Over the years, software applications have emerged to also include the patient in the process attempting to make “patient engagement” a reality. There are validated efforts in achieving care coordination between the provider and the patient.
Now comes artificial intelligence (AI) in healthcare. How could AI assist providers in realizing their goals for value-based care? Here are ideas:
1) Decision-support – The AI ingests and analyzes the contract between the payer and provider and helps the provider remain informed for what aspect of the care coordination the provider has responsibility providing relevant information, recommendations, and any pertinent patient data to the provider’s care team
2) AI-driven Intelligent Process Automation – The AI decides the next step in the process flow depending upon: a) The AI’s knowledge of the provider/payer contract and agreed upon best practice augmented with prior experience gained over time by the AI, and b) The AI ensures transparency of the process between the provider and the patient, and c) The AI recommends how and when the provider care team should initiate contact with the patient and manage the authorizing of additional care or managing a care delivery issue for the patient regardless of how the patient contact is initiated – inward bound by the patient or outward bound by the provider’s care team
3) Patient Engagement – An AI-driven scenario:
a. The AI apprises the patient of the provider care coordinator who is managing their inquiry, next the AI requests and informs the patient of request status, and if other follow-up actions are necessary such as informing the patient to schedule an appointment with a particular provider
b. The AI requests additional information from the patient, as well as provides approved patient education information
c. The AI notifies the provider’s care coordination teams of communications with the patient and those initiated by the patient while the AI maintains a documented care coordination journal to monitor the execution of a patient’s care plan.
This scenario achieves a key objective: Collaboration between the provider and patient. This collaboration results in the right patient care options evaluated, and the right form of care delivery authorized which could also consider SDoH (i.e., Social Determinants of Health). The expected results: Ensuring improved patient outcomes, along with the higher probability of lowering the total cost of care while meeting performance targets for overall quality – and finally – active engagement of the patient.
4) Active Contact with Complex Patient Care Cases – The AI identifies, informs and recommends actions for care coordinators and patients with multiple chronic conditions and morbidities (i.e., pre-defined and identified “special” populations), and is triggered by an event or pattern of medical events and/or services identified by the AI to indicate with a level of reliable probability that a provider care coordinator reaches out to the patient whether or not currently under a coordinated care plan.
There are additional AI-driven scenarios to discuss for enabling value-based care’s success. Note that the applications of AI like those described above are under development.