intervention of an EP system. However, the EP system facilitated the systematic extraction of data to enable analysis of prescribing errors. In this way, the EP system can serve as a tool to identify all kinds of prescribing error, even those that would not be prevented by an EP system.
Quantitative studies on the operation of EP systems have also been published by centres in Europe. Van Doormaal et al. [ 18 ] studied the effect of an EP system with clinical decision support on the incidence of medication errors and adverse drug events (ADEs) in a hospital in the Netherlands. They found that the percentage of medication orders containing at least one error dropped from 55 % prior to imple- mentation of the EP system to 17 % post-implementation. While there was also a reduction of ADEs in the hospital after implementation of the EP system, a causal link between the two could not be demonstrated, because of the interrupted time- series design of the study.
In a quantitative evaluation of an EP system in Switzerland, Bonnabry et al. [ 19 ] noted that the patient safety bene fi ts of the EP system were dependent on the exact EP functions implemented, and how easy they are for the user to operate. This fi nding correlates well with Barber’s observation that EP systems are not “plug and play” systems, but sociotechnical systems , where the safety of the whole system depends on the interaction of the human operator as well as the operation of the computer [ 20 ] .
67 Effect of EP Systems on Medication Error Rates in Paediatrics
prescribing error rate decreased from 8.8 % prior to implementation to 8.1 % 1 week after implementation (a non signi fi cant reduction), and then to 4.6 % 6 months after implementation. Omitted doses were reduced signi fi cantly from 8.1 % before implementation, to 1.6 % 6 months after implementation, albeit with a rise to 10.6 % in the week immediately following implementation.
Kazemi et al. [ 24 ] studied the error rate for an EP system in a paediatric environ- ment, comparing intervals of physician order entry (POE) and nurse order entry (NOE). They found that prescribing errors decreased from 10.3 % during POE to 4.6 % during the NOE period, and compliance with warnings was 44 % for POE and 68 % with NOE. These results suggest that nurse involvement in the EP process reduced error rates and improved compliance and that, if physicians are resistant to EP implementation, nurse use of the system might facilitate the implementation process.
A US study [ 25 ] compared the number of adverse events during 1,200 paediatric hospital admissions, before and after installation of an EP system. Seventy six and ninety four adverse events occurred before implementation, compared to 37 and 35 after implementation, a signi fi cant decrease. The system was most effective at reducing adverse events associated with prescribing of aminoglycosides and cepha- losporins. The authors concluded that an EP system with comprehensive decision support functions reduced errors associated with paediatric hospital admissions , but that re fi nements were required to gain further safety bene fi ts.
Jani et al. [ 26 ] studied the use of a commercial EP system at a tertiary care chil- dren’s hospital in the UK. They found that, prior to EP implementation, prescribing errors occurred in 88 of 3,929 items prescribed (2.2 %), and in 57 of 4,784 items prescribed (1.2 %) after implementation. A decrease in the severity rating of errors was also noted after EP implementation. The authors concluded that, while EP appeared to reduce medication errors in the paediatric setting, larger studies were required to assess the impact of EP on error severity, and in different settings.
Current studies suggest that EP has the potential to reduce risks associated with prescribing speci fi cally in a paediatric patient population, in particular errors asso- ciated with omission of prescription information. However, as with EP in general patient populations, further studies are required to assess the impact of EP on pre- scribing errors in detail.
Role of Barcodes in EP Systems
Barcode technology has also been used by EP systems in order to reduce errors in the medication administration process on the ward. The patient’s wristband barcode is scanned prior to a medicine administration event to con fi rm patient identity , and the barcode on the medicine is scanned to con fi rm the identity of the medicine to be administered. Medicines administration with the assistance of barcodes to identify either the patient or the drug may contribute to reductions in levels of medicine administration errors at the point of administration.
The EP system installed at Charing Cross Hospital, London, UK [ 15 ] is a closed loop system, which forced nurses to use the barcode system for patient identi fi cation, and it was found that the percentage of patients who were not de fi nitively identi fi ed prior to medicines administration decreased from 82.6 % before EP system imple- mentation, to 18.9 % after system implementation.
Poon et al. [ 27 ] conducted a study of 115,164 medicines administration events before implementation of barcode medicine administration, and 253,984 adminis- tration events after implementation. They found that target adverse events were reduced by 74 % and all adverse events were reduced by 63 %, and that the greater the proportion of doses scanned, the higher the error reduction rates possible. Nolen and Rodes [ 28 ] studied the use of barcode medicine administration (BCMA) in anaesthetics for cardiac surgery cases (n = 870). They found that the BCMA process increased the available information on peri-operative drug administration by 21.7 %, and the availability of drug cost data by 18.8 %. Furthermore, the time required to process the operating room anaesthesia record was reduced by 8 min per case, fol- lowing full implementation of the system.
Miller et al. [ 29 ] has indicated that BCMA can reduce medication errors and improve patient safety. However, because the process of medicines administration by barcode scanning is potentially interruptive, there are various work-arounds (BCMA work-arounds) that nurses and pharmacists may use to bypass the system.
In a study of fi ve hospitals, Koppel et al. [ 30 ] have studied BCMA work-arounds and identi fi ed 15 work-arounds, with 31 causes of different types. Reasons for work-arounds [ 31 ] include:
Inability to scan medicines because a scanner is not available at the point of
•
medicines administration
Lack of awareness of the hospital’s BCMA process (bank/agency staff, but also
•
new staff and staff who are not usually involved with medicines administration) Shortage of time
•
Delay in computer response
•
Administration of a medicine prior to prescribing
•
McNulty et al. [ 32 ] discussed strategies for dealing with the problem of BCMA work-around. These include:
(a) encouraging a better culture of ownership of the system among nursing staff (b) improving the infrastructure to address known technical issues (e.g. wireless
black spots)
(c) an effective staff training programme, and better engagement of staff during the implementation period.
(d) greater use of “ hard stops ” in the system (i.e. ensuring that a medicine is not available for administration without following the procedure – e.g. linking BCMA to ward cabinets). However, hard stops may be highly disruptive and implementers should consider the unintended consequences in each scenario.
The culture of ownership of the system is important and, while BCMA has the potential to resolve many medication errors at the point of administration, managers
69 Effect of EP Systems on Medication Error Rates in Paediatrics
should remember that different staff groups with have different priorities with BCMA implementation [ 33 ] . Pharmacy staff will want to ensure that the stock and inventory is controlled, whereas nursing staff will want to ensure that the system is usable at the point of medicine administration.
The limitations with use of barcodes are the availability, con fi guration and scal- ability of appropriate hardware, and also the fact that there are a proportion of medi- cines that do not have a correct barcode identi fi er. The use of barcodes may also be limited by harmonisation issues and obsolescence, due to development of RFID (radio frequency identi fi cation) technology [ 34 ] . In addition, use of barcodes for reduction of medicine administration errors relies on use of original packs (with barcodes) at ward level in a hospital. The use of barcodes in pharmacy professional practice and the pharmacy supply chain will be discussed at greater length in Chap. 7 .
Increases in Medication Errors due to the Introduction of EP Systems
An important fi nding in some US studies is that the implementation of an EP system can actually increase the number of medication errors reported, at least at the outset.
In the fi rst of these studies, the Bates 1999 study [ 6 ] , an increase in error rate was noted during the initial stages of the study, as noted earlier. The increase in error rate was attributed to the EP system’s functionality for dealing with potassium orders, which was only fi nalised later in the implementation process. In the second study, an observational review of a CPOE implementation at the University of North Carolina Hospitals [ 35 ] , the increase in medication error rate was attributed to (a) increased ability to identify errors due to enhanced data capture on an electronic system, (b) errors generated by staff unfamiliar with a new system, or (c) error detection bias (due to either pre-conceived ideas about EP by users, or evaluators keen to report errors in a new system).
More worryingly, it has been suggested in some studies that the very design of EP systems could facilitate errors , leading to hitherto uncharacterised errors.
Koppel et al. [ 36 ] did a study of medication errors generated by a commercial EP system that had been in operation in a US hospital for 7 years. They found that the EP system facilitated 22 error types, which fell into two groups: (a) errors generated by the fragmentation of data by the system and lack of integration between the different components of the system, (b) errors arising from the human-machine interface .
Nebeker et al. [ 9 ] commented on how a high rate of adverse drug events (ADEs) could occur even at a hospital where there was a high level of IT usage, to support hospital processes. The study looked at ADEs across the electronic prescribing pro- cess, by performing a prospective daily review of the electronic medical record for a random sample of all admissions over a 20 week period at a US hospital. The study showed that, of 937 admissions, there were signi fi cant ADEs in 483 admissions.
Ninety-nine percent of the ADEs identi fi ed resulted in serious harm to the patient, and 27 % of the ADEs were due to medication. The study observed that ADE rates were still relatively high after CPOE introduction, if decision support systems are not present as an integral part of the system. The role of decision support systems in reducing prescribing risk with electronic systems is discussed in detail in the next section.
This phenomenon has also been noted in the UK. As mentioned previously in this chapter, an initial increase in prescribing error rate following implemen- tation of EP (leading to an eventual reduction in error rate), was demonstrated at the Ayrshire and Arran Trust, Scotland [ 13 ] , for discharge prescriptions , and this observation was responsible for the reduction of prescribing error rate for discharge prescriptions not reaching overall statistical signi fi cance. This effect was also noted in the comparative study of an EP system with handwritten prescriptions in the intensive care unit context [ 14 ] , where the three major errors occurred with the EP system. The authors concluded that clinicians should not become complacent about the use of automated systems to eradicate errors.
A number of subsequent studies have identi fi ed the potential for EP systems to generate new errors that would not have occurred prior to use of the system. Flebbe et al. [ 37 ] examined the use of an EP system on an orthopaedic surgery ward in a Danish hospital. They found that the EP system could generate new types of error, and that some of these errors could be prevented by improvements in the user inter- face/work fl ow, and user training.
Magrabi et al. [ 38 ] examined prescribing practice with an EP system in a cohort of hospital doctors under laboratory conditions, to assess the errors that EP system operation could introduce. In the study, 32 doctors completed four tasks, with planned interruptions, and across the four prescribing tasks, an error rate of between 0.5 and 16 % was noted; the wide variation is probably due to the small sample size. A range of different errors occurred in the prescribing process – failure to enter allergy information, incorrect medicine selection, incorrect route, dose, for- mulation and frequency of administration, and omission of start date, administra- tion times and discontinuation date. Unsurprisingly, complex tasks took longer to complete. The authors indicated that prompts in the work fl ow may have prevented errors due to task interruption, but that more research was required to evaluate the effects of interruption.
Reckmann et al. [ 39 ] conducted a review of 13 hospital EP studies published between 1998 and 2007. While nine of the studies demonstrated a signi fi cant reduction in prescribing errors, several studies reported errors such as increased rates of duplicate medicines, and failure to discontinue medicines (although these errors types are possible with paper-based prescribing). The authors con- cluded that the evidence for the safety bene fi ts of EP systems is not compelling and that further research was required using larger sample sizes, more controlled conditions and standard de fi nitions of errors and adverse events. The issues relating to the methodology of EP study design are discussed in more detail later in this chapter.
71 Effect of EP Systems on Medication Error Rates in Paediatrics
Reduction of Medication Errors due to the Availability
of Electronic Decision Support Tools at the Point of Prescribing
In the study by Nebeker et al. [ 9 ] , documenting 937 hospital admissions, it was found that 483 admissions had signi fi cant adverse drug events associated with them and that 27 % of these were associated with medication. Of the medication-related adverse drug events, 61 % were associated with prescribing errors and 25 % with monitoring errors and the authors concluded that EP with decision support (DS) features would have a major impact on these error rates, by reducing inappropriate prescribing at the outset, and by providing suitable monitoring tools when certain drugs are prescribed (e.g. digoxin, lithium, theophylline). Indeed, the consensus among electronic pre- scribing specialists is that decision support tools should be an integral part of EP systems, as they have the potential to “add value” to the system as a clinical tool. The above data suggest that DS functions are particularly valuable in reducing selection errors and inappropriate selection at the medicine ordering stage of the medicines management cycle , and thus reduce risks associated with prescribing errors .
Clinical decision support facilities may be classi fi ed into active decision sup- port , or passive decision support . Active decision support functions provide a clini- cal alert to the user automatically as part of the work fl ow of the system, without the user having to actively seek the clinical information. Active decision support mechanisms are built into the EP system software. Passive decision support func- tions, however, are stand-alone medicines information reference sources mounted on the internet, an intranet or a local server, and accessible via a “hot key” or quick link by a clinical user, when the user is actively seeking information to resolve a clinical problem.
Clinical decision support warnings and information would include some or all of the following:
(a) sensitivity checking (b) drug interactions
(c) duplicate therapy / drug doubling (d) precautions / contraindications (e) dose checking
(f) formulary status , and (g) monitoring warnings.
The key issue with active DS functions is that they must be suf fi ciently compre- hensive to be of clinical value, but designed in such a way that they are not exces- sively presented to the clinical user, which might lead to important warnings being disregarded by the user (warning fatigue). This is a delicate balance and requires considerable thought if the rules are going to be con fi gured within the EP applica- tion – for this reason, some implementers have chosen not to support DS functions at all, rather than implement them in a partial manner or without full evaluation; this is the reason why the EP project at Southmead Hospital, Bristol, did not implement any DS functions [ 40 ] . In the past, for example, there have been some systems that
have limited the number of drug interaction warnings to two or three per drug, or limited the number of allergens for sensitivity checking to two per patient.
Limitations of this nature are clearly not acceptable if an EP system is to provide comprehensive DS functions.
However, with a comprehensive DS tool, based on a drug database from a third party data supplier , these issues can be addressed by the mode of implementation of the DS functions in the EP application; with, for example, the use of graded drug interactions , where the system can be con fi gured so that the most clinically signi fi cant drug interactions are displayed prominently to clinical users, or fl agging of absolute contraindications as a priority. The advantages and disadvantages of using a third party data supplier to enable DS functions will be discussed in the next chapter.
The issue of warning fatigue is well-recognised. On a retrospective analysis of allergy alerts in an EP system, Huntemann et al. [ 41 ] found that, out of 49,887 medi- cation orders, 643 orders gave rise to an allergy alert, but that 625 of the 643 alerts (97 %) were overridden, either because the patient had previously tolerated the medication, the bene fi ts outweighed the risk, or for some other reason. The quanti- tative impact of allergy alerts is therefore low.
Another question to be addressed in the provision of DS is whether there is any type of prescription that should be completely disallowed by a DS function on the EP system – i.e. whether a hard stop should be placed on the prescribing process.
There are some prescriptions that are absolutely dangerous and that should (and could) be prevented automatically by an EP system – so-called “ never events ”, for example, intrathecal use of vincristine, daily dosing of methotrexate. The EP sys- tem used on the renal unit at the Queen Elizabeth Medical Centre, Birmingham, UK [ 42 ] disallowed some orders because of sensitivities and serious drug interac- tions . However, careful consideration should be given to the issue of disallowing prescriptions because of relative contraindications ; if too many prescription types are automatically disallowed, clinicians may choose to bypass the system and write prescriptions by hand, thus defeating the object of an EP system and com- promising the completeness of the electronic prescribing record . Strom et al. [ 43 ] looked at the use of a hard stop on the co-prescribing of warfarin and co-trimox- azole. They found that, while the alert was highly effective in changing prescrib- ing behaviour (i.e. the prescriber had no choice but to abandon the prescription), it led to clinically signi fi cant delays in treatment initiation, which caused the termi- nation of the study.
There are also issues concerning the usefulness of decision support algorithms for alerting on contraindications and high doses. In designing a decision support algorithm for contraindications, Ferner and Coleman [ 44 ] remarked that many con- traindications were due to co-morbidities, and the decision support algorithm was reliant on relevant clinical data being available in the patient’s electronic patient record , which was by no means always the case. Seidling et al. [ 45 ] designed an algorithm to alert for high doses of some 170 drugs, taking into account age, renal function and contraindications, but the high dose alert was triggered on only 4.5 % of prescriptions and that clinicians were responsive to only one in four high dose