Clinical Decision Support SystemsDarla K. OwensUniversity of IndianapolisHealthcare InformaticsNUGR-522Charlie RandolphNovember 22, 2018Clinical Decision Support Systems “Clinical decision support systems (CDSS) are computer systems designed to impact clinical decision making about individual patients at the moment those decisions are made by presenting contextually appropriate information” (Alexander & Frith, 2015). These systems work through the electronic health record (EHR) to give clinicians the needed information about patients and to provide advice. The goal of CDSSs is to present information to healthcare providers, patients, and others in a timely manner to assist in making decisions about health care. CDSSs can only be effective if they have high quality of research literature that is up to date. Some CDSS tools would be “order sets specific to disease or health condition, suggestions for care, databases that give pertinent information of specific health conditions, reminders of upcoming appointments, and alerts about potentially dangerous situations” (Decision, 2013). CDSSs are designed to help organizations improve quality of care, be more efficient, and reduce costs. CDSSs have a type of software that enables them to be trained. “Artificial intelligence is the ability of a computer to perform human-like behavior and/or analysis” (Alexander & Frith, 2015). Principles of artificial intelligence combined with information science allows CDSSs to “provide active knowledge with patient data to generate clinical, patient-specific advice” (Alexander & Frith, 2015). CDSSs must contain certain elements in order to function properly. The knowledge base contains the research literature and must be maintained and updated continuously to provide the best evidence-based research. The reasoning engine works together with the knowledge base to “exchange information to pre-established rules” (Alexander & Frith, 2015). This means that the knowledge base, which has the evidence-based research interacts with the reasoning engine to determine if the preset data and patient data are the same or if the patient’s data is abnormal, which would then trigger an alert.
“The American Recovery and Reinvestment Act of 2009 (ARRA) set a mandate for technology to increase patient safety and reduce health care costs” (Alexander ; Frith, 2015). Under this act, hospitals were incentivized to adopt and implement the use of an electronic health record (EHR). The EHR was to include technology such as “computerized provider order entry, electronic prescribing, drug to drug and drug to allergy interactions, active medication lists, trending of patients vital signs, and clinical decision rules”, and have a functioning CDSS (Alexander ; Frith, 2015). Under the ARRA is the Health Information Technology for Economic and Clinical Health Act (HITECH) (Blumenthal ; Tavenner, 2010). The purpose of HITECH is not just adopting the EHR, but for the EHR to have meaningful use. Certain criteria had to be met in order for hospitals and health care providers to receive the incentives. “These meaningful use objectives include improve quality, safety, efficiency, and reduce health disparities, engage patients and their family, improve care coordination and population and public health, maintain privacy and security of patient health information” (HealthIT, 2017). “The meaningful use rule is part of a coordinated set of regulations to help create a private and secure 21st-century electronic health information system” (Blumenthal ; Tavenner, 2010). “Benefits of meaningful use include maintenance of complete and accurate health records about patients, improved access to information for providers, and empowerment of patients to take a more active role in their health” (Alexander ; Frith, 2015).
The use of CDSS in the perioperative setting has provided many benefits to the end users. CDSSs can help surgeons by providing surgery-specific order sets, allowing for quick order entry, safer medication administration, and evidence-based post-operative care. These order sets can be up dated quickly and easily allowing for current evidence-based literature to be used. These systems help the nursing staff by having all available patient information at their fingertips. This allows nursing staff to know about the patient’s past medical history providing safer care for the patient. The use of bar code scanning will alert nursing staff about any issue regarding allergies to medications, the system will alert providers of abnormal pre-op labs, and makes providers address the verification of surgical procedure, making sure they have the right patient, operating on the correct site.
There are many benefits and drawbacks to implementing CDSSs. Some benefits would include, the cost savings that could occur after the initial expense, would provide consistency in the care of patients, help clinical workflows improve, provide patients with feedback at time of appointment, and the quality of care stays constant (Benefits, 2013). The drawbacks could have providers feeling threaten due to clinical judgement, can increase time of patient appointments because of taking longer to input data, and keeping research up to date offering the latest evidence-based practices. There are also issues that could be pros or cons when implementing a CDSS, user acceptance, attitude of end users, how easy the system is to use, are there enough resources during implementation, the quality and reliability of the system, and the provision of evidence, advice or recommendations (Benefits, 2013). As with any type of implementation, proper education and buy in from end users are keys for success.
CDSSs in conjunction with the EHR can continue to provide safe and high quality of care to patients. When hospitals and HCPs use the same system of charting, patient information is continuous across all aspects of care allowing for safer care. There are many advances being made to continue to provide the most current data in hopes to expand the knowledge of CDSSs. Health care providers will continue to see vast amounts of research being added to assist in the decision-making process. The future will bring more evidence-based research to the end users, making the care provided more focused on their patient’s health.
Alexander, S., ; Frith, K. H. (2015). Applied Clinical Informatics for Nurses. Jones and Bartlett Learning.
Benefits, P. (2013, July 8). Potential benefits and drawbacks of the use of CDSSs; Factors which can help determine the successful use of CDSSs in clinical practice. Retrieved from Open Clinical: https://www.openclinical.org/dssSuccessFactors.html
Blumenthal, D., ; Tavenner, M. (2010, August 5). The “Meaningful Use” Regulation for Electronic Health Records. Retrieved from The New England Journal of Medicine: https://www.nejm.org/doi/full/10.1056/NEJMp1006114
Decision, C. (2013, February). Clinical Decision Support. Retrieved from Agency for Healthcare Research and Quality: https://www.ahrq.gov/professional/prevention-chronic-care/decision/clinical/index.html
HealthIT. (2017, September 5). Meaningful Use and the Shift to the Merit-based Incentive Payment System. Retrieved from Health IT: https://Healthit.gov/topic/federal-incentive-programs/meaningful-use
Sim, I., Gorman, P., Greenes, R., Haynes, R. B., Kaplan, B., Lehmann, H., ; Tang, P. (2001, February 19). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Retrieved from National Center for Biotechnology Information: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063
Support, C. D. (2011, April 14). Clinical Decision Support. Retrieved from American Medical Informatics Association: https://www.amia.org/fact-sheets/clinical-decision-support
Support, C. d. (2016, October 14). Four ways clinical decision support can improve outcomes. Retrieved from Managed Healthcare Executives: http://www.managedhealthcareexecutive.com/business-strategy/four-ways-clinical-decision-support