Health care organizations have compelling reasons for interest in outpatient e-prescribing. The evidence summarized in and indicates that a primary care provider with 3,000 patients could expect 45–90 preventable ADEs per year among his or her patients, with more than half of these resulting from a prescribing error. In academic hospitals, CPOE with e-prescribing has been associated with dramatic improvements in medication errors and in guideline adherence. However, the generalizability of these findings to commercial systems and community outpatient settings remains uncertain.13
Moreover, the structural and cultural variance among health care organizations combined with the variance in functional capabilities among e-prescribing systems will likely make any evidence about overall e-prescribing effects difficult to generalize.
This paper describes a conceptual framework for evaluating e-prescribing that accounts for variation among clinical settings and among e-prescribing systems. Using our approach, evaluators first construct a version of the medication management process model () that is localized for the intended patient care setting. Next, they identify the e-prescribing functional capabilities that might influence each activity in the process (). Finally, they consider the effects from each functional capability, integrating specific evidence where it is available. Since e-prescribing systems can introduce errors that did not exist with handwritten prescribing,44
the framework specifically guides users to consider both the positive and the negative effects of each system feature.
Our framework organizes potential e-prescribing features using a process model of the activities in medication management. We believe that this organization of the framework provides a comprehensive view of a system's potential effects within its environment, using a sequence of activities that are familiar to most clinicians and administrators. Other investigators have used a similar process model to simulate the results that could be expected from different degrees of ADE reduction within each medication management activity.66,67
Our approach differs in that it opens the “black box” of each activity to consider how
system design features would alter the activity.
One limitation of the proposed conceptual framework is the relative paucity of evidence available to support judgements about the effects from different e-prescribing design features. Although we found some evidence to support 8 specific effects for six of the 14 functional capabilities we catalogued (five effects related to safety, one to formulary adherence, and two to clinician labor), all of this evidence needs to be considered preliminary given the limited number of studies. Overall, the evidence for functional capabilities is not yet sufficient to support quantitative modeling of effects. Thus, the statements resulting from an application of our framework to individual vendor systems need to be considered hypotheses—of varying strength—rather than conclusions.
Users of our model should also be aware that the 14 functional capabilities we identified do not represent an exhaustive list. In identifying functional capabilities, we focused primarily on the “prescribing” step of medication management. Capabilities that we have not explicitly considered, especially involving later steps of the process model, may also be important in determining the effects of e-prescribing systems. However, the proposed evaluation framework has the virtue of being able to accommodate additional functional capabilities that we have not considered.
Though the current evidence is incomplete, it nonetheless supports a set of e-prescribing design priorities. Because errors in medication selection are most common, functional capabilities such as medication selection menus and safety alerts may be the most important for preventing ADEs. Because the administration and monitoring steps are also common sources of outpatient preventable ADEs, adequate support of outpatient education and follow-up monitoring may also reduce health risks significantly. Because the transmission and dispensing steps accounted for fewer than 5% of preventable ADEs, electronic transmission of prescriptions may have a lesser impact on patient safety than it does on process efficiency. When evidence is incomplete, a Delphi panel process can help to codify expert opinion about best practices.68
We are currently conducting an expert panel process to develop quality standards for e-prescribing.
Another limitation in the evidence base is the lack of information about factors that can lead to unintended hazards. The experience with potassium ordering screens at Brigham and Women's Hospital44
demonstrated that occult hazards can exist in e-prescribing systems, but the specific factors that caused this hazard were not identified. Analysis of the Therac-25 radiation therapy accidents found that rare, fatal overdoses arose from interactions between software and hardware flaws coupled with frequent, cryptic error messages to which users became insensitive.69
Some unintended hazards may be prevented by better software engineering and evaluation,70
but identifying rare hazards may still depend on user vigilance.69
Future work integrating the functional capability approach with cognitive models of human errors71,72
may prove valuable for predicting user interface hazards. Until then, our framework may still help evaluators to systematically consider possible hazards by applying basic principles of user interface design73
when examining each system capability. More importantly, implementations of e-prescribing should be accompanied by specific efforts to monitor for and respond to unexpected quality and efficiency problems.
Clearly, more research is needed in e-prescribing effects. Given the high costs of controlled trials, some experts have argued that less-rigorous evidence must be accepted for safety-related information technology (IT).74
However, others have countered that the harms potentially arising from IT interventions justify significant investments in evaluation before the nation commits to the high costs of implementation.75
We posit that experimentation might focus most productively on alternative functional capabilities within different clinical environments rather than on e-prescribing vs. traditional prescribing. As more complete evidence is accumulated, our framework may serve as a starting point for quantitative models. Future work might also expand the framework to deal with other aspects of CPOE, such as computerized test ordering and results retrieval.
Finally, little information is available about the costs of creating and maintaining e-prescribing systems. These costs would likely be greatest for institutions starting with less-developed informatics infrastructures. Whether e-prescribing systems will be cost-saving or whether their potential health benefits will require additional net investments in health care remains unknown. Future studies of e-prescribing systems will be most useful if they can capture implementation costs.