Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review

Abstract

While medications can improve patients' health, the process of prescribing them is complex and error prone, and medication errors cause many preventable injuries. Computer provider order entry (CPOE) with clinical decision support (CDS), can improve patient safety and lower medication-related costs. To realize the medication-related benefits of CDS within CPOE, one must overcome significant challenges. Healthcare organizations implementing CPOE must understand what classes of CDS their CPOE systems can support, assure that clinical knowledge underlying their CDS systems is reasonable, and appropriately represent electronic patient data. These issues often influence to what extent an institution will succeed with its CPOE implementation and achieve its desired goals. Medication-related decision support is probably best introduced into healthcare organizations in two stages, basic and advanced. Basic decision support includes 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-pregnancy checking, and drug-disease contraindication checking. In this paper, the authors outline some of the challenges associated with both basic and advanced decision support and discuss how those challenges might be addressed. The authors conclude with summary recommendations for delivering effective medication-related clinical decision support addressed to healthcare organizations, application and knowledge base vendors, policy makers, and researchers.

Identification and weighting of the most critical “real-life” drug–drug interactions with acenocoumarol in a tertiary care hospital

“…Various studies recommend reducing alert fatigue by lowering the number of alerts being sent to the clinician and by increasing alert specificity [61,62], but the identification of high-priority DDIs for use in electronic health records is still the subject of ongoing debate and can differ from one institution to another [63]. The major advantage of our method, which is adapted from the FMECA, lies in its simplicity and the quantitative evaluation it allows by combining three complementary factors based on pharmacological characteristics and clinical effects of drugs.…”

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

“…The work of many authors supports the evidence that decision support systems can be extremely effective in improving the quality and safety of healthcare (e.g. [3,[7][8][9][10]). The most famous expert systems were built as diagnosis assistants and therapy advisors in different medical areas (MYCIN [11], ONCOCIN [12], etc.)…”

A Modal Specification Approach for On-Demand Medical Systems

“…For example, modern computerized physician order entry (CPOE) systems automatically check if a doctor prescribes drugs that may interact adversely. In many cases hospitals have worked with vendors to disable these checks because these checks are too conservative (i.e., generate an overwhelming number of alerts) and the caregivers are competent enough to know how the drugs will interact and whether or not the risk is justi ed for a particular patient [21]. The answer to this question will depend on the complexity of on-demand medical systems, our level of understanding of patient physiology, and the delity of care those system are intended to deliver.…”