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PRACTICE REPORT Medication safety effectiveness

2218 Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

P R A C T I C E R E P O R T

Comparison of medication safety effectiveness among nine critical access hospitals

GARY L. COCHRAN AND GLEB HAYNATZKI

GARY L. COCHRAN, PHARM.D., S.M., is Assistant Professor, De- partment of Pharmacy Practice, College of Pharmacy; and GLEB HAYNATZKI, PH.D., is Professor, Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha.

Address correspondence to Dr. Cochran at the Department of Pharmacy Practice, College of Pharmacy, University of Nebraska Medical Center, 986045 Nebraska Medical Center, Omaha, NE 68198-6045 ([email protected]).

The authors acknowledge the critical access hospitals that partici- pated in this study, the Collaborative Alliance for Nursing Outcomes for its assistance with the direct observation methodology, and

Kathleen Mohan, Pharm.D., for her assistance with data collection and management.

Supported by grant number K08HS018059 from the Agency for Healthcare Research and Quality. The content is solely the respon- sibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

The authors have declared no potential conflicts of interest.

DOI 10.2146/ajhp130067

This article is part of a series focusing on pharmacy practice in small and rural hospitals. Stephanie Y. Crawford, Ph.D., M.P.H., B.S.Pharm., and Glen T. Schumock, Ph.D., Pharm.D., M.B.A., serve as field editors for the series, soliciting and reviewing future articles. Unsolicited submissions are also welcome.

Since 1999, when the Institute of Medicine published “To Err Is Human: Building a Safer Health System,” we have learned a great deal about medication error epidemiol- ogy, the root causes of errors, and technologies that can reduce the frequency of medication errors, particularly in large urban hospi- tals.1 Despite these advances, two important limitations exist regard- ing previous research for small rural hospitals. First, the medication-use systems (from prescribing through monitoring) in large hospitals dif- fer greatly from the medication-use systems in place in small hospitals, making the generalization of study results difficult.2-6 Second, little is known about the comparative ef- fectiveness of technologies such as telepharmacy and a bedside barcode

Purpose. The rates of medication errors across three different medication dispens- ing and administration systems frequently used in critical access hospitals (CAHs) were analyzed. Methods. Nine CAHs agreed to participate in this prospective study and were assigned to one of three groups based on similarities in their medication-use processes: (1) less than 10 hours per week of onsite pharmacy support and no bedside barcode system, (2) onsite pharmacy support for 40 hours per week and no bedside barcode system, and (3) onsite pharmacy support for 40 or more hours per week with a bedside barcode system. Errors were characterized by severity, phase of origination, type, and cause. Characteristics of the medication being administered and a number of best practices were collected for each medica- tion pass. Logistic regression was used to identify significant predictors of errors.

Results. A total of 3103 medication passes were observed. More medication errors originated in hospitals that had onsite pharmacy support for less than 10 hours per week and no bedside barcode system than in other types of hospitals. A bedside barcode system had the greatest impact on lowering the odds of an error reaching the patient. Wrong dose and omission were common error types. Human factors and communication were the two most frequently identified causes of error for all three systems. Conclusion. Medication error rates were lower in CAHs with 40 or more hours per week of onsite pharmacy support with or without a bedside barcode system compared with hospitals with less than 10 hours per week of pharmacy support and no bedside barcode system. Am J Health-Syst Pharm. 2013; 70:2218- 24

system on the reduction of medica- tion errors in critical access hospi- tals (CAHs).7

Lack of onsite pharmacist support is an important example of how large and small hospitals differ. In 2006, Casey et al.3 reported that 27.7% of

the small rural hospitals (50 beds or fewer) surveyed had pharmacist staffing of 0.5 full-time equivalent or fewer. A similar survey revealed that 48% of small rural hospitals with an average daily census of fewer than five had a pharmacist onsite for

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2219Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

five or fewer hours per week2; this represented approximately 25% of all responding small rural hospitals. Adoption of technology also lags in small hospitals. For example, 90% of hospitals with an average census of fewer than five reported using a handwritten medication administra- tion record (MAR) compared with 22% of large urban facilities.2 These differences in structure and process are likely to affect medication error epidemiology.

Small hospitals must select care- fully among competing options to improve medication safety because most institutions lack the financial and human resources to implement multiple systems simultaneously.8 Options include hiring an onsite pharmacist who works 40 hours per week, implementing 24–7 telephar- macy review and order entry, install- ing a bedside or pharmacy barcode system, leasing automated dispensing cabinets, and implementing com- puterized prescriber order entry.9-12 Decision-makers would benefit from a better understanding of er- ror epidemiology across the phases of medication use, including the types of errors that persist. Without comparative-effectiveness informa- tion, administrators of small hospi- tals may delay the adoption of im- portant safety interventions, waiting for better estimates of effectiveness before making the necessary large financial investments.

The need for a better understand- ing of errors in small hospitals is not trivial, as CAHs (facilities with 25 or fewer acute care beds) comprise approximately 26% of all commu- nity hospitals in the United States.13 CAHs are a special category of rural hospitals that receive cost-based reimbursement from Medicare. To qualify as a CAH, rural hospitals must have a maximum of 25 inpa- tient acute care beds, maintain an annual average length of stay of no more than 96 hours, and be located at least 35 miles from another hospi-

tal. There are currently 1328 certified CAHs in the United States.14

The primary objective of this study was to compare the rates of medication errors that reached the patient across three different medica- tion dispensing and administration systems frequently used in CAHs. A secondary analysis to identify inde- pendent predictors of errors was also conducted.

Methods Study participants. An invitation

to participate in this study was pre- sented at two Nebraska CAH network meetings. Nine hospitals agreed to participate and were assigned to one of three groups based on similarities in their medication-use processes. Two hospitals had less than 10 hours per week of onsite pharmacy support and no bedside barcode system and were grouped together. Pharmacists in these hospitals were primarily responsible for inventory manage- ment. Pharmacists did not regularly review prescriber orders or dispense medications. A second group con- sisted of four hospitals with onsite pharmacy support for 40 hours per week but no bedside barcode system. Pharmacists were available in these facilities Monday through Friday during the day. Pharmacist coverage during nights and weekends varied. The third group consisted of three hospitals with onsite pharmacy sup- port for 40 or more hours per week and bedside barcode systems. Hos- pitals in the third group provided day time and evening pharmacy services (from 7 a.m. to 7 p.m., at a minimum) Monday through Friday and daytime support on Saturday and Sunday.

Data collection. This study used the direct obser vation method, with observers recording detailed information about each medication administered.15 After each observa- tion was complete, the observer com- pared the medications administered to the physician’s orders in the chart.

Rather than relying on the current MAR, current orders are deter- mined by preparing a list of ordered medications beginning at the time of admission and updating the list as orders change over the patient’s stay. This is done to identify any missed orders (new or discontinued) and to identify transcription errors.

At least two nurses or pharmacists from each hospital attended one of two training sessions to learn the direct observation methodology. Ex- perienced staff from the Collabora- tive Alliance for Nursing Outcomes (CALNOC) conducted the training. Draft data collection forms were presented at these training sessions. Forms were modified and vari- able definitions were agreed upon through group discussion.

A convenience sample of 350 observations for each hospital was chosen based on the available budget and expected hospital census. The trained observers at each hospital identified the nurses to be observed. The medication-use process changes significantly during nights and week- ends (e.g., reduction in nurse and pharmacist staffing), possibly lead- ing to differences in error rates. For this reason, hospitals were asked to observe at least 200 daytime, 75 nighttime, and 75 weekend medica- tion passes (i.e., times during which medications were administered). Weekend medication passes were defined as medications administered during Saturday or Sunday morn- ing. Nighttime medication passes were defined by each hospital based on staffing changes. Most hospitals defined nighttime medication passes as medications administered after 7 p.m., since pharmacy and nursing staff changes frequently occurred at that time. Hospitals were asked not to observe the same patient during the same shift on more than 1 occa- sion. Patients observed during a day- time shift could be observed again on evenings or weekends, as long as the same nurse had not already been

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2220 Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

observed on another shift. The intent of these directives was to reduce the correlation between observations. Observations were conducted be- tween February and August 2011.

Medication, system, and process variables. Data collected for each administered medication included drug name, dosage, route, and dos- age form. Observers also recorded the type of order (routine, as needed, urgent) and the source of the medi- cation (e.g., dispensed by a pharma- cist, acquired by a nurse from the drug closet or pharmacy, automated dispensing cabinet, floor stock). The presence or absence of several bed- side best practices was also recorded, including the following:

• Was there a comparison of the drug to the MAR at the bedside?

• Was the drug labeled from drug ac- quistion to bedside administration?

• Were two forms of patient identifica- tion checked?

• Was the purpose of the medication explained to the patient?

• Was the medication’s barcode scanned at the bedside before administration?

Because duplicate doses can oc- cur if a medication’s administration is not listed on the MAR, the timing of medication charting was recorded (before administration, after adminis- tration, or not charted). The number of interruptions and distractions that occurred throughout the observed medication pass was also recorded.

Outcome variables. A medication error was defined as a dose adminis- tered differently than ordered by the prescriber as recorded in the patient’s MAR.16 A wrong-time error was de- fined as a dose given at least one hour before or after the scheduled admin- istration time.

The direct observation technique was designed to identify errors that originate during administration. Transcription and dispensing errors are also regularly identified during the chart review process that occurs

after medication administration is complete. We did not attempt to identify errors that originated during medication reconciliation or errors identified by a pharmacist during order review, because these errors could not be identified through di- rect observation or the subsequent chart review.

Error severity was described using the National Coordinating Council for Medication Error Reporting and Prevention taxonomy.17 Errors that were identified and prevented before medication administration were recorded as category B, or near- miss errors. All errors including near misses underwent further investi- gation by the institution’s quality- improvement staff. Because category A events are not actual errors, they were not included in the analysis. Ad- ditional information regarding the phase of error origination, the error type, and the cause of the error was gathered.

All data collection forms were reviewed before data entry into the study database. Hospitals were asked to clarify missing or unclear data. In some cases, error severity was reclassi- fied after review of the description of the error or following discussion with the reporting hospital. Four errors were identified by one of the hospitals through a medication reconciliation process that was conducted after observation was complete. Because reconciliation was not part of the di- rect observation training, and recon- ciliation was not conducted similarly in all hospitals, these errors were not included in the analysis. Hospitals were reimbursed for training and data collection time. The study was reviewed and approved by the Uni- versity of Nebraska Medical Center Institutional Review Board.

Study endpoints and statistical analysis. The primary endpoint was the number of errors that reached the patient (categories C–I) per 100 doses administered. The total error rate for each system (near misses

and errors that reached the patient) was also compared. Error rates for nighttime and weekend medication passes were compared to determine whether error rates increased when staff levels were reduced. The nu- merator for all comparisons was the number of errors that occurred in each system. The denominator was the number of doses evaluated us- ing direct observation. Fisher’s exact test was used to compare error rates between systems. Logistic regres- sion was used to identify significant predictors of errors that reached the patient. Finally, all errors were clas- sified by error type, phase of origina- tion, and cause. A two-sided p value of <0.05 was considered significant. The statistical package SAS 9.2 (SAS Institute, Cary, NC) was used for all analyses.

Results A total of 3103 medication passes

were observed. At least 350 medica- tion administrations were observed at eight of the nine hospitals (200 daytime, 75 nighttime, and 75 week- end medication administrations). One hospital was only able to com- plete 138 observations over the study period due to low census. Patient and system characteristics are displayed in Table 1.

Forty-four errors were identified, including 13 errors that did not reach the patient (category B). All of the 31 errors that reached the patient were either errors that reached the patient but did not cause patient harm (cat- egory C) or errors that reached the patient and required monitoring to confirm that they resulted in no harm (category D). None of the errors iden- tified were harmful (categories E–I).

More medication errors origi- nated in hospitals that had onsite pharmacy support for less than 10 hours per week and no bedside bar- code system than in other types of hospitals (Table 2). Similarly, fewer errors reached the patient in hospi- tals that had 40 hours per week of

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onsite pharmacy support without a bedside barcode system or hospitals with 40 or more hours per week of onsite pharmacy support and a bed- side barcode system compared with hospitals with less than 10 hours per week of onsite pharmacy support and no barcode system.

Table 3 details the impact of sever- al system or process variables on the odds of an error reaching the patient. A bedside barcode system had the greatest impact on lowering the odds of an error reaching the patient. Hav- ing the medication dispensed by a pharmacist also significantly reduced the odds of a medication error.

Errors were also categorized by the phase of the medication-use process in which the error originated (Table 4). The majority of errors that reached the patient originated in the administration phase for all three systems. No significant differences in error rates were noted among day- time, nighttime, and weekend shifts.

Wrong dose and omission were common error types and represented

approximately 46% of all errors. Wrong-time errors were rare. Human factors and communication were the two most frequently identified causes of error for all three systems.

Discussion Pharmacists and bedside barcode

systems are important safety systems used by rural hospitals. This study compared the rates of medication errors among systems that differed in the availability of pharmacy support and a barcode system.

Hospitals with 40 or more hours per week of onsite pharmacy support and a bedside barcode system had the lowest overall error rates among the three groups. Hospitals with onsite pharmacy support for 40 hours per week and no barcode system had a lower error rate than did hospitals with less than 10 hours per week of onsite pharmacy support and no barcode system. Despite the best ef- forts of these facilities, the errors that reached patients represent current system vulnerabilities that allowed

the errors to originate, progress through the medication-use process undetected, and be administered to a patient. As expected, hospitals with pharmacy support and a bedside barcode system had fewer errors that reached patients than did facilities with less than 10 hours per week of onsite pharmacy support and no barcode system.

If error risk was similar in all in- stitutions, we would expect similar overall error rates across all hospitals, with fewer errors reaching patients (more near misses identified) in hos- pitals with double checks built into their medication-use system. Instead, errors were much less likely to occur in hospitals with regular pharmacy support and a bedside barcode sys- tem. A bedside barcode system does not reduce the likelihood that an error will originate in a hospital; rather, its role is to increase the likeli- hood that the error will be identified before drug administration and thus be prevented. For this reason, the presence of a barcode system does

Characteristic

Onsite Pharmacy Support Available <10 hr/wk

Without Bedside Barcode System (n = 2)

No. observations Mean ± S.D. patient age, yr Male, % Mean ± S.D. no. medications per

patient Mean ± S.D. no. patients cared

for by nurse administering medication

No. (%) medications dispensed by pharmacist

No. (%) medications with bedside barcode system

Hospitals within group having computerized provider order entry, %

Hospitals within group having automated dispensing cabinets, %

Table 1. Characteristics of Patients and Critical Access Hospitals in the Study

1136 76 ± 15

8.3 ± 4.4

2.05 ± 0.90

805 (70.1)

1055 (92.9)

100

Onsite Pharmacy Support Available 40 hr/wk Without

Bedside Barcode System (n = 4)

Onsite Pharmacy Support Available ≥40 hr/wk With

Bedside Barcode System (n = 3)

489 75 ± 11

7.3 ± 4.3

2.97 ± 1.26

60 (12.3)

1478 78 ± 14

6.2 ± 3.9

2.65 ± 0.98

625 (42.3)

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2222 Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

not explain the differences in er- ror rates observed in this study. In contrast, pharmacist dispensing may partially explain the lower error rates in the two groups of facilities with pharmacy support. Because phar- macists have a unique knowledge of drugs, fewer dispensing errors oc- curred when a pharmacist dispensed a medication as opposed to another health care professional. Pharma- cists can also prevent errors through order review, medication reconcili- ation, formulary management, de- sign of medication-use policies and procedures, and participation on quality-improvement projects. While none of these activities were directly evaluated in this study, their impact on errors may be reflected in each hospital’s overall error rate. It is also likely that other system differences unrelated to pharmacy support or a bedside barcode system are respon- sible for the differences observed.

We would expect more errors to be prevented in facilities using sys- tems with built-in double checks, such as pharmacist dispensing or a barcode system, as opposed to sys- tems in which the same nurse both selects a medication and administers the drug without any system double checks. A significant difference in near-miss rates was identified be- tween hospitals with 40 hours per week of pharmacy support without a barcode system and hospitals with less than 10 hours per week of phar- macy support without a barcode sys- tem. The near-miss rate in hospitals with a bedside barcode system did not differ significantly from the other hospital groups.

We conducted a secondary analy- sis by combining the observations from all nine hospitals and evaluat- ing the impact of several indepen- dent variables on the likelihood that an error would reach the patient. Pharmacist-dispensed medications and the presence of a a bedside bar- code system were associated with lower odds of an error reaching the

patient. Previous research has sug- gested an association between nurse staffing and medication errors.18 In this study, workload was represented by recording the number of patients being cared for by the administering nurse at the time of the observed medication pass. We found that increasing the number of patients being cared for by a nurse was associ- ated with an increased risk of an er- ror reaching the patient. The impact of interruptions and distractions has also been identified as a significant predictor of medication errors.19,20 We compared medication passes with one or more interruptions and distrac- tions with medication passes without them and found no significant as- sociation between interruptions or distractions and errors reaching the patient (Table 3).

It is common for staffing to be reduced on nights and weekends in small rural hospitals. This led to our hypothesis that errors would be more frequent during those times. Howev- er, Patrician et al.18 reported that 45% fewer medication administration er- rors occurred on critical care units at night. They postulated that the lower rate might be the result of the fact that fewer new or modified medica- tion orders are received at night or that there are fewer interruptions and distractions. No significant dif- ferences in error rates were identified in our study.

The overall error rate observed in this study was notably lower than what was reported in larger facilities that used direct observation to detect errors. CALNOC reported a median error rate of 8% from observations submitted by 27 hospitals between 2006 and 2008.21 In 2009, Helmons et al.22 observed a 10.7% error rate in the medical–surgical units of a southern California academic teach- ing hospital before the implementa- tion of a bedside barcode system. The observed error rate fell to approxi- mately 8% after implementation of a barcode system. A 2002 study that

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PRACTICE REPORT Medication safety effectiveness

2223Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

observed medication administration in 36 institutions reported that 19% of the doses observed were given in error; wrong time (43%), omission (30%), wrong dose (17%), and un- authorized drug (4%) were the most frequent types of errors reported.23 In comparison, our study identified 31 errors that reached the patient from 3103 medication passes (1.0%) in 9 CAHs. An additional 13 errors were intercepted before reaching the patient, representing an overall error rate of 1.4%. Only 2 (5%) of the 44 errors were wrong-time errors.

The difference in overall error rates is surprising, given that CAHs lack

technology, quality-improvement re- sources, and regulatory drivers (Joint Commission accreditation) that are common in larger facilities. There are several possible explanations that would support a lower overall error rate and fewer wrong-time errors in CAHs. First, differences in patient acuity may be associated with a less- complex medication regimen, lead- ing to fewer errors. Second, a lower nurse:patient ratio may explain the lower rates. The average number of patients being cared for by the admin- istering nurse in our study was about 2.5. A report issued by the California Workforce Initiative in 2000 indicated

that patient:nurse ratios in acute care hospitals ranged from 4.7 to 7.2.24

Fewer wrong-time errors may be a result of hospital size. CAHs are small; most are a single floor with the medication room close to patient care areas, reducing drug acquisi- tion and delivery times. Hospitals without regular pharmacy support do not need to wait for the pharmacy to review or dispense medications. This may reduce wrong-time (late) errors but would presumably in- crease errors that would arise during dispensing.

The lower-than-expected error rate reduced our ability to evaluate relationships between predictors and outcomes. We planned to construct a multivariable logistic regression model to control for confounding and address possible data correla- tion (clustering) at the hospital and patient levels. Our sample only pro- vided the statistical power for a crude analysis of relationships. For these reasons, the results of this pilot study need to be interpreted cautiously. While error rates differed among the three types of hospitals, other differ- ences between hospitals (teamwork

Phase of Medication- Use Process

Onsite Pharmacy Support Available <10 hr/wk Without Bedside

Barcode System

Prescribing Total no. errors (%) No. near misses Transcription Total no. errors (%) No. near misses Dispensing Total no. errors (%) No. near misses Administration Total no. errors (%) No. near misses Other Total no. errors (%) No. near misses

Table 4. Origins of Errors in Critical Access Hospitals

Onsite Pharmacy Support Available 40 hr/wk

Without Bedside Barcode System

Onsite Pharmacy Support Available ≥40

hr/wk With Bedside Barcode System

1 (6.25) 0

3 (18.75) 0

3 (18.75) 1

8 (50.00) 0

1 (6.25) 0

0 0

1 (16.67) 0

1 (16.67) 1

4 (66.67) 1

0 0

2 (9.52) 1

4 (19.05) 2

6 (28.57) 4

8 (38.10) 2

2 (9.52) 1

Hospital Practice

Bedside barcoding performed Medication dispensed by a pharmacist Presence of at least 1 interruption or

distraction Nurse:patient ratio No. medications administered to patient

Table 3. Independent Predictors of Errors That Reached Patientsa

aOdds ratio and p calculated using logistic regression.

Odds Ratio (95% Confidence Interval) p

0.21 (0.06–0.68) 0.37 (0.17–0.84)

1.05 (0.50–2.19) 1.42 (1.05–1.91) 0.93 (0.85–1.02)

0.01 0.02

0.91 0.02 0.14

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2224 Am J Health-Syst Pharm—Vol 70 Dec 15, 2013

and communication, policies and procedures, culture, other technolo- gies) may explain the differences in error rates.

Selection bias and the Hawthorne effect could have influenced the observed error rates. Observers did not randomly choose nurses to ob- serve. It is possible that nurses with fewer patients or patients with less- complex problems were more likely to be observed. Observers, however, reported that nurses rarely asked that they not be followed and that partici- pation by nurses was widespread and generally well received. In addition, the mean number of medications administered per patient was similar across hospital groups.

The Hawthorne effect could have influenced error rates. The concern for either an increased error rate (ob- servation increases subject nervous- ness) or a decreased error rate (sub- ject becomes more careful) has been investigated. A study evaluating the validity and reliability of observation to identify medication administra- tion errors concluded that those con- cerns might be unfounded.25 Accord- ing to Flynn and Barker,26 pioneers of the direct observation technique, “if observation is unobtrusive and nonjudgmental, the observed subject will quickly resume normal behav- ior.” During our exit interviews, how- ever, observers found that in three hospitals, at least one nurse in the institution was more likely to follow hospital protocols when being ob- served. While we cannot rule out the Hawthorne effect, we do not believe it could explain the large differences between our rural findings and those observed in an urban setting.

Conclusion Medication error rates were lower

in CAHs with 40 or more hours per week of onsite pharmacy support with or without a bedside barcode

system compared with hospitals with less than 10 hours per week of pharmacy support and no bedside barcode system.

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