Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS is used to augment clinicians in their complex decision-making processes. Since their firstuse in the 1980s, CDSS has seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities.
INTRODUCTION: WHAT IS A CLINICAL DECISION SUPPORT SYSTEM?
A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. A traditional CDSS is comprised of software designed to be direct aid to clinical decision making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision.
CDSSs today are primarily used at the point-of-care, for the clinician to combine their knowledge with information or suggestions provided by the CDSS. Presently, CDSS often make use of web applications or integration with electronic health records (EHR) and computerized provider order entry (CPOE) systems. They can be administered through desktops, tablets, smartphones, and other devices such as biometric monitoring and wearable health technology.
CDSS has been endorsed by the US Government’s Health and Medicare acts, financially incentivizing CDS implementation into EHRs.10 In 2013, an estimated 41% of U.S. hospitals with an EHR, also had a CDSS, and in 2017, 40.2% of US hospitals had advanced CDS capability.
The scope of functions provided by CDSS is vast, including diagnostics, alarm systems, disease management, prescription (Rx), drug control, and much more. They can manifest as computerized alerts and reminders, computerized guidelines, order sets, patient data reports, documentation templates, and clinical workflow tools.
ADVANTAGES OF CDSS
One of the advantages of CDSS is patient safety. CDSS targeting patient safety through CPOE and other systems have generally been successful in reducing prescribing and dosing errors, contraindications through automated warnings, drug-event monitoring and more. CDSS also improve patient safety through reminder systems for other medical events, and not just those that are medication related.
Another advantage of CDSS is supporting clinical management. Studies have shown CDSS can increase adherence to clinical guidelines. Furthermore, CDSS can assist with managing patients on research/treatment protocols, tracking and placing orders, follow-up for referrals, as well as ensuring preventative care. CDSS can also alert clinicians to reach out to patients who have not followed management plans; or are due for a follow-up, and help identify patients eligible for research based on specific criteria. A CDSS designed and implemented at Cleveland Clinic provides a point-of-care alert to physicians when a patient’s record matches clinical trial criteria. The alert prompts the user to complete a form that establishes eligibility and consent-to-contact, forwards the patient’s chart to the study coordinator, and prints a clinical trial patient information sheet.
CDSS can be cost-effective for health systems, through clinical interventions, decreasing inpatient length-of-stay, CPOE- integrated systems suggesting cheaper medication alternatives, or reducing test duplication. A CPOE-rule was implemented in a pediatric cardiovascular intensive care unit (ICU) that limited the scheduling of blood count, chemistry and coagulation panels to a 24-h interval. CDSS can notify the user of cheaper alternatives to drugs, or conditions that insurance companies will cover. In Germany, many inpatients are switched to drugs on hospital drug formularies. The CDSS could switch 91.6% of 202 medication consultations automatically, with no errors, increasing safety, reducing workload and reducing cost for providers.
CDSS provide support for clinical and diagnostic coding, ordering of procedures and tests, and patient triage. Designed algorithms can suggest a refined list of diagnostics codes to aid physicians in selecting the most suitable one(s). A CDSS was conceived to address inaccuracy of ICD-9 emergency department(ED) admission coding (ICD is International Statistical Classification of Diseases, standardized codes used to represent diseases and diagnoses). The tool used an anatomographical interface (visual, interactive representation of the human body) linked to ICD codes to help ED physicians accurately find diagnostic admission codes faster. CDSS can directly improve the quality of clinical documentation. Documentation accuracy is important because it can directly aid clinical protocols.
CDSS supports diagnostics. CDSS for clinical diagnosis is known as diagnostic decision support systems (DDSS). These systems have traditionally provided a computerized ‘consultation’ or filtering step, whereby they might be provided data/user selections, and then output a list of possible or probable diagnoses. Given the known incidence of diagnostic errors, particularly in primary care, there is a lot of hope for CDSS and IT solutions to bring improvements to diagnosis. We are now seeing diagnostic systems being developed with non-knowledge-based techniques like machine learning, which may pave the way for more accurate diagnosis.
Diagnostics support: imaging. Knowledge-based imaging CDSS are typically used for image ordering, where CDSS can aid radiologists in selecting the most appropriate test to run, providing reminders of best practice guidelines, or alerting contraindications to contrast.
Diagnostics support: laboratory and pathology. Another subset of diagnostics where CDSS can be useful is laboratory testing and interpretation. Alerts and reminders for abnormal lab results are simple and ubiquitous in EHR systems. CDSS can also extend the utility of lab-based tests for the purpose of avoiding riskier or more invasive diagnostics. Pathology reports are crucial as decision points for many other medical specialities. Some CDSS can be used for automated tumour grading.
With the advent of the ‘Personal Health Record’ (PHR), we are seeing CDS functionality integrated, similar to EHRs, with the patient as the end-user or ‘manager’ of the data. This is a great step towards patient-focused care, and CDS-supported PHRs are the ideal tool to implement shared decision-making between patient and provider, specifically because CDSS can remove a ‘lack of information as a barrier to a patient’s participation in their own care. PHRs are frequently designed as an extension of commercial EHR software, or as standalone web-based or mobile-based applications. When connected to EHRs, PHRs can have a two-way relationship, whereby information entered directly by the patient can be available to their providers, and also information in the EHR can be transmitted to the PHR for patients to view. Furthermore, PHRs and other patient monitoring applications can be designed to collect information from health devices and other wearables, to create actionable insights for providers. It is worth noting that as PHRs have become more advanced with CDSS capabilities, there has also been increasing emphasis on the design of these systems to serve shared decision making between patient and provider, and to be interactive tools to make patients more knowledgeable/involved in their own care.
CDSS has been shown to augment healthcare providers in a variety of decisions and patient care tasks, and today they actively and ubiquitously support the delivery of quality care.
Reference(s)
Sutton, R., Pincock, D., Baumgart, D., Sadowski, D., Fedorak, R., & Kroeker, K. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digital Medicine, 3(1). doi: 10.1038/s41746–020–0221-y
