Friday, May 3, 2019

AI Technology Adaption will Face ‘Iron Triangle’ in Healthcare Test to Prove its Worth


By Sanjay Pathak - Head Healthcare and Insurance Solutions, 3i Infotech

Looking at the upcoming trends globally and across industry ‘Artificial Intelligence/Machine Learning (AI/ML)’ tops the charts. Generally, first thing which comes to mind is machine/cyborg taking over human elements and this has been depicted to various degree in many sci-fi movies. While, the reality is far away from that, it will be unjust to ignore how healthcare is evolving and adopting AI in real life to reduce cost and improve patient outcomes.

In current context, AI means simulation of human elements by machines/computers, where they acquire information (learning), process it to reach reasonable conclusions (action) and adapt themselves to situations (course corrections). AI leverages various technologies like Machine/Deep learning, Vision, NLP, Robots or autonomous machines etc.

As per Gartner, most organizations are in early stage of AI adoption. Only around 6% have it in use and more than 60% organizations are still trying to understand it. It will take a while before real benefits of AI can be leveraged. Below are areas where AI has already made its way or can bring in difference in future.

Leveraging vision, deep learning on sensor based vital data, physicians will be better equipped to diagnose ailments. Medical imaging can be taken to new levels where AI on top can accurately diagnose and in some cases even predict diseases. Blood smears will use vision to count cells and anomalies. ECG & cardio data can pass through AI to predict outcomes and assist physicians in accurate diagnosis.
Hospital re-admission has been a grave concern and millions wasted due to lack of post operation care. AI can help predict situation like this and can assist providers take extra precautions.
Based on the patient case and required procedures, AI can help in planning surgery, help doctors in accurate measurements, and assist during surgery by tracking vital and other data. AI can help surgeons understand surgery outcomes better based on correlations from similar cases.
Using NLP and vision, AI can assist doctors with diagnosis, running pharmacy correlations with other drugs, allergy, food etc. AI can help physicians with transcripts and voice assisted case management. All these integrated with EHR system will bring in the best of the best values.
Virtual health assistants are tools like chat bots or a conversational service using smart speakers helping customer answer health related quires, symptoms checker or assist them with appointments etc.
AI can assist hospitals in better management of assets, emergency management and better planning of the hospital processes and functions.
In the field of telemedicine, AI can bring wonders by enabling accurate remote health monitoring, predictive diagnosis leading to cheaper & effective remote/rural health management.
If we flip to other side of healthcare, i.e., ‘insurance’, AI can bring many value added services together with care side to bring down the overall healthcare spending globally.

Outcome, risk and cost comparison for similar cases in different hospitals/cities will help insurance companies compare cost and better optimize the plans offered and their premiums.
Predictive element of care can assist providers in better reach out to patients and proactive care management, which can save significant amounts for both sides.
Predictive AI for care, claims and other information can also help providers come up with health plans, which are cheaper and more effective.
AI systems can sift through clinical and claims data to highlight errors in diagnosis, payments, frauds and workflow issues, thus providing a true value based care system.
The real test for AI system will depend on solutions’ ability to integrate with the hospital or doctors’ workflow. AI systems should not be perceived as extra process, as that will reduce the value such systems can potentially bring. Adoption of AI in healthcare, both clinical and insurance will be slow and will face some challenges like:

Ethical concerns due to reduction in Hu element - who takes the liability for a negative event?
Regulation & compliance will play a big role in adaption of AI as they will govern the process and procedures that are followed.
Initial adoption both by physicians and patients will see hiccups mostly related to trust factors, till the time both parties build confidence in such systems.
Lack of requisite skillsets for technology adoption, followed by trainings of end users.
Finally, AI or any new age technology adaption will face ‘Iron Triangle’ of healthcare (access, quality, and cost) test to prove its worth. For an industry which has always lacked skilled manpower to manage everyone’s health, AI can do wonders in times to come.

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