To build a holistic view of patients with AI, data needs to follow the patient, Manoharan says. “We need to connect the dots across various medical conditions the patient may have, and along the continuum of care. Linking hospitals to the home, primary care, etc.,” she says. “For this, you also need continuous patient engagement and feedback to understand their experience with the prescribed treatment, and to factor in the patient-reported outcomes into clinical decision-making.”
This, of course, will present some legal challenges. Medical data is sensitive and subject to privacy regulations that vary across jurisdictions. Companies that develop AI solutions for health care and medicine can run afoul of privacy laws if they’re not careful, and the industry is still feeling its way toward finding the balance between access to data and protecting sensitive health data.
“Regional legislation has to enable secure exchange and access to properly annotated data for medical research and clinical practice while safeguarding patient privacy,” Manoharan says.
The effort also requires new approaches from vendors of health technology, which will streamline the development of AI solutions.
“Medical equipment vendors have to start enabling the creation of applications by third parties, like innovative start-ups, or academic clinical centers, through publishing application programming interfaces (API),” Manoharan says.
Manoharan stresses that before an AI-powered product reaches adoption and has an impact on health care, it will face other technical and non-technical barriers such as monetization, implementation effort, actual workflow improvement, and trust. “We need to think of things like, where does the money come from, how our customers will pay for the AI, what are the reimbursement and efficiency gains,” Manoharan says.