Access to personal health information (2)

28 June, 2020

Richard Fitton, UK writes

"Patients' reactions to an abundance of information involves a decision to ignore most of it, using the rest and interpreting it using past experience to make as good a judgement as possible about what is going to happen in the future. Access to information by a patient which has been sorted and coded by a trained and specialized doctor reduces uncertainty and increases the judgemental abilities of patients. The doctor's notes can reduce the abundance of information that a patient has to choose from."

The 2020-06-28 issue of bims-librar, which I just brought out, has a paper that seems---from as little as I understand about it---relevant to your concern

http://biomed.news/bims-librar/2020-06-28#p3 [*see note below]

That paper seems to suggest the use of machine learning, rather than the expensive labour of a specialized doctor.

You could create your own tailored report Biomed News report to watch developments in this area. BTW, Biomed News is also heavily aided by machine learning.

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Cheers,

Thomas Krichel http://openlib.org/home/krichel

skype:thomaskrichel

HIFA profile: Thomas Krichel is Founder of the Open Library Society, United States of America. Professional interests: See my homepage at http://openlib.org/home/krichel Email address: krichel@openlib.org

[*Note from HIFA moderator (Neil PW): Here are the citation and abstract:

The Personal Health Library: A Single Point of Secure Access to Patient Digital Health Information.

Ammar N, Bailey JE, Davis RL, Shaban-Nejad A.

Traditionally, health data management has been EMR-based and mostly handled by health care providers. Mechanisms are needed to give patients more control over their health conditions. Personal Health Libraries (PHLs) provide a single point of secure access to patients' digital health information that can help empower patients to make better-informed decisions about their health care. This paper reports a work-in-progress on leveraging tools and methods from artificial intelligence and knowledge representation to build a private, decentralized PHL that supports interoperability and, ultimately, true care integration. We demonstrate how a social application querying such a decentralized PHL can deliver a tailored push notification intervention focused on improving self-care behaviors in diabetic adults from medically underserved communities.

DOI: https://doi.org/10.3233/SHTI200200 ]