Medications are powerful and commonly used modern therapies that can yield many benefits. Yet, they can also cause considerable harm, 1,2,3,4
especially if prescribing clinicians fail to consider relevant patient characteristics. For example, renal insufficiency and advanced patient age call for lower than usual medication doses, and drug–drug interactions are sometimes lethal. Electronic health record (EHR) systems can improve the reliability, quality, and safety of medication use. 5,6
Computerized provider order entry (CPOE) with clinical decision support (CDS) can improve medication safety and reduce medication-related expenditures because it introduces automation at the time of ordering, a key process in health care. Electronic order communication can occur instantly, accurately, and reliably and computer-generated orders are more legible than those written by hand. A knowledge-based CDS review can assure that the order is safe and compliant with guidelines. 7,8
For CDS to be effective, adequate expertise must go into defining and representing medical knowledge. Also, data that are critical for CDS, such as the patient’s weight and allergy status, must be captured and made available to the CDS system. CDS systems must support, rather than impede, clinical workflows through speedy, available, and usable algorithms that provide parsimonious, clear, concise, and actionable warnings and advice. 8,9
To help understand the state of the art of the capability of CDS to improve medication safety, efficiency, and health care quality, the authors convened a CPOE conference in San Francisco in June of 2005. Participants reviewed the common categories of medication-related CDS within CPOE. For each category of CDS, we considered: How does it work? What is the potential benefit? What (if any) are the results of studies that have documented the benefits and/or undesirable side effects? What are outstanding issues (e.g., knowledge-base management, user interface issues) that prevent the benefit from being realized to its fullest? And what are some next steps that might help the evolution of the specific category of decision support?
This review provides a literature-based summary of the discussions. Rather than exhaustively reviewing the literature on these topics, we selected papers that reflect exemplary current practice and have direct actionable relevance to system designers working to implement these features in today’s technical environment. We also identified papers that illustrate the limitations of today’s technology and can help point the way forward for future developments in the field. For each category of decision support, we make recommendations for how the effectiveness of the feature can be optimized and we conclude the paper with summary recommendations to healthcare organizations, application and knowledge-base vendors, policy makers, and researchers for how to advance the delivery of effective medication-related clinical decision support.
We divided the CDS categories into two stages—basic and advanced. The issues associated with basic CDS are more straightforward than the advanced categories, and may represent a suitable starting point for most health care organizations. In basic CDS, we included drug-allergy checking, basic dosing guidance, formulary decision support, duplicate therapy checking, and drug–drug interaction checking. Advanced decision support includes dosing support for renal insufficiency and geriatric patients, guidance for medication-related laboratory testing, drug–disease contraindication checking, and drug–pregnancy checking.