National Health Service to improve patient care and safety. Under the terms of the agreement, Microsoft will supply code based on the full shipping versions of its desktop software that can be used by independent vendors and supply customized versions of Office and Windows (NHS, 2004). However, use of common coding to link and present data is only one aspect of improving the user interface. Addressing user interface issues will require greater attention to the cognitive and social factors influencing clinicians in their daily workflow and interaction with technologies (van Bemmel and Musen, 1997). Yet little emphasis has been placed on physicians’ ability to learn and use these systems or on the technologies’ effects on physicians’ reasoning. From the perspective of cognitive psychology, designers must develop a better understanding of how clinicians best comprehend information, as well as of the limits of human perception and memory. The context of the clinical environment, in which clinicians must perform multiple tasks simultaneously and manage numerous interruptions by beepers, telephones, and colleagues,
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Preventing Medication Errors must be taken into account (Ash et al., 2004). Designers should understand that cognitive overload can result from overemphasizing complete information entry or retrieval. A prime example of how comprehensive design strategies such as these have been successful in transforming health technology interfaces to improve patient safety is in the high-risk area of anesthesia (IOM, 2000; Hallinan, 2005; Pierce, 2006). Anesthesiology has reduced anesthesia mortality rates from two deaths per 10,000 administrations to one death per 300,000 administrations (JCAHO, 1998). This success was accomplished through a combination of the following: Technical changes (new monitoring equipment, standardization of existing equipment) Information-based strategies, including the development and adoption of guidelines and standards Application of human factors to improve performance, such as the use of simulators for training Formation of the Anesthesia Patient Safety Foundation to bring together stakeholders from different disciplines (physicians, nurses, manufacturers) Having a leader who could serve as a champion for the cause (Leape et al., 1998; IOM, 2000) No single one of these changes has been sufficient to have a clear-cut impact on mortality, yet, the application of human factors principles in conjunction with the other factors has been highly effective (Leape at al., 2002; Sawa and Ohno-Machado, 2002; Wachter at al., 2003). Measuring progress over time and regularly integrated lessons learned into clinical systems created a dynamic process for ongoing quality and safety improvement. It can be challenging to capture the richness and complexity of clinical data in a manner that is concise and precise, but still comprehensive enough for medical care (Cimino et al., 2001). Screen layout and the visual salience of the information presented critically affect the way the information is interpreted by clinicians using decision-support systems (Kushniruk et al., 1996; Kaufman et al., 2003). Menus, graphics, and colors can all help differentiate data and make systems more attractive and simpler to learn and use. Interfaces must offer clear presentations, avoid unnecessary detail, and provide consistent interaction to be effective (Shortliffe et al., 2001). Designers also should recognize the inherent differences among clinician user groups, and seek to design multidimensional interfaces that can accommodate the information requirements of individual clinicians and comprehensive conceptual views of patient information.
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Preventing Medication Errors REFERENCES Abookire SA, Teich JM, Sandige H, Paterno MD, Martin MT, Kuperman GJ, Bates DW. 2000. Improving allergy alerting in a computerized physician order entry system. Proceedings of American Medical Informatics Association Symposium 2–6. Ahern MD, Kerr SJ. 2003. General practitioners’ perceptions of the pharmaceutical decision-support tools in their prescribing software. Medical Journal of Australia 179(1):34–37. Alexander KP, Peterson ED. 2003. Evidence-based care for all patients. American Journal of Medicine 114(4):333–335. Allen D. 2002. The Next Chapter: Unit-of-Use Packaging. Pharmaceutical and Medical Packaging News. [Online]. Available: http://www.devicelink.com/pmpn/archive/02/11/004. html [accessed May 27, 2006]. ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial). 2002. Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs. diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). Journal of the American Medical Association 288(23):2981–2997. Anton C, Nightingale PG, Adu D, Lipkin G, Ferner RE. 2004. Improving prescribing using a rule-based prescribing system. Quality and Safety in Health Care 13(3):186–190. Aronson JK. 2004. Medication errors resulting from the confusion of drug names. Drug Safety 3(3):167–172. Ash JS, Berg M, Coiera E. 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):104–112. ASTM (American Society for Testing and Materials). 1988. Standard D4267-89. Philadelphia, PA: ASTM. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. 2003. Ten commandments for effective clinical decision support: Making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association 10(6):523–530. Becker LA, Glanz K, Sobel E, Mossey J, Zinn SL, Knott KA. 1986. A randomized trial of special packaging of antihypertensive medications. Journal of Family Practice 22(4):357–361. Berman A. 2004. Reducing medication errors through naming, labeling, and packaging. Journal of Medical Systems 28(1):9–29. Billman G. 2004. A Medical Center’s Experience Using Smart Infusion Pumps to Manage Medication Administration. San Diego, CA: ALARIS Center for Medication Safety and Clinical Improvement. Blumenthal D. 2004. Doctors and drug companies. New England Journal of Medicine 351(18): 1885–1890. Brennan TA, Rothman DJ, Blank L, Blumenthal D, Chimonas SC, Cohen JJ, Goldman J, Kassirer JP, Kimball H, Naughton J, Smelser N. 2006. Health industry practices that create conflicts of interest: A policy proposal for academic medical centers. Journal of the American Medical Association 295(4):429–433. Brown, SH. 2006. Diagram of NDF-RT Data Elements. Personal Communication. Califf RM, DeMets DL. 2002a. Principles from clinical trials relevant to clinical practice: Part I. Circulation 106:1015–1021. Califf RM, DeMets DL. 2002b. Principles from clinical trials relevant to clinical practice: Part II. Circulation 106:1172–1175. CERTS (Centers for Education and Research on Therapeutics). 2003. Risk assessment of drugs, biologics, and therapeutic devices: Present and future issues. Pharmacoepidemiology and Drug Safety 12(8):653–662.
OCR for page 304
Preventing Medication Errors Charatan F. 2001. Hospital bans free drug samples. Western Journal of Medicine 174(4): 236–237. Chew LD, O’Young TS, Hazlet TK, Bradley KA, Maynard C, Lessler D. 2000. A physician survey of the effect of drug sample availability on physicians’ behavior. Journal of General Internal Medicine 15(7):478–483. Cimino JJ, Patel VL, Kushniruk AW. 2001. Studying the human-computer-terminology interface. Journal of the American Medical Informatics Association 8(2):163–173. Cipriano PF. 2003. A nursing perspective on bedside scanning systems. Hospital Pharmacy 38(11):S14–S15. Classen DC, Pestotnik SL, Evans RS, Lloyd JF, Burke JP. 1997. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. Journal of the American Medical Association 277(4):301–306. Cohen MR. 2000. Medication Errors: Causes, Prevention, and Risk Management. Sudbury, MA: Jones and Bartlett Publishers. Cohen MR. 2002. Trade name, INNs, and medication errors. Archives of Internal Medicine 162(22):2636–2637. Cole WG, Stewart JG. 1994. Human performance evaluation of a metaphor graphic display for respiratory data. Methods of Information in Medicine 33(4):390–396. Combes JR. 2004. Understanding the Challenges of Implementation of Point Care Bar Code Systems. Presentation to the IOM Committee on Identifying and Preventing Medication Errors, March 19, 2004. Crowley WF, Sherwoo, L, Salber P, Scheinberg D, Slavkin H, Tilson H, Reece EA, Catanese V, Johnson SB, Dobs A, Genel M, Korn A, Reame N, Bonow R, Grebb J, Rimoin D. 2004. Clinical research in the United States at a crossroads: Proposal for a novel public-private partnership to establish a national clinical research enterprise. Journal of the American Medical Association 291(9):1120–1126. Davia J. 2003. Encourage Use of Generics to Rein in Cost of Medications. [Online]. Available: http://www.democratandchronicle.com/news/extra/fighting/health/story4.shtml [accessed March 12, 2006]. DHA (Australian Department of Health and Ageing). 2002. BMMS: Alerts Discussion Paper (Version 3.0 edition). Canbera, Australia: DHA. Dodds-Ashley ES, Kirk K, Fowler VG. 2002. Patient detection of a drug dispensing error by use of physician-provided drug samples. Pharmacotherapy 22(12):1642–1643. Edwards R, Faich G, Tilson H, and International Society of Pharmacovigilance. 2005. Points to consider: The roles of surveillance and epidemiology in advancing drug safety. Pharmacoepidemiology and Drug Safety 14(9):665–667. Erickson G. 1998. Unit-of-Use Packaging: The Wave of the Future? Pharmaceutical and Medical Packaging News. [Online]. Available: http://www.devicelink.com/pmpn/archive/ 98/06/003.html [accessed May 27, 2006]. FDA (U.S. Food and Drug Administration). 2005a. Overview of the Office of Medication Errors and Technical Support. Submission to the IOM Committee on Identifying and Preventing Medication Errors. Rockville, MD: FDA. FDA. 2005b, July–August 2005. Drug name confusion: Preventing medication errors. FDA Consumer Magazine. FG (Freedonia Group). 2003. World Pharmaceutical Packaging: Forecasts to 2007 and 2012. [Online]. Available: http://www.gii.co.jp/sample/pdf/fd17351.pdf [accessed April 2, 2006]. Fields M, Peterman J. 2005. Intravenous medication safety system averts high-risk medication errors and provides actionable data. Nursing Administration Quarterly 29(1):78–87. Fitzgerald GA. 2005. Opinion: Anticipating change in drug development: The emerging era of translational medicine and therapeutics. Drug Discovery 4(10):815–818.
OCR for page 305
Preventing Medication Errors Fleming TR, DeMets DL. 1996. Surrogate end points in clinical trials: Are we being misled? Annals of Internal Medicine 125:605–613. Forester AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. 2003. The incidence and severity of adverse events affecting patients after discharge from the hospital. Annals of Internal Medicine 138(3):161–167. Forester AJ, Halil RB, Tierney MG. 2004. Pharmacist surveillance of adverse drug events. American Journal of Health System Pharmacists 61(14):1466–1472. Fortescue EB, Kaushal R, Landrigan CP, McKenna KJ, Clapp MD, Federico F, Goldman DA, Bates DW. 2003. Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients. Pediatrics 111(4):722–729. FR (Federal Register). 2004. Bar Code Label Requirements for Human Drug Products and Biological Products: Final Rule. Washington, DC: National Archives and Records Administration. Glassman PA, Simon B, Belperio P, Lanto A. 2002. Improving recognition of drug interactions: Benefits and barriers to using automated drug alerts. Medical Care 40(12):1161– 1171. Grasso BC, Rothschild JM, Genest R, Bates DW. 2003. What do we know about medication errors in psychiatry? Joint Commission Journal on Quality and Safety 29(8):391–400. Groves KEM, Sketris I, Tett SE. 2003. Prescription drug samples: Does this marketing strategy counteract policies for quality use of medicines? Journal of Clinical Pharmacy and Therapeutics 28:259–271. Gurwitz JH. 2004. Polypharmacy: A new paradigm for quality drug therapy in the elderly? Archives of Internal Medicine 164(18):1957–1959. Gurwitz JH, Field TS, Judge J, Rochon P, Harrold LR, Cadoret C, Lee M, White K, LaPrino J, Mainard JF, DeFlorio M, Gavendo L, Auger J, Bates DW. 2005. The incidence of adverse drug events in two large academic long-term care facilities. The American Journal of Medicine 118(3):251–258. Hallinan TJ. 2005. Once Seen as Risky, One Group of Doctors Changes Its Ways. [Online]. Available: http://webreprints.djreprints.com/1254400029287.html [accessed May 29, 2006]. Han YY, Carcillo JA, Venkataraman ST, Clark RSB, Watson RS, Nguyen TC, Bayir H, Orr RA. 2005. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 116(5):1506–1512. HCPC (Healthcare Compliance Packaging Council). 2003. HCPC Response to Public Comments Requested in 68 FR 115, CPSC Petition PP 03-1. Falls Church, VA: HCPC. HIBCC (Health Industry Business Communications Council). 2001. The Use of the Health Industry Bar Code for Product Labeling and Device Tracking. [Online]. Available: http:// www.hibcc.org/PUBS/WhitePapers/HIBCFeatures.pdf [accessed December 19, 2005]. Hoffman JM, Proulx SM. 2003. Medication errors caused by confusion of drug names. Drug Safety 26(7):445–452. Hsieh TC, Kuperman GJ, Jaggi T, Hojnowski-Diaz P, Fiskio J, Williams DH, Bates DW, Gandhi TK. 2004. Characteristics and consequences of drug allergy alert overrides in a computerized physician order entry system. Journal of the American Medical Informatics Association 11(6):482–491. Huang HY, Maguire MG, Miller ER, Appel LJ. 2000. Impact of pill organizers and blister packs on adherence to pill taking in two vitamin supplementation trials. American Journal of Epidemiology 152(8):780–787. Hulley S, Grady D, Bush T, Furberg C, Herrington D, Riggs B, Vittinghoff E. 1998. Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. Heart and Estrogen/progestin Replacement Study (HERS) Research Group. Journal of the American Medical Association 280(7):605–613.
OCR for page 306
Preventing Medication Errors Ientile C, Stokes J, Hendry M, Jensen A, Lewis G. 2004. Literature Review for the Effectiveness and Cost Effectiveness of Dose Administration Aids Project. Queensland, Australia: University of Queensland. IMS Health. 2004. Total U.S. Promotional Spending by Type, 2003. Notes: Provided to IOM from PhRMA. IOM (Institute of Medicine). 2000. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press. IOM. 2001. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press. IOM. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. IOM. 2006. Developing a Registry of Pharmacologic and Biologic Clinical Trials. Washington, DC: The National Academies Press. ISMP (Institute for Safe Medication Practice). 2002. ISMP Medication Safety Alert July 10. Huntington Valley, PA: ISMP. ISMP. 2005. Medication Safety Alert. December 15. Huntington Valley, PA: ISMP. JCAHO (Joint Commission on Accreditation of Healthcare Organizations). 1998. Medication Use: A Systems Approach to Reducing Errors. Oakbrook Terrace, IL: JCAHO. JCAHO. 2003. 2003 JCAHO National Patient Safety Goals: Practical Strategies and Helpful Solutions for Meeting These Goals (Special Report 2003). Oakbrook Terrace, IL: JCAHO. JCAHO. 2006. National Patient Safety Goals: Look-Alike/Sound-Alike Drug List. [Online]. Available: http://www.jointcommission.org/PatientSafety/NationalPatientSafetyGoals [accessed June 13, 2006]. Johnson CL, Carlson RA, Tucker CL, Willette C. 2002. Using BCMA software to improve patient safety in Veterans Administration Medical Centers. Journal of Healthcare Information Management 16(1):46–51. Kaufman DR, Patel VL, Hilliman C, Morin PC, Pevzner J, Weinstock RS, Goland R, Shea S, Starren J. 2003. Usability in the real world: Assessing medical information technologies in patients’ homes. Journal of Biomedical Informatics 36:45–60. Kaushal R, Bates DW, Landrigan C, McKenna KJ, Clapp MD, Federico F, Goldmann DA. 2001. Medication errors and adverse drug events in pediatric inpatients. Journal of the American Medical Association 285(16):2114–2120. Kilbridge P, Welebob E, Classen D. 2001. Overview of the Leapfrog Group Evaluation Tool for Computerized Physician Order Entry. Washington, DC: The Leapfrog Group. Kilbridge PM, Welebob EM, Classen DC. 2006. Development of the Leapfrog methodology for evaluating hospital implemented inpatient computerized physician order entry systems. Quality and Safety in Health Care 15(2):81–84. Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, Strom BL. 2005. Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Association 293(10):1197–1203. Kushniruk AW, Kaufman DR, Patel VL, Levesque Y, Lottin P. 1996. Assessment of a computerized patient record system: A cognitive approach to evaluating medical technology. MD Computing 13:406–415. Leape LL, Kabcenell A, Berwick DM, Roessner J. 1998. Breakthrough Series Guide: Reducing Adverse Drug Events. Boston, MA: Institute for Healthcare Improvement. Leape LL, Berwick DM, Bates DW. 2002. What practices will most improve safety? Evidence-based medicine meets patient safety. Journal of the American Medical Association 288(4): 501–507.
OCR for page 307
Preventing Medication Errors Levy S. 2002. TrialCard Puts Drug Samples in R.Ph.’s Hands. [Online]. Available: http:// www.drugtopics.com/drugtopics/article/articleDetail.jsp?id-116704 [accessed January 23, 2006]. Malashock CM, Shull SS, Gould DA. 2004. Effect of smart infusion pumps on medication errors related to infusion device programming. Hospital Pharmacy 39(5):460–469. March JS, Silva SG, Compton S, Shapiro M, Califf R, Krishnan R. 2005. The case for practical clinical trials in psychiatry. American Journal of Psychiatry 162(5):836–846. MHPRA (Medicines and Healthcare Products Regulatory Agency). 2003. Guidance Note 25: Best Practices for Labeling and Packaging of Medicines. London, United Kingdom: MHPRA. Miller RA, Gardner RM, Johnson KB, Hripcsak G. 2005. Clinical decision support and electronic prescribing systems: A time for responsible thought and action. Journal of the American Medical Informatics Association 12(4):403–409. NCC MERP (National Coordinating Council for Medication Error Reporting and Prevention). 2001. Recommendations to Reduce Medication Errors Associated with Verbal Medication Orders and Prescriptions. [Online]. Available: http://www.nccmerp.org/ council/council2001-02-20.html [accessed December 23, 2005]. NCVHS (National Committee on Vital and Health Statistics), 2003. Letter to Secretary of DHHS Tommy Thompson: PMRI Terminology Standards. [Online]. Available: http:// www.ncvhs.hhs.gov/reptrecs.htm [accessed May 23, 2006]. Nemeth C. 2003. Report on Infusion Pump Operation by Healthcare Professionals. Chicago, IL: Cognitive Technologies Laboratory. NHS (National Health Service). 2004. Microsoft and Partners to Invest £40 Million in Development Resources to Improve Clinical Care in the NHS. [Online]. Available: http:// www.connectingforhealth.nhs.uk.news/0311041 [accessed March 15, 2006]. Paterson JM, Anderson GM. 2002. “Trial” prescriptions to reduce drug wastage: Results from Canadian programs and a community demonstration project. American Journal of Managed Care 8(2):151–158. Patterson ES. 2003. Addressing human factors in bar code medication administration systems. Hospital Pharmacy 38(11):S16–S17. Patterson ES, Cook RI, Render ML. 2002. Improving patient safety by identifying side effects from introducing bar coding in medication administration. Journal of the American Medical Informatics Association 9(5):540–553. Petersen M. 2000, November 15. Growing opposition to free drug samples. New York Times. Business. Pierce EC. 2006. 34th Rovenstine Lecture: Enhancing Patient Safety from the 1980’s through the Present. Pittsburgh, PA: Anesthesia Patient Safety Foundation. Prentice RL. 1989. Surrogate end points in clinical trials: Definition and operational criteria. Statistics in Medicine 8:431–440. Psaty BM, Furberg CD. 2005. COX-2 inhibitors—Lessons in drug safety. New England Journal of Medicine 352(11):1133–1135. Rasmussen JE. 1988. Free drug samples. Archives of Determatology 124:135–137. Reddan D, Szczech LA, O’Shea S, Califf RM. 2003. Anticoagulation in acute cardiac care in patients with chronic kidney disease. American Heart Journal 145(4):586–594. Reichley RM, Seaton TL, Resetar E, Micek ST, Scott KL, Fraser VJ, Dunagan C, Bailey T.C. 2005. Implementing a commercial rule base as a medication order safety net. Journal of the American Medical Informatics Association 12(4):383–389. Roberts R, Rodriguez W, Murphy D, Crescenzi T. 2003. Pediatric drug labeling: Improving the safety and efficacy of pediatric therapies. Journal of the American Medical Association 290:905–911.
OCR for page 308
Preventing Medication Errors Rogers ML, Patterson E, Chapman R, Render M. 2005. Usability Testing and the Relation of Clinical Information Systems to Patient Safety. [Online]. Available: http://www.ahrq.gov/ downloads/pub/advances/vol2/Rogers.pdf [accessed December 20, 2005]. Santell JO, Camp S. 2004. Similarity of drug names, labels, or packaging creates safety issues. U.S. Pharmacist 29(7). Sawa T, Ohno-Machado L. 2001. Generation of dynamically configured check lists for intra-operative problems using a set covering algorithm. Proceedings of American Medical Informatics Association Symposium 593–597. Schneider PJ, Murphy JE, Pedersen CA. 2006, In press. Adherence and treatment outcomes in elderly outpatients with hypertension using specially packaged medications. Journal of the American Pharmacists Association. Schünemann HJ, Cook D, Grimshaw J, Liberati A, Heffner J, Tapson V, Guyatt G. 2004. Antithrombotic and thrombolytic therapy: From evidence to application. Chest 126(Suppl.): 688–696. Security Biometrics, Inc. 2004. Security Biometrics, Inc. Subsidiary eMedRx, Forms Exclusive Strategic Alliance with Univec. [Online]. Available: http://findbiometrics.com/viewnews. php?id=831 [accessed January 23, 2006]. Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, Recklet EG, Bates DW, Gandhi TK. 2006. Improving acceptance of computerized prescribing alerts in ambulatory care. Journal of the American Medical Informatics Association 13(1):5–11. Shortliffe EH, Perreault LE, Wiederhold G, Fagan LM. 2001. Medical Informatics: Computer Applications in Health Care and Biomedicine. 2nd ed. New York: Springer. Simmons D, Upjohn M, Gamble GD. 2000. Can medication packaging improve glycemic control abd blood pressure in Type 2 diabetes? Diabetes Care 23(2):153–156. Simon SR, Majumdar SR, Prosser LA, Salem-Schatz S, Warner C, Kleinman K, Miroshnik I, Soumerai SB. 2005. Group versus individual academic detaining to improve the use of antihypertensive medications in primary care: A cluster-randomized controlled trial. American Journal of Medicine 118:521–528. Sipkoff M. 2003. Getting Serious About Generics. [Online]. Available: http://www.managed caremag.com/archives/0301/0301.generics.html [accessed March 12, 2006]. Szeinbach SL, Baron M, Guschke T, Torkilson EA. 2003. Survey of state requirements for unit-of-use packaging. American Journal of Health-System Pharmacists 60(18):1863– 1866. Taira DA, Iwane KA, Chung RS. 2003. Prescription drugs: Elderly enrollee reports of financial access, receipt of free samples, and discussion of generic equivalents related to type of coverage. American Journal of Managed Care 9(4):305–312. Taxis K, Barber N. 2003. Ethnographic study of incidence and severity of intravenous drug errors. British Medical Journal 326(7391):684. Tourville J. 2003. Automation and error reduction: How technology is helping Children’s Medical Center of Dallas reach zero-error tolerance. U.S. Pharmacist 28:80–86. Tsintis P, La Mache E. 2004. CIOMS and ICH initiatives in pharmacovigilance and risk management: Overview and implications. Drug Safety 27(8):509–517. Tunis SR, Stryer DB, Clancy CM. 2003. Practical clinical trials: Increasing the value of clinical research for decision making in clinical and health policy. Journal of the American Medical Association 290(12):1624–1632. USAN (United States Adopted Name Council). 2005. Stem List. [Online]. Available: http:// www.ama-assn.org/ama1/pub/upload/mm/365/usanstmlist_10_19_05.doc [accessed December 23, 2005]. USP (U.S. Pharmacopeia). 1993. Unit-of-Use Packaging: Contemporary Issues. Rockville, MD: USP. USP. 1998. USP Quality Review. No. 62. Rockville, MD: USP.
OCR for page 309
Preventing Medication Errors USP. 2005. CAPSLink Newsletter. Rockville, MD: USP. Valero G. 2005. Patient Compliance via Unit-Dose Packaging. Pharmaceutical Business Strategies. [Online]. Available: http://www.pbsmag.com/ArticlePrinterFriendly.cfm?ID=176 [accessed May 27, 2006]. van Bemmel JH, Musen MA. 1997. Handbook of Medical Informatics. Heidelberg, Germany: Springer-Verlag. Vanderveen T. 2005. Smart Pumps and Patient Controlled Analgesia Machines. Submission to the IOM Committee on Identifying and Preventing Medication Errors, July 2006. Wachter SB, Agutter J, Syroid N, Drews F, Weinger MB, Westenskow D. 2003. The employment of an iterative design process to develop a pulmonary graphical display. Journal of the American Medical Informatics Association 10(4):363–372. Weary PE. 1988. Free drug samples: Use and abuse. Archives of Determatology 124: 135–137. Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. 2003. Physicians’ decisions to override computerized drug alerts in primary care. Archives of Internal Medicine 163(21):2625–2631. Weinger MB, Slagle J. 2002. Human factors research in anesthesia patient safety. Journal of the American Medical Informatics Association 9(6):S58–S63. WHI Steering Committee (Women’s Health Initiative). 2004. Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: The Women’s Health Initiative randomized controlled trial. Journal of the American Medical Association 29:11701– 1712. Wogalter MS, Vigilante WJ. 2003. Effects of label format on knowledge acquisition and perceived readability by younger and older adults. Ergonomics 46(4):327–344. Wood AJ. 1999. The safety of new medicines: The importance of asking the right questions. Journal of the American Medical Association 281(18):1753–1754. Wright JM, Htun Y, Leong MG, Forman P, Ballard RC. 1999. Evaluation of the use of calendar blister packaging on patient compliance with STD syndromic treatment regimens. Sexually Transmitted Disease 26(10):556–563. Yusuf S, Collins R, Peto R. 1984. Why do we need some large, simple randomized trials? Statistics in Medicine 3:409–422. Zarin DA, Tse T, Ide NC, 2005. Trial registration at ClinicalTrials.gov between May and October 2005. New England Journal of Medicine 353(26):2779–2787. Zoeller J. 1999. Searle weighs Celebrex name change: New cox-2 has been confused with other similarly-named drugs. American Druggist 216(5):15.
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