Research

Risk models to improve safety of dispensing high-alert medications in community pharmacies Michael R. Cohen, Judy L. Smetzer, John E. Westphal, Sharon Conrow Comden, and Donna M. Horn Received September 30, 2010, and in revised form June 17, 2011. Accepted for publication July 22, 2011.

Abstract Objectives: To determine whether sociotechnical probabilistic risk assessment can create accurate approximations of detailed risk models that describe error pathways, estimate the incidence of preventable adverse drug events (PADEs) with highalert medications, rank the effectiveness of interventions, and provide a more informative picture of risk in the community pharmacy setting than is available currently. Design: Developmental study. Setting: 22 community pharmacies representing three U.S. regions. Participants: Model-building group: six pharmacists and three technicians. Model validation group: 11 pharmacists; staff at two pharmacies observed. Intervention: A model-building team built 10 event trees that estimated the incidence of PADEs for four high-alert medications: warfarin, fentanyl transdermal systems, oral methotrexate, and insulin analogs. Main outcome measures: Validation of event tree structure and incidence of defined PADEs with targeted medications. Results: PADEs with the highest incidence included dispensing the wrong dose/ strength of warfarin as a result of data entry error (1.83/1,000 prescriptions), dispensing warfarin to the wrong patient (1.22/1,000 prescriptions), and dispensing an inappropriate fentanyl system dose due to a prescribing error (7.30/10,000 prescriptions). PADEs with the lowest incidence included dispensing the wrong drug when filling a warfarin prescription (9.43/1 billion prescriptions). The largest quantifiable reductions in risk were provided by increasing patient counseling (27–68% reduction), conducting a second data entry verification process during product verification (50–87% reduction), computer alerts that can't be bypassed easily (up to 100% reduction), opening the bag at the point of sale (56% reduction), and use of barcoding technology (almost a 100,000% increase in risk if technology not used). Combining two or more interventions resulted in further overall reduction in risk. Conclusion: The risk models define thousands of ways process failures and behavioral elements combine to lead to PADEs. This level of detail is unavailable from any other source. Keywords: Risk assessment, high-alert medications, preventable adverse drug events, event trees. J Am Pharm Assoc. 2012;52:584–602. doi: 10.1331/JAPhA.2012.10145

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Michael R. Cohen, BSPharm, MS, FASHP, is President; and Judy L. Smetzer, RN, BSN, FISMP, is Vice President, Institute for Safe Medication Practices, Horsham, PA. John E. Westphal, BS, is a consultant; and Sharon Conrow Comden, BS, MPH, DrPH, is a consultant, Outcome Engenuity, LLC, Plano, TX. Donna M. Horn, BSPharm, is Director of Patient Safety for Community Pharmacy, Institute for Safe Medication Practices, Horsham, PA. Correspondence: Judy Smetzer, RN, BSN, FISMP, Institute for Safe Medication Practices, 200 Lakeside Dr., Suite 200, Horsham, PA 19044. Fax: 215-914-1492. E-mail: jsmetzer@ ismp.org Disclosure: The authors declare no conflicts of interest or financial interests in any product or service mentioned in this article, including grants, employment, gifts, stock holdings, or honoraria. Funding: Agency for Healthcare Research and Quality contract no. 1P20HS017107. Previous presentations: 10th International Probabilistic Safety Assessment & Management Conference, Seattle, WA, June 7–11, 2010, and Agency for Healthcare Research and Quality Annual Conference, Bethesda, MD, September 14, 2009.

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dverse drug events (ADEs), which are defined as injuries from drug therapy,1 are among the most common causes of harm during the delivery of health care.2 At least a quarter of these events are preventable.1,3,4 On an annual basis, up to 450,000 inpatients experience a preventable ADE (PADE).3–5 PADEs lead to about 4% (range 1.4–15.4%) of hospital admissions.6–12 Few prospective data detail the incidence of PADEs in ambulatory patients.12 Four retrospective studies that examined community pharmacy dispensing errors using similar definitions, detection methods, and expression of incidence rates reported a wide range of errors (from 1.7% to 24%).13–16 The lowest dispensing error rate (1.7%) translates to approximately four errors per 250 prescriptions per pharmacy per day2 or to

At a Glance Synopsis: A model-building team built 10 event trees that estimated the incidence of preventable adverse drug events (PADEs) for four high-alert medications and found that sociotechnical probabilistic risk assessment (ST-PRA) was able to define thousands of ways process failures and behavioral elements combine to lead to PADEs. PADEs with the highest incidence included dispensing the wrong dose/strength of warfarin as a result of data entry error (1.83/1,000 prescriptions) and dispensing warfarin to the wrong patient (1.22/1,000 prescriptions). The greatest quantifiable reductions in risk were provided by factors such as increasing patient counseling (27–68% reduction) and conducting a second data entry verification process during product verification (50–87% reduction). Analysis: The ST-PRA models created in this study were effective at identifying dispensing system vulnerabilities that were largely correctable before reaching patients given environmental, technological, system/process, and behavioral conditions that are within the reach of most community pharmacies and pharmacy staff. A sensitivity analysis identified that using automated dispensing and barcoding technology, conducting a second data entry verification process during final product verification, counseling patients more frequently and effectively, opening the bag at the point of sale to view all filled prescriptions, and other interventions will reduce prescribing and dispensing errors that reach patients by as much as 87%, or more when combining several interventions. The authors noted that the interventions discussed here are not meant to suggest and/or establish a standard of care for community pharmacies; instead, they are intended to represent future patient safety improvements.

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60 million errors during the dispensing of 4 billion prescriptions annually.2,17 Few studies have reported the frequency of harm caused by PADEs in the community pharmacy setting. Ghandi et al.12 found that 5% of ambulatory patients experienced a PADE with medications dispensed from community pharmacies. Gurwitz et al.18 identified that almost one-half of serious, life-threatening, or fatal ADEs related to medications dispensed from pharmacies were preventable. Several studies suggested that dosing errors occur frequently and have the highest rate of clinical significance among types of medication errors.19–22 An estimate in 2000 determined that hospital admissions caused by PADEs accounted for $121.5 billion or 70% of total costs of drug-related problems in the United States.23 The drugs associated with the most harmful PADEs in acute care settings were first coined “high-alert” medications by the Institute for Safe Medication Practices (ISMP) in 1998.24 High-alert medications carry a major risk of causing serious injuries or death to patients if misused. Errors with these drugs are not necessarily more common, but the consequences are devastating.25–27 Appendix 1 (electronic version of this article, available online at www.japha.org) notes the characteristics, medications, and types of errors involved in patient harm from PADEs in the ambulatory setting. Traditionally, health care systems have relied on root cause analysis (RCA) and failure mode and effects analysis (FMEA) to understand the risks involved in prescribing, dispensing, and administering medications.28–30 RCA and FMEA are the most basic types of risk analysis that focus largely on system and process errors.30–35 Both offer qualitative information about risk and error, but neither helps quantify the level of risk or model the dependencies and effects of combinations of failures.36 Sociotechnical probabilistic risk assessment (STPRA) is a prospective technique that advances the qualitative work of FMEA and RCA into a quantitative realm by linking process failures with estimates of human error and behavioral norms, yielding a more accurate picture of why and how often these failures affect patient outcomes.28,30,36,37 Online Appendix 2 summarizes the advantages of ST-PRA over FMEA and RCA. ST-PRA, which is derived from a probabilistic risk assessment (PRA) tool that originated in the mid-1970s to improve safety in nuclear power plants, allows all possible combinations of task or system failures to be considered in combination with one another.36–40 Although PRA is predominantly used to model mechanical systems, ST-PRA is especially suited for modeling human systems and is the more appropriate tool for health care.28,30,36,37 Although ST-PRA use in health care remains relatively new,28,30,36,37,41–44 a previous study using ST-PRA to model medication system risk in long-term care strongly suggests that application of this process to high-alert medications dispensed from community pharmacies will be successful in assessing risks and gauging the impact of system and behavioral changes on these risks.30,36

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Objectives The objectives of this study were to (1) identify a list of highalert medications dispensed from community pharmacies; (2) determine whether ST-PRA can create accurate approximations of detailed risk models that describe error pathways, estimate the incidence of PADEs involving high-alert medications dispensed in community pharmacies, and rank the effectiveness of interventions to prevent PADEs; and (3) determine whether ST-PRA provides a more informative picture of risk in the community pharmacy setting than currently available through typical sources, such as retrospective event reporting, RCA, and FMEA.

Methods The Temple University Office for Human Subjects Protections Institutional Review Board approved the study before initiation. During 2007–08, ISMP partnered with several community pharmacy organizations, from which a purposive sample of 22 pharmacies from three regions was selected to ensure diversity in setting, prescription volume, staffing, hours of operation, and population served. Model-building sample The modeling team consisted of two trained ST-PRA facilitators from Outcome Engenuity, LLC, two clinical research staff from ISMP, and six pharmacists and three pharmacy technicians from nine of the sample pharmacies in the same central southwest region of the United States. The participating pharmacies served urban, suburban, and small community areas and were diverse regarding prescription volumes, hours of operation, and access to drive-through services. Pharmacy staffing patterns ranged from a single pharmacist on duty to multiple pharmacists and technicians on duty. The participants had diverse ethnic backgrounds, 5 to 18 years of experience (median 10), and included both genders. Model validation sample Pharmacists who worked in 11 community pharmacies in the New England and mid-Atlantic regions participated in structured interviews to validate the risk models. The pharmacies and participants were diverse in regards to experience, practice settings, daily prescription volume, and gender/ethnic backgrounds. Observations were also conducted at two pharmacies selected from a convenience sample. Identifying high-alert medications A list of high-alert medications dispensed from community pharmacies was compiled using qualitative methods, including analysis of data about PADEs from the following sources: ISMP National Medication Errors Reporting Program,45 the Pennsylvania Patient Safety Reporting System,46 the Food and Drug Administration MedWatch database,47 databases from participating pharmacies, community pharmacy survey data,48 public litigation data,49 and literature review.7,9–18,49,50

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Steps in the modeling process Recruit the modeling team. A voluntary modeling team was recruited using a noncoercive protocol; all members were from different pharmacies. Build a process and control system map. A process and control map of the pharmacy dispensing process was created. Observations in pharmacies and discussion with pharmacy staff verified that any differences in the workflow among the sample pharmacies were minimal, allowing agreement upon one standard process map. The map is a visual aid that clearly shows how work inputs, outputs, and tasks are linked and shows the embedded control systems that aid in the prevention and detection of errors. Mapping occurred at two levels. First, using an iterative progression, the dispensing process steps and decision points were identified and linked according to the current workflow. Then, control systems were identified and added to the map. Active control systems are deliberate steps in the process that specifically help manage the risk of errors, such as data entry verification of prescriptions entered into the computer. Passive controls are features inherent in the system that might help control risks but are not specifically set up for that purpose, such as differences in tablet appearance that may alert a pharmacist to an incorrect medication.28 Identify failure modes. An abbreviated FMEA process was used as a hazard identification technique to describe possible failure points (e.g., errors, at-risk behaviors, equipment failures) during the dispensing process and for each targeted high-alert medication. Online Appendix 3 shows small cross sections of the FMEAs related to warfarin and fentanyl transdermal systems. The FMEAs were used to determine PADEs to be modeled for the targeted drugs. Six PADEs for warfarin and one PADE each for fentanyl systems, methotrexate, and insulin analogs were selected (Table 1). Build the risk models. During February to April 2007, the modeling team met six times and created 10 event trees for the PADEs. An event tree is a graphical quantitative risk model that represents the complex relationships among process steps, organizational culture, human errors, equipment failure, behavioral norms, and undesirable outcomes.28,30,36,37 The event trees decompose the dispensing system as a whole into subsystems and components. The process and control map was used to guide this step. Each individual event tree defined the event sequences that could lead to the specific PADE of interest, based on what was currently known about the dispensing process and the behavior of systems and pharmacy staff under given conditions. The risk model building process starts with an initiating error that could lead to a PADE. Each initiating error then was followed through the dispensing process steps, which were called “basic events” in the event trees. The basic events represent (1) exposure rates, or how often certain activities occur; (2) fundamental failures, such as human error, at-risk behavior,1 or equipment failure rates; or (3) capture opportunities when errors can be detected and corrected (online Appendix 4). These basic events flowed through the branches of the event tree, linking them together (Figure 1) Journal of the American Pharmacists Association

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Prescribing error: Wrong dose/strength tablets prescribed. Dispensing errors: (1) Wrong warfarin dose/ strength selected when manually filling a warfarin prescription. (2) Wrong warfarin dose/strength selected when filling an automated dispensing cabinet with warfarin. Dispensing error: Wrong dose/strength selected or entered during data entry of a warfarin prescription. Prescribing error: Warfarin prescription included directions to take the drug more often than daily.

Wrong dose/strength of warfarin dispensed Wrong dose/strength of warfarin dispensed

Rationale for selection Drug: Warfarin is a commonly prescribed oral medication (consistently among the top 200 drugs dispensed each year)72,73 that exposes patients to bleeding or thrombosis (subtherapeutic doses) if used in error. Initiating errors: Selected to represent a variety of prescribing and dispensing errors associated with wrong patient, wrong drug, wrong dose, and wrong directions; three causes of wrong dose errors selected because dosing errors represent the greatest risk to patients.19–21

Dispensing error: Warfarin prescription entered into the wrong patient’s drug profile. Dispensing errors: (1) Warfarin vial placed in a bag containing another patient’s medications. (2) Wrong patient’s medication(s) selected from the will-call area at the point of sale. Prescribing error: Oral methotrexate for nononcoDrug: Methotrexate is an oral antineoplastic agent also commonly Methotrexate dislogic use prescribed with directions to take the drug used in less frequent dose intervals to treat other immune-modulated pensed with directions to take daily daily. diseases (e.g., rheumatoid arthritis, psoriasis). Initiating error: Selected because daily doses that exceeded 5 days have been fatal.63,64 Incorrect or inappro- Prescribing error: Incorrect dose or inappropriate Drug: Fentanyl transdermal system of delivering opioid pain medicapriate dose of fentan- dose prescribed for a patient based on opioid toler- tion exposes patients to over sedation, respiratory depression and yl patches dispensed ance and type/duration of pain. arrest. Initiating error: Selected because fatalities have happened to a patient repeatedly after prescribing doses too high for opioid-naive patients or when using the drug to treat acute, not chronic, pain.80,81 Wrong insulin analog Dispensing error: Wrong insulin analog selected Drug: Insulin is a commonly prescribed injectable drug used to treat dispensed to a patient from the screen during data entry of an insulin pre- type 1 diabetes, a prevalent chronic illness that affects 700,000 Ameriscription. cans.82 It is one of the most common chronic diseases in children and adolescents.83 Initiating error: Selected due to frequency of reported mixups between insulin products with look-alike names and the serious adverse effects of those mixups.84,85

Wrong dose/strength of warfarin dispensed Warfarin dispensed with wrong directions for use Warfarin dispensed to the wrong patient Warfarin dispensed to the wrong patient

Initiating error(s) Dispensing errors: (1) Wrong drug selected when manually filling a warfarin prescription. (2) Wrong drug selected when filling an automated dispensing cabinet with warfarin.

Selected PADE Wrong drug dispensed

Abbreviation used: PADE, preventable adverse drug event.

Insulin analogs

Fentanyl patches

Methotrexate

High-alert medication Warfarin

Table 1.   Selected PADEs for warfarin, methotrexate, fentanyl systems, and insulin analogs

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Event trees are particularly well suited for displaying the order of events and the dependencies between events, such as when the failure of subsystem B may depend on the status of subsystem A.51 To cite a simplified example, subsystem F fails (e.g., patient receives wrong medication) given that the initiating event has happened (e.g., pharmacy technician selected Top level event

wrong product when filling prescription) and subsystem A has succeeded (e.g., pharmacist checked final prescription), subsystem B has failed (e.g., pharmacist did not capture error during product verification), subsystem C has succeeded (e.g., patient was counseled when picking up prescription), subsystem D has failed (e.g., prescription vial not opened to view tablets

Medication dispensed to wrong customer at the point of sale Gate 1

And gate

Q: 0.00138

Medication given to wrong customer

Wrong customer not detected at the point of sale

Gate 2

Gate 3

Q: 0.0034

Or gate

And gate

Q: 0.405

Wrong customer’s medications selected by pharmacy at the point of sale when dispensing medication

Medication was placed in wrong customer’s bag

Pharmacy staff do not detect identification error at point of sale

Customer does not catch identification error at point of sale

Event 1

Event 2

Gate 6

Event 3

Q: 0.0004

Q: 0.45

Q: 0.003

Initiating errors

Or gate

Identification error not caught when following customer identification process

Q: 0.9

Identification error not caught when customer identification process not followed

Gate 4

Q: 0.0005

Basic event

Gate 5

And gate

And gate

Q: 0.45

Exposure rate for following customer identification process

Pharmacy staff fail to detect the error when following customer identifcation process

Exposure rate for not following customer identification process

Pharmacy staff fail to detect the error when customer identifcation process does not occur

Event 4

Event 5

Event 6

Event 7

Q: 0.5

Q: 0.001

Q: 0.5

Q: 0.9

Exposure rate

Basic event

Exposure rate

Basic event

Figure 1.  Illustration of fault tree for dispensing medication to wrong customer Abbreviations used: PADE, preventable adverse drug event. Example of a small section of a fault tree associated with dispensing a prescription to wrong customer (e.g., patient, family member, friend, caregiver). Events 1 and 2 represent the initiating errors for one pathway leading to the PADE (top-level event in gate 1) of dispensing a medication to the wrong customer. The probability of selecting the wrong customer’s medications (event 1) was estimated to occur with 3 of 1,000 (0.003) prescriptions. The probability of placing the medication in the wrong patient’s bag (event 2) was estimated to occur with 4 of 10,000 (0.0004) prescriptions. These initiating errors were combined with an “or” gate (gate 2), meaning that one or the other must happen for the medication to be given to the wrong customer. Reading from the bottom of the tree, from left to right, events 4 and 6 represent exposure rates for adherence to the patient identification process. In the example, the probabilities were set at 0.5 for each, meaning that 50% of the time, the patient identification process is followed and 50% of the time it is not followed. Events 5 and 7 describe how often pharmacy staff fail to notice that the wrong patient’s medications are in the bag. When following the identification process (event 5), the probability of failing to notice the error was estimated to occur in 1 of 1,000 (0.001) prescriptions. But when the patient identification process is not followed (event 7), the probability of failing to notice the error was estimated to occur in 9 of 10 (0.9) prescriptions. Events 4 and 5 and events 6 and 7 are connected with “and” gates (gates 4 and 5) because both of the basic events below them must occur for the gates to be true. The fault tree software calculated the combined effects of how often the identification process is followed (events 4 and 6) and the estimated rate of failing to detect the error (events 5 and 7). Gates 4 and 5 are connected with an “or” gate (gate 6) because the error was not detected either when following the patient identification process or not following the identification process. Again, the fault tree calculated the combined effects of gates 4 and 5 to arrive at the probability expressed in gate 6. Event 3 to the right of gate 6 shows the probability that the customer will fail to catch the error at the point of sale: 9 of 10 (0.9) opportunities. Pharmacy staff inability to capture the error (gate 6) was then combined with the customer’s inability to capture the error (event 3) through an “and” gate (gate 3) because both failures need to happen for the error to continue through the dispensing process and reach the patient. Gate 3, which expresses the combined effects of the two initiating errors, and gate 4, which expresses the combined effects of inability by pharmacy staff and customers to capture the error, are then combined with an “and” gate to reach the top level event (gate 1). In this example, for illustrative purposes only, the medication dispensed for the wrong patient at the point of sale is estimated to occur in 1.4 of 1,000 (0.00138) prescriptions.

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to aid in error detection), and subsystem E has failed (e.g., patient does not detect error at point of sale). The event trees went through multiple iterations until the modeling team was satisfied that they accurately captured the components of the dispensing process and dependencies among the different tasks.

Quantify event rates. The modeling team quantified the probability of failure or frequency of occurrence for each basic event in the event trees. The data used to support the quantification process came from documented component-specific information (e.g., rates of barcode scanning overrides for a particular drug), generic reference points (e.g., well-established

Table 2.   Human error probabilities Description of error probabilities High probability of error Unfamiliar task performed at speed with no idea of likely consequences86 Failed task involving high stress levels54 Inspection/verification of tasks with moderate stress87,86 Failed complex task requiring high level of comprehension and skill86 Failed task involving complex math computation88 Failed task conducted in the first 30 minutes of an emergency54 Failure to detect an error after it has happened89 Fairly simple task performed rapidly or given scant attention86 Moderate probability of error Misidentify/misdiagnose given like symptoms/appearance86 Failure to select ambiguously labeled control/package89 Failure to perform a check correctly90 Wrong conclusion drawn with competing/unclear information86 Failed execution of maintenance/repair86 Failed task with cognitive or task complexity86 Failure to act correctly after the first few hours in a high-stress situation54 Symptoms noticed, but wrong interpretation86 Failed task related to values/units/scales/indicators86 Failed task related to selection of items from among groups of items88 Failed routine, highly practiced, rapid task, involving a relatively low level of skill86 General mental slip without knowledge deficit91 Failed task related to known hazards/damage86 Failed communication among workers91 Failed task involving both diagnosis and action86 Failed diagnosis task86 Error in a routine operation where care is required54 Set a switch in wrong position89 Low probability of error Procedural omission86 Errors during read-back91 Counting/volume errors88 Selection of the wrong control/package (well labeled)89 Operate spring-loaded switch until proper position reached89 Selection of the wrong switch/package (dissimilar in shape/appearance)54 Lowest limits of human error Completely familiar, well-designed, highly practiced, routine task occurring several times per hour, performed to highest possible standards by a highly motivated, highly trained, and experienced person, totally aware of implications of failure, with time to correct potential error but without the benefit of important job aids86 Human-performance limit: single person working alone54 Responds correctly to system command when there is an augmented or automated supervisory system providing accurate interpretation of system state86 Human performance limit: team of people performing a well-designed task54 Journal of the American Pharmacists Association

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Error probability

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0.5 0.3 0.2 0.15 0.15 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.006 0.005 0.004 0.003 0.003 0.001

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equipment failure rates that could be reasonably assumed for pharmacy dispensing equipment), and expert opinion. The failure rate estimates needed as inputs in the event trees were obtained using Bayesian statistical methods, which work directly with estimated probabilities, rather than classical statistical methods, which work primarily with counts of data. Judgment and expert opinions are required with all PRAs and ST-PRAs because the available data about components of the systems are not of the precise form required for use of classical statistical methods.52 Most health care practitioners do not have actual rate data for the underlying basic events.2 At best, data collection systems only capture the end result, with the rate of intermediate failures relatively unknown.31 Failures, at-risk behaviors, and adverse events are so underreported in health care that using any data sources relying on reporting systems is suspect. In addition, PADEs can be masked by the patient's illness and thereby underestimated in occurrence data.2 Thus, some components of the event trees were developed purely through Bayesian methods, which provided a formal and rigorous way of combining expert judgments with observed data to obtain a probability. These probabilities then were propagated in the ST-PRA models to express the likelihood that a particular event would happen and the conditional uncertainty associated with that event.28,31,51,53 The substantive accuracy of the ST-PRA depends on how well the assessors know the problem under consideration. Thus, the modeling team was led by experts in human factors, probability theory, ST-PRA modeling, and medication safety. The pharmacists and technicians possessed deep domain knowledge of the processes under assessment. Internal pharmacy operational data verified the team's estimates of exposure rates (e.g., how often technicians enter prescriptions into the computer, how often prescriptions are received via fax, percent of a specific drug filled via automation). Pharmacists and technicians relied on their work experiences regarding frequencies of at-risk behaviors. Evidence shows that expert opinion–based probabilities are biased toward the low values of failure rates.52 Therefore, team facilitators anchored the group estimates of human error rates on data reported in the literature, setting lower- and upper-bound human error probability limits as reference points for specific conditions. Table 2 summarizes the human error probabilities that helped inform and verify team estimates. Numerous sophisticated techniques have been used since the early 1980s to estimate probabilities of human error.54 Although the formulas and tables for estimating human error probabilities vary from technique to technique, each factors in error type and performance-shaping factors (PSFs) to make judgments about error rates. Examples of common PSFs can be found in online Appendix 5. PSFs have a positive or negative effect on performance. For example, staff training can influence performance either positively (e.g., when training emphasizes the appropriate learned responses) or negatively (e.g., when training is absent). The modeling team referenced the FMEAs to uncover the most relevant PSFs before making estimates. 590 • JAPhA • 5 2 : 5 • S e p / O c t 2012

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In very general terms, given a human performance limit of 0.0001 (10−4 or 1/10,000) for a single worker operating in absolutely ideal conditions,54 the modeling team often started with an error rate of 0.001 (10−3 or 1/1,000) to account for the negative influence of a single PSF such as time constraints. Identification of additional PSFs (e.g., illegible prescriptions, look-alike product names, complex tasks, minimal worker training) was part of the group process. The number of PSFs and their degree of influence helped the team adjust its estimates upward or downward through an iterative process before deciding on a final probability. The team quickly gained comfort in the task of estimating error and at-risk behavior probabilities, which is typical of ST-PRA modeling teams.30,36,37 Experience indicates that these team estimates are more accepted than rates derived from event data and are often more accurate than rates predicted by senior management.36 More information on ST-PRA and the risk modeling process are available in Marx and Slonim36 and Comden et al.30 Model validation process Observations were conducted at two pharmacies to validate the event trees’ representation of the dispensing process and confirm the presence or absence of visible process risks (e.g., infrequent patient counseling) and PSFs (e.g., look-alike products next to each other). The observations also served to understand the relationship and dependencies among the various components of the dispensing systems and to validate that the structure of the event trees accurately represented the “as is” dispensing process at the participating pharmacies. A survey instrument about exposure rates, capture opportunities, at-risk behaviors, and failures most predictive of the PADEs was constructed and tested for interviewing pharmacists who did not participate in the modeling sessions. Probabilities estimated by the modeling group were not shared with the validation group. Well-constructed published studies of drug mishaps were examined to provide, where possible, evidence to support the probability estimates derived for the PADEs and initiating errors in the event trees. Quantifying the impact of risk-reduction interventions (sensitivity analysis) Event tree software55 calculated combinations of failures and the total combined probability of occurrence of each PADE. All unique combinations that could lead to PADEs were identified and ranked, producing cut sets or a “risk portfolio” for each event tree.30 The portfolios defined which components of the dispensing system were truly important to risk in that they contributed most frequently among all of the different sequences of events that could lead to PADEs. The portfolios then were used to identify the best interventions to reduce the probability of errors and at-risk behaviors or to change the tree's structure by building into the process new opportunities for capturing errors. After the interventions were identified, the event trees were updated to test and quantify the impact of each strategy.

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Results

Community pharmacy high-alert medications Table 3 lists the drugs that were identified as high-alert medications dispensed from community pharmacies. Warfarin, fentanyl systems, oral methotrexate, and insulin analogs were selected for ST-PRA modeling. Examples of risk factors with these targeted drugs can be found in online Appendix 6. Validation of event trees Based on observations and survey findings, 2 of 52 probability estimates associated with at-risk behaviors were adjusted because of minor differences between modeling team and validation group estimates. No changes were made to 306 exposure rates or 211 failure rates, as modeling team and validation group estimates were very similar. No changes occurred as a result of comparison with error rates in published studies. No changes were made to the structure of the event trees, which were determined to be accurate. Risk of PADEs reaching patients This study produced 10 event trees for PADEs associated with warfarin, fentanyl systems, oral methotrexate, and insulin analogs. These 10 event trees produced more than 200,000 failure pathways that could lead to PADEs. Table 4 shows the estimated rate of PADEs reaching patients for each event tree. These rates include all errors that reach patients after they leave the pharmacy counter, even if patients discover the error after leaving the pharmacy and do not take or use any of the erroneous medications. PADEs with the highest incidence included dispensing the wrong dose/strength of warfarin because of a data entry error (1.83/1,000 prescriptions), dispensing

warfarin to the wrong patient (1.22/1,000 prescriptions), and dispensing an inappropriate fentanyl system dose because of a prescribing error (7.30/10,000 prescriptions). PADEs with the highest incidence were associated with single-pathway failures, meaning that no key opportunities were available to capture the error from the time it was made until it reached the patient. PADEs with the lowest incidence included dispensing the wrong drug when filling a warfarin prescription (9.43/1 billion prescriptions) and dispensing the wrong dose when filling a warfarin prescription (9.25/10 million prescriptions). PADEs with the lowest incidence were associated with consistent use of barcode scanning technology. A sensitivity analysis conducted to evaluate the impact of selected interventions (Table 5) showed that the largest quantifiable reductions in risk were provided by (1) consistently using barcoding technology (up to 100% reduction), (2) building computer alerts that can't be bypassed easily (up to 100% reduction), (3) conducting a second data entry verification process during product verification (50–87% reduction), (4) increasing patient counseling (27–68% reduction), (5) opening the bag at the point of sale (56% reduction), and using tall man letters to distinguish insulin products with similar names (50% reduction). Combining two or more interventions resulted in further overall reduction in risk. Further description of the analyses of several PADEs follows. Wrong warfarin dose/strength dispensed because of data entry error Event tree analysis: High vulnerability of data entry errors. Initially entering a wrong dose or strength of warfarin into the patient's profile during data entry was estimated to occur

Table 3.   High-alert medications in community pharmacy Drug class/category Antiretroviral agents Chemotherapy, oral (exclusion: hormonal agents) Hypoglycemic agents, oral Immunosuppressant agents Insulin Opioids, all formulations Pregnancy Category X drugs Pediatric liquid medications that require measurement Individual drugs Carbamazepine Chloral hydrate liquid (for sedation of children) Heparin (unfractionated and low molecular weight) Metformin Methotrexate (nononcologic use) Midazolam liquid (for sedation of children) Propylthiouracil Warfarin Journal of the American Pharmacists Association

Examples Abacavir, atazanavir, diaveridine, lamivudine, ritonavir, zidovudine. Combination products such as Combivir, Atripla, Epzicom, Kaletra Busulfan, chlorambucil, cyclophosphamide, lomustine, melphalan, mercaptopurine methotrexate, procarbazine, temozolomide Chlorpropamide, glipizide, glyburide, repaglinide Azathioprine, cyclosporine, daclizumab, mycophenolate, sirolimus, tacrolimus NPH/regular, aspart, detemir, glargine, glulisine, lispro Butorphanol, fentanyl, hydromorphone, meperidine, methadone, morphine, opium tincture, oxycodone Atorvastatin, bosentan, estazolam, isotretinoin, simvastatin, temazepam

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Table 4.   Probabilities of PADEs for warfarin, fentanyl systems, methotrexate, and insulin analogs Initiating error rate per 1,000 prescriptions

Medication, PADE Warfarina Prescribing error: wrong dose 10 Prescribing error: wrong direc2 tions Data entry error: wrong dose 100 Filling error: wrong drug Automated dispensing, 0.1; manual dispensing, 1; combined rate, 1 Filling error: wrong dose Automated dispensing, 0.1; manual dispensing, 100; combined rate, 100 Fentanyl transdermal patchesb Prescribing error: wrong dose 1 Methotrexatec Prescribing error: wrong direc1 tions Insulin analogsd Data entry error: wrong drug 10 All prescription medicationse Data entry error: wrong patient 5 Point-of-sale error: wrong Select wrong patient’s patient bag, 3; place in wrong patient’s bag, 0.4; combined rate, 3.4

Capture before reaching patients %

Rate of PADEs reaching patients per 1,000 prescriptions

94.3 99.9

0.569 (5.69/10,000) 0.0001 (1.34/10 million)

98.2 99.9

1.83 (1.83/1,000) 0.000009 (9.43/1 billion)

99.9

0.0009 (9.25/10 million)

27.0

0.730 (7.30/10,000)

99.9

0.0009 (9.64/10 million)

96.9

0.306 (3.06/10,000)

99.0 64

0.052 (5.15/100,000) 1.22 (1.22/1,000)

No. PADEs reaching patients annually among all U.S. community pharmacies (n = 56,000) 15,022 4 48,312 0.25 (once every 4 years)

24

3,431 4

6,426 197,849 4,641,856

Abbreviation used: PADE, preventable adverse drug event. a Annual prescription volume for all U.S. community pharmacies: 26,400,000 (2007).72–74 b Annual prescription volume for all U.S. community pharmacies: 4,700,000 (2007).72–74 c Annual prescription volume for all U.S. community pharmacies: 4,400,000 (2007).72–74 d Annual prescription volume for all U.S. community pharmacies: 21,000,000 (2007).72–74 e Annual prescription volume for all U.S. community pharmacies: 3,804,800,000 (2007).72–74

with 1 in 10 warfarin prescriptions. Of these, 1.83 data entry errors per 1,000 prescriptions reached patients. The dispensing system is vulnerable to this type of data entry error because nine different strengths of warfarin tablets exist from which to choose. These errors are rarely corrected if data entry verification and patient counseling do not occur. During data entry, pharmacy staff have a greater chance of detecting the error if the patient had previous warfarin prescriptions filled at that pharmacy. But failure to detect the error is high (75%) given a 90% probability that these patients will have multiple strengths of warfarin in their drug profile history. An independent data entry verification process by a pharmacist who has not entered the prescription was estimated to capture up to 99% of errors if an out-of-range dose alert occurs and is not bypassed. However, dose alerts are not likely to occur if the wrong strength tablets are selected during data entry. Duplicate therapy alerts occur during data entry with about 80% of warfarin prescriptions but are not reliable as a means of detecting a data entry error. Patient counseling was estimated to occur with 30% of patients picking up warfarin 592 • JAPhA • 5 2 : 5 • S e p / O c t 2012

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prescriptions. If the prescription bottle is opened during counseling, a patient who knows what color tablets to expect has a 99% chance of capturing the data entry error. However, the bottle is only opened about 30% of the time. Sensitivity analysis: Impact of data entry verification and patient counseling. We determined the impact of four interventions on the incidence of dispensing the wrong warfarin dose because of a data entry error: (1) reducing the incidence of a skipped, rushed, or inattentive data entry verification process by 50%, (2) increasing patient counseling from 30% to 80%, (3) more frequent (90%) independent verification by another pharmacist of prescriptions entered by pharmacists, and (4) the addition of a second data entry verification process during the product verification step. The most effective interventions involved the second data entry verification process and patient counseling. Increasing patient counseling to 80% resulted in a 67% reduction in dispensing the wrong warfarin dose because of data entry error; errors that reached patients decreased from 1.83 to 0.6 per 1,000 prescriptions. Conducting a second data entry verification process during product verification by comparing Journal of the American Pharmacists Association

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Table 5.   Sensitivity analysis of selected interventions to reduce PADEs

Evaluated interventionsa PADE: Medication dispensed to the wrong patient due to a bagging error or bag selection error at the point of sale A: Open the bag at the point-of-sale to view all filled prescriptions B: Increase adherence with following a patient identification process from 50% to 80% C: Increase patient counseling from 30% to 50% D: Reduce at-risk behavior of working on more than one patient’s medications during product verification and bagging (which lowers the bagging error rate from 0.4 to 0.1 per 1,000 prescriptions) Action A and action B Action A and action B and action C Action A and action B and action C and action D PADE: Medication dispensed to the wrong patient caused by entering the prescription into the wrong profile A: Reduce at-risk behavior of conducting inattentive data entry verification from 1 in 10 to 5 in 100 prescriptions (requires changes in the system/ environment to support a consistent, cognitive checking process) B: Increase patient counseling from 30% to 50% C: Increase the frequency of an independent double-check for data entry verification when a pharmacist enters prescriptions from 50% to 90% D: Conduct a second redundant data entry verification during the final product verification step E: Reduce the incidence of entering prescriptions into the wrong patient profile from 5 to 1 per 1,000 prescriptions by requiring entry of two unique patient identifiers (name, birth date) in the patient profile Action A and action B Action A and action B and action C Action A and action B and action C and action D Action A and action B and action C and action E Action A and action B and action C and action D and action E PADE: Wrong or inappropriate dose of fentanyl patches dispensed due to a prescribing error A: Conduct an intake history of opioids when receiving a prescription for fentanyl patches; pharmacist review history before data entry (which results in capture of 40% of prescribing errors) B: Increase patient counseling from 10% to 80%, and increase the ability to recognize inappropriate doses from 10% to 80% by reviewing the patient’s opioid history during counseling session Action A and action B PADE: Methotrexate dispensed with directions to take daily instead of weekly due to a prescribing error A: Include a diagnosis or indication for use on the prescription B: Set dose alert as a hard stop that does not allow the entry of methotrexate prescriptions with daily dosing for more than 1 consecutive week C: Eliminate computer warning about daily dosing of methotrexate Action A and action B PADE: Wrong insulin analog dispensed due to selecting the wrong drug during data entry

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Errors before action, per 1,000 prescriptions

Errors after action, per 1,000 prescriptions

Decrease in risk (increase in risk) %

1.22 (1.22/1,000) 0.534

56

0.804 0.889

34 27

1.11 0.233 0.169 0.154

9 81 86 87

0.034 0.037

35 29

0.024

17

0.007

87

0.010 0.024 0.020 0.002 0.004 0.0004

81 54 62 96 92 99

0.439

40

0.263 0.159

64 78

0.0007

22

0.00000001 (1/100 billion) 0.006 0.00000001 (1/100 billion)

100 (522) 100

0.052 (5.15/100,000)

0.730 (7.30/10,000)

0.0009 (9.64/10 million)

0.306 (3.06/10,000)

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Table 5.   Sensitivity analysis of selected interventions to reduce PADEs Table 5  continued A: Reduce the frequency of misreading prescriptions for insulin products with similar names by increasing electronic prescriptions by 20% (lowers initiating error rate from 10 to 8 per 1,000 prescriptions) 0.245 B: Reduce the rate of selecting the wrong insulin product during order entry by using tall man letters to distinguish products with similar names (lowers initiating error rate from 10 to 5 per 1,000 errors) 0.153 C: Increase frequency of patient counseling from 30% to 80% 0.100 D: Conduct a second redundant data entry verification during the final product verification step 0.153 Action A and action B and action C 0.028 Action A and action B and action C and action D 0.014 PADE: Wrong drug or dose dispensed due to a selection error while (1) Wrong drug, filling a prescription for warfarin 0.000009 (9.43/1 billion); (2) wrong dose, 0.0009 (9.25/10 million) A: Eliminate barcoding technology during the dispensing process (1) 0.009 (9/1 million), (2) 0.900 (9/ 10,000) B: Use a cheat sheet to scan a barcode for warfarin 30% of time rather (1) 0.025 (2.5/100,000, (2) than scanning the bar code on the stock bottle/carton 0.200 (2/10,000) C: No pill image on the product verification screen (and label) (1) 0.00004 (4 /100 million), (2) 0.004 (4/1 million) D: Increase the automated filling of warfarin prescriptions from 20% to (1) 0.000007 (7/1 billion), 50% (2) 0.0006 (6/10 million) E: Increase the frequency of patient counseling from 30% to 80% (1) 0.000003 (3/1 billion, (2) 0.0003 (3/10 million) Action A and action C (1) 0.042 (4.2/100 thousand), (2) 4.20 (4.2/1,000) Action D and action E (1) 0.000002 (2/1 billion) (2) 0.0002 (2/10 million) PADE: Wrong dose/strength of warfarin tablets dispensed due to a pre0.569 scribing error (5.69/10,000) Increase patient counseling from 30% to 80% 0.274 PADE: Wrong warfarin dose dispensed due to a data entry error 1.83 (1.83/1,000) A: Reduce at-risk behavior of conducting inattentive data entry verification from 1 in 10 to 5 in 100 prescriptions (requires changes in the system/ environment to support a consistent, cognitive checking process) 1.19 B: Increase patient counseling from 30% to 80% 0.600 C: Increase the frequency of an independent double-check for data entry verification when a pharmacist enters prescriptions from 50% to 90% 0.865 D: Conduct a second redundant data entry verification during the final product verification step 0.366 Action A and action B 0.393 Action A and action B and action C 0.283 Action A and action B and action C and action D 0.174 PADE: Warfarin prescription dispensed with the wrong directions due 0.0001 (1.34/10 to a prescribing error million) Make data entry for more frequent than daily dosing of warfarin result in an alert with a hard stop 0.000000001 (1/1 trillion) Increase patient counseling when picking up prescriptions from 30% to 80% 0.00005 (5/100 million)

20

50 67 50 91 95

(1) (95,340), (2) (97,197) (1) (265,011), (2) (21,521) (1) (324), (2) (332) (1) 25, (2) 35 (1) 68, (2) 67 (1) (445,287), (2) (453,954) (1) 78, (2) 78

52

35 67 53 80 79 85 91

100 50

Abbreviation used: PADE, preventable adverse drug event. a Most evaluations measure the positive impact of increasing an existing risk-reduction strategy or implementing a new risk-reduction strategy. A few of the evaluations measure the negative impact of reducing or eliminating an existing risk-reduction strategy.

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the scanned prescription in the computer with the prescription label reduced the risk of this error reaching patients by 80%. More frequent independent checks and less skipped, rushed, or inattentive checks during data entry verification reduced the risk of PADEs by 53% and 35%, respectively. All four interventions together lowered the risk of dispensing the wrong warfarin dose from 1.83 to 0.174 per 1,000 prescriptions. Wrong drug or dose dispensed because of selection error while filling prescription for warfarin Event tree analysis: Vulnerability of selecting wrong dose higher than wrong drug. Initially selecting the wrong drug while manually filling a warfarin prescription was estimated to occur in 1 of 1,000 prescriptions. Mixups between warfarin and another medication have been reported rarely, although a risk exists with the branded warfarin product Jantoven (Upsher-Smith), which could be confused with Januvia or Janumet.56 However, pharmacies often stock warfarin on shelves according to generic names, thereby lessening the risk of such an error. Initially selecting the wrong dose while manually filling a prescription for warfarin was estimated to occur more frequently (1 of 10 prescriptions). This estimate is in line with a study that found that more than 5% of medications first selected to fill a prescription were wrong,57 as more errors are expected with warfarin doses because of nine different tablet strengths. The 1-mg and 10-mg strengths also are prone to mixups, particularly if a trailing zero is used to express the 1-mg dose (1.0 mg) on pharmacy or product labels.58 The probability of an error reaching the patient is 9.43 per 1 billion warfarin prescriptions for wrong drug errors and 9.25 per 10 million prescriptions for wrong dose errors. These low estimates are primarily the result of using barcoding technology while manually filling prescriptions and the availability of a tablet image for product verification. We estimated that technology would fail to capture the error in 1 of 100,000 opportunities to account for an occasional problematic barcode or scanner malfunction. If the error is not picked up through barcoding, an image of the correct drug and dose on the screen during product verification facilitates capture of the error in 99 of 100 occurrences. The final opportunity to capture these errors is during patient counseling, which was estimated to occur 30% of the time, mostly for patients with a new prescription or dose change. If the bottle label is viewed and the bottle is opened during the counseling session, the chance of capturing the error during this process step was estimated to increase from 90% (bottle not opened) to 99% (bottle opened), as pharmacists and patients often know what color tablet to expect for a given strength. The impact of this intervention is lessened in the overall estimate of PADE occurrence because patient counseling does not always occur. Sensitivity analysis: Impact of barcode product verification, automated dispensing, tablet imaging, and patient counseling. Our event trees for warfarin drug and dose selection errors add evidence to existing knowledge about the Journal of the American Pharmacists Association

effectiveness of using barcoding technology during dispensing.16,17,59–61 With the technology, 99.9% of selection errors were detected and corrected. However, without it, the probability of dispensing the wrong drug increased from 9.43 per 1 billion to 9 per 1 million, and the probability of dispensing the wrong dose increased from 9.25 per 10 million to 9 per 10,000. Similar increases were seen if an image of the correct tablet was not available during product validation. When barcoding and tablet imaging are absent, the probability of dispensing the wrong drug increased from 9.43 per 1 billion prescriptions to 4.2 per 100,000 prescriptions and the probability of dispensing the wrong dose increased from 9.25 per 10,000,000 prescriptions to 24.2 per 1,000 prescriptions. We also evaluated the impact of increasing automated dispensing of warfarin from 20% to 50% and increasing patient counseling from 30 to 80%, which reduced the risk of allowing either a wrong drug or wrong dose dispensing error to reach the patient by 78%. Incorrect or inappropriate dose of fentanyl systems dispensed because of prescribing error Event tree analysis: Dispensing system unreliable in detecting prescribing errors. Prescribing an incorrect or inappropriate dose of fentanyl systems was estimated to occur in 1 of 1,000 prescriptions. Fentanyl systems that are prescribed to treat acute pain (not an approved indication) and/or prescribed to opioid-naive patients were classified as an incorrect or inappropriate dose, as were doses that exceeded safe limits based on the patient's previous opioid use, age, general medical condition, conditions associated with hypoxia or hypercapnia, and/ or concomitant analgesics. Including all of these wrong dose prescribing errors resulted in dispensing 0.73 wrong doses per 1,000 prescriptions. The dispensing system in participating pharmacies was largely unreliable in its ability to detect this prescribing error; only 27% of the errors were estimated to be captured and corrected. The low capture rate was primarily associated with inadequate knowledge about the patient's prior opioid use, underlying health conditions, and type of pain for which the fentanyl system had been prescribed. Drug use review and patient counseling are two steps during which fentanyl system prescribing errors can be captured, but with limited success. The acceptable dose range for fentanyl systems is wide, from 12.5 μg/hour to 100 μg/hour or more, depending on the patient's opioid tolerance and pain level. Doses up to 300 μg/hour are recommended for patients with a 24-hour intake history of oral morphine in doses from 1,035 to 1,124 mg/day.62 An out-of-range dose alert would occur in just 1 of 100 prescriptions, as the computer cannot detect an inappropriate dose within such a wide acceptable therapeutic range. If the computer issues an out-of-range dose alert, the modeling team estimated that the error would be detected 98% of the time, but the low rate of dose alerts makes this an unreliable capture opportunity. The frequency of patient counseling was estimated to be 10% because many patients on fentanyl systems do not pick up their prescriptions and counseling is often declined by the www. japh a. or g

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caregiver or friend picking up the prescription. When counseling occurs, focusing on how to use the system correctly rather than appropriateness of the dose leads to an estimated capture of the dosing error just 10% of the time. Sensitivity analysis: Impact of obtaining opioid use history. We determined the impact of two interventions on the incidence of dispensing an inappropriate dose of fentanyl system because of a prescribing error. Both strategies involved obtaining a history of opioid use from the patient or health care provider. In the first case, we added a process step during receipt of the prescription for pharmacists to request information from patients, caregivers, and/or health care providers about prior opioid use and for pharmacists to review the information (and the patient's drug profile as necessary) before the prescription is entered into the computer. The team estimated a 40% capture rate of inappropriately prescribed doses with this intervention. Even with this modest capture rate, the process change allowed capture of 56% of prescribing errors. The second intervention involved collecting an opioid history during face-to-face or phone counseling. First, the frequency of patient counseling was increased from 10% to 80%. The pharmacist's ability to detect an inappropriate dose during the counseling session was increased from 10% to 80% by estimating the impact of using a checklist that gathered information about the patient's opioid use history. This reduced the probability of errors reaching patients from 0.730 to 0.263 per 1,000 prescriptions, which represents a prescribing error capture rate of 74%. Oral methotrexate dispensed with directions to take daily due to a prescribing error Event tree analysis: Computer alert leads to a high capture rate of prescribing errors. Prescriptions for oral methotrexate with incorrect directions to take the drug daily instead of a weekly dosing schedule were estimated to occur in 1 of 1,000 prescriptions. Of these, 0.0009 errors per 1,000 prescriptions (9.64/10 million prescriptions) were estimated to actually reach patients. Oral methotrexate intended for nononcologic use typically entails a single weekly dose sometimes spread over three doses every 12 hours instead of daily doses. Given that methotrexate doses for oncologic use do not typically exceed courses of 5 days repeated after a week with no therapy, the PADE includes all prescriptions for methotrexate that were prescribed daily for more than 1 consecutive week. Pharmacies in our study have a robust dispensing process that captures and corrects 99.9% of methotrexate prescriptions with directions to take the drug daily. The reasons for a high capture rate included computer alerts that appeared when entering a daily dose of oral methotrexate and heightened pharmacy staff awareness of this type of PADE. Such conditions may not exist in other pharmacies. Sensitivity analysis: Impact of including indication on prescription and computer alerts. Knowing the indication could help detect a methotrexate prescription with the wrong directions for use.63,64 Estimating 80% prescriber adherence 596 • JAPhA • 5 2 : 5 • S e p / O c t 2012

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with including the indication on the prescription and estimating modest increases (≤50%) in the ability of pharmacy staff to detect the error when the indication is provided, the rate of the PADE was reduced from 0.0009 to 0.0007 of 1,000 methotrexate prescriptions. We also tested the effect of making the computer alert a hard stop that does not allow entry of oral methotrexate prescriptions with daily dosing for more than 1 consecutive week. This lowered the incidence of dispensing methotrexate with directions for daily use to once every 100 billion prescriptions. We evaluated the reliability of the dispensing process to detect the error if the computer system does not warn pharmacy staff about daily dosing of methotrexate. Removing this important safety feature resulted in more than a 500% increase in risk, with the probability of capturing the error decreasing from 0.0009 to 0.006 events per 1,000 methotrexate prescriptions. Wrong insulin analog dispensed because of selecting wrong drug during data entry Event tree analysis: Look- and sound-alike product names contribute to errors. Dispensing the wrong insulin analog as a result of data entry error was estimated to occur in 1 of 100 insulin analog prescriptions. The insulin analogs considered were Humalog (Eli Lilly), Humalog Mix 75/25, Humalog Mix 50/50, NovoLog (Novo Nordisk), NovoLog Mix 70/30, Apidra (sanofi-aventis), Lantus (sanofi-aventis), and Levemir (Novo Nordisk). The participating pharmacies were capable of capturing 96.9% of these data entry errors, leaving 0.306 errors per 1,000 prescriptions that reached patients. The error rate for selecting the wrong insulin analog during order entry was estimated to be higher than a typical human error rate due to several well-known PSFs (online Appendix 5), including look- and sound-alike product names, similar strengths and mixtures, handwritten prescriptions susceptible to being misread, and long insulin analog pick lists in the computer from which to select the correct product. Data entry verification was found to be the most opportune time in the dispensing process to detect these errors. However, only 10% of the errors would be recognized if the pharmacist who initially entered the prescription in error also verified the prescription entry. Drug use review is not a reliable step for capturing this particular type of error. Dosing ranges overlap; therefore, out-of-range dose alerts occur infrequently. Duplicate therapy alerts are common and occur with about 80% of insulin prescriptions because many patients with diabetes require more than one type of insulin. The frequency of duplicate insulin therapy alerts led the modeling team to estimate a 30% rate of bypassing the alert without sufficient attention and the frequency of patients receiving more than one type of insulin led to an estimated 90% failure rate for detecting the error even if the duplicate therapy alert was investigated. The ability to detect the data entry error would increase from 90% to 99% if the counseling session included visual inspection by the patient of the insulin carton, vial, or pen. To be

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effective, this requires counseling at the counter, not the drivethrough window. Sensitivity analysis: Impact of electronic prescriptions, tall man letters, and data entry verification. Two of the risk-reduction interventions that were evaluated focused on reducing the initial data entry error by changing PSFs. We evaluated the impact of increasing electronic or computerprinted prescriptions by 20% to reduce the risk of misreading handwritten prescriptions for insulin products with similar names. This was estimated to lower the initiating error rate from 10 to 8 per 1,000 prescriptions, yielding a reduction in errors reaching patients from 0.306 to 0.245 per 1,000 prescriptions. We also evaluated the use of tall man letters to draw attention to the differences in insulin names (e.g., HumaLOG, HumuLIN, NovoLOG, NovoLIN) on computer screens, which lowered the rate of selecting the wrong insulin during data entry from 10 to 5 per 1,000 prescriptions and lowered the probability of dispensing the wrong insulin to a patient from 0.306 to 0.153 events per 1,000 prescriptions. Conducting a second data entry verification during the product verification step had a similar effect. By increasing the frequency of patient counseling from 30% to 80%, the probability of capturing the error increased from 96.9% to 99%, which reduced the risk of dispensing the wrong insulin to a patient from 0.306 to 0.100 per 1,000 prescriptions. Dispensing medication to wrong patient at point of sale Event tree analysis: High vulnerability to dispensing prescriptions to the wrong patient. Pharmacies are vulnerable to dispensing correctly filled prescriptions to the wrong individual at the point of sale, which is a risk that has been substantiated in the literature.21,65–67 This PADE is not influenced by the attributes of a specific medication; dispensing any prescription medication to the wrong patient at the point of sale carries a similar risk of occurrence. Infrequent patient counseling and flawed patient identification procedures were the most frequent contributory factors leading to an estimated error rate of 1.22 per 1,000 prescriptions. With close to 4 billion prescriptions filled annually, this error rate suggests that 386,821 prescriptions will be dispensed to the wrong patient each month or an average of seven errors per month for every U.S. pharmacy. Two initiating errors most often led to this PADE: errors when placing the products into a bag for pick up (0.4/1,000 prescriptions) and errors when retrieving the medications at the point of sale (3/1,000 prescriptions). Bagging errors often stem from working on more than one patient's medications during the product verification and bagging process. Because the bag is not opened at the point of sale, customers rarely capture the errors before leaving the pharmacy. A flawed or absent patient identification process most often led to errors when retrieving the medications at the point of sale. Although patient verification is expected at the point of sale, the modeling team reported difficulty obtaining a birth Journal of the American Pharmacists Association

date when prescriptions were picked up by caregivers, family, or friends. Using an address as a second identifier is suboptimal, as patients with the same last name often live together. Modeling team members who worked in stores with lower prescription volumes felt that they were able to visually identify most patients; however, they were also more likely to skip a formal verification process, during which errors could occur. Unless patient counseling occurs at the point of sale, dispensing a prescription to the wrong patient is a single-pathway failure, meaning that no key opportunities to capture the error occurred from the time it was made to when the prescription reached the patient. Sensitivity analysis: Impact of opening the bag at the point of sale, adherence to patient identification process, and patient counseling. A simple process change such as opening the bag at the point of sale to view the products yielded improvement, from 1.22 to 0.534 errors per 1,000 prescriptions. We tested opening the bag to view its contents, with a modest rate (90%) in capturing a wrong patient error compared with when the bag was not opened; the intervention was very effective in reducing the risk of an error reaching the patient. The modeling team estimated that pharmacy staff followed a patient identification process at the point of sale only half of the time. The process included verifying the patient's last name along with one other unique identifier: birth date or address. With an increase from 50% to 80% in the frequency of carrying out the patient identification process at the point of sale, the incidence of dispensing a prescription to the wrong patient decreased from 1.22 to 0.804 errors per 1,000 prescriptions. Combining 80% adherence with following the patient identification process and opening the bag at the point of sale further lowered the incidence of this error to 0.233 errors per 1,000 prescriptions, representing 81% improvement. Increasing the frequency of counseling patients who pick up prescriptions from 30% to 50% reduced the incidence of the error from 1.22 to 0.899 per 1,000 prescriptions. Adding this intervention to the previous two interventions—opening the bag and following the identification process 80% of the time—resulted in a further reduction to 0.169 per 1,000 prescriptions (86% improvement). This reduction in risk changes the rate of dispensing a prescription medication to the wrong patient at the point of sale at a U.S. pharmacy from seven per month to less than 1 per month.

Discussion Data reported in the literature are not directly comparable with the estimates in our event trees or the calculated probabilities of occurrences for each PADE. The incidence of community pharmacy errors and PADEs for each of our targeted high-alert medications and the specific types of errors is not readily available in the literature. Most studies have identified general rates of medication errors, ADEs, and/or PADEs. To cite one example, in 2003, Flynn et al.16 identified a community pharmacy dispensing error rate of 17.2 errors per 1,000 prescriptions. Dispensing error rates per 1,000 prewww. japh a. or g

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scriptions were also reported for wrong drug (1.2) and wrong strength (1.9), but the rates were not categorized according to the class of drugs involved in the errors. Data entry errors accounted for the largest type of dispensing error (11.2/1,000 prescriptions), but details regarding the type of data entry error (e.g., wrong strength, wrong patient) were not provided, making comparison with our results unworkable. The study of Flynn et al. also showed that about 48% of data entry errors and 45% of drug selection errors were captured and corrected before reaching patients. Our results differed in that 96.9% to 99.0% of data entry errors, and virtually all drug selection errors, were captured during the dispensing process. Differences in the capture of data entry errors may be explained by our exclusionary focus on four high-alert medications for which computer alerts were functional to help capture data entry errors. Differences in the capture of data entry errors also can be explained by our sole focus on wrong medication, wrong dose, and wrong patient data entry errors. Our study did not evaluate data entry errors associated with entering the wrong directions for use, which was an important source of errors in the Flynn et al. study. Differences in the capture of drug selection errors between the Flynn et al. study and our study were expected because of consistent use of barcoding systems in all pharmacies participating in our study, which was not present in all pharmacies in the study of Flynn et al. Direct comparisons of our data with previous studies also were limited by differences in event definitions, event categories, study settings, detection methods, and outcomes evaluated. Further, many previous studies do not distinguish between inpatient and outpatient prescriptions; errors, PADEs, and ADEs; types of errors with each drug; or forms of the drugs involved in the errors (e.g., oral, parenteral, transdermal). The event trees created during our study enhance findings from previous studies on medication errors by demonstrating important and largely correctable community pharmacy dispensing system vulnerabilities, identified by the people who work within those systems. The event trees represent the expert opinions of experienced, practicing pharmacists and pharmacy technicians and define thousands of ways process failures and behavioral elements combine to lead to each PADE. They provide insight into deep system weaknesses, human errors, and behavioral choices that define medication dispensing risks because the models incorporate operational understanding of system design, interdependencies, and human reliability concepts not easily visualized with traditional quantitative studies. This level of detail, which is not available from any other source, identified dispensing system vulnerabilities and a remarkable capacity to detect and correct errors before reaching patients given certain environmental, technological, system/process, and behavioral conditions that are well within the reach of most community pharmacies and pharmacy staff. Prescribing errors With prescribing errors, the event trees suggest that community pharmacy dispensing systems may be designed to capture straightforward mistakes, such as prescribing warfarin twice 598 • JAPhA • 5 2 : 5 • S e p / O c t 2012

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a day, more often than errors associated with inappropriate drugs or doses, such as prescribing fentanyl systems for an opioid-naive patient or warfarin in a dose not appropriate for the patient based on international normalized ratio values. Detecting the former type of error requires knowledge about the drug and/or system safeguards, such as computer alerts, to help capture these errors. Detecting the latter type of error requires knowledge of the drug and knowledge about the patient's medical history and laboratory values—important information rarely accessible in community pharmacies. Lack of information about patients’ diagnoses and/or prescriptions without a specified indication for the drug further hamper the pharmacist's ability to detect errors. It is not surprising that the event trees demonstrated a 99.9% capture rate for prescribing errors associated with warfarin directions for use and only a 27% capture rate for prescribing errors associated with the dose of fentanyl systems, which is dependent on the patient's type of pain and prior opioid tolerance. Sensitivity analysis identified that more frequent and effective patient counseling will reduce prescribing errors that reach patients by as much as 64%. ISMP has long been a staunch supporter of the need for community pharmacy access to clinical patient information and reimbursement systems that compensate the time pharmacists spend on clinical review of prescriptions and counseling.68 Improvement in these areas will considerably improve the success of detecting prescribing errors before they reach patients in the outpatient setting. Dispensing errors Community pharmacies in the study exhibited highly reliable systems for preventing and detecting drug or dose selection errors when filling prescriptions for warfarin. Reliability was driven by available technologies, including barcoding, automated dispensing, scanned prescription images, and computer-generated tablet images. Selecting the wrong drug while filling a warfarin prescription was estimated to reach patients once every 4 years among U.S. pharmacies if barcoding technology was used for verification. On the other hand, wrong dose selection errors were estimated to reach patients 24 times each year. Increasing automated dispensing of warfarin to 50% and the frequency of patient counseling to 80% lowered the probability of wrong drug selection errors to once every 20 years and wrong dose selected and dispensed to once every 5 years among U.S. pharmacies if barcoding technology was used for verification. Community pharmacies in the current study were vulnerable to wrong drug and wrong dose data entry errors, which is consistent with previous reports.16,69,70 Sensitivity analysis revealed that conducting a second data entry verification process at the time of product verification reduced this risk by one-half (50%) for wrong insulin analog data entry errors and by 80% for wrong warfarin dose data entry errors. Wrong patient errors also present a risk, with about 5 errors per 100,000 prescriptions originating from data entry into the wrong patient's profile and 1 error per 1,000 prescriptions originating at the point of sale. These findings are higher than Journal of the American Pharmacists Association

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rates reported in the literature,16,21,65–67 although errors captured at any point after the patient leaves the pharmacy counter were included in the event tree incidence rates. Opening the bag at the point of sale to view products reduced the error risk by more than one-half (56%). In practice, privacy at the point of sale is difficult; thus, confidentiality concerns and the Health Insurance Portability and Accountability Act of 1996 Privacy Rule will affect the degree to which this intervention can be applied. The interventions discussed and evaluated in the study are not meant to suggest and/or establish a standard of care for community pharmacies. Rather, the interventions are intended to represent future improvements that will affect patient safety positively and raise the bar in community pharmacies.

Limitations This study produced event trees that are representations of the dispensing systems used in community pharmacies. Although community pharmacy dispensing systems share many elements in common, the study results are not generalizable to all community pharmacies because of potential differences in process steps, technology, frequency of at-risk behaviors, and PSFs that increase or decrease risk. Samples sizes were constrained by funding limits and to avoid disrupting operations at participating pharmacies. The modeling sessions required a manageable number of participants, limiting the sample size. The small sample sizes of the model-building team and the validation group could reduce the certainty of the probability estimates based on expert opinions, particularly for components of the dispensing system for which failure was rare and no preexisting reference data were available to assist with estimations. Keeping reasonable travel times for the modeling team necessitated a convenience sample from a single geographic location. To ensure diversity among the model-building and validation samples, staff representing minorities may have been overrepresented. The event trees may not include important community pharmacy systems or practice variations, and estimates of PADEs could differ in pharmacies not in the study; however, the event trees represent a starting point for understanding how and why serious errors occur. We view these event trees as living models of risk that can be expanded to accommodate a broader scope of pharmacy practices and refined by replacing expert opinion estimates with known probabilities. Use of expert opinions and uncertainty with ST-PRA probabilities Misunderstandings surround the use of ST-PRA, which uses a Bayesian statistical model that relies in part on expert opinions to determine probability distributions during the analytic process.51,53 Although users of conventional statistical methods rarely dispute the mathematical foundations of the Bayesian approach,71 they are unaccustomed to mixing objective data with subjective judgments and may feel that ST-PRA lacks scientific rigor or validity to guide decision making.52,71 This viewpoint is misguided. First, many important patient safety deciJournal of the American Pharmacists Association

sions cannot wait until all questions can be answered with empirical data. Next, ST-PRAs are built on an accepted probability model and provide important information regarding the conditional occurrence of a particular event, given what is currently known and accepted assumptions.51–54,71 All ST-PRAs and PRAs require the use of subjective data. It is the uncertain and probabilistic nature of risk that requires the inclusion of subjective data.51 Instead of making inferences based purely on empirical data, with ST-PRA, inferences must be made despite uncertain parameters and missing data. Uncertainty and risk are highly interconnected. Thus, estimates of risk resulting from ST-PRAs will be uncertain, in large part because of the sparse nature of data on system components.51,53 Nevertheless, the use of subjective and uncertain probabilities in making decisions is theoretically founded; it is rational for people to make decisions based on the best information available.53 All risk modeling represents a rough approximation of true risk. The uncertainty attached to individual failure rates and the presence of unidentified process variations speak to the uncertainties in risk models as a whole. The task for the riskmodeling team was to determine whether, in a restricted time frame, the ST-PRA technique would provide a more informative picture of actual risk in the community pharmacy setting than that currently available through typical sources, such as retrospective event reporting, RCA, and FMEA. Through this work, we have provided additional knowledge and understanding of the risks imposed on the pharmacy dispensing system and the effects of various interventions on reducing these risks. Further discussion on the Bayesian statistical model, uncertainty, and arguments that prove the mathematical rigor in using probability distributions to describe uncertainties is beyond the scope of the current report; however, interested readers are referred to Zimmerman and Bier,51 Bier,53 and Bernardo71 for further information.

Conclusion The ST-PRA models created during the study were exceptionally robust for identifying process and/or behavioral failures, estimating the frequency of adverse outcomes, and evaluating the effectiveness of interventions. The event trees revealed important systems relationships, unintended consequences of behavioral choices, and valuable risk-reduction interventions that can guide and accelerate community pharmacy safety improvements. They serve as visible diagrams for shared understanding of the failure pathways that lead to harm, which facilitates communication, shared goals, trust, and agreement between stakeholders because everyone owns the same risk model. The greatest value of the ST-PRA process often lies less in the quantitative estimates of potential adverse events and more in the qualitative, robust risk models that define the interdependencies and combination of failure pathways in complex systems that lead to adverse events. Each of the ST-PRA risk models we developed identified tens of thousands of failure pathways that could lead to a single adverse event. The risk models were then used to prioritize the process steps, behavioral choices, and sequence of events that most often contribwww. japh a. or g

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uted to the adverse outcome and to evaluate the impact of selected risk-reduction strategies. This study demonstrates the strength and value of ST-PRA and its application in health care, its advantages over current qualitative risk assessment methods, its capabilities to forecast combinations of risk leading to PADEs, and how it might be used to facilitate development and implementation of high-leverage interventions to reduce medication errors. The techniques required to model human error and behavioral risks with ST-PRA are still advancing, and the process can be lengthy, complex, and require expert facilitation. However, the alternative to ST-PRA is to assume the system is safe, wait for events to happen, investigate, and remove the newly seen risk. This study demonstrates that ST-PRA, even with its uncertainties, can lead to learning and improvements not achievable by current prospective risk assessment processes or retrospective event investigations. The process is proving to be highly useful in health care, although the elicitation and use of expert opinions in safety studies and risk management is an area for further study.52 Although every community pharmacy conducting its own ST-PRA modeling may not be practical, application of the lessons learned from these existing models can lead to widespread improvement in community pharmacies nationwide. Logic dictates that the event trees will be useful across a broad range of community pharmacies, as they inform pharmacists about the systems design and behavioral elements that can produce or prevent a dispensing error. We anticipate that the results of this study will contribute greatly to the growing body of knowledge about the application of ST-PRA in health care and lead to further exploration regarding how the process can be demystified and used as a practical tool in health care settings. Since 2008, ISMP and Outcome Engenuity, LLC, have been developing a streamlined, computer-based, community pharmacy risk assessment tool that uses the risk models developed during this study to compute quantifiable risks associated with various types of prescribing and dispensing errors. The user will be required to provide easily accessible data gathered during a series of approximately one dozen to three dozen online questions. A scorecard will estimate the rate of PADEs that reach patients based on each pharmacy's unique processes, computer support, and staff behaviors. The scorecard will also suggest risk-reduction strategies and allow the user to recalculate the frequency of PADEs that reach patients to demonstrate the anticipated reduction in risk with the selected risk-reduction strategies. The tool will be freely available in 2012.

Note added in proof Following acceptance of the manuscript, the tool High-Alert Medication Modeling and Error-Reduction Scorecards (HAMMERS) was completed and is now available on the ISMP website at www.ismp.org/tools/HAMMERS. References 1.  

Bates DW, Spell N, Cullen DJ, et al. The cost of adverse drug events in hospitalized patients. JAMA. 1997;277:307–11.

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2.  

Aspden P, Wolcott JA, Bootman JL, Cronenwett LR, Preventing medication errors: quality chasm series. Washington, DC: National Academies Press; 2007.

3.  

Bates DW, Boyle DL, Vander Vliet MB, et al. Relationship between medication errors and adverse drug events. J Gen Intern Med. 1995;10:199–205.

4.  

Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;274:29–34.

5.  

Classen DC, Pestotnik SL, Evans RS, et al. Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality. JAMA. 1997;277:301–6.

6.  

Porter J, Jick H. Drug-related deaths among medical inpatients. JAMA. 1977;237:879–81.

7.  

Phillips J, Beam S, Brinker A, et al. Retrospective analysis of mortalities associated with medication errors. Am J Health Syst Pharm. 2001;58:1835–41.

8.  

Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998;279:1200–5.

9.  

Howard R, Avery A, Bissell P. Causes of preventable drug-related hospital admissions: a qualitative study. Qual Saf Health Care. 2008;17:109–16.

10.   Howard RL, Avery AJ, Slavenburg S, et al. Which drugs cause preventable admissions to hospital? A systematic review. Br J Clin Pharmacol. 2007;63:136–47. 11.   Budnitz DS, Pollock DA, Weidenbach KN, et al. National surveillance of emergency department visits for outpatient adverse drug events. JAMA. 2006;296:1858–66. 12.   Ghandi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care. N Engl J Med. 2003;348:1556–64. 13.   Buchanan TL, Barker KN, Gibson JT, et al. Illumination and errors in dispensing. Am J Hosp Pharm. 1991;48:2137–45. 14.   Flynn EA, Barker KN, Berger BA, et al. Dispensing errors and counseling quality in 100 pharmacies. J Am Pharm Assoc. 2009;49:171–80. 15. 

Allan EL, Barker KN, Malloy MJ, et al. Dispensing errors and counselingincommunitypractice.AmPharm.1995;NS35:25–33.

16.   Flynn EA, Barker KN, Carnahan BJ. National observational study of prescription dispensing accuracy and safety in 50 pharmacies. J Am Pharm Assoc. 2003;43:191–200. 17.   Szeinback S, Seoane-Vazquez E, Parekh A, et al. Dispensing errors in community pharmacy: perceived influence of sociotechnical factors. Int J Qual Health Care. 2007;19:203–9. 18.   Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003;289:1107–16. 19.   Lesar TS, Briceland L, Stein DS. Factors related to errors in medication prescribing. JAMA. 1997:277:312–7. 20.   Leape LL, Bates DW, Cullen DJ, et al. Systems analysis of adverse drug events. JAMA. 1995;274:35–43. 21.   Dean BS, Allan EL, Barber ND, et al. Comparison of medication errors in an American and a British hospital. Am J Health Syst Pharm. 1995;52:2543–9. 22.   Field TS, Gilman BH, Subramanian S, et al. The costs associated with adverse drug events among older adults in the ambulatory setting. Med Care. 2005;43:1171–6. Journal of the American Pharmacists Association

risk models in community pharmacies Research

23.   Ernst FR, Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model. J Am Pharm Assoc. 2001;41:192–9.

41.   Reason J. Hazards, defences and losses. In: Reason J. Managing the risks of organizational accidents. Burlington, VT: Ashgate; 2000:1–18.

24.   Cohen MR, Proulx SM, Crawford SY. Survey of hospital systems and common serious medication errors. J Healthc Risk Manag. 1998;18:16–27.

42.   Stockwell DC, Slonim AD. Quality and safety in the intensive care unit. J Intensive Care Med. 2006;21:199–10.

25.   Davis NM, Cohen MR. Today's poisons: how to keep them from killing your patients. Nursing. 1989;19:49–51.

43.   Wreathall J, Nemeth C. Assessing risk: the role of probabilistic risk assessment (PRA) in patient safety improvement. Qual Saf Health Care. 2004;13:206–12.

26.   Institute for Safe Medication Practices. ISMP 2007 survey on high-alert medications; difference between nursing and pharmacy perspectives still prevalent. ISMP Medication Safety Alert! 2007;12(10):1–3.

44.   Cook RI, Wreathall J, Smith A, et al. Probabilistic risk assessment of accidental ABO-incompatible thoracic organ transplantation before and after 2003. Transplantation. 2007;84:1602–9.

27.   Smetzer JL, Cohen MR. Medication error reporting systems. In: Cohen MR, Ed. Medication errors. 2nd ed. Washington, DC: American Pharmacists Association; 2007:513–50.

45.   Institute for Safe Medication Practices. The National Medication Errors Reporting Program (ISMP MERP). Accessed at https://www.ismp.org/orderforms/reporterrortoismp.asp.asp, July 1, 2012.

28.   Battles JB, Kanki BG. The use of socio-technical probabilistic risk assessment at AHRQ and NASA. In: Spitzer C, Schmocker U, Dang VN, Probabilistic safety assessment and management. 4th ed. Berlin: Springer; 2004:2212–7. 29.   Burkhardt M, Lee C, Taylor L, et al. Root cause analysis of medication errors. In: Cohen MR, Ed. Medication errors. 2nd ed. Washington, DC: American Pharmacists Association; 2007:513–50. 30.   Comden SC, Marx D, Murphy-Carley M, Hale M. Using probabilistic risk assessment to model medication system failures in long-term care facilities. Accessed at www.ahrq.gov/downloads/pub/advances/vol2/Comden.pdf, August 15, 2010. 31.   Senders JW, Senders SJ, Smetzer JL, et al. Heath care failure mode and effects analysis. In: Cohen MR, Ed. Medication errors. 2nd ed. Washington, DC: American Pharmacists Association; 2007:561–86. 32.   The Joint Commission. Failure mode and effects analysis in health care: proactive risk reduction. 2nd ed. Oakbrook Terrace, IL: Joint Commission Resources; 2005. 33.   Helmreich RL. On error management: lessons from aviation. BMJ. 2000;320:781–5. 34.   Van der Schaaf TW. Medical applications of industrial safety science. Qual Saf Health Care. 2002;11:205–6. 35.   Spencer FC. Human error in hospitals and industrial accidents: current concepts. J Am Coll Surg. 2000;191:410–8. 36.   Marx DA, Slonim AD. Assessing patient safety risk before the injury occurs: an introduction to socio-technical probabilistic risk assessment in healthcare. Qual Saf Health Care. 2003;12(suppl 2):33–8. 37.   Marx DA, Westphal JE. Socio-technical probabilistic risk assessment: its application to aviation maintenance. Int J Aviat Psychol. 2008;18:51–60. 38.   Stamatelatos M, Apostolakis G, Dezfuli H, et al. Probabilistic risk assessment procedures guide for NASA managers and practitioners. Accessed at www.hq.nasa.gov/office/codeq/ doctree/praguide.pdf, June 3, 2010. 39.   Garrick BJ, Christie RF. Probabilistic risk assessment practices in the USA for nuclear power plants. Saf Sci. 2002;40:177–201. 40.   Forester JA, Whitehead DW, Kolaczkowski AM, et al. Results of a nuclear power plant application of a new technique for human error analysis (ATHEANA). Accessed at www.osti.gov/ bridge/servlets/purl/543650-eNlI7i/webviewable/543650.pdf, June 3, 2010. Journal of the American Pharmacists Association

46.   Pennsylvania Patient Safety Authority. Pennsylvania Patient Safety Reporting System. Accessed at http://patientsafetyauthority.org/PA-PSRS/Pages/PAPSRS.aspx, August 15, 2010. 47.   Food and Drug Administration. FDA MedWatch: the FDA safety information and adverse event reporting program. Accessed at www.fda.gov/medwatch, February 7, 2010. 48.   Institute for Safe Medication Practices. ISMP survey on high-alert medications in community/ambulatory settings. Accessed at www.ismp.org/survey/newsletter/comsurvey200606.asp, June 10, 2010. 49.   Rothschild JM, Federico FA, Ghandhi TK, et al. Analysis of medication-related malpractice claims: causes, preventability, and costs. Arch Intern Med. 2002;162:2414–20. 50.   Moore TJ, Cohen MR, Furberg CD. Serious adverse drug events reported to the Food and Drug Administration, 1998– 2005. Arch Intern Med. 2007;167:1752–9. 51. 

Zimmerman R, Bier VM. Risk assessment for extreme events. Accessed at www.ldeo.columbia.edu/chrr/documents/meetings/ roundtable/white_papers/zimmerman_wp.pdf,June10,2010.

52.   Apostolakis G. The concept of probability in safety assessments of technological systems. Science. 1990;250:1359–64. 53.   Bier VM. Challenges to the acceptance of probabilistic risk analysis. Risk Analysis. 1999;19:703–9. 54.   Shelton CP. Human interface/human error. Accessed at www. ece.cmu.edu/~koopman/des_s99/human, February 10, 2010. 55.   Relex software [computer program]. Version 7.6. Greensburg, PA: Relex Software Corporation; 2003. 56.   Institute for Safe Medication Practices. Look-alike names: safety briefs. ISMP Medication Safety Alert! 2008;13(6):1–2. 57.   Koutnik E. Bar coding: working toward error-free pharmacy. Accessed at www.pharmacytimes.com/issues/articles/2003--09_690.asp, January 10, 2010. 58.   Institute for Safe Medication Practices. ISMP's list of error-prone abbreviations, symbols, and dose designations. Accessed at www.pharmacytimes.com/issues/articles/2003--09_690.asp, January 22, 2010. 59.   Poon EG, Cina JL, Churchill W, et al. Medication dispensing errors and potential adverse drug events before and after implementing bar code technology in the pharmacy. Ann Intern Med. 2006;145:426–34.

www. japh a. or g

S e p /O c t 2012 • 52:5 •

JAPhA • 601

Research

risk models in community pharmacies

60.   Ukens C. New study sheds light on medication errors. Drug Topics. 2002;146(21):33. 61.   Maviglia SM, Yoo JY, Franz C, et al. Cost-benefit analysis of a hospital pharmacy barcode solution. Arch Intern Med. 2007;167:788–94. 62.   Lexi-Comp. FentaNYL monograph (Lexi-Drugs). Accessed at http://online.lexi.com/lco/action/doc/retrieve/ docid/patch_f/6903, August 16, 2010. 63.  

Institute for Safe Medication Practices. Methotrexate overdose due to inadvertent administration daily instead of weekly. Accessed at www.ismp.org/hazardalerts/ha.pdf, August 16, 2010.

64.   Moore TJ, Walsh CS, Cohen MR. Reported medication errors associated with methotrexate. Am J Health Syst Pharm. 2004;61:1380–4. 65.   Schulmeister L. Chemotherapy medication errors: descriptions, severity, and contributing factors. Oncol Nurs Forum. 1999;26:1022–42. 66.   Chassin MR, Becher EC. The wrong patient. Ann Intern Med. 2002;136:826–33. 67.   Institute for Safe Medication Practices. Oops, sorry, wrong patient! Applying the JCAHO “two-identifier” rule beyond the patient's room. ISMP Medication Safety Alert! 2004;9(11):1–2. 68.   Institute for Safe Medication Practices. “20/20” captures community pharmacy errors, but vision seems to be shortsighted. ISMP Medication Safety Alert! 2007;12(7):1, 3. 69.   Knudsen P, Herborg H, Mortensen AR, et al. Preventing medication errors in community pharmacy: frequency and seriousness of medication errors. Qual Saf Health Care. 2007;16:291–6. 70.   Bobb A, Gleason K, Husch M, et al. The epidemiology of prescribing errors. Arch Intern Med. 2004;64:785–92. 71.   Bernardo JM. Bayesian statistics. Accessed at www.uv.es/bernardo/BayesStat.pdf, August 9, 2009. 72.   Top 200 generic drugs by units in 2007. Accessed at http:// drugtopics.modernmedicine.com/drugtopics/data/articlestandard//drugtopics/072008/491181/article.pdf, August 9, 2009. 73.   Top 200 brand drugs by units in 2007. Accessed at http:// drugtopics.modernmedicine.com/drugtopics/data/articlestandard//drugtopics/072008/491207/article.pdf, August 9, 2009. 74.   IMS Health. 2009 channel distribution by U.S. dispensed prescriptions. Accessed at www.imshealth.com/deployedfiles/ imshealth/Global/Content/StaticFile/Top_Line_Data/Channel%20Distribution%20by%20U.S.%20RXs.pdf, August 15, 2010. 75.   Wu JS, Apostolakis GE. Experience with probabilistic risk assessment in the nuclear power industry. J Hazard Mater. 1992;29:313–45.

602 • JAPhA • 5 2 : 5 • S e p / O c t 2012

www.j aph a. or g

76.   Forester JA, Whitehead DW, Kolaczkowski AM, et al. Results of nuclear power plant application of a new technique for human error analysis. Risk Analysis. 1996;16. 77.   Hsueh KS, Mosleh A. The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants. Reliab Eng Syst Saf. 1996;52:297–314. 78.   Sues RH, Chen HC, Oswald EA, et al. Integrating internal events in an external probabilistic risk assessment: Tornado PRA case study. Reliab Eng Syst Saf. 1993;40:173–86. 79.   System design and analysis harmonization and technology update. Fed Regist. 1996;61:26246–7. 80.   Moore TJ, Cohen MR, Furberg CD. Quarter watch: 2008 quarter 1. Accessed at www.ismp.org/QuarterWatch/2008Q1.pdf, February 10, 2010. 81.   Moore TJ, Cohen MR, Furberg CD. QuarterWatch: 2008 quarter 2. Accessed at www.ismp.org/QuarterWatch/200901.pdf, August 10, 2010. 82.   Vertical Health LLC. Type 1 diabetes: symptoms, diagnosis, & treatment of type 1 diabetes. Accessed at www.endocrineweb. com/diabetes/1diabetes.html, February 18, 2010. 83.   Centers for Disease Control and Prevention. Diabetes public health resource. Accessed at www.cdc.gov/diabetes/projects/ cda2.htm, February 18, 2010. 84.   Institute for Safe Medication Practices. Proliferation of insulin combination products increases opportunity for errors. ISMP Medication Safety Alert! 2002;7(24):1–2. 85.   Institute for Safe Medication Practices. Getting the right insulin is becoming a real crapshoot. ISMP Medication Safety Alert! 2004;9(14):2–3. 86.   Gertman D, Blackman H, Marble J, et al. The SPAR-H human reliability analysis method. Washington, DC: Nuclear Regulatory Commission; 2005. 87.   System Reliability Center. Technique for human error rate prediction (THERP). Rome, NY: Alion Science and Technology; 2005. 88.   Garnerin P, Pellet-Meier B, Chopard P, et al. Measuring human-error probabilities in drug preparation: a pilot simulation study. Eur J Clin Pharmacol. 2007;63:769–76. 89.   Lewis M. THERP: technique for human reliability analysis. Accessed at www.pitt.edu/~cmlewis/THERP.htm, June 15, 2010. 90.   Institute of Petroleum. Human reliability analysis. London: Institute of Petroleum; 2003. 91.   Gibson WH, Hicking B, Kirwan B. Feasibility study into the collection of human error probability data. Brussels, Belgium: European Organisation for the Safety of Air Navigation, Eurocontrol Experimental Centre; 2006.

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Appendix  1.  Medications  involved  in  harmful  errors  in  ambulatory  settings   Medications or Medication   Class  

Analgesics  

Phillips     et  al.     20017   Reported   fatal  drug   errors  in   hospitals,   ambulatory   care  settings,   patients’   homes  

Rothschild   et  al.     200249   Analysis  of   malpractice   claims  for   preventable   adverse  drug   events   (inpatient  and   outpatient)  

Ghandhi     et  al.   200312   Preventable   adverse  drug   events  in   adults  18  and   older  self-­ administered   prescription   medications  

Studies Budnitz   et  al.   200611  

Flynn     et  al.   200316  

Gurwitz et al. 200318

Medication   dispensing   errors  in   ambulatory   pharmacies   considered   clinically   significant  

Preventable adverse drug events in older individuals in ambulatory clinical settings

X   opioids  and   nonnarcotic    

 

X   nonnarcotic  

 

X nonnarcotic

 

 

 

X   insulin  

 

 

 

  X

X  

X  

X   penicillins  

X  

 

 

X   wrong  label   information,   clindamycin    

X   warfarin,   heparin    

X  

 

 

 

 

Antipsychotic/   antidepressants   Antirheumatic   Antitussive  

 

X  

 

 

 

 

 

 

 

 

Cardiovascular   drugs    

X  

X  

Diuretics   Electrolytes   Hormones  and   synthetic   substitutes     Ipratropium   bromide  inhaler     Lithium   Nonsteroidal  

 

 

X   calcium  channel   blockers,  beta   blockers,  ACE   inhibitors    

X   promethazine   with  codeine   X   wrong  form   (Norpace)    

X

 

 

X  

 

 

X   Depo-­‐Medrol  

 

 

 

 

 

 

X  

 

 

 

 

 

 

X  

 

 

 

 

 

Anticonvulsants     Antidiabetic   agents,     including  insulin     Antiinfective/   antibiotics   Antineoplastics   Anticoagulants     Antiplatelets  

Budnitz     et  al.   200611  

Howard     et  al.     200710  

Moore    et  al.    200750  

Howard     et  al.     20089  

Emergency   department   visits  for  an   adverse  drug   event  that  led   to  hospital-­ ization  

Identified  the   drugs  most   often   responsible   for  hospital   admission   (review  of  17   studies)  

Increase  in   reported   deaths  and   serious   injuries   associated   with  drug   therapy  

Causes  of   preventable   drug-­related   hospital   admissions  

X   opioids    

X     opioids    

X  

 

X  

 

 

X   insulin,  oral   hypoglycemics  

X   insulin  

X  

X      insulin  

X  

X   amoxicillin-­‐ containing   agents   X  

 

 

 

Emergency   department   visits   classified  as   an  uninten-­ tional   overdose  

 

  X

 

X  

X  

 

X   warfarin  

X    

X  

X  warfarin  

X      warfarin  

 

 

 

 

 

 

 

 

X   aspirin,  Plavix    

X  

 

 

 

 

X  

 

 

 

 

 

 

 

 X  digoxin,  beta   blockers  

 

X   beta  blockers,   angiotensin   inhibitors,   inotropes   X  

 

X  

 

 

 

 

 

 

 

X  estrogens  

 

 

 

 

 

 

  X

 

X   digitalis   glycosides  

X

 

 

 

 

 

 

X  

X   COX-­‐2  inhibitors  

 

X  

 

 

 

 

antiinflammatory  

agents     Theophylline  

X   opioids    

Appendix  2.  Advantages  of  ST-­‐PRA  over  FMEA  and  RCA30,37–43,75–79   • Presents  a  comprehensive  risk  assessment  of  the  “as  is”  state  of  predominantly  human   processes    Accommodates  latent  failures  and  active  risks    Includes  the  identification  and  effects  of:   o Human  error*   o At-­‐risk  behaviors**   o Intentional  procedural  deviations   o Mechanical  failures    Permits  examination  in  complex  systems  of  high  impact  events  of  both  low  and  high   frequency    Models  unique  combinations  of  events,  processes,  and  behaviors  that  increase  or   decrease  risk    Models  the  effect  of  a  combination  of  more  than  one  failure  point  in  complex   systems  that  are  most  likely  to  lead  to  errors    Objective  evaluation  and  comparison  of  different  configurations  are  possible      Models  can  incorporate  the  cumulative  knowledge  of  operations  experts  when   complete  data  sets  from  other  sources  are  not  available     • Quantifies  risk  and  makes  it  visible    Uses  probability  estimates  culled  from  an  expert  panel      Uses  software-­‐facilitated  calculation  to  quantify  combinations  of  risks      Predicts  the  quantitative  impact  of  specific  system  or  practice  changes    Provides  a  visual  depiction  of  the  dispensing  system  and  ways  that  errors  reach   consumers     • Informs  decision  making  and  promotes  rapid  assessment/improvement      Predicts  the  most  common  error  pathways  to  ensure  risk  reduction  or  mitigation   efforts  are  effective    Permits  ranking  of  relative  risks  to  determine  where  to  concentrate  limited   resources  for  maximum  risk  reduction    Models  different  interventions  to  identify  the  variation  with  the  most  benefit    Identifies  immediate  high-­‐impact  changes      Prioritizes  interventions  based  on  quantitative  risk       • Facilitates  the  safety  culture    Key  stakeholders  in  healthcare  can  see  their  roles  in  the  error  process,  their   interdependencies,  and  how  their  actions  increase  or  decrease  risks  for  each  other   and  ultimately,  for  patients       *Human  error  is  defined  as:  inadvertently  doing  other  than  what  should  have  been  done;  the  failure  of  a   planned  action  to  be  completed  as  intended  (error  of  execution)  or  the  use  of  a  wrong  plan  to  achieve  an  aim   (error  of  planning).     **At-­risk  behavior  is  defined  as:  behavioral  choice  that  increases  risk  where  risk  is  not  recognized  or  is   mistakenly  believed  to  be  justified.  At-­‐risk  behaviors  are  often  employed  and  tacitly  encouraged  as  a   workaround  for  various  system,  process,  technological,  or  environmental  weaknesses.  

 

Appendix  3.  Sample  section  of  FMEA  for  warfarin  and  fentanyl  transdermal  patches   Process   Failure   Causes  of  Failure/   Effect  of     Upstream/Downstream     Mode   Performance  Shaping   Failure   Controls   Factors     Example  for  Warfarin   Prepare   medication  

Select  the   wrong   drug  or   dose      

• Look-­‐alike  products  stored  

near  each  other  (e.g.,   different  strengths)     • Look-­‐alike  drugs  mistakenly   sent  by  wholesaler  and/or   misplaced  in  pharmacy  stock   • Label  ambiguity   • Knowledge  deficit  

Allergic   reaction       Overdose:     bleeding,  death     Subtherapeutic   dose:   thrombosis  

 

Upstream  controls   • Separate  look-­‐alike  products   • Warning  messages  in  computer  system   for  serious  product  labeling  issues  or   look-­‐alike  packages   • Checking  process  to  verify  stock  upon   arrival  from  wholesaler   Downstream  controls   • Independent  double  check  before   anticoagulants  are  dispensed  from  the   pharmacy     • Reviewing  the  medication  with  the   patient  before  dispensing  it   • Patient  inspection  at  the  point-­‐of-­‐sale    

Example  for  Fentanyl  Transdermal  Patches   Prescribe  the   drug  

Prescribe   the  wrong   dose    

• Knowledge  deficit  about  dose   Overdose:   •

• • • • •

 

 

conversions  from  opioids  to   fentanyl  patches     Knowledge  deficit  regarding   use  of  drug  in  opioid-­‐naïve   patients,  elderly  patients,  or   patients  who  do  not  require   a  total  daily  dose  of  opioids   equivalent  to  25  mcg/hour     Patient’s  clinical  situation   not  known  or  considered     Confuse  size  of  patch  as  dose   (e.g.,  10  cm2  for  a  25   mcg/hour  patch)   Mental  slip   Wrong  dose  selection  from   list  of  doses  if  prescribed   electronically   Increasing  the  dose  before   peak  fentanyl  levels  achieved   in  24-­‐72  hours  (change   recommended  no  sooner   than  3  days  after  the  current   dose  has  been  administered)  

respiratory   arrest,  death     Subtherapeutic   dose:   uncontrolled   pain  

Upstream  controls   • Dose  conversion  guidelines  readily   accessible   • Warning  messages  in  computer  when   prescribing  the  drug  using  electronic   system   Downstream  control   • Warning  messages  in  computer  when   entering  prescription  into  the  patient   profile    

Appendix  4.  Elements  of  event  trees   Event  Tree  Element   Description   Top  Level  Event   The  adverse  outcome  that  occurs  without  capture  before   reaching  the  victim.  Example:   • Dispense  the  wrong  dose  of  fentanyl  patch  to  a  patient   due  to  a  prescribing  error  that  was  not  captured.     Initiating  Error   The  initiating  error  under  study  that  may  result  in  the   adverse  outcome  or  be  captured  before  it  occurs.  Example:   • Prescribe  the  wrong  dose  of  fentanyl  patch.     Basic  Event   The  fundamental  failures,  loss  of  function,  unavailability,   exposure  rates,  or  capture  opportunities  that  create  the   branches  in  the  event  tree  when  combined  with  AND  or  OR   gates  (see  description  of  AND  and  OR  gates  below).   Example:   • Pharmacy  technician  does  not  catch  prescribing  error   during  data  entry  into  an  existing  patient  profile.   Exposure  Rate   The  frequency  with  which  certain  process  steps  or   conditions  occur;  used  to  split  sections  of  the  tree  into   appropriate  categories  that  may  have  differing  rates  of   contribution  to  the  top-­‐level  event.  Example:   • Categories  based  on  how  the  prescription  was  received   in  the  pharmacy  (e.g.,  50%  fax,  10%  telephone,  39%   patient  delivery,  1%  electronic).   Human  Error     Inadvertently  doing  other  than  what  should  have  been  done;     the  failure  of  a  planned  action  to  be  completed  as  intended   (error  of  execution)  or  the  use  of  a  wrong  plan  to  achieve  an   aim  (error  of  planning).  Example:   • Physician  orders  wrong  dose  of  a  fentanyl  transdermal   patch  due  to  knowledge  deficit.   At-­Risk  Behavior     Behavioral  choice  that  increases  risk  where  risk  is  not   recognized  or  is  mistakenly  believed  to  be  justified.  At-­‐risk   behaviors  are  often  employed  and  tacitly  encouraged  as  a   workaround  for  various  system,  technological,  or   environmental  weaknesses.  Example:   • Patient  identification  process  not  followed  at  point-­‐of-­‐ sale.   Equipment  Failure   Any  equipment  failure  that  can  affect  the  top-­‐level  event.   Example:   • Failure  of  the  barcode  reader  to  accurately  scan  the   barcode.     Capture  Opportunities   Activities  or  conditions  that  actively  or  passively  help  staff   detect  and  correct  the  initiating  error.  Failed  capture   opportunities  may  be  caused  by  human  error  or  at-­‐risk   behavior.  Example:   • Pharmacist  fails  to  capture  a  prescribing  error  during  

 

AND  Gates   OR  Gates  

 

 

drug  use  evaluation.     Many  missed  capture  opportunities  do  not  represent  human   error,  but  the  opportunity  to  catch  a  mistake  that  was  not   realized.  Example:   • Patient  fails  to  capture  a  dispensing  error  at  point-­‐of-­‐ sale.   A  method  of  combining  basic  events  in  which  all  the  basic   events  are  required  to  satisfy  the  condition  above  the  gate.   A  method  of  combining  basic  events  in  which  only  one  of  the   conditions  below  the  gate  is  required  to  satisfy  the  condition   above  the  gate.  

Appendix  5.  Performance-­‐shaping  factors  (PSFs)38–41   EXTERNAL   Task  complexity   Information  complexity   Ergonomics/Human-­‐ machine  interface   Procedures,  including  job   aids   Work  environment     Communication/Informatio n  exchange     Workflow/Work  processes   Stress     Time  available/Time   urgency     Design  of  products  and   labels   Organizational   culture/Management     INTERNAL   Training   Experience   Familiarity  with  task   Mental  and  physical   health/Fitness  for  duty   Task  tension  and   engagement   Stress     Motivation   Previous  actions  

 

Appendix  6.  Risk  factors  with  warfarin,  fentanyl  patches,  methotrexate,  and  insulin   analogs   Medication Warfarin

Fentanyl Patches

Methotrexate

Insulin analogs

 

Examples of Known Risk Factors

Warfarin  is  a  narrow  therapeutic  index  drug  for  which  small  changes  in   systemic  concentration  can  lead  to  significant  changes  in  pharmacodynamic   response.  This  may  result  in  potentially  subtherapeutic  or  toxic  effects,   particularly  in  patients  with  advanced  age,  comorbid  illness,  or  those   receiving  multiple  medications.     Warfarin  requires  close  monitoring  of  INR  values  for  proper  dosing.  Too   frequent  dose  changes  can  lead  to  fluctuating  and  suboptimal  levels  of   anticoagulation.     Due  to  frequent  dose  changes,  warfarin  prescription  labels  often  do  not  list   the  most  current  dosing  directions,  or  prescribers  list  “take  as  directed”  on   prescriptions  without  explicit  directions.     Serious  drug  interactions  are  common  with  warfarin.   Fentanyl  should  only  be  used  to  treat  moderate  to  severe  chronic  pain,  not   acute  pain.     Fentanyl  should  only  be  prescribed  for  patients  who  are  opioid-­‐tolerant   (have  taken  other  opioids  previously  for  pain).   Dose  conversion  from  another  opioid  to  fentanyl  can  result  in   overestimation  of  the  dose.   Each  patch  must  be  removed  before  applying  a  subsequent  patch.   Patches  that  fall  off  or  are  not  disposed  securely  can  be  lethal  to  children   because  a  significant  amount  of  drug  remains  in  the  patch  after  disposal.     Application  on  open  skin  or  exposure  to  heat  (e.g.,  sun,  heating  pad)  can   result  in  overdoses.   When  used  for  rheumatoid  arthritis,  the  drug  should  be  prescribed  as  a   single  weekly  dose  or  in  smaller  doses  every  12  hours  for  3  doses/week,  but   never  daily.     Weekly  versus  daily  dosing  for  most  drugs  is  uncommon  and  thus  prone  to   error.     Serious  drug/drug  and  drug/disease  interactions  and  adverse  effects  can   occur  with  methotrexate.   Monthly  hematology  and  bimonthly  renal  and  hepatic  lab  studies  are   required  during  treatment.     Handwritten  orders  for  methotrexate  2.5  mg  can  be  mistaken  as  minoxidil   2.5  mg,  and  vise  versa.     There  are  about  a  dozen  different  types  of  insulins  and  several  dozen   different  brands,  many  of  which  have  names  and/or  packages  that  look  or   sound  alike.     The  onset  of  action  for  insulin  types  varies  widely.  Depending  on  the   product,  the  onset  may  vary  from  mere  minutes  to  8  hours.  This  makes  the   typical  time  for  insulin  administration  and  its  relationship  to  meals   confusing.     Insulin  is  available  in  multiple  concentrations  (100  units/mL  and  500  

units/mL).     Patients  often  receive  widely  variable  doses  and  more  than  one  type  of   insulin  concurrently.     Many  insulin  products  are  available  over-­‐the-­‐counter.   The  abbreviation  “u”  for  units  can  be  misread  as  a  zero,  risking  a  10-­‐fold   error.