Bridging the 17-year knowledge gap with clinical decision support
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Bridging the 17-year knowledge gap with clinical decision support

17 years. That’s the average length of time it takes from the moment new clinical knowledge is released, to the time it is practically applied in a healthcare setting.*

In this article, we take a look at innovative ways to reduce this gap, in order to ensure more accurate, timely and relevant clinical decision support (CDS).

Actionable, evidence-based approach

We’ve already discovered in a previous article, how active, medication-related CDS is surfacing as a critical enabler for greater patient safety, and how standardisation and integration of patient-specific parameters are central to realising an evidence-based approach.

We looked at the study by the American Journal of Health System Pharmacy, Improving medication-related clinical decision support, which revealed how CDS systems should incorporate more patient-specific information into decision-making algorithms and employ human factors design principles.

Importantly, the report also called for healthcare teams to be more accountable for improving interoperability, and ensure more regular updates of CDS systems to optimise accurate information sharing.

“Standardisation, integration of patient-specific parameters, and consideration of human factors design principles are central to realising the potential benefits of medication-related CDS,” the report said.

CDS Systems and semantic interoperability

Now let’s take this concept of interoperability one step further. What if we could deliver the latest medication safety information even more intuitively and accurately, within the context of a specific patient history? This is where semantic interoperability can be a game changer to CDS systems.

“Semantic” interoperability provides interoperability at the highest level, enabling two or more systems or elements to exchange information and to use the information that has been exchanged.5

Semantic interoperability takes advantage of both the structuring of the data exchange and the codification of the data, including vocabulary, so that the receiving information technology systems can interpret the data. Think of it as the Tower of Babel in digital data form: where information is reconstructed and unscrambled, so everyone understands the same language.

Applied to healthcare, this level of interoperability supports the electronic exchange of patient health information and data via health digital ecosystems, with the aim to improve quality, safety, efficiency, and efficacy of healthcare delivery.6

More specifically, the application of semantic interoperability means the entire CDS journey is more efficient, from publication, through to discovery by third parties, through to analysis, understanding and information exchange.

Importantly, innovative mechanisms help automate discovery and analysis of information, and can ultimately, contribute to improve health care, reduce costs and support access to the latest evidence.**

For instance, better, more innovative CDS Systems can be particularly valuable to clinical decision support around chronic disease management and treatment, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification.

So imagine a digital ecosystem of semantic interoperability, where AI-enabled CDS systems can analyse a patient’s characteristics and provide tailored recommendations for diagnosis, treatment, patient education, adequate follow-up, and timely monitoring of disease indicators – all at a fraction of the time and with more accuracy than ever before.

Supporting the future of CDS systems

The future of CDS looks exciting. And already, clinical ontologies such as SNOMED CT and AMT have reached a point of maturity where they can usefully facilitate semantic interoperabilitywhile keeping security front on mind.

By combining clinical application expertise, industry leading medicines content services and advances in AI-enabled machine learning, MedicalDirector is in an exciting position to continue to innovate in this area, so that together, we can make people healthier around the world.


* The answer is 17 years, what is the question: understanding time lags in translational research, Morris, Wooding, Grant, J R Soc Med 2011: 104: 510 –520.

** Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach, Marco-Ruiz, Pedrinaci, Maldonado, Panziera, Chen, Bellika, Journal of Biomedical Informatics 62 (2016) 243–264

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