D
Medication Errors: Prevention Strategies

Many organizations and researchers have recommended specific interventions for preventing medication errors (See Box D-1). This appendix reviews the empirical evidence in support of these interventions in three care settings (hospital, nursing home, and community care), in pediatric and psychiatric care, and in relation to over-the-counter (OTC) and complementary and alternative medications.

PREVENTION STRATEGIES FOR HOSPITAL CARE

The committee reviewed published error reduction strategies of 10 organizations (see Box D-1). The methods used by these organizations for selecting and supporting their recommended interventions varied from those based on expert opinion to more rigorous evaluation of the literature; some organizations did not explicitly state the method used. Most proposed interventions are based on expert opinion.

The most evidence-based summaries were produced by the Agency for Healthcare Research and Quality (AHRQ). These summaries of specific practices were derived from a rigorous review of the published literature using strict article inclusion criteria and a standardized hierarchy for rating the strength of evidence for any particular intervention. These recommendations have been criticized, however, because of issues related to applying the usual evidence criteria to safety interventions (Leape et al., 2002). The National Quality Forum followed a rigorous process of interpreting the AHRQ recommendations to develop standards using an expert panel.



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Preventing Medication Errors D Medication Errors: Prevention Strategies Many organizations and researchers have recommended specific interventions for preventing medication errors (See Box D-1). This appendix reviews the empirical evidence in support of these interventions in three care settings (hospital, nursing home, and community care), in pediatric and psychiatric care, and in relation to over-the-counter (OTC) and complementary and alternative medications. PREVENTION STRATEGIES FOR HOSPITAL CARE The committee reviewed published error reduction strategies of 10 organizations (see Box D-1). The methods used by these organizations for selecting and supporting their recommended interventions varied from those based on expert opinion to more rigorous evaluation of the literature; some organizations did not explicitly state the method used. Most proposed interventions are based on expert opinion. The most evidence-based summaries were produced by the Agency for Healthcare Research and Quality (AHRQ). These summaries of specific practices were derived from a rigorous review of the published literature using strict article inclusion criteria and a standardized hierarchy for rating the strength of evidence for any particular intervention. These recommendations have been criticized, however, because of issues related to applying the usual evidence criteria to safety interventions (Leape et al., 2002). The National Quality Forum followed a rigorous process of interpreting the AHRQ recommendations to develop standards using an expert panel.

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Preventing Medication Errors BOX D-1 Organizations with Published Prevention Strategies for Hospital Care Agency for Healthcare Research and Quality (Shojania et al., 2001) American Society of Health-System Pharmacists (American Society of Health-System Pharmacists, 1996) Institute for Healthcare Improvement (IHI, 2005) Institute of Medicine (IOM, 2000) Institute for Safe Medication Practices (ISMP, 2005b), Joint Commission on Accreditation of Healthcare Organizations (JCAHO, 2005a) Massachusetts Coalition for the Prevention of Medical Errors (MCPME, 1999) National Quality Forum (NQF, 2003) National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP, 2005b) Pathways for Medication Safety (Pathways for Medication Safety, 2002) U.S. Pharmacopeia (USP, 2005) The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) selects its patient safety goals from a pool of recommendations that are first identified by members of the Sentinel Event Advisory Group. These recommendations are selected because they are considered either evidence-based or, much more typically, consensus-based or practical. Similarly, the Institute for Safe Medication Practices generally develops its guidelines based on careful analysis of reported errors. These recommendations are then peer-reviewed prior to their release. Recommendations for preventing medication errors are made by the National Coordinating Council for Medication Error Reporting and Prevention’s committee of experts in the field. The Massachusetts Coalition for the Prevention of Medical Errors developed best-practice recommendations for the prevention of medication errors based on a special consensus panel. The American Society of Hospital Pharmacists (ASHP) convened a multidisciplinary conference on preventing medication errors. Recommendations from the ASHP derive largely from these expert panels. Along with the ASHP, both the American Medical Association and the American Nurses Association participated in this multidisciplinary conference. Recommended Approaches Table D-1 summarizes the error reduction strategies recommended by the organizations listed in Box D-1. In general, most organizations

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Preventing Medication Errors TABLE D-1 Recommendations for the Prevention of Medication Errors in Hospital Care Recommended Practice Recommending Body Strength of Evidence Supporting Efficacy Technological Interventions Implement computerized provider order entry (CPOE) IOM, NCCMERP, MCPME, ASHP, IHI, NQF, PMS, AHRQ Medium strength Implement bar coding technology at the point of care NCCMERP, MCPME, ASHP, PMS, AHRQ Limited evidence Ensure availability of pharmaceutical decision support IOM, MCPME, ASHP Limited evidence Use pharmaceutical software IOM, MCPME, ASHP Lower strength Use automated medication dispensing devices AHRQ Lower strength Ensure free-flow protection on all general-use and patient-controlled analgesia (PCA) intravenous (IV) infusion pumps NCCMERP, JCAHO Limited evidence Interventions Utilizing Clinical Pharmacists Have a central pharmacist supply high-risk IV medications and pharmacy-based admixture systems IOM, MCPME, PMS Limited evidence Include a pharmacist during rounds of patient care units IOM, MCPME, ASHP, AHRQ Medium strength Utilize pharmacist counseling of patients NCCMERP Limited evidence Have a pharmacist available on call after hours of pharmacy operation MCPME Medium strength Have a pharmacist review all medication orders before first doses ASHP, NQF Limited evidence Interventions Related to the Medication-Use Process Establish a controlled formulary in which the selected medications are based more on safety than on cost PMS Limited evidence Standardize prescription writing and prescription rules, and eliminate certain abbreviations and dose expressions IOM, NCCMERP, ASHP, IHI, ISMP, NQF, JCAHO, USP Limited evidence

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Preventing Medication Errors Recommended Practice Recommending Body Strength of Evidence Supporting Efficacy Limit and formally structure verbal communication of medication prescriptions NCCMERP, ASHP, NQF, JCAHO Limited evidence Implement unit dosing IOM, MCPME, NQF, AHRQ Lower strength Implement standard processes for medication doses, dose timing, and dose scales in a given patient care unit IOM, IHI, ISMP, USP Limited evidence Monitor for look-alike and sound-alike medications IHI, ISMP, JCAHO, USP Limited evidence Limit the number of different kinds of common equipment IOM Limited evidence Do not store concentrated solutions of hazardous medications on patient care units, and limit the number of drug concentrations available in the organization IOM, MCPME, JCAHO, ISMP Limited evidence Employ special procedures and written protocols for the use of high-risk IV and oral medications OM, MCPME, IHI, INQF, PMS, ISMP, AHRQ, USP Medium strength Institute policies and procedures regarding labeling of all medications NCCMERP, NQF, ISMP, JCAHO, USP Limited evidence Miscellaneous Nontechnological Interventions Adopt a systems-oriented approach to medication error reduction IOM, NCCMERP, MCPME, ASHP, IHI, PMS, ISMP, USP Limited evidence Use improved communication practices, such as always resolving medication discrepancies prior to administration NCCMERP, ASHP, IHI, JCAHO Limited evidence Take steps to reduce workplace fatigue, such as planned naps, careful scheduling, or light therapy IHI, ISMP, USP Lower strength Create a culture of safety NCCMERP, ASHP, IHI, NQF, PMS, ISMP, USP Limited evidence Collect a medication history, and reconcile the list with the patient and other providers during care transitions ISMP, JCAHO, USP Limited evidence

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Preventing Medication Errors Recommended Practice Recommending Body Strength of Evidence Supporting Efficacy Improve the work environment for medication preparation, dispensing, and administration IOM, ASHP, IHI, NQF Limited evidence Improve error detection and reporting, and promote a nonpunitive atmosphere NCCMERP, MCPME, ASHP, NQF, PMS Limited evidence Make relevant patient information available at the point of care IOM, MCPME, IHI Indirectly supported through evidence on CPOE, electronic medication administration record (MAR), and bar coding Use failure modes and effects analysis or other strategies for risk management NCCMERP, PMS, ISMP Limited evidence Improve patients’ knowledge about their treatment IOM, MCPME, IHI, PMS Limited evidence NOTE: AHRQ = Agency for Healthcare Research and Quality; ASHP = American Society of Hospital Pharmacists; IHI = Institute for Healthcare Improvement; IOM = Institute of Medicine; ISMP = Institute for Safe Medication Practices; JCAHO = Joint Commission on Accreditation of Healthcare Organizations; MCPME = Massachusetts Coalition for the Prevention of Medical Errors; NCCMERP = National Coordinating Council for Medication Error Reporting and Prevention; NQF = National Quality Forum; PMS = Pathways for Medication Safety; USP = U.S. Pharmacopeia. SOURCE: ASHP, 1993; No Author, 1996; MCPME, 1999; IOM, 2000; Shojania et al., 2001; PMS, 2002; NQF, 2003; IHI, 2005; ISMP, 2005b; JCAHO, 2005a; NCCMERP, 2005b. recommend implementing computerized provider order entry (CPOE) and bar coding at the bedside, although the evidence supporting bar coding remains weak. Other specific interventions supported by multiple groups include involving clinical pharmacists in patient rounds, implementing and utilizing unit dosing, standardizing prescription writing and prescription rules and eliminating certain abbreviations, utilizing special written protocols for high-risk medications, and limiting as well as standardizing verbal medication orders. Additional general recommendations embraced by most organizations include adopting a systems-oriented approach to medication errors, creating a culture of safety, and improving medication error identification and reporting. For studies of interventions to reduce medication errors, inclusion criteria derived from AHRQ-sponsored analysis of patient safety practices were used (Shojania et al., 2001). Only studies with the following study design

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Preventing Medication Errors were included: (1) randomized controlled trial; (2) nonrandomized controlled trial; (3) observational studies with controls. The same grading scale was modified to indicate the strength of evidence supporting a particular intervention: greatest, high, medium, lower, limited. This approach results in an overly conservative assessment of the evidence supporting a particular procedure, and these limitations have been well characterized previously (Leape et al., 2002). Most of the recommendations have limited evidence to support their efficacy. The evidence appears strongest for recommendations to implement CPOE, include pharmacists in medication-intensive areas in the hospital, and use standardized written protocols for high-risk medications. Evaluation of Recommended Approaches Interventions to reduce medication errors can be divided into four categories: CPOE and decision-support systems, use of clinical pharmacists, automated medication dispensing systems, and a final category that includes all other proposed strategies. Computerized Provider Order Entry and Decision-Support Systems Ten studies evaluated CPOE and decision-support systems for medication error reduction. Two of these studies were randomized controlled trials, and the remaining eight used a before–after design. All ten studies demonstrated a statistically significant reduction in medication errors. Rates of medication errors were reduced by 13–86 percent, and rates of preventable adverse drug events (ADEs) by 17–62 percent. Two studies at Brigham and Women’s Hospital, Boston, Massachusetts, examined the impact of CPOE with clinical decision support on medications errors and ADEs. The first found that nonintercepted serious medication errors decreased by 55 percent, from 10.7 to 4.86 events per 1,000 patient-days (p = 0.01). Preventable ADEs declined by 17 percent, but this was not statistically significant (Bates et al., 1998). The second study, consisting of a baseline period followed by the implementation of CPOE with decision support and then three study periods, demonstrated significant reductions in all medication errors (excluding missed-dose errors) and nonintercepted serious medication errors (Bates et al., 1999). The non-missed-dose medication error rate fell 81 percent, from 142 per 1,000 patient-days in the baseline period to 26.6 per 1,000 patient days in period 3 (p <0.0001). The nonintercepted serious medication error rate declined 86 percent over the same time frame (p = 0.0003). However, the decline in ADEs/1,000 patient days from 14.7 to 9.6 was not statistically significant.

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Preventing Medication Errors Neither study was sufficiently powered to detect a difference in the preventable ADE rate. The remaining eight studies examined more focused aspects of the medication-use process. Another study at Brigham and Women’s Hospital studied the impact of the implementation of a range of clinical decision-support tools for improving physician prescribing practices. Following the implementation of computerized decision support, use of the recommended histamine2-blocker rose from 15.6 to 81.3 percent of orders (p <0.001); the standard deviation of drug doses decreased by 11 percent (p <0.001); the proportion of doses that exceeded the maximum decreased from 2.1 to 0.6 percent (p <0.001); use of the approved dosing frequency for ondansetron hydrochloride increased from 6 to 75 percent of orders (p <0.001); and use of subcutaneous heparin for thrombosis prophylaxis in patients on bed rest increased from 24 to 46 percent of eligible cases (p <0.001). At the Regenstrief Institute for Health Care, Indianapolis, Indiana, a study investigated the impact of computerized reminders on physician test-ordering behavior (Overhage et al., 1997). During a 6-month trial, reminders about corollary orders were presented to 48 intervention physicians and withheld from 41 control physicians. Intervention physicians executed the suggested corollary orders in 46.3 percent of instances when they received a reminder, compared with 21.9 percent among control physicians (p <0.0001). Two studies were carried out at LDS Hospital, Salt Lake City, Utah. The first compared a computerized antibiotic selection consultant with physician antibiotic selection (Evans et al., 1994). The antibiotic consultant suggested an antibiotic regimen to which all isolated pathogens were shown to be susceptible for 453 out of 482 culture results (94 percent), while physicians ordered an antibiotic regimen to which all isolated pathogens were susceptible for 369 out of 482 culture results (77 percent) (p <0.001). The second study found that computer-assisted decision support for ordering antibiotics in an intensive care unit (ICU) resulted in improved quality of care (Evans et al., 1998). During the intervention period, all 545 patients admitted were cared for with the aid of the anti-infectives management program. Measures of processes and outcomes were compared with those for the 1,136 patients admitted to the same unit during the 2 years before the intervention period. Use of the program led to significant reductions in orders for drugs to which the patients had reported allergies (35 versus 146 during the preintervention period, p <0.01), excess drug dosages (87 versus 405, p <0.01), and antibiotic-susceptibility mismatches (12 versus 206, p <0.01). There were also marked reductions in adverse events caused by anti-infective agents (4 versus 28, p <0.02). A study at Brigham and Women’s Hospital, Boston, Massachusetts, examined a computerized decision-support system for prescribing drugs

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Preventing Medication Errors that adjusted drug dose and frequency based on the patient’s renal insufficiency. The proportion of prescriptions deemed appropriate by dose increased from 54 to 67 percent after the intervention (p <0.001), and by frequency increased from 35 to 59 percent (p <0.001) (Chertow et al., 2001). The Section of General Internal Medicine, University of Illinois at Chicago, investigated the impact of computer alerts relating to the appropriate monitoring and use of digoxin (Galanter et al., 2004). Checking for unknown serum values rose after implementation from 6 to 19 percent for digoxin levels, 9 to 57 percent for potassium, and 12 to 40 percent for magnesium (p <0.01 for all comparisons). A study at the Division of General Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, examined the impact of a CPOE system with clinical decision support designed to adjust medication doses of psychotropic medications in geriatric patients (Peterson et al., 2005). The intervention increased prescription of the recommended daily dose from 19 to 29 percent (p <0.001), reduced the incidence of ten-fold dosing from 5 to 2.8 percent (p <0.001), and reduced the prescription of nonrecommended drugs from 10.8 to 7.6 percent of total orders (p <0.001). Patients in the intervention group had a lower in-hospital fall rate—0.28 falls per 100 patient-days as compared with 0.64 falls per 100 patient-days (p = 0.001). Researchers at the Mount Sinai Medical Center, New York, New York, investigated the impact of a CPOE system with clinical decision support linked to pharmacist and nurse feedback, designed to adjust medication doses in patients with renal insufficiency (Nash et al., 2005). The baseline rate of excessive dosing was 23.2 percent of administered medications requiring adjustment for renal insufficiency given to patients with renal impairment on the participating units. The rate fell to 17.3 percent with nurse feedback and 16.8 percent with pharmacist feedback in the participating units (p <0.05 for each, relative to baseline). The rates of excessive dosing for the rest of the hospital were largely unchanged over the same time periods. One recent critique (Berger and Kichak, 2004) of two key studies on the medication-related safety benefits of CPOE (Bates et al., 1998, 1999) suggested that while CPOE (with decision support) has the potential to deliver benefits, there was some question as to whether these benefits had been adequately demonstrated since the 1998 study did not show that the preventable ADE rate had been reduced. The problem with this argument is that the studies that have been conducted were powered to detect a difference not in the preventable ADE rate, but in the serious medication error rate; no adequately powered studies of the preventable ADE rate have been

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Preventing Medication Errors carried out. A number of subsequent studies have also found that CPOE can reduce medication error rates in the inpatient setting. It is clear as well that computer systems can introduce errors of their own (Ash et al., 2004; McDonald et al., 2004; Koppel et al., 2005), and may even worsen outcomes in some instances. A recent study that raises substantial concern observed an unexpected increase in mortality coincident with CPOE implementation (Han et al., 2005). Although this study had a number of methodological flaws, the increase in mortality was large, and the authors postulated that the increase may have been due to delays in care caused by policies related to the introduction of CPOE and the technology itself. The committee acknowledges that there can be unintended consequences if health care providers do not carefully plan and implement major clinical transformations such as CPOE (Phibbs et al., 2005). Successful implementation requires redesign of health care delivery processes (Levick, 2005) and continuous monitoring for problems during the implementation phase, followed by the rapid introduction of system fixes (Bates, 2005). Finally, high rates of ADEs may continue after the implementation of CPOE in the absence of decision support for drug selection, dosing, and monitoring (Nebeker et al., 2005). Role of the Clinical Pharmacist Three hospital-based studies evaluated the role of the clinical pharmacist. In two of these studies, a clinical pharmacist accompanied the medical team during daily rounds and was available throughout the day for consultation. One study, carried out in a medical ICU in a large urban teaching hospital, demonstrated a reduction in the preventable ADE rate of 66 percent, from 10.4 to 3.5 per 1,000 patient-days (p <0.001) (Leape et al., 1999). The pharmacist made 366 recommendations related to drug ordering, 362 of which (99 percent) were accepted by physicians. Another study, carried out in a general medicine unit, demonstrated a reduction of 78 percent in preventable ADEs, from 26.5 to 5.7 per 1,000 patient-days (Kucukarslan et al., 2003). There were 150 documented interventions recommended by the pharmacist during the rounding process, 147 (98 percent) of which were accepted by the team. A third study utilized a clinical pharmacist to review prescriptions for vancomycin to determine the appropriateness of use (Anglim et al., 1997). The proportion of inappropriate prescriptions written was reduced from 61 to 30 percent of orders (p <0.001). An additional study did not study pharmacists, but evaluated the impact on medication errors of nurses with special medication safety education (Greengold et al., 2003). In this study, the dedicated medication safety nurses had no effect.

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Preventing Medication Errors Automated Medication-Dispensing Systems Four studies evaluated automated medication-dispensing devices. In the only randomized trial, now more than 20 years old, a bedside automatic medication-dispensing machine was associated with a statistically significant reduction in medication error rate from 15.9 percent within the control group (a decentralized unit dose system) to 10.6 percent within the intervention group (Barker et al., 1984). In another study, implementation of an automated drug-dispensing system led to a reduction in medication errors, largely those related to time of administration, from 16.9 to 10.4 percent (p <0.001) (Borel and Rascati, 1995). This result is consistent with that of a later study demonstrating that the introduction of an automated medication-dispensing device led to an increase in the number of medications administered as scheduled from 59 to 77 percent of doses (p = 0.02) (Shirley, 1999). The remaining study reported on the introduction of automated medication-dispensing devices into a cardiovascular surgery unit and a cardiovascular ICU. Medication error rates decreased for patients in the surgical unit but increased for patients in the cardiothoracic unit; neither difference was statistically significant (Schwarz and Brodowy, 1995). The AHRQ-funded review of patient safety practices concluded that the evidence provided by the above studies does not support the use of automated dispensing devices to reduce medication errors (Shojania et al., 2001). Other Studies National disease registries have become an important mechanism for correcting both under- and overprescribing of medications in hospitals for certain important groups of patients—those with ST elevation and non–ST elevation acute coronary syndrome and acute heart failure (Ferguson et al., 2003; Peterson et al., 2004; Jha et al., 2005). A randomized control trial in a cardiac surgical ICU tested the efficacy of smart IV infusion pumps (incorporating an integrated decision-support system)—the intervention period. In the control period, the decision-support software was inactive. Although many errors were found that would not otherwise have been detected, the rates of serious medication errors in the control and intervention periods were not different (Rothschild et al., 2005). A randomized controlled trial evaluated a continuous quality improvement initiative designed to increase the use of preoperative beta-blockers in patients undergoing coronary artery bypass graft surgery (Ferguson et al., 2003). The intervention included a call to action to a physician leader at the study site; educational products; and periodic longitudinal, site-specific feed-

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Preventing Medication Errors back benchmarked on national averages. Over a 2.5-year period, the use of beta-blockers increased by 7.3 percent in the intervention sites compared with 3.6 percent in the control sites (p = 0.04). Two randomized controlled trials examined the impact of patient educational interventions on medication errors. In one study, geriatric patients either participated in a self-medication program or received standard care (Pereles et al., 1996). Participation in the self-medication program did not increase the proportion of patients who were able to self-medicate on discharge from the hospital. Adherence, however, was improved by the program. The self-medication group had made statistically significant fewer medication errors than the control group at 1-month follow-up. In the other trial, intervention patients received drug safety information and their medication list, and the control group received drug safety information only. There was a nonsignificant difference between intervention patients and controls in the ADE and close-call rates (Weingart et al., 2004). A final study evaluated whether medical interns exposed to an ICU schedule designed to minimize sleep deprivation might make fewer medication errors (Landrigan et al., 2004). The intervention group schedule was designed to eliminate extended work shifts and reduced the number of hours worked per week. The control group followed the traditional ICU call schedule. The interns allocated to the traditional call schedule made more serious medication errors than the intervention group (99.7 versus 82.5 per 1,000 patient-days, p = 0.03). The interns on the traditional schedule also made more than five times as many serious diagnostic errors (18.6 versus 3.3 per 1,000 patient-days, p <0.001). The results of this study suggest that limiting extended work shifts can reduce the medication error rate. PREVENTION STRATEGIES FOR NURSING HOME CARE Interventions to prevent medication errors in nursing home care fall into five categories: regulation, education and academic detailing, profiling and feedback, medication therapy management, and the use of technology. Regulation Much of the research relevant to medication safety in nursing homes has focused on documenting the overuse of psychotropic drugs prior to 1990 and evaluating the factors that influenced changes, and in some cases improved use of these drugs. Federal regulation of the use of psychotropic drugs in Medicaid- and Medicare-certified nursing homes became law in 1987 as the Nursing Home Reform Amendments of the Omnibus Budget Reconciliation Act (OBRA) of 1987 (P.L. 100-203), also known as OBRA-

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Preventing Medication Errors TABLE D-2 Recommendations for the Prevention of Medication Errors in Pediatric Care Recommended Practice Recommending Body Evidence Specific to Children Source of Supporting Evidence Computerized provider order entry PPAG, ISMP, AHA, AHRQ, IOM, NQF, MHA, NCCMERP, AAP/NICHQ None Adult data/ expert opinion Automated medication-dispensing devices PPAG, ISMP, AHA, AHRQ None Adult data/ expert opinion Pediatric presence with formulary management PPAG, ISMP, AHA, AAP/NICHQ None Expert opinion Appropriate and competent pharmacy personnel and environment PPAG, ISMP, AHA, NQF, NCCMERP, AAP/NICHQ None Expert opinion Pharmacist available on call when pharmacy is closed PPAG, ISMP, AHA, MHA None Expert opinion Policies on verbal orders PPAG, ISMP, AHA, NQF, NCCMERP, AAP/NICHQ, JCAHO None Expert opinion Clear and accurate labeling of medications PPAG, ISMP, AHA, NQF, NCCMERP None Expert opinion Quality improvement efforts with drug-use evaluation and medication error reporting and review PPAG, ISMP, AHA, MHA, NCCMERP, AAP/NICHQ None Expert opinion Access of health care workers to current clinical information and references PPAG, ISMP, AHA, IOM, MHA, NCCMERP, AAP/NICHQ None Expert opinion Emergency medication dosage calculation tools PPAG, ISMP None Expert opinion Accurate documentation of medication administration PPAG, ISMP, MHA, NCCMERP None Expert opinion Medication standardization and appropriate storage ISMP, AHA, IOM, NCCMERP, JCAHO None Expert opinion Training of all health care providers in appropriate medication prescribing, labeling, dispensing, monitoring, and administration PPAG, ISMP, IOM, NQF, MHA, NCCMERP, AAP/NICHQ, JCAHO None Expert opinion Patient education on medications ISMP, AHA, IOM, MHA, NCCMERP, AAP/NICHQ None Expert opinion

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Preventing Medication Errors Recommended Practice Recommending Body Evidence Specific to Children Source of Supporting Evidence Direct participation of pharmacists in clinical care AHRQ, IOM, NQF None Expert opinion Computer detection/alert systems for adverse drug events (ADEs) AHRQ None Adult studies Reduction of ADEs related to anticoagulants AHRQ None Adult studies Unit-dose drug distribution systems AHA, AHRQ, NQF, MHA None Adult studies/ expert opinion Special procedures and written protocols for high-alert medications AHA, IOM, NQF, MHA, JCAHO None Expert opinion Use of pharmaceutical software AHA, IOM None Expert opinion Pharmacy-based intravenous (IV) admixture systems MHA None Expert opinion Use of bar coding for medication administration MHA, NCCMERP None Expert opinion Standardized equipment (e.g., pumps, weight scales) AAP/NICHQ None Expert opinion Standardized measurement systems (kilograms) AAP/NICHQ None Expert opinion Standardized order sheets including areas for weight and allergies AAP/NICHQ None Expert opinion Team environment for review of orders among nurses, pharmacists, prescribers AAP/NICHQ None Expert opinion NOTE: AAP/NICHQ = American Academy of Pediatrics/National Initiative for Children’s Healthcare Quality (Berlin et al., 1998; Lannon et al., 2001; Gorman et al., 2003; NICHQ, 2005); AHA = American Hospital Association (AHA, 2002, 2005); AHRQ = Agency for Healthcare Research and Quality, report on Making Healthcare Safer (Shojania et al., 2001; AHRQ, 2005); IOM = Institute of Medicine (IOM, 2000); ISMP = Institute for Safe Medication Practices (ISMP, 2005b); JCAHO = Joint Commission on Accreditation of Healthcare Organizations (JCAHO, 2005a,b); MHA = Massachusetts Hospital Association/Massachusetts Coalition for the Prevention of Medical Errors (MCPME, 2005a,b); NCCMERP = National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP, 2005a,b); NQF = National Quality Forum (NQF, 2003, 2005); PPAG = Pediatric Pharmacy Advocacy Group (Levine, 2001; ISMP, 2002, 2005b).

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Preventing Medication Errors medications are taken on the advice of physicians. It is up to consumers to diagnose their problem properly, select the best medical product if it is necessary, read and understand the instructions for its use, take it properly, and know when it is time to terminate the treatment. Many OTC medication errors are due to misdosing or adverse drug–drug interactions. Patients need to understand that OTCs are drugs and, like prescription medications, have both therapeutic value and potential side effects. A key approach for reducing OTC medication errors is the Food and Drug Administration’s (FDA) labeling requirements, mentioned in Chapter 2, which provide information on active ingredients, what the drug is for, dosing levels, and warnings about use. The information requirements on the packaging of complementary and alternative medications are more limited. Patient self-education is the other major prevention strategy. Much information to improve patient awareness of OTC and complementary and alternative medications is currently available from the Internet, television, books, magazines, and newspapers. In addition, there are specialized packaging technologies designed to decrease the chances of misuse, such as tamper-resistant and childproof containers and blister packaging that numbers each pill to help the user remember whether the product was taken. Pillboxes divided by time of day and day of week are another low-technology solution. Still another approach is for the pharmacist to ask those picking up prescription drugs whether they are using any OTC or vitamin/mineral products and to advise them of any issues involved. For example, people taking blood thinners should be advised to speak to their doctor before taking vitamin E. The committee could find no study on the efficacy of any of the above strategies for preventing medication errors in the use of OTC and complementary and alternative medications. REFERENCES AHA (American Hospital Association). 2002. Pathways for Medication Safety. [Online]. Available: http://www.medpathways.info/medpathways/tools/tools.html [accessed December 5, 2005]. AHA. 2005. American Hospital Association. [Online]. Available: www.aha.org [accessed June 17, 2005]. Ahrens J. 2003. Combatting medication errors in home health. Caring 22(1):56–59. Ahrens J, Feldman PH, Frey D. 2002. Preventing medication errors in home care. Center for Home Care Policy and Research Policy Briefs 12:1–6. AHRQ (Agency for Healthcare Research and Quality). 2005. Agency for Healthcare Research and Quality. [Online]. Available: www.ahrq.gov [accessed June 17, 2005]. American Society of Health-System Pharmacists. 1996. Top-priority actions for preventing adverse drug events in hospitals. Recommendations of an expert panel. American Journal of Health-System Pharmacy 53(7):747–751.

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