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Clinical Decision Support

What is Clinical Decision Support?

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.


Why Is It Important to Have a Good Clinical Decision Support Model?

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.

What Internal Data Should I Have for a Good CDS Model?

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.

What External Data is Essential for a Good Model?

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.

What External Data May Prove Useful for a Good Model?

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.

What Are the Main Challenges of the Clinical Decision Support Use Case?

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.

Interesting Case Studies and Blogs to Look Into

IEEE Xplore: Patient safety & clinical decision support systems (CDSS): A case study in Turkey
ResearchGate: A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Models

Tangible Examples of Impact

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.

Thieme E-Journals: Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Relevant datasets

FDNA Telehealth Data

by FDNA Telehealth

FDNA Telehealth Data helps identify genetic conditions and rare diseases with medical publications data and biometric AI. The program identifies diseases through an initial analysis of facial features in a photo.

FDNA Telehealth makes use of published medical data and a network of hospitals, clinics, and professionals across the nation.

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EMIS Health Hospital Medicines Management

by EMIS-logo

Hospital Medicine Management services by EMIS Health allows clinicians to stay informed of vital patient data to make informed prescription decisions that reduce risks and streamline processes.

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Pollen Sense Data

by Pollen Sense

Pollen Sense Data offers live pollen, mold, & dust counts and forecasts. They can even identify mold and pollen species up to a 30 kms away

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Definitive Healthcare Medical Claims Data & Insights

by Definitive-Healthcare

Definitive Healthcare Medical Claims Data & Insights provides analysis of procedures, diagnosis, and prescription volumes for hospitals and healthcare businesses to identify market growth opportunities.

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Quantium Q

by Quantium

Quantium Q provides a range of data and artificial intelligence services for real estate, healthcare, retail, and banking industries

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