4. Prognosis

View

Prognosis

Another application of digital health is prognosis and prediction of response to a treatment, disease progression, and health condition to help make the right treatment decision. Also, the quality of care can be improved because of improved predictive analytic techniques. These techniques are available using machine learning algorithms and ultimately support development of innovative solutions to improve the quality of care and outcomes [1].

A central feature of predictive analytics is the contribution to what is described as “learning” health systems, whereby robust analytics track health outcomes to enable systems to learn and define the care delivery strategies that achieve the best conditions under which best outcomes can be achieved for individuals and the population [2].

 

References

[1] Park MPH S, Garcia-Palacios J, Cohen A, Varga Z. DIGITAL RESEARCH: A SUBSTITUTE FOR BIOLOGICAL MODELS? From treatment to prevention: The evolution of digital healthcare AUTHOR
[2] Scobie S, Castle-Clarke S. Implementing learning health systems in the UK NHS: Policy actions to improve collaboration and transparency and support innovation and better use of analytics. Learn Health Syst. 2019 Dec 15;4(1):e10209. doi: 10.1002/lrh2.10209. PMID: 31989031; PMCID: PMC6971118.