A clinical decision support system (CDSS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support (CDS), that is, assistance with clinical decision-making tasks. A working definition has been proposed by Robert Hayward of the Centre for Health Evidence: "Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care". CDSSs constitute a major topic in artificial intelligence in medicine.
But, now that patients are becoming more empowered with information and expected to make more and more decisions, it's time we create patient decision support systems. Giving digital records to patients, like electronic dog tags, is not a new idea.
There are 5 steps (Ds) requiring support, not just one. They include :
a ) data gathering support, including information that might not be in the medical record, like socioeconomic and behavioral issues
b) diagnosis making tools, like algorithms that incorporate many different variable like evidence based guidelines, literature reviews or standards of care
c) discussion support tools, helping to explain and explore options with patients
d) decision support tools that help to distill all the previous data into actionable information that serves at the basis for a therapeutic recommendation
e) discovering whether you made the right decision and correcting it as quickly as possible.
There are several drivers that should result in the evolution of patient decision support systems:
- Patient centered interoperable medical records
- The recognized value of shared decision making for preference sensitive conditions
- Precision medicine, incorporating biomarkers and -omics into prescribing and monitoring effectiveness and adverse events
- Patient engagement tools
- Economic incentives for patients to make smart sick care decisions
- Economic incentives to stay healthier and avoid risk factors for disease
- Incorporating and quantifying the social determinants of adverse outcomes
- Better mobile, cloud, AI, blockchain, patient reported outcomes and IT technologies
- Remote care access
- The Internet of medical things.
Unfortunately, getting patients to use and pay for apps, like chronic disease management apps, are substantial and dissemination and implementation has been disappointing. Adoption will require a multifaceted approach and appropriate patient incentives to use them.
Some examples of data driven patient decisions might be which medicine to take for a given condition, whether and how to participate in a clinical trial, various treatment options for their symptoms or disease, evidence based guidelines, pharmaceutical compliance and adherence suggestions, immunization and preventive health recommendations, and much more.
Decision support tools, in some instances, might be the same for doctors and patients. However, in some instances, based on many factors and practice patterns, both doctors and patients need an individualized version.
Apple recently announced a platform where patients can access their EMR data on their phones. This is but the first step.
With everyone being quantified, the data is a commodity. The real value lies in translating it into patient centered impact and behavior change. That won't be easy.
Look for a "support" button on your digital therapeutics keyboard soon.
Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs
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