AI and machine applications for healthcare
Paolo Beffagnotti , this is a fascinating topic and I am investigating a little bit at the moment. Of course the big advantage of AI is to collect Big Data and convert this into Smart Data to be able to predict different scenarios. Healthcare practitioners and insurance companies collect information from the various regular and one-time studies. AI enables to create a virtual model of the patient, a "Digital Twin". To make this more precise, wearables can deliver a flow of additional information,
The healthcare industry an use this to predict potential diseases, but also to identify potentials opportunities. No need to say that this may also develop a sinister side, as for example insurance companies may decide that based on the virtual model, "investing" into the patient does not make sense. If such information gets shared with the patient's employer, this may have additional consequences for the career.
AI and machine learning could helps in urgent and primary care where they could be "taught" to diagnose diseases thereby reducing doctor to patient loads and also speed to attain health care support. Furthermore AI and machine learning could reduce the risk of wrong or mis-diagnosis by doctor as AI could provide a list of possible diagnosis for the doctor to consult as a 2nd opinion. Furthermore AI and machine learning could also helps in breaking the language barrier between health care provider and the patient where language may no be a significant barrier in doctor to patient communication.
A. I. and Digital Health
New AI and digital models change the way the insurers interact with patients.
For example, digital insurers have reworked the trust equation with the patient, outsourced much of their value chain to their members, and now know much more about them. Digital business models tend to also blur the lines between payer and care giver organizations.
Some of the first-movers already crossed the line and started to offer services which have previously been provided exclusively by doctors and nurses. The ten digital business models are defined as follows:
- Digitally assisted member acquisition is a freemium business model concept.
- Mobile health concierge is a business approach designed for members to complete all health insurance tasks using mobile phones with the support from a concierge team.
- Peer-to-peer (P2P) insurance refers to a risk-sharing community.
- Mobile micro-insurance refers to the health insurance plans that cover short-term small health events or minimal ongoing health insurance.
- Health insurers tech platforms license their technology for the management of health plans and members to their customers.
- On-demand insurance is a usage-based model that enables members to access desired health plans upon request with the help of a mobile app.
- High-risk patient preventive care model concentrates on insuring and managing potentially costly patient groups.
- The payer & provider collaboration model stands for a closer, digitally enabled partnership between payers and care providers, especially hospitals.
- The API health insurance model uses a list of pre-defined health insurance products accessible to websites and app providers via an application programming interface (API).
- Direct primary care model. Within this model, a care provider or a hospital act like a health insurance company using a monthly subscription model.