Providers appear to be rapidly adopting electronic health records and computerized prescribing, and one of the major anticipated benefits is expected to be through medication-error reduction. However, many of these benefits may not be realized if the computerized prescribing applications are not mature. In this study of computerized prescriptions received by an outpatient pharmacy chain in three states, we found that just over one in 10 computer-generated prescriptions contained errors, and about a third of the errors in prescriptions had the potential for harm. Roughly six in 10 errors related to omitted information, which should be relatively easy to eliminate. The rates of errors varied by vendor, and some had higher potential ADE rates than others.
Our results in terms of error frequency with electronic prescription are consistent with outpatient handwritten and electronic prescription error rates that are reported in the literature, although we evaluated more systems than in prior reports.2
In one study, Gandhi et al
reported that 7.6% of computer-generated and handwritten outpatient prescriptions contained errors, of which 43% were potential ADEs. In that study, the use of basic computerized prescribing systems was not associated with reduced error rates. Similarly, Devine et al
found an 8.2% prescription error rate after implementation of a CPOE system that included basic decision support and dosage calculators in a community-based, multispecialty health system.18
Although we found an average error rate that is consistent with these studies, we found in addition that the number, type, and severity of prescribing errors varied significantly according to which computerized prescribing system was used, suggesting that either the system designs differed, for example, with a superior user interface or more advanced functionality; or implementation varied, since, for example, better clinician training may result in safer prescribing.19
Based on our framework (), we identified several strategies to minimize the errors associated with computer-generated prescriptions, including errors that may not be seen in hand-written prescriptions. These strategies include both computer-based interventions and provider-based interventions.
Examples of computer-based interventions are outlined below, and include forcing functions, specific drug decision-support systems such as maximum dose checkers, and calculators.
- Forcing functions can be designed to prevent omitted information, incomplete drug names, medications, with instructions to be taken as needed without a specific indication, and inappropriate abbreviations. In our sample, forcing functions could have eliminated 71.7% of total errors and 63.2% of potential ADEs.
- Specific drug decision support, including features such as maximum dose checking, have the capacity to eliminate clinical errors such as wrong dose or frequency, which comprised 7.5% of errors and 13.5% of potential ADEs associated with the computer-generated prescriptions in our sample.20
21 These represent some of the errors that have been most likely to result in harm in the inpatient setting, suggesting that this may be an important priority.22
- Calculators can resolve inconsistent quantity errors by eliminating redundant data entry. For example, instead of entering the final quantity to dispense, the system calculates this quantity from the duration of treatment and the frequency of administration entered by the physician. In our sample, a simple calculator could have eliminated 5.6% of total errors and 1.2% of potential ADEs, namely those errors where the quantity to dispense does not match the quantity in the patient directions. Although this seems like a small number of errors prevented, 3.5 billion prescriptions are written per year, and with a 10% error rate, 6% of errors prevented is equivalent to 21 million errors.
When implementing any computer-based intervention, the benefits of reduced errors and improved patient safety must be weighed against the cost of physician resistance to using an inflexible system while under significant time pressure. If the intended benefits are well conveyed, the system is well designed and flexible enough to allow for non-standard specifications, and training is sufficient, acceptance of the system can be very good.11
However, specific issues around human-factors design in the decision support should receive careful consideration. For example, alerts should steer providers in the right direction early on instead of providing retrospective warnings after users have invested time entering the information in question.21
In order to incorporate computer-based interventions into current systems without generating significant physician resistance, the content for decision support should be developed and tested with clinicians in the field. User groups may be a good forum for iterative testing and clinician feedback on system design.
Unlike computer-based interventions, which focus on eliminating errors on a per-prescription basis, provider-based interventions focus on ensuring that the computerized prescribing system's design and implementation support the elimination of errors. Provider-based interventions may include rigorous vendor selection, aligned financial incentives, and strong training.
To ensure a minimum standard of system functionality, rigorous vendor selection should begin with vendors who have certified electronic prescribing systems. However, certified electronic prescribing systems alone do not guarantee success, as the current certification criteria are pass/fail for all criteria, and remain subject to change. For example, while the criteria require drug–drug interaction checking, there is no check of whether or not the key interactions are in place or whether the warnings when delivered adhere to human factors design principles. As the criteria for certification continue to evolve, it is important not only to choose certified systems but also to monitor total errors (see ) and error distribution (see ) on an ongoing basis. The vendor selection process should also eliminate vendors that are unwilling to commit to resolving problems as they arise through system revisions such as the addition of forcing functions.26
Finally, implementation and adoption may be facilitated if vendors commit to providing long-term, on-site training that covers all shifts in order to minimize workflow disruptions.27
Internists prescribe an especially large proportion of prescriptions, and thus should be especially sensitive to how good the prescribing application is in the electronic health record they select.
In addition to vendor selection, adoption and implementation of electronic prescribing systems could include financial incentives to encourage physician adherence to meaningful system use. Simply purchasing a certified, fully functional electronic prescribing system is not enough to achieve improvements in quality and patient safety. Key system functionality must be used in order to improve safety and quality. Beginning in 2011, providers will be rewarded by the government for achieving ‘meaningful use’ of certified systems.28
These financial incentives will likely improve adoption of advanced electronic prescribing systems with forcing functions, which can be expected to result in better error-reduction rates. For example, the prevention of incomplete prescription information (eg, drug name, dose, frequency, duration of treatment, and quantity to dispense) could have resulted in the elimination of more than 70% of prescribing errors in our sample. Pharmacy feedback to providers on error rates may serve as a mechanism for monitoring meaningful use as a basis for financial incentives. Future meaningful-use regulations in this area should also include some testing of whether key safety checks are included in provider system implementations, since data from the inpatient setting have demonstrated that there is substantial variability in whether such checks are implemented.29
Training also represents a key determinant of successful adoption. While the vendor's training capabilities should be part of the vendor selection process, organizations need to ensure themselves that enough training of adequate types occurs.27
Training when a new electronic health record is implemented typically should account for 30–40% of costs, yet is often given short shrift (JP Glaser, personal communication, 2010). Tracking of how users are using electronic prescribing after implementation is also likely to be helpful, as some users may adopt less slowly or have specific issues with the system.
Our study has several limitations. First, we were not able to distinguish between true electronic prescriptions (ie, those that were electronically transmitted directly to pharmacies) and computer-generated prescriptions that were printed and either faxed or hand-delivered by the patient to the pharmacy. However, we believe that our results apply regardless of mode of prescription transmission. Second, although we identified a difference in error rate between various electronic prescribing systems in our sample, we were not able to assess whether this difference was due to variations in system design or the implementation process. This limitation occurred because these data represent many different systems being used by providers across three states, and the details on specific system functionality and implementation process were not available to us. Further research should investigate specific system characteristics that are associated with increased error rates. Third, our sample, while large, did not include all vendors and also included systems that appeared to be home-grown and not commercially available. Fourth, this study focuses on prescribing errors and was not designed to investigate whether the errors were intercepted at the pharmacy level, or other pharmacy workflow issues. Future studies should investigate subsequent error interception and resulting patient outcomes. Finally, this study was not designed to identify errors that are not evident on a prescription, such as the wrong patient receiving a medication, an incorrect diagnosis causing the wrong medication to be prescribed, or a drug–drug interaction. When considering these other sources of error, actual prescription error rate may be higher than the 11.7% we have identified.
In summary, about one in 10 computer-generated prescriptions in our sample included one or more errors, about two-thirds of which involved omissions. The number, type, and severity of prescribing errors varied significantly according to which computerized prescribing system was used, suggesting that systems with more advanced functionality, or those used by physicians with improved computer training, were better able to prevent errors. Implementing a computerized prescribing system without comprehensive functionality and processes in place to ensure meaningful use of the system does not decrease medication errors. To enable stakeholders to realize more of the potential benefits of computerized prescribing systems, vendors and healthcare providers may consider implementing several of the outlined computer-based and provider-based interventions, which combined have the potential to eliminate more than 80% of the errors. These data are especially important now because providers are currently rapidly adopting electronic health records, yet may not realize the full range of benefits if the prescribing applications have some of the issues we identified.