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Medication-Related Clinical Decision Support System: Benefits and Drawbacks

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Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs

This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made.

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Clinical informatics is the application of informatics and information technology to support healthcare delivery services. Its role is rapidly evolving toward providing better clinical decision-making by integrating state-of-the-art knowledge with medical record systems. As medicine moves into an era of personalized treatment and precision pharmaceuticals, the application of expertise in electronic health/medical record (EHR/EMR) systems and translational research will enhance operating efficiencies for hospitals and reduce costs. In reality, the populating and analyzing of large amounts of accumulating data in standardized format from EHRs has yet to happen, since protocols and resources have not yet sufficiently matured. Recognition of the importance of applying digitized data and information for patient care has spurred the first class of physicians to become board-certified in the newly-created subspecialty of clinical informatics. A critical step for achieving precision medicine will be to integrate old and new data into validated information and to convert this information into knowledge directly applicable to diagnosis, prognosis, or treatment. This will entail developing an integrated knowledge environment that continually captures information, grows, accumulates, organizes, and institutionalizes new information, making it accessible to health care providers

Knowledge accumulated from scientific research and clinical data contained in EHRs will be shared and will impact the discovery of novel therapeutic methods and the application of precision medicine. It now takes 17 years for a laboratory discovery to reach widespread clinical application.

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Medications are powerful and commonly used modern therapies that can yield many benefits. Yet, they can also cause considerable harm, (Gandhi TK, Weingart SN, 2003) 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 (Fernandopulle R, Ferris T, Epstein A, 2003). 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 (Kuperman GJ, Teich JM, 2001). 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.

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All in all, recommendations for this clinical decision tool to be more effective and accurate would be to increase the knowledge base and this could improve the lack of reference annotations to provide more data to support a clinical diagnosis suggested. Future work suggestions for Cerner to improve this clinical data support tool would be to employ and consult with more academic healthcare institutions to acquire more data and knowledge to ultimately expand the database and begin building out the specialty branches, which could equip residents and fellows in an academic setting with a better way to quality check their work. Future work could also include doing further research and evaluation into whether the document quality review tool has a significant impact on billing because diagnosis codes are aligned with supportive data such as imaging reports and lab results.

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Fernandopulle R, Ferris T, Epstein A, et al. A research agenda for bridging the “quality chasm.” Health Aff (Millwood) 2003.

Bates DW, Gawande AA. Improving safety with information technology N Engl J Med 2003.

Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient safety and computerized medication ordering at Brigham and Women’s Hospital Jt Comm J Qual Improv 2001.

Gandhi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care.

Kaushal R, Bates DW, Landrigan C, et al. Medication errors and adverse drug events in pediatric inpatients JAMA 2001

Bates DW, Cullen DJ, Laird N, et al. ADE Prevention Study Group Incidence of adverse drug events and potential adverse drug eventsImplications for prevention.

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