Clinical Decision Support and Electronic Prescribing Systems: A Time for Responsible Thought and Action

doi:10.1197/jamia.M1830

J Am Med Inform Assoc. 2005 Jul-Aug; 12(4): 403–409.

PMCID: PMC1174885

Clinical Decision Support and Electronic Prescribing Systems: A Time for Responsible Thought and Action

Randolph A. Miller, MD, Reed M. Gardner, PhD, Kevin B. Johnson, MD, MS, and George Hripcsak, MD, MS

Affiliations of the authors: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN (RAM, KBJ); Department of Medical Informatics, University of Utah, Salt Lake City, UT (RMG); and Department of Biomedical Informatics, Columbia University, New York, NY (GH).

Correspondence and reprints: Randolph A. Miller, MD, Eskind Biomedical Library–LL, 2209 Garland Avenue, Nashville, TN 37232-8340; e-mail: <randolph.a.miller/at/vanderbilt.edu>.

Received May 3, 2005; Accepted May 4, 2005.

This article has been cited by other articles in PMC.

Electronic prescribing (e-prescribing) systems can provide computer-based support for the creation, transmission, dispensing, and monitoring of pharmacological therapies. In the United States and other countries, such systems have been documented, under certain conditions, to increase the safety and quality of patient care.¹^,²^,³^,⁴^,⁵ The authors applaud the initial efforts of Teich and colleagues in the Joint Clinical Decision Support Workgroup (Joint CDS WG) to outline e-prescribing desiderata, as reported in this issue of JAMIA by Teich et al.⁶ Their article is published as an endorsed policy of the American Medical Informatics Association (AMIA). Previously, Bell et al. published an excellent list of desiderata for outpatient e-prescribing and sorted the desiderata into functional categories.⁷ Subsequently, Wang et al. surveyed e-prescribing vendor systems to determine that existing systems on average met only half the desiderata, with none exceeding 64% fulfillment.⁸ The recommendations outlined in the tables of the Joint CDS WG provide a useful point of departure for future discussions. Of note, the Joint CDS WG guidelines were developed as a “commissioned work” with externally determined foci, time limitations, and priorities, so that those guidelines do not fully cover all relevant areas. The Joint CDS WG document therefore represents an important first step in an evolving approach to a complex set of problems.

The Joint CDS WG recommendations present a scenario of how e-prescribing features might be rolled out. The authors of this commentary would like to supplement, from what we believe is a broader perspective, the focused set of Joint CDS WG recommendations. The Joint CDS WG proposal has several strengths, including the recommendations that the United States should develop and promote shareable standards for e-prescribing and related decision support systems, a consensus should be developed on how to implement and evaluate decision support systems, and certain organizations, (such as the Office of the National Coordinator for Health Information Technology, the Agency for Healthcare Research and Quality, the U.S. Food and Drug Administration (FDA), the National Library of Medicine, AMIA, the e-Health Initiative, and the Health Information and Management Systems Society) should take leadership roles in the e-prescribing efforts. The authors note that developers and implementers should consider the variability that currently exists among users, clinical settings, information systems, and environments when determining how and when to install and support an e-prescribing system. For example, even when clinical systems provide net benefits to an institution, the implementation of electronic systems to improve the quality of care can introduce unwanted, potentially harmful side effects that must be detected, monitored, and addressed.⁹^,¹⁰ It is therefore important to consider the potential adverse effects of e-prescribing implementation.

Electronic prescribing systems represent only one genre of electronic health record system activity (others include departmental pharmacy, radiology, and laboratory systems and systems for record keeping, ordering, results display, monitoring, and decision support). Because the current state of the art for complex, comprehensive electronic health record systems is immature,¹¹ there is not yet a scientific basis for selecting among the many potential courses of action related to implementation and use of e-prescribing systems. It is the authors' opinion that human (end-user) factors and electronic information interchanges among e-prescribing and other clinical systems play critically important roles in determining the success or failure of e-prescribing systems. These considerations should be combined with the Joint CDS WG suggestions when making implementation decisions. Several articles in the current issue of JAMIA illustrate how intricate and difficult it is to implement and evaluate such systems. There are few operational systems in place that have documented the success or failure of e-prescribing guidelines outlined. The authors note that e-prescribing systems alone may not suffice; more comprehensive electronic health systems may be required to address the needs of both healthcare facilities and individual practitioners. Clinicians should be wary of developing a false sense of security and unrealistic expectations based on use of e-prescribing applications alone, when more complex systems may be required.

To reap the benefits of several decades of dedicated work by biomedical informaticians, commercial vendors, and health care providers (institutions and individuals), a responsible approach to e-prescribing must be advocated. All parties with a stake in e-prescribing must develop a common, overarching framework for its development and dissemination. The remainder of this commentary examines the environmental factors, technical factors, and strategic factors relevant to e-prescribing before concluding with a recommended framework that builds on and supplements the Joint CDS WG e-prescribing recommendations.

Environmental Factors Relevant to Electronic-Prescribing Systems

Environmental factors relevant to e-prescribing include individual practitioners' specialties and roles; the variety of practice settings in which care is delivered in the United States; the standard of care in the clinical community; and end-user constraints imposed by human limitations in knowledge, habits, and work flows. Pragmatically, clinicians' individual practice circumstances should determine their access to, choice of, and use of e-prescribing technology.

Currently in the United States, the status of e-prescribing systems varies by geographic regions and by federal, state, or local governmental jurisdictions; practice setting/type; and commercial vendor application. As a result, there are widely varying e-prescribing adoption rates. It might be argued that in today's health care environment, a rural general practitioner who manages inpatients in the morning, outpatients in a small office in the afternoon, and who makes house calls as needed cannot and should not use the same e-prescribing system in each setting, even though in the future one system should suffice. Today's systems with one underlying knowledge base cannot easily switch between inpatient and outpatient formularies (which differ significantly). The rich information environment of hospital-based electronic medical record and computerized provider order entry (CPOE) systems is not easily replicated in outpatients' homes, even with remote wireless connectivity. Large academic medical centers have the resources (adequate teams of talented informaticians, available expert clinicians, and an annual budget of millions of dollars to support their work) to develop or purchase, customize, roll out, and evolve state-of-the-art e-prescribing systems. By contrast, both solo providers and small rural hospitals have limited access to informatics expertise, and little time or money to develop or install complex systems.¹²^,¹³

In the inpatient setting, when a clinician orders a medication, there is typically only one inpatient pharmacy system through which the order will be processed, and it is the same system for all providers and all orders. In the outpatient setting, patients typically receive multiple prescriptions from multiple care providers and may fill them at different pharmacies. Each retail pharmacy store (or pharmacy chain) may have its own software system that provides various levels of alerts regarding doses and drug interactions to pharmacists as they fill prescriptions, but the same prescription taken to different pharmacies will generate different alerts. Electronic connectivity is rare between free-standing outpatient pharmacies and the hospital or clinic-based, patient information–rich practice settings where providers generate prescriptions. Most connectivity that exists takes the form of fax machines, which, it is hoped, produce legible prescriptions but which still do not preclude transcription (and other) errors. For example, it would not be uncommon for a physician to write, “warfarin 5 mg one tablet by mouth daily” and the pharmacist to dispense (due to a temporary shortage) 2.5 mg tablets, with the instruction “take two tablets daily.” When in the following week, the physician receives a subtherapeutic international normalized ratio (INR) blood test result for the patient (indicating that the dose should be increased), a phone call to the patient's home to “take one and one-half pills daily” may have disastrous consequences. The patient would take a decreased dose (3.75 mg) instead of the physician's intended dose (7.5 mg) due to failed communication among systems, providers, and the patient.

“Closed loop” e-prescribing feedback that matches clinicians' orders with pharmacy dispensing annotations requires a bidirectional interface from prescribing site to the dispensing pharmacy. What is required is that the systems “match up” the dispensing record with the prescribing order in a manner in which any clinician reviewing the patient's chart could easily determine what was ordered and how it was dispensed, so as to avoid the previous scenario. The few bidirectional interfaces now in existence often generate a request for the attending physician's countersignature whenever the pharmacist changes the dispensing record. The latter model is unworkable in terms of introducing an unnecessary and often confusing extra burden on the physician, who may not understand which medication order is being changed for what dispensing reason if multiple changes are made.

Clinicians who are told to use an e-prescribing system cannot be expected to do so if using the system compromises the clinicians' existing standard of care for patients. Weight-based dosing represents the standard of care for many medications and ages in the pediatric population. A 1998 article in Pediatrics titled “Prevention of Medication Errors in the Pediatric Inpatient Setting” listed “confirm that the patient's weight is correct for weight-based dosages” as its first recommendation under “medication ordering to reduce errors.”¹⁴ In a related article in 2004, Kaushal et al. cited error-prone behaviors in clinicians' manual calculations of weight-based pediatric medication dosages.¹⁵ The authors believe that weight-based dose calculations for pediatric patients is an established standard of practice in the community that should be adopted within e-prescribing systems immediately (i.e., by 2006), even though many current vendor pharmacy and CPOE products poorly support pediatric dosing (and especially dosing in premature neonates). A number of academic centers have demonstrated that pediatric dosing can be done as part of electronic prescribing. Those centers have developed, deployed, and evaluated reliable pediatric and neonatal dosing systems over the past decade.⁴

In the current issue of JAMIA, Horsky et al.¹⁶ present a sobering case report of a therapeutic misadventure that caused potentially life-threatening hyperkalemia, in part facilitated by improper use of an inpatient e-prescribing system. This report emphasizes the importance of complex interactions among system and environmental factors that can lead to errors whenever inadequate training, testing, and feedback occur. Systems that alter clinician workflow by not integrating all relevant information for informed decision making into one place run the risk of distracting already busy clinicians. If the clinician must still check the traditional paper record (or a nonintegrated clinical results reporting system) as well as deal with an e-prescribing system simultaneously, the result can be more work and frustration for the clinician as well as more opportunities to err by missing important cues. Similarly, implementing a suboptimal system, or doing so with inadequate training, can cause a substantial risk of errors.

The report in this issue by Nelson and colleagues¹⁷ provides a good example of how systems that might otherwise be “technically perfect” can demonstrate suboptimal effects when implemented in the clinical environment due to a lack of user training, failure of users to employ the system correctly despite training, clinical urgencies of the environment, and differing mind-sets of various users. Imperfect communications among different electronic systems, among clinicians, or between clinicians and systems can lead to errors. Care must be given to the environment as well as to the systems being implemented.

In the current issue of JAMIA, the study by Saleem et al. analyzed the factors that improved clinician adherence with reminders and found that environmental factors can play key roles in either facilitating or impairing guideline utilization.¹⁸ Impediments to reminder use included “(1) lack of coordination between nurses and providers; (2) using the reminders while not with the patient, impairing data acquisition and/or implementation of recommended actions; (3) workload; (4) lack of clinical recommendation flexibility; and (5) poor interface usability.” Facilitators included “(1) limiting the number of reminders at a site; (2) strategic location of the computer workstations; (3) integration of reminders into workflow; and (4) the ability to document system problems and receive prompt administrator feedback.”¹⁸ In previous issues of JAMIA, Ash et al. emphasized the human factors required for successful implementation of clinical systems,¹⁹ and Waitman and Miller argued that the most important aspectsof automated guideline implementation are locally determined.²⁰

Technical Factors Relevant to Electronic-Prescribing Systems

Technical factors relevant to e-prescribing systems include the information content (“knowledge base”) of a system; the target setting for system use; the development processes for the system (e.g., the extent to which clinician end-users participate in feedback at the design, implementation planning, rollout, and steady-state use levels); the electronic interface of the system to other electronic systems; the user interface of the system; the availability of a combined clinical-technical “swat team” who detects and addresses system problems efficiently; the ability of local users to have meaningful input into system evolution processes, and the ability to carry out critical changes within hours to days, not quarterly or annually; and internal and external mechanisms for ongoing quality improvement involving both users and developers of such systems.

Although an evidence base exists in the literature demonstrating that, under appropriate circumstances, e-prescribing systems can improve patient safety and quality of care, the conditions under which such demonstrations occurred cannot be easily replicated. Most studies demonstrating the beneficial impact of CPOE and electronic medical record systems have come from uniquely qualified, leading-edge academic medical centers that possess the resources and in-house expertise (in both informatics and pharmacotherapy) to create and maintain one-of-a-kind institutional clinical decision support systems.¹^,²^,³^,⁴^,⁵^,²¹ Furthermore, the pharmacy/drug information knowledge bases underlying commercial CPOE and pharmacy system vendor products are typically created by third-party commercial enterprises (such as Multum®, MicroMedex®, First DataBank®, and others) and licensed to CPOE vendors. The quality and reliability of these pharmacy information bases have rarely been formally evaluated, and their reliability has at times been called into question.²²^,²³^,²⁴^,²⁵ The editing efforts reported by Reichley et al.²⁶ are symptomatic of this problem; although the success reported by Reichley indicates that such problems, when encountered, can be overcome.

The performance and safety of e-prescribing systems depend on their underlying pharmacy information data, the design of the software, and the circumstances of software and system implementation. In general, the systems available to the majority of hospitals and office-based practices in the United States were developed by commercial vendors, whose systems typically lag behind the state of the art in the leading academic centers by several years. For example, experience suggests that most vendor systems are weak in the domain of pediatric prescribing. The authors are aware of a number of anecdotal reports of large hospitals purchasing pharmacy systems and turning off or ignoring the pediatric prescribing components due to unacceptably large numbers of errors generated by the systems. An unpublished audit of one commercial vendor's drug information system (used in an e-prescribing system) found numerous errors (such as registering dose strengths of syrups in milligrams rather than milligrams per milliliter and incorrect error bounds for minimum and maximum doses) that prevented use of the system until extensive local editing was done.

The report of Reichley and colleagues²⁶ typifies the kinds of problems seen with many commercial vendor systems (and are not unique to the product described). Most commercial pharmacy information systems generate a clinically intolerable (and irrelevant) number of drug interaction warnings (Reichley et al. report a 9.2% rate for all prescriptions), based on a failure of the vendors to discriminate adequately how often interactions occur in actual patient populations, and the severity of those reactions when they occur. As Reichley et al. note, institutions often limit interaction checking at the physician level (as opposed to in the pharmacy). Reichley et al. also describe the substantial level of institutional resources that must be dedicated to screening and optimizing the already-expensive commercial drug information databases—a process that must be repeated at every major medical center nationally that adopts such systems. The incremental costs of replicating these edits at each local medical center across the nation are both excessive and at least partially unnecessary, although some unique local customization is required at most sites.

Electronic-prescribing system design desiderata, including use of standards, were well covered in the reports of Bell et al.,⁷ the Joint CDS WG (Teich et al.),⁶ and in the “ten commandments” for system developers outlined by Bates and colleagues.²⁷ In addition to design desiderata, it is essential to consider end-users' workloads and expertise when implementing e-prescribing systems. For example, information should be collected from end users via keyboards only when the information will be used for important decisions. Furthermore, the data should be collected only once, from the individual most likely to know the correct information. Having a clinician type in patient diagnoses or laboratory results just so that the information can be displayed as indications or precautions on a written prescription may be less than useful. Preferably, the e-prescribing system should contain decision support logic that considers laboratory results or diagnoses immediately, if available, to provide informed, patient-specific dosing recommendations and warnings.

The ability to audit e-prescribing system records (including reviewing alerts issued and actions taken after alerts) is essential for quality control in e-prescribing. As illustrated by Melton and Hripcsak in this issue of JAMIA,²⁸ new technologies such as natural language processing may facilitate the capture of coded data for use by decision support systems and may be used to uncover evidence of e-prescribing errors, facilitating postmarketing surveillance of e-prescribing.

Strategic Factors Relevant to Electronic-Prescribing Systems

Strategic factors relevant to e-prescribing include issues of whether or how to validate e-prescribing systems through published evaluations versus certification; pragmatic issues related to the timing of e-prescribing system feature rollout; regulatory issues (such as governmental and Joint Commission on Accreditation of Healthcare Organizations compliance); issues related to privacy and confidentiality; and “system-wide” issues such as development of standards bodies, centralized monitoring strategies, etc.

Performance of an e-prescribing system over time depends on three factors:

the quality and validity of the knowledge base underlying the e-prescribing system at any given point in time;
the quality and reliability of the software system applying the knowledge base to a patient's clinical condition for prescribing purposes;
the quality, methods, and schedule for updates of both the knowledge base and the software.

The Joint CDS WG proposed that drug information “knowledge clearinghouses” should be created. However, if a significant number of such clearinghouses are created independently of one another, and if their disparate knowledge bases lack standardization and validation (in terms of format, content, quality of information, and maintenance over time), they might not contribute to improving the quality of drug information systems.

The concept of “certification” for e-prescribing systems is an interesting but untested idea. Until careful evaluations occur, we cannot definitively know how well each of the three broad approaches to certification described by the Joint CDS WG will work. If organizations attempt to undertake certification; it is important to separate certification of the underlying drug information knowledge base from certification of the related software. Outstanding software cannot compensate for poor quality drug information, and an exceptional quality knowledge base may be compromised by substandard software. Both aspects can rapidly change, independent of one another so that a system “certified” as acceptable today may become far from ideal over time if the knowledge base is not maintained carefully, or if new software updates include serious “bugs.” The authors believe that it is too early to know if certification will work or if the newly formed Certification Commission for Healthcare Information Technology is the right venue for such efforts, presuming certification can be demonstrated to be practical and add benefit.

Suggestions for Moving Forward: Outline of a Proposed Framework

The authors believe that a framework should be developed and shared to promote appropriate adoption of e-prescribing systems in specific settings. We outline here the characteristics that such a framework might embody, both now and in the near-term future. Electronic-prescribing system capabilities must improve over time in a scalable and sustainable manner. Recommendations are limited to e-prescribing systems and their components and do not address more comprehensive medication safety procedures such as bar-coding of both patients and dispensed medications and real-time reconciliation of medication orders and dispensed doses at the point of care prior to administration.

A systematic approach to objectively measuring what has been and will be accomplished through e-prescribing is essential to making progress. Electronic prescribing cannot become an evidence-based activity until evidence is developed for its efficacy in settings outside leading academic centers. System evaluation cannot be carried out in simulated situations alone (although evaluations should not involve real patients initially). An otherwise perfect e-prescribing system that is fed the wrong patient information from an external system can produce suboptimal results. After “passing” simulation testing, extensive clinical testing (involving real patients) should occur in carefully monitored settings.

Roles for Governmental and Professional Organizations and Academic Research Centers

At Present

The governmental and professional organizations should encourage the development of standards for content, transmission, and monitoring of pharmacy-related information and play a role in the development and dissemination of quality drug information knowledge bases. The United States government has taken a leading role, through interagency cooperation, in developing standards for the prescribing and dispensing of medications. The FDA Orange Book and related RxNorm and NDF-RT (National Drug File Reference Terminology) products represent excellent starts.

A logical next step would be to develop consensus standards for e-prescribing systems' information content. For example, a common format is needed for capturing and representing drug allergies, including the nature and severity of allergic reactions. Similarly, standards for recording the nature and severity of drug-drug interactions within drug information databases should be developed. Currently, each vendor develops its own systems for capturing such information and storing it. Not only does this lead to an excess of clinically irrelevant e-prescribing alerts, as reported by Reichley et al.²⁶ in this issue, but it also creates potentially dangerous situations in which, for example, a CPOE system cannot transmit to pharmacy system (or vice versa) what is already known to be potentially dangerous for a given patient (e.g., the CPOE system might recognize “PCN allergy” as “allergy to penicillin class compounds”, but the pharmacy system might not be able to decode the abbreviation (or vice versa).

A national standard for drug interaction information (or for adverse effects of individual drugs) should have an agreed-on format; it might include, for example, the following components: (a) generic names of the drugs that interact (or of the single drug with an adverse effect); (b) a brief human-readable but “computable” standard set of descriptions for the clinical nature of the interactions (“drug A increases the level of drug B”; “drug A and drug B together impair renal function”; or “drug A diminishes bioavailability of drug B”); (c) an indication (on a 1–5 scale) of the strength of the evidence base for the interaction/effect (including references) (e.g., theoretical, based on testing in animals or presumed to occur based on knowledge of similar chemical compounds but never reported in humans), reported as isolated case reports or reported as a confirmed series of carefully done studies replicated by multiple reputable observers; (d) a scale for the severity of interactions/effects (e.g., minimal: noticeable effect but no major discomfort or potential for harm; mild: minor, reversible discomfort or organ dysfunction; moderate: significant discomfort or organ dysfunction requiring therapeutic intervention; severe: causing potentially devastating permanent or life-threatening complications; and extreme: observed to directly cause death in humans); (e) a frequency listing on a logarithmic scale of how often each severity reaction has been reported to occur, for example, such a scale might use: 5 = more than 25% of administrations; 4 = 5% to 25% of administrations; 3 = one in 100 to four in 100; 2 = per 1,000 to nine per 1,000; 1 = less than 1 per 1000 administrations. With such an evidence-based standard set of descriptors for drug adverse effects and drug interactions, sites installing e-prescribing systems could more accurately determine locally how to “tune” the level of alerts to the proper level for end users and potentially display more severe alerts in a more disruptive manner than less severe alerts.

There are a large number of academic and commercial developers who now have “battle scars” from developing e-prescribing systems. System design involves common sense, such as never “covering up” critical information (such as current patient weight, active medications, or allergies) with a pop-up menu or dialog box when the user is attempting to prescribe a new medication. Similarly, if the system displays appropriate “most recent” laboratory results during medication ordering (such as showing recent electrolytes, and serum creatinine levels when an intravenous fluid is prescribed), it is important to display the date and time that the laboratory results were obtained because a physician might mistake yesterday's “normal” creatinine level and absolute white cell count as being current when ordering today's chemotherapy when in fact the laboratory results are pending today (and might come back abnormal if the clinician had waited). In the current issue of JAMIA, Rosenbloom et al. show that the same educational message delivered through different interface mechanisms can lead to dramatically different utilization rates by physicians.²⁹ Specifically, their “intervention” interface led to a tenfold greater than control rate for physicians reading on-line drug information monographs during e-prescribing. The government should develop a resource that expands on the ten commandments of Bates et al.²⁷ and the general recommendations of Ash et al.¹⁹ to compile useful information for future developers and for sites planning to install e-prescribing systems.

Federal agencies, professional specialty societies, and academic centers engaged in research should catalyze further development of e-prescribing standards as “test harnesses” for both information content and software. Currently, most vendors consider their drug information knowledge bases to be proprietary and confidential, and as a result, they are not subjected to an appropriate, objective level of scrutiny by outside experts. In the current environment, institutions purchasing e-prescribing systems may not even have access to the internal drug information knowledge bases underlying the systems, making quality control a challenge. In an ideal world, both vendors rating their own systems with such standardized tools and independent external groups rating the same systems with the same tools would produce the same results.

Through grants or contracts, federal agencies can stimulate creation and dissemination of evaluation methodologies for drug information knowledge bases themselves. Correspondingly, evaluation protocols for e-prescribing system software might embody standardized, known-quality “plug-in” drug information knowledge bases to create a level playing field. Such evaluations should ensure that the clinical standard of care in the community, such as weight-based dosing for pediatrics, is maintained in e-prescribing systems. When it is not, appropriate disclaimers should be prominently displayed, and system utilization under such circumstances might be blocked (e.g., if weight-based dosing is not available, the system could not generate prescriptions for children younger than perhaps 12 years). Sites should be developed that provide data to consumers about which vendor product components and systems have been evaluated at what points in time (allowing the vendors and/or external evaluators to supply the evaluation results directly).

Individual professional societies or academic medical centers should develop frameworks, based on evaluations of vendor (or nonprofit) e-prescribing systems that are available for use, illustrating which types of systems or system features (such as the desiderata of the Joint CDS WG) are acceptable and/or recommended for caregivers in various settings. A matrix approach might be taken. One axis might indicate the current degree of automated practice environment on a scale that begins with zero. Another axis of the matrix would indicate the category of the person/entity contemplating e-prescribing use: a solo practitioner in an isolated setting; a small stand-alone group practice; a large stand-alone group practice; a group practice or clinic affiliated with a moderate to large medical center; a small (< 200 beds) hospital; a medium-sized (200–400 beds) hospital; a large nonacademic hospital (>400 beds); and a large academic medical center (>400 beds). Another axis would rate various types of systems (or system features) as entry-level, intermediate, or advanced and explain the resources involved in time, money, and information infrastructure required to use each. Matrix-based recommendations for e-prescribing systems would then vary with each setting so that a solo practitioner with no e-prescribing system now might be directed to select one of a number of objectively evaluated, “acceptable” handheld computer-based e-prescribing systems and to only use it for prescribing advice when needed (so as to not disrupt practice but to provide information conveniently and quickly in any setting). By contrast, for a large academic medical center with an installed electronic medical record system, the matrix might recommend selection of a CPOE-based e-prescribing approach and indicate relevant criteria to use in selecting such a system.

Finally, government, specialty organizations, and academic medical centers should promote and distribute validated tools for adverse event detection with anonymous centralized reporting.¹¹

Near-Term Future

Although creating a “terminology system for prescribing” is an important first step, the authors suggest that only the federal government has the objectivity, permanency, and sustained budgetary resources to convene the appropriate groups of experts to create a shared, common, reliable, tested, maintained, standardized database of drug information to support e-prescribing over time. Federal efforts to create NDF-RT/RxNorm, as well as the decade-long National Library of Medicine effort to create and distribute the Unified Medical Language System (UMLS), can serve as models for how a national drug information knowledge base could be developed. The UMLS effort has involved a partnership among governmental agencies, leading academic medical centers, professional societies, and commercial enterprises to develop and maintain a clinically useful resource. If the federal government undertook such an effort, the resulting high-quality, standards-based, peer-reviewed drug information database of medications might include (among others) recommended doses for each medication dose form by indication, age, and weight; drug-drug, drug-food, and drug-laboratory interactions with supporting references and indices indicating severity of interaction, quality of evidence, number of actual reports of the events, supporting references, and other factors; educational monographs; and national pricing data. The federal drug information knowledge base could be distributed “at cost” to consumers, researchers, and commercial vendors.

Roles for Developers and Vendors of Electronic-Prescribing Systems

At Present

In the absence of a government-supplied, standardized drug information knowledge base, developers and vendors should submit both the drug information data underlying their e-prescribing products and the related software to objective third-party evaluators for auditing and quality control (which will NOT constitute “certification”). Governmental agencies should encourage, through grant and contract initiatives, academic research centers and professional societies to develop the capabilities to carry out such testing and develop standard methods for reporting the results. Vendors should make public the results of third-party evaluations of their products once they are tested.

Vendors must evolve the tools and techniques used at local sites to customize drug information databases, making them more user friendly, efficient to use, and able to integrate serial updates from the vendors.

Near-Term Future

Vendors should actively participate in any governmental effort to create a shared, standardized drug information knowledge base (as recommended above). Commercial enterprises now developing and selling proprietary drug information knowledge bases and e-prescribing systems need not go out of business if the federal government at some future date provides a drug information knowledge base for all to use. The vendors could adopt the government-supplied “standard” knowledge base at a very low cost and distinguish themselves by providing both quality “add-on” information resources and useful e-prescribing software packages with advanced decision support capabilities.

Vendors, through common forums such as the Health Information and Management Systems Society, should be encouraged to develop a common postmarketing surveillance system that frequently (at least weekly, preferably daily) collects “complaint button” requests from end users and demonstrates the capability to provide incremental updates to their systems on a daily or weekly (not quarterly or yearly) basis to improve patient safety. A set of process standards similar to ISO-9000³⁰ should be developed for the e-prescribing industry.

Roles for Clinical Practice Sites and Providers Using Electronic-Prescribing Systems

Sites that deploy e-prescribing systems have the responsibility to carefully select safe products and to collect ongoing feedback for quality improvement. Sites must determine that the burden of installing and using e-prescribing systems does not compromise the overall quality of care, even if the quality of prescribing per se is improved. For example, if the cost of using e-prescribing is for each practitioner to see 25% fewer patients per day, then the net benefit of an otherwise excellent system may be negative.

Similarly, individual care providers must first decide to use e-prescribing systems, must carefully select safe products, and must provide ongoing feedback for quality improvement. By informing patients that the provider is using an e-prescribing system, the provider promotes the technology and his or her use of safe practices, and the provider alerts the patient in case some unusual circumstance arises (e.g., the patient is surprised by a new dose, which turns out to be an untoward effect of a new system).

Conclusion

The time for developing a coordinated national approach to e-prescribing is at hand. The desiderata presented by the Joint CDS WG present an important step forward. While this list of desiderata is useful, it is only a first step. Additional criteria should be developed over time to account for variation among users, settings, and information systems. In addition, the authors note that individual e-prescribing recommendations may not always be optimal for all settings (e.g., pediatric dosing). Alternative viewpoints on some of the recommendations are possible and constructive debate will help to improve the guidelines over time. Evaluation is critical, including the documentation of untoward effects of such systems, with subsequent development of reliable mechanisms to report “adverse effects” in a timely manner. The authors believe that certification of such systems will be complex and difficult. However, we encourage the careful testing and evaluation of “certification” concepts. Interventions that affect patient safety cannot be done in an arbitrary manner or without mechanisms to detect and correct problems promptly as they arise. Much additional work will be required through coordinated participation of governmental agencies, academic institutions, professional specialty societies, and industry. The authors applaud the Joint CDS WG recommendations as a bold step forward that will evolve based on feedback and evaluation in coming years.

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