Bhuvanesh.applying SHERPA to Analyze Medication Administration in the Cardiac Telementry Unit

8/2/2019 Bhuvanesh.applying SHERPA to Analyze Medication Administration in the Cardiac Telementry Unit

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Proceedings of the 2008 Industrial Engineering Research Conference J. Fowler and S. Mason, eds.

Applying SHERPA to Analyze Medication Administration in theCardiac Telemetry Unit

Abhinesh Bhuvanesh, Shengyong Wang, Mohammad Khasawneh, Sarah S. Lam,Krishnaswami Srihari

Department of Systems Science and Industrial EngineeringState University of New York at Binghamton

Binghamton, NY 13902

Tejas GandhiManagement Engineering, Virtua Health

Voorhees, NJ 08043

Abstract

Medication administration is one of the most important and complex processes touching almost every patient that

comes into the hospital. Consisting of five stages, namely prescribing, documenting, dispensing or preparation,administering and monitoring, the process is highly dependent on the effective and efficient interactions of health professionals from different disciplines. Previous research indicated the benefits of applying human factors to studymedication errors and highlighted the need for healthcare providers to be more aware of human limitations and thatsystem changes should be made to accommodate them. The Systematic Human Error Reduction and PredictionApproach (SHERPA), which is based on human errors taxonomy, analyzes tasks and identifies potential solutions toerrors in a structured manner. The application of SHERPA for analyzing drug administration will be especially

beneficial for hospital decision makers as they plan to integrate various technologies to improve this process.Therefore, this paper illustrates the application of SHERPA to the process of administering drugs to patients of thecardiac telemetry unit at Virtua Health, a multi-hospital healthcare system headquartered in Marlton, NJ, particularlyas a means for preventing (or reducing) errors and comparing alternative administration technologies.

Keywords

Medication administration, SHERPA, human error, cardiac telemetry unit.

1. IntroductionMedication errors are a global problem and are typically defined as deviations from a physician's order, withhospital medication error rates reaching as high as 1.9 per patient per day [9]. Even with this high incidence manygo unreported because nurses fear the consequences of reporting an error [19]. Many major medical error studieshighlight medication errors as a cause of adverse events suffered by patients [14, 16]. Ferner and Aronson [6] definea medication error as ‘‘a failure in a drug treatment process that leads to or has the potential to lead to harm to the

patient.’’ Even though medication errors may or may not result in an adverse effect, they indicate a low level of safety in health assistance [18]. According to the National Coordinating Council for Medication Error Reporting andPrevention (NCCMERP), drug-related incidents may be classified into groups and include adverse reactions,adverse effects, and medication errors [18]. An Adverse Drug Reaction (ADR) is defined as every harmful andundesirable effect occurring after a drug is administered in doses usually used by man for prophylaxis, diagnosis, or

treatment of a disease or with the aim of changing a biological function [18]. An Adverse Drug Event (ADE)includes medication errors and adverse drug reactions, which may be classified as avoidable or unavoidable [18].Finally, a dispensing error is defined as the discrepancy between the written order in a medical prescription and itsfulfillment. Dispensing errors, are typically made by the pharmacy staff, including pharmacists, while dispensingdrugs to hospital units [2, 8], comprise the types of medication errors that are most harmful to patients.

Medication administration is one of the most important and complex processes touching almost every patient thatcomes into the hospital. It consists of five stages, namely prescribing, documenting, dispensing or preparation,administering and monitoring. This makes the process is highly dependent on the successful and efficient interactionof health professionals from different disciplines [15]. There are many different causes of medication errors like

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Bhuvanesh, Wang, Khasawneh, Lam, Srihari and Gandhi

workload, constant interruptions, shift patterns [5], communication failures [7], long working hours [11] and theseoften combine to cause the incident. Researchers also cite other causes such as the lack of proficiency in thecalculation of drug doses [23] and rates of administration and lack of information about the patient [16]. Aconsistent medication administration process is necessary to support an integrated approach to delivering andmanaging pharmaceutical care in an organized delivery system. According to Wolf [24], the responsibility for theerror is often placed on the nurse, as she or he is the last person in the drug administration chain. Therefore, thefocus of this study was on the acquisition of meds by nurses to the drug administration to the patients in the cardiac-telemetry unit.

2. The Medication Administration Task At Virtua Health, the hospitals use a ward pharmacy system, wherein all the floor medications, both common and

patient specific, are stored in the automated point-of-use unit dose drug distribution Pyxis machines in medicationrooms. As soon as the nurse starts his/her shift, he/she receives a patient assignment for the shift, which represents alist of patients (usually 4 to 5) that he/she has to take care of. The nurse checks patient records for any neworders/appointments, radiology/doctors instructions, etc. followed by a check of the Medication AdministrationRecords (MARs), which are located at the nurse station. The MARs have to be the most recent and should have beenupdated by the night shift nurse at 10 PM daily. The nurse is required to crosscheck the MARs in the patient recordswith the Computerized Medication Administration Records (CMARs), which is automatically generated whenever a

pharmacists profiles a patient order. CMARs are located on the medication-carts in the hallways of each unit, and

are checked at the beginning of the shift on a daily basis, to ascertain the correctness of the medications. The nursethen checks the patient specific compartment in the Pyxis system for medications not present in the patient specificcompartments on the med-carts. If these are not available, then the nurse has to fill a medication order for the patientand give it to the unit secretary for further processing. Once all the medications are available, the nurse prepares andadministers the medications and updates both the nursing and patient records. Nurses make a medicationadministration round four to six times on a daily basis, depending on the type of unit.

Giving medications to patients is a fundamental nursing role. However, it is a complex activity that carries a highrisk of error due to the involvement of different healthcare professionals, which makes it inherent that errors mayoccur at any stage of the process [10]. Even though nurses seek to give medications correctly, their efforts are oftenconfounded by hard-to-read prescriptions, constant interruptions, conflicting demands and high workloads.Successful medication administration is dependent on an effective patient information and patient monitoringsystem. Some medications that have a limited therapeutic range and various indicators (such as blood pressure or anticoagulant levels) have to be checked before being administered in the appropriate quantity. Medical staff alsoneed to know which medication is needed and when, this information cannot be accurately ascertained in theabsence of the medication chart. Physicians use the patient’s chart to indicate to the nurse which medications the

patient is to receive. Patient charts are kept at the nurse station and used to record medication administration, whichinclude the medication name, dose, route, and the medication administration round when it is to be administered.Each dose administered is recorded on the medication chart, which usually shows 3-4 days of documentationallowing for viewing of the patient’s most recent medication history.

3. A Human Factors Approach to Medication Administration ErrorsThe focus of this study was to explore the procedure for administering drugs to patients and examine the task steps,the equipment used, and the relationships among these factors. The systematic analysis of the medicationadministration process at task level will assist in assessing interventions to reduce errors through various designsolutions like technology, procedural changes etc. Human error analysis facilitates the understanding of how humaninteraction with drug administration tasks might lead to incidents. Hierarchical Task Analysis (HTA) helps inisolating the critical characteristics of the tasks by delineating them in an orderly way at different levels of details.At each level, the methods required to improve specific aspects of the task can be identified, which aids inevaluating what type of training is needed to bring the personnel to superior performance levels. HTA has beenextensively used to study human error, human-machine interface design, human-computer interaction, training, job-design, and allocation of functions and assessment [21]. The data used in HTA can be obtained through a variety of modalities, including observation, shadowing, structured and un-structured interviews, appropriate verbal protocolanalysis, and analysis of company wide procedures/documentation and training manuals [3, 20]. In this study, directobservation and shadowing were carried out of nurses in the cardiac-telemetry unit at Virtua Health’s Voorheescampus for developing the HTA (see Figure 1).

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The high frequency tasks performed by the Registered Nurses (RNs) across these units have been highlighted ingreen, and every task associated with documentation is highlighted in light blue. Medication is a task that nurses dorepetitively for every patient (typically 9 AM and 4 PM meds), which makes it a vital part of patient treatment. Thetask begins, as the nurse first checks the patient records for any new notes, followed by a check of the patient’smedication administration record, and then the nurse goes to the med-cart/Pyxis to obtain the scheduled medications.Once the nurse has acquired the medications, then he/she prepares the correct dose and administers the drug to the

patient, followed by immediate updating of the patient’s medication chart. The goal of patient treatment consists of several daily level 1 nursing tasks, such as patient admit, patient records, medication, patient rounds, patientdischarge, and shift change. The HTA shows all the activities that an RN is required to do in the cardiac telemetryunit. The involvement of cardiac monitors adds tasks that are unique to this unit. As soon as a patient is admitted tothe unit, based on the patient’s condition, RNs have to attach a cardiac monitor to the patient. A critical task thatRNs have to do in this unit is to frequently monitor the telemetry displays for patient condition, which makes it aninherent initial step in the medication administration process.

The HTA was reviewed by nurse managers, the director of quality management, and the patient safety expert. Thetop-level goal of the system is to administer medications to the patient. The steps necessary to do this are listed astasks 1–6 on the next level of the hierarchy. Plan 0 indicates the activities or sub-goals that should be carried out toachieve the goal. These activities are further broken down into operations at the lower levels. The order in whichthese activities are carried out is determined by the plan defined in the diagram.

Figure 1. Hierarchical Task Analysis for Nurse Tasks

4. SHERPA AnalysisThe Systematic Human Error Reduction and Prediction Approach (SHERPA) was developed by Embery [4] for usein the process industries (conventional and nuclear power generation, petrochemical processing and oil and gasextraction). The technique is based on the taxonomy of human error, which, in the original form, specifies the

psychological mechanism underlying the error, which can be used to analyze tasks and identify potential solutions tothe errors in a structured manner. Ongoing development of the technique has removed this reference to theunderlying psychological mechanism. This human error identification technique allows the analyst to define theinformation that is useful for error reduction strategies. Kirwan [12, 13] compared SHERPA with five other humanerror identification techniques on the criteria of comprehensiveness, accuracy, consistency, theoretical validity,

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usefulness, resource usage and auditability/acceptability to see if the incidents predicted by those techniquesmatched those that had actually occurred and found that SHERPA was the most highly rated by expert users. Baber and Stanton [1] reported the concurrent validity as 0.8 and reliability as 0.9, whereas Stanton and Stevenage [22]reported concurrent validity and reliability statistics of 0.74 and 0.65, respectively for the application of SHERPA by25 novice users to the prediction of errors on a confectionery vending machine. These reliability and validitystatistics are generally encouraging. Moreover, Lane et al. [15] demonstrated the application of SHERPA for themedication administration process for the UK-based hospital systems. The study presented in this paper is anextension of Lane et al.’s work and application of SHERPA for the medication administration process in the cardiac-telemetry unit at Virtua Health.

SHERPA uses the bottom-level actions of the HTA as its inputs, which represent the operations or task steps carriedout to achieve the higher-level goal. The operations are evaluated for potential error using the human error taxonomy shown in Table 1. The types of error that may occur fall into one of the five behavior categories: action,checking, retrieval, communication, and selection. Each error type in the taxonomy is coded and associated with anerror mode. The task steps from the HTA are examined in turn and are classified into one of the error types, with theconsideration of the most likely error modes associated with that operation. For example, task step 2.3 in the HTA(i.e., “Cardiac Monitor Records’’, as shown in Figure 1) is classified as a “checking” activity. Looking at theassociated checking error modes in Table 1, only the most credible errors for the task step are taken into account. Itis possible that prior to medication administration, the RN may fail to check telemetry monitor for the patientscondition, or he/she may be called away to attend to another patient, and hence, not complete the search. In nursingterms, these are not strictly errors. However, with regards to SHERPA, these actions would prevent the goal of accurate and complete patient record, which also affects medication administration and patient treatment.

Table 1: SHERPA Error Modes [15]

The results of the SHERPA analysis specific to cardiac monitors are recorded in tabular form (see sample in Table2). The error table for the rest of the tasks can be reviewed in the study conducted by Lane et al. [15]. The firstcolumn indicates the number of the task step (i.e., 3.1). The error mode C1 is entered in the second column, whichdenotes that a check has been missed. In the third column (i.e., “Description”), an outline of the error is described as:‘‘ fail to check telemetry display’’ . At this stage of the analysis, it is possible to make a prediction of what theconsequence of that error might be. The fourth column (i.e., “Consequence”), a description of the potentialconsequence of the activity is entered, with the fifth column indicating whether or not the error can be recovered. It

p = frequency of occurrence1: Low2: Medium3: High

p = frequency of occurrence1: Low2: Medium3: High

q = severity of occurrence1: Small adverse effect, likely recovery2: Small adverse effect, unlikely recovery3: Medium adverse effect, likely recovery4: Medium adverse effect, unlikely recovery5: Significant adverse effect, likely recovery6: Significant adverse effect, unlikely recovery

q = severity of occurrence1: Small adverse effect, likely recovery2: Small adverse effect, unlikely recovery3: Medium adverse effect, likely recovery4: Medium adverse effect, unlikely recovery5: Significant adverse effect, likely recovery6: Significant adverse effect, unlikely recovery

Risk Priority Number = Σ p x q Risk Priority Number = Σ p x q

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may be that by completing further task steps, the nurse will be able to go back and correct the original error or omission. If it is not possible to recover the error then this column is left blank. The probability of the error occurring is denoted in the table by P , which is categorized as low (hardly ever occurs), medium (has occurred onceor twice), or high (occurs frequently). The final column shows the measures (e.g., product design or technologicalsystem) that could be taken to reduce errors. There are various technological interventions that hospitals are lookingto reduce such errors. Digitization of patient records and active use of computer technology by the hospital staff for documentation and updating patient treatment information will enable real time access of patient information byrelevant parties. Bar-coding and RFID are examples of technologies that can alleviate issues with patientidentification during medication administration and by using various alerting measures, ensure that the RN checksthe necessary conditions (like checking heart rate, etc.) prior to medication administration. It is important to notethat, in order to be effectively implemented, any design solution needs to be regulated by appropriate managementand organizational controls.

Table 2: Example of SHERPA Application

5. Discussion and ConclusionsThe main purpose for carrying out this analysis was to demonstrate the types of error that occur during medicationadministration in the cardiac telemetry unit and the location in the process where these errors occur. The analysis

predicts what steps can be taken to achieve resolution and highlights those aspects of the drug administration processwhere design solutions would have the greatest impact. Many of the tasks presented in the HTA could be subdividedinto further levels of component tasks and operations, thereby revealing a highly detailed description of the drugadministration task. The error mode taxonomy prompts the analyst to consider potentially unforeseen errors and theerror reduction strategies are readily identified. The strength of the SHERPA technique is that it can be used toanalyze tasks or processes at many different levels.

According to Lane et al. [15], medication administration errors occur due to a number of varying and ofteninteracting factors that may originate from organizational practices, the working environment, or personal and

professional practices. There is an underlying assumption that the SHERPA taxonomy is able to capture the fullrange of error-producing activity whereas this is not the case. Communication with patients and their relatives,colleagues and various departments all impinge on the process of drug administration. These factors cannot beanalyzed effectively using the taxonomy and would require other techniques. The task analysis could be extended tothe medication delivery systems from pharmacy and predict errors that might occur due to unavailability and delaysin acquiring medications in the unit. Furthermore, validation of the errors predicted in the model needs to becompared with data gathered by observation of actual medication delivery in the hospital for different ward settingsto analyze both opportunity for error and the types of error that actually occur. Whilst this paper focuses on thenursing activities on the cardiac-telemetry unit, similar extensions could be carried out at different hospital units andcan be adapted to different ward settings for a range of other healthcare procedures. This would facilitate theunderstanding of critical tasks which may lead to medication errors, and identify strategies to mitigate them usingtechnology, process redesign and system changes.

References1. Baber, C., and Stanton, N.A., 1996, “Human Error Identification Techniques Applied to Public

Technology: Predictions Compared with Observed Use,” Applied Ergonomics, 27, 119–131.2. Cohen, M.R., 1999, “Medication Errors: Causes, Prevention and Risk Management,” Washington, D.C.,

American Pharmaceutical Association Foundation.3. Diaper, D., 1998, Task Analysis for Human-Computer Interaction. Ellis Horwood Ltd., West Sussex, UK.4. Embery, D.E., 1986, “SHERPA: A Systematic Human Error Reduction and Prediction Approach,”

International Topical Meeting on Advances in Human Factors in Nuclear Power Systems, Knoxville, TE.5. Ferner, R.E., 1995, “Misleading Drug Packaging,” BMJ (Clinical Research Ed.), 311, (7003), 514.

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6. Ferner, R.E., and Aronson, J.K., 2000, “Medication errors, worse than a crime,” Lancet, 355, 947–948.7. Fiesta, J., 1998, “Failure to Communicate,” Nurse Manager, 29 (2), 22–25.8. Flynn, E.A., Barker, K.N., and Carnahan, B.J., 2003, “National Observational Study of Prescription

Dispensing Accuracy and Safety in 50 Pharmacies,” Journal of American Pharmacy Association, 43, 191-200.

9. Fontan, J.E., Maneglier, V., Nguyen, V.X., et al., 2003, “Medication Errors in Hospitals: ComputerizedUnit Dose Drug Dispensing System Versus Ward Stock Distribution System,” Pharmacy World Science,(25), 112–117.

10. Hand, K., and Barber, N., 2000, “Nurses’ Attitudes and Beliefs about Medication Errors in a UK Hospital,”International Journal of Pharmacy Practice, 128–134.

11. The Joint Commission (JCAHO). http://www.jcaho.org (Last Accessed August 2007).12. Kirwan, B., 1992, “Human Error Identification in Human Reliability Assessment. Part 2: Detailed

comparison of techniques,” Applied Ergonomics, 23, 371–381.13. Kirwan, B., 1994, “A Guide to Practical Human Reliability Assessment,” Taylor & Francis, London, 426–

436.14. Kohn, L.T., Corrigan, J.M., and Donaldson, M.S. (Eds.), 1999, To Err Is Human: Building A Safer Health

System, National Academy Press, Washington, DC, USA.15. Lane, R., Stanton, N. A., and Harrison, D., “Applying Heirarchical Task Analysis to Medication

Administration Errors” Applied Ergonomics 37, 2006, 669–679.16. Leape, L.L., Bates, D.W., Cullen, D.J., Cooper, J., Demonaco, H.J., Gallivan, T., Hallisey, R., Ives, J.,

Laird, N., Laffel, G., Nameskal, R., Petersen, L.A., Porter, K., Servi, D., Shea, B.F., Small, S.D., Sweitzer,B.J., Thompson, B.T., Vliet, M.V., 1995, “Systems analysis of adverse drug events,” The Journal of theAmerican Medical Association, 274 (1), 35–43.

17. Lesar, T., Briceland, L., and Stein, D., 1997, “Factors Related to Errors in Medication Prescribing,” Journalof the American Medical Association, (277), 312–317.

18. National Co-coordinating Council for Medication Error Reporting and Prevention, “What is a MedicationError?”, http://www.nccmerp.org/aboutMedErrors.html, 1998-2007 (last accessed August 2007).

19. Pape, T., (2001). Searching for the final answers: Factors contributing to medication administration errors.The Journal of Continuing Education in Nursing, 32(4), 152-160.

20. Shepherd, A., “HTA as a Framework for Task Analysis”, Ergonomics, 41 (11), 1998, 1537-1552.21. Stammers, R.B., and Shepherd, A., 1995, Evaluation of Human Work: A Practical Ergonomic

Methodology, Taylor and Francis, London, UK.22. Stanton, N.A., and Stevenage, S.V., “Learning to predict human error: issues of acceptability, reliability

and validity.” Ergonomics 41, 1998, 1737–1756.23. Thornton, P.D., Simon, S., Matthew, T.H., 1999, “Towards safer drug prescribing, dispensing and

administration in hospitals,” Journal of Quality in Clinical Practice, (19), 41–45.24. Wolf, Z.R., 1993, “Medication Errors: the Nursing Experience,” Delmar, Albany, NY.

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Bhuvanesh.applying SHERPA to Analyze Medication Administration in the Cardiac Telementry Unit