Clinical Decision Support provides clinicians, staff, and patients with patient information, usually at the point of care. CDS takes over many routine tasks from clinicians and flags potential problems and solutions for the medical team and patient.
Clinicians primarily use CDS systems to help diagnose and determine the best care for patients. Therefore the systems must be accurate and efficient; inaccurate systems models can seriously harm patients.
Most of CDSS data is external, but the most important internal data should be the analysis of your system’s past performance in different circumstances. In particular, pay attention to diagnosis, treatments, and medication.
Finally, note that machine learning systems learn and self-correct so the data must be of the highest quality.
The objective of CDSS models is well-informed patient care. Therefore, the most important data is the patient’s medical history and current symptoms, especially documentation from a patient’s Electronic Health Records (EHRs). You should also include public records and disease and treatment documentation. Finally, include qualified and standardized studies and textbooks with disease information and treatment guidelines.
Additional data is genomic and genetic data of the population segments, as well as of the patient in question. You can also enlarge the CDSS model with other health or medical records and databases.
In many cases, the main challenge for CDSS is the acceptance of these systems, or lack thereof, among physicians. Many physicians and other clinical staff have trouble relying on automated decision support, leading to suboptimal performance.
Finally, written guidelines build a large part of the CDSS models; these are difficult to translate to algorithmic features.
Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.
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