Clinical Decision Support Systems
Brittany Percy
University of Miami
Professor Catherine Turner, BSN, MBA, RN-BC
HCS685 Y – Intro to Health Informatics
October 21, 2017
Clinical Decision Support Systems
Technological advances developed in the last few decades have greatly influenced changes in the delivery of healthcare internationally. With health organizations’ goals of increasing the quality of care, improving patient outcomes, and reducing healthcare costs, implementing clinical decision support systems are vital to attaining Triple Aim and Meaningful Use. Throughout history, improvements in clinical decision support systems have enabled several benefits to be realized. Contained within this document, the use of clinical decision support systems will be thoroughly analyzed, including the history, purpose, and scope. In addition, healthcare organizations’ projects involving clinical decision support systems and vendors supplying the technology will be described. By the end of this document, readers will recognize the influence of clinical decision support systems on patient care and healthcare overall.
Clinical decision support systems (CDSS) are defined as “the use of information and communication technologies to bring relevant knowledge to bear on the health care and well-being of a patient” (Greenes, 2014). The aim of clinical decision support (CDS) is to make patient data easier to access and foster evidence-based action based on the information provided. By grouping together pertinent knowledge, the user can make decisions based on recommendations. Not only does CDS provide clinicians with relevant information and knowledge but also patients and individuals, so that better health processes are promoted as well as better patient care. For example, CDSS can trigger a message to a clinician during the electronic prescribing process when a medication has a dangerous interaction with another medication the patient is currently taking. Also, CDSS can prompt a patient that their flu shot is now due. While some CDSS involve simple types of decision support, such as alerting that a laboratory test result is abnormal, the type of CDS that “can be delivered depends to a large extent on the existence of the electronic health record (EHR) and/or on the various applications such as computerized physician order entry (CPOE), electronic prescribing, and patient-facing applications that need to access data from or otherwise interact with the EHR” (Greenes, 2014). Combining all of those applications and the EHR, CDSS can be most effective and achieve positive outcomes for populations.
The history of CDSS began in 1959, where clinical decision support ran separately from other systems. To employ a standalone system, “a clinician had to intentionally and purposefully seek the system out, log into it, enter information about his or her case, and then read and interpret the results” (Wright and Sittig, 2008). Robert Ledley and Lee Lusted’s 1959 paper about physician reasoning of medical diagnosis lead to the field of medical decision making. Proposing the use of a computer to sort cards which represented symptoms, “the number of cards supporting a particular diagnosis represented the likelihood of that diagnosis” (Wright and Sittig, 2008). Then, the system would be updated as new patients were seen and diagnoses added, as clinicians filled out a new card and placed it into the sorter. Later in 1964, Morris Collen developed the “Automated Multiphasic Screening and Diagnosis System” used at Kaiser Permanente (Wright and Sittig, 2008). During patient exams, patients were given cards with symptoms or questions. Cards that represented symptoms the patient was experiencing and questions with affirmative answers were collected in a “Yes” box. Then, the cards were entered into the system which offered an initial medical diagnosis.
The first CDSS integrated into existing clinical systems occurred in 1967 at the LDS Hospital in Salt Lake City, Utah. The Health Evaluation through Logical Processing (HELP) System was initially implemented in the cardiac catheterization laboratory but was expanded to “provide sophisticated clinical decision-support capabilities to a wide variety of clinical areas such as the clinical laboratory, nurse charting, radiology, pharmacy, respiratory therapy, ICU monitoring, and the emergency department” (Wright and Sittig, 2008). The immense success of the HELP System enabled many clinical departments to use this CDSS into the 21st century. As CDS technology became more progressive, systems were able to suggest a therapy in addition to producing a medical diagnosis. In 1971, de Dombal created a probabilistic model for diagnosing patient’s abdominal complaints. Compared to senior clinicians, “the computer’s preliminary diagnosis was accurate 91.8% of the time” and significantly cut “the error rate in half” (Wright and Sittig, 2008). By 1975, CDSS were able to prescribe antibiotics based on a patient’s infectious process due to the introduction of artificial intelligence. Shortliffe’s system, MYCIN, could interpret patient data supplied by a clinician, and then MYCIN would suggest a therapy based on the system’s knowledge base. “Early evaluation showed it suggested acceptable therapy 75% of the time, and it got better as more rules were added” (Wright and Sittig, 2008).
Throughout the early 1980s to 1990s, hospitals began to implement their own CDSS along with EHR adoption. Vanderbilt’s WizOrder System, Brigham and Women’s Hospital’s Brigham Integrated Computing System (BICS), and Veterans Health Administration’s Computerized Patient Record System (CPRS) are just a few of the organizations who applied CDS for patient care. BICS could direct clinical workflow pathways, such as guiding clinicians “through data entry and ordering tasks according to a specific clinical purpose” and “reminding clinicians to complete orders relevant to a pathway which are not yet done” (Wright and Sittig, 2008). Some advantages of using these new CDS platforms include proactive alerts and electronic storage of information. The new systems enabled users to save time by not having to reenter patient information and be alerted of dosing errors and drug-drug interactions without the user seeking support. The only disadvantage of the new CDSS was the lack of interoperability between larger clinical systems.
The purpose of CDSS is to assist healthcare personnel in making informed, higher quality and timely patient care decisions. According to the Healthcare Information and Management Systems Society (HIMSS), “CDS encompasses a variety of tools including, but not limited to: computerized alerts and reminders for providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information” (Clinical Decision Support and Meaningful Use, 2016). By utilizing all of these tools, the major objective of health information technology (HIT) is achieved, which involves the improvement of healthcare quality, safety, and cost-efficiency for populations.
CDSS can accomplish the HIT goal by providing interventions based on the Five Rights concepts. The Right Information, which is “evidence-based, derived from a set of recognized guidelines, or based on a national performance measure”, must be offered at the point of care (Campbell, 2013). The Right People should be notified, including the entire care team and the patient and/or healthcare proxy. Although CDS can be implemented in various formats, it is imperative for CDS to be applied in the best format with only enough information required for informed action. In addition, there are several channels for the delivery of CDS, including the EHR, CPOE, mobile health application, and patient portal. Due to the possibility of cognitive overload and alert fatigue, CDS must provide recommendations for interventions at the right time in the clinician’s workflow. According to the Center of Medicare and Medicaid Services (CMS), “effective CDS must be relevant to those who can act on the information, in a way that supports completion of the right action” at the right time (CLINICAL DECISION SUPPORT: More Than Just ‘Alerts’ Tipsheet, 2014).
CDSS implementation is essential to the achievement of Meaningful Use (MU). Since the enactment of the American Reinvestment and Recovery Act (ARRA) in 2009, the Health Information Technology for Economic and Clinical Health (HITECH) Act has initiated the process of achieving the “MU of interoperable EHRs throughout the United States health care delivery system” (Meaningful Use, 2017). Ensuring that the nation’s providers are using certified EHRs in a manner that improves the quality of patient care, the Office of the National Coordinator for Health IT (ONC) established the method for submission of quality care measures to CMS. According to Murphy (2014), “an essential component of “meaningful use” is the development of EHRs that are capable of CPOE with CDSS that will integrate into workflow and facilitate clinical outcome objectives”. Some of the Meaningful Use goals include “improving medical quality, patient safety, healthcare efficiency and reducing disparities; engaging patients and families; and improving population and public health (Hoyt and Yoshihashi, 2014). As a result of the development of the MU program, healthcare organizations are required to integrate CDS into their EHR systems. In order for organizations to get reimbursed by CMS, hospitals and eligible healthcare personnel must demonstrate engagement in implementing certified EHRs to improve quality of care and decrease federal costs.
The Meaningful Use of certified EHRs was implemented in a series of phases beginning in 2011. Currently, the Stage 3 objective for 2018 incorporates implementing CDS interventions concentrated on “improving performance on high-priority health conditions” (Clinical Decision Support and Meaningful Use, 2016). Hospitals and eligible personnel must implement CDS functions, such as drug-to-drug and allergy interaction checks, as well as adopt five CDS interventions “related to four or more clinical quality measures at a relevant point in patient care for the entire EHR reporting period” (Clinical Decision Support and Meaningful Use, 2016). The process of measuring patient care and outcomes demonstrates providers’ adherence to evidence-based care delivered to patients. Examples of some of the measures included in the documentation of achieving Stage 3 includes proof of reduced length of stay, fewer medication-related adverse events and reduced morbidity for patients with chronic conditions (Meaningful Use, 2017).
In order for hospitals and healthcare providers to be reimbursed for achieving Meaningful Use, CDSS within EHRs has been implemented in a variety of healthcare departments. Applying CDSS in pharmacology, pharmacy, and pathology has greatly impacted how healthcare personnel utilize CDS in their workflow. In recent studies, several benefits are attained by utilizing CDSS. For example, “case studies have identified increased pathogen susceptibility to selected antimicrobial agents, decreased rates of adverse drug effects, toxic drug levels, and bleeding events on patients taking anticoagulants” (Castaneda, Nalley, Mannion, Bharracharyya, … and Suh, 2015). Another study by the Vermont Diabetes Information System demonstrated that use of patient summaries with CDS alerts and reminders resulted in “decreased probability of hospitalization, fewer emergency room visits, and statistically significant cost savings in both hospitalizations and ER visits” (Murphy, 2014). Substantial CDS benefits are also realized in outpatient settings. The Mobile Diabetes Intervention Study combined CDSS, mobile health tracking, and communication within a patient portal. Collectively, these interventions succeeded in reducing the intervention group’s mean glycosylated hemoglobin determination (HgbA1c) level from 9.9% to 7.9% within a year. In another study, EHR reminders provided to patients at 6 months post-fracture resulted in a statistically significant “51.5% of patients receiving recommended osteoporosis care” (Murphy 2014). It is evident that there are substantial benefits to patients when organizations adopt CDSS.
Realizing the importance and advantages of adopting CDSS within their practice, several healthcare organizations have implemented successful CDSS within their current workflow. New England’s White River Family Practice (WRFP) developed a CDSS project after recognizing a number of deficiencies in their pre-EHR management of diabetic and asthmatic patients. Prior to attaining an EHR, WRFP used paper documentation and were unable to identify if patients were in need of a HgbA1c level or an updated asthma action plan (AAP). Assisted by the Vermont Information Technology Leaders, WRFP installed an integrated EHR with standardized language to aid in the identification of the required populations within the registry. WRFP acknowledged the project’s possibility of improving patient care to these chronic populations and developing better techniques for consumer health maintenance.
“WRFP’s EHR was configured with appropriate alerts and clinical decision supports to facilitate standardized provision of guideline-recommended care for populations with either asthma or diabetes” (Nunlist, 2013). With an integrated EHR, the office could electronically notify patients of potential gaps in their care. Identifying patients who required a “current HgbA1c, fasting low-density lipoprotein (LDL), creatinine, or urine microalbumin determination (as defined in our standing orders)”, medical assistants could have “these analyses drawn (or ordered) without requiring a specific request by that patient’s practitioner” (Nunlist, 2013). In addition, similar interventions were performed for the asthmatic patients. The new standard required medication assistants to obtain peak flow measurements and an asthma control test during certain intervals as well as update the asthma action plan at least annually. The project’s result showed significant changes over a period of two years. More patients had a current HgbA1C level and asthma control test in their EHR. As a result of positive patient outcomes due to the availability of recent results, WRFP was able to attest to Meaningful Use Stages 1 and 2 and achieve Triple Aim goals, which include “improving health of population, engaging patients and families in managing health and making decisions about care, removing waste and achieving effective, affordable care” (Nunlist, 2013).
In addition to WRFP’s CDSS project, Truman Medical Centers (TMC) in Missouri employed a CDSS project to improve patient safety and reduce the risk of hospital acquired pressure ulcers (HAPUs). Since implementing their system-wide EHR in 2009, TMC began a series of performance improvement projects based on their data analysis results. Unknown prior to TMC’s EHR use, 33% of their HAPUs were contributed to medical devices. “Without preventative technology and analytical data to monitor these factors, the treatment policy was ineffective at monitoring and preventing HAPU risk” (Briley, 2014). As a result of their findings, TMC began to implement interventions to reduce the occurrence of HAPUs in 2012.
TMC’s new protocol was designed using evidence-based knowledge via a comprehensive healthcare personnel team. By phasing in the protocol one unit at a time and identifying a wound care champion on each unit, TMC was able to audit nursing compliance at the point of care and encourage further training. The HAPU protocol was incorporated in the current workflow to prompt prophylaxis interventions for at risk patients. “The protocol outlined how to apply, maintain, and change prophylactic dressings, and aligned with industry evidence that protective dressings can be an effective component of HAPU prevention strategies” (Briley, 2014). With the development of an algorithm that worked along with the nurse’s existing workflow, the HAPU prevention protocol began as soon as the patient was admitted. Based on the Braden score identified in the patient assessment, a care plan with recommendations was built and the nurse could customize the interventions and outcomes according to the patient’s needs. After collecting audits for one year, data analysis showed an increase in compliance of evidence-based guidelines and discovered HAPU-causing devices that have now been replaced with safer materials. Oxygen tubing was established as the major contributing device to HAPUs within the system. Since the identification of the cause of HAPUs within TMC, the protocol’s “interventions reduced the prevalence of HAPUs by 78 percent across our downtown and suburban campuses and corresponded with savings exceeding $4 million” (Briley, 2014). TMC’s success of their CDSS project demonstrates the numerous prospects CDS has in increasing patient safety, improving population’s outcomes, and decreasing federal healthcare costs.
Recognizing the need for having CDSS, many commercial vendors are supplying CDS technology for healthcare organizations. According to a recent article by Monica (2017), First Databank is considered to be the “#1 vendor among physician practices” and the “highest-rated drug database by KLAS”. First Databank’s most widely used application is MedKnowledge. Utilized internationally, MedKnowledge contains the advanced medication decision support features required to attest to Meaningful Use within the ARRA and HITECH Act. According to “FDB MedKnowledge” (n.d.), some of the essential resources of the MedKnowledge application includes “EHR, CPOE, electronic medication administration record (EMAR), pharmacy dispensing, medication reconciliation, and much more”. First Databank’s CDSS technology provides clinicians with immediate electronic messages, which offer current evidence-based references within existing applications for active CDS. Since MedKnowledge can be integrated into other vendor applications, this CDSS can be utilized by other commercial EHR’s, such as MEDITECH and Epic. The importance of MedKnowledge use by healthcare organizations is displayed by how First Databank designed the CDSS. MedKnowledge “ensures that clinicians are aware of the potential hazards of a medication and how these relate to their patient before the medication is prescribed, dispensed or administered, thus helping to reduce the occurrence of medication errors and improve patient safety” (“FDB Medknowledge”, n.d.). First Databank’s design is based on the Five Rights of CDSS, and recognizes the importance of getting the right CDS information to the right person, in the right format, through the right channel, at the right time in the clinician’s workflow. This is especially necessary for meaningful use of the system. As stated by Khalifa (2014), “one of the most important success factors for CDSS is to make them fit smoothly into regular and normal clinical workflow, otherwise any CDSS will not have any positive effect and will never be used”. Without CDSS use by healthcare organizations, Meaningful Use by hospitals and eligble personnel will not be accomplished.
In addition to First Databank, another widely-recognized CDSS is supplied by Zynx Health. This particular company developed a series of applications that work together to provide CDS within multiple areas of the EHR. ZynxEvidence provides the “clinical recommendations for interventions, along with article summaries, complete references, abstracts, and links to full-text articles are integrated within your EHR, so clinicians always have the vital information they need to guide decision making at the point of care” (“Evidence-Based Clinical Decision Support Solutions”, n.d.). Meanwhile, ZynxOrder contains evidence-based CPOE order set templates where the references can be viewed directly within the CPOE system. ZynxOrder has full interoperability with most major EHR vendors, including Cerner, MEDITECH, Epic, and Allscripts. In addition, ZynxAnalytics’ Knowledge Analyzer is used by organizations to maintain and validate CDS information. By bringing together the latest evidence-based content, healthcare disparities are reduced and patient outcomes are improved. Hospital organizations who use Zynx Health as their CDSS have the possibility of reducing “costs, length of stay, mortality, and readmissions” (“Evidence-Based Clinical Decision Support Solutions”, n.d.).
In conclusion, CDSS are important tools in the HIT community. After reviewing the history, purpose, scope, and benefits, it is evident that CDSS enables healthcare organizations to not only attest Meaningful Use but also achieve Triple Aim goals. With so many examples of organizations completing CDSS projects, the impact on healthcare and consumers is realized. Due to the immense number of commercial vendors supplying CDS technology, it is vital for organizations to research the various options and choose a system that will help accomplish their clinical and financial objectives. Over time, CDSS use will improve the quality of patient care and the performance of clinicians for a better healthcare experience.
References
Briley, A. (2014). Truman Medical Center Pressure Ulcer Case Study (Rep.). Retrieved October 13, 2017, from HIMSS website: http://www.himss.org/sites/himssorg/files/FileDownloads/TMC%20Davies%20HAPU-Menu%20Case%20Study_FINAL%20with%20Edits.pdf
Campbell, R. (2013, October). The Five Rights of Clinical Decision Support: CDS Tools Helpful for Meeting Meaningful Use. Retrieved October 12, 2017, from http://library.ahima.org/doc?oid=300027#.Wd_EZBNSxmA
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., Goy, A., and Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5, 4. http://doi.org/10.1186/s13336-015-0019-3
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Greenes, R. (2014). Clinical Decision Support The Road to Broad Adoption (2nd ed.). Burlington: Elsevier Science.
Hoyt, R. E., & In Yoshihashi, A. (2014). Health informatics: Practical guide for healthcare and information technology professionals.
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Monica, K. (2017, April 07). Top Clinical Decision Support System (CDSS) Companies by Ambulatory, Inpatient Settings. Retrieved October 12, 2017, from https://ehrintelligence.com/news/top-clinical-decision-support-system-cdss-companies-by-ambulatory-inpatient
Murphy, E. (2014). Clinical Decision Support: Effectiveness in Improving Quality Processes and Clinical Outcomes and Factors That May Influence Success. The Yale Journal of Biology and Medicine, 87(2), 187-197.
Nunlist, M. (2013). WRFP Diabetic Management Case Study (Rep.). Retrieved October 13, 2017, from HIMSS website: http://www.himss.org/sites/himssorg/files/FileDownloads/2013%20Ambulatory%20Award_White%20River%20Family%20Practice%20POPULATION%20MANAGEMENT.pdf
Wright, A., & Sittig, D. F. (2008). A Four-Phase Model of the Evolution of Clinical Decision Support Architectures. International Journal of Medical Informatics, 77(10), 641–649. http://doi.org/10.1016/j.ijmedinf.2008.01.004