engleski

Health, social and financial consequences of the application of AI, ML and digital health – Going far beyond the traditional healthcare system

We employed the case of the aging population to make an inquiry into the nature and causes of the transition of the healthcare system and health- related industries to the value-based healthcare model. The concept is based on the idea that purchasers of health care such as government, public and private employers, and individual consumers and single payers, multiple payers, individuals hold the health care delivery system accountable for the best possible quality of care. Increasing cost burden from chronic health conditions and aging population is the key driver for digital health solutions such as m-Health applications, RPM devices, telehealth platforms, and PERS. Furthermore, favorable reimbursement policies towards clinically relevant digital health applications. There are significantly expanding care delivery models beyond physical medicine to include behavioral health, wellness therapies, dentistry, nutrition, and prescription management – critical areas of care for the 65+ population. Healthcare is at a tipping point for leading tech 80 LIJEČ VJESN 2020 ; godište 142 ; supplement 1 ; 67–88 Main Topic B Smart Technologies for Age-Friendly Ecosystems companies (Amazon, Apple, IBM, Google, and Microsoft) developing technologies suited for healthcare industry use cases. Applications for healthcare are gaining prominence as tech moguls are aggressively developing applications such as quick medical scribes and transcription speech-based guided interactions to vetted clinical use cases such as elderly care and chronic condition management. The key “tool/instrument” of transition is AI/ML across clinical and non- clinical arena bolstering the growth of healthcare space. AI/ ML will begin to see fruition, particularly in the risk analytics applications for the aging population. The application of digital health will continue to go far beyond the traditional system and empower individuals to be able to manage their own health what is of critical importance for the self-dependent aging population. It is expected that digital health tech catering to out of hospital settings, including services for the 65+ population will grow by 30% crossing $25 billion markets globally. The application of AI/ML algorithms in healthy aging is highly dependent on data availability and integrity. Ericsson Nikola Tesla has developed solution “Smart Habits” for independent living of elderly people which by collecting information from sensors learns the patterns of daily living using ML algorithms and provides actions on behaviors different than usual. Integration of various data sources from activity to machine- generated vital parameters readings, in addition, will serve as basis for prediction modeling in chronic disease management.

artificial intelligence ; machine learning ; health consequences

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