How artificial intelligence can slowly change the healthcare landscape
Looking at 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 lifeto reduce cost and improve patient outcomes.
In the 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 organisations are in the early stage of AI adoption. Only around 6 percent use it; more than 60 percent organizations are still trying to understand it. It will take a while before the real benefits of AI can be leveraged. Here are the sectors where AI has already made a mark or can make a difference in the future.
1. 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 can accurately diagnose and, in some cases, even predict diseases. Blood smears will use vision to count cells and anomalies. ECG and cardio data can pass through AIto predict outcomes and assist physicians in accurate diagnosis.
2. Hospital re-admission has been a grave concern and millions are wasted due to lack of post-operative care. AI can help predict situations like this and can assist care providers in taking extra precautions.
3. Based on the patient case and required procedures, AI can help in planning surgery, help doctors with 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.
4. 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 systems will bring in the best-of-the-best values.
5. Virtual health assistants are tools like chat bots or a conversational service using smart speakers and can help answer health-related quires, check symptoms, or assist with appointments etc.
6. AI can assist hospitals in better management of assets, emergency management, and more efficient planning of processes and functions.
7. In the field of telemedicine, AI can bring wonders by enabling accurate remote health monitoring and predictive diagnosis, leading to cheaper and effective remote/rural health management.
If we flip to other side of healthcare, that is ‘insurance’, AI can bring many value-added services together with the care side to bring down the overall healthcare spending globally.
1. Outcome, risk, and cost comparison for similar cases in different hospitals/cities will help insurance companies compare cost and better optimise the plans offered and their premiums.
2. Predictive element of care can assist providers in reaching out to patients and for proactive care management, which can save significant amounts for both sides.
3. Predictive AI for care, claims, and other information can also help providers come up with cheaper and more effective health plans.
4. 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 systems will depend on the solution’s ability to integrate with the hospital or doctors’ workflow. AI systems should not be perceived as an extra process as that will reduce the value such systems can potentially bring.
However, the adoption of AI in healthcare, both clinical and insurance, will be slow and will face some challenges like:
1. Ethical concerns due to reduction in Hu element: who takes the liability for a negative event?
2. Regulation and compliance will play a big role in adaption of AI as they will govern the process and procedures followed.
3. 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.
4. Lack of requisite skill sets for technology adoption, followed by trainings of end users.
Finally, AI or any new-age technology will face the ‘Iron Triangle’ of healthcare (access, quality, and cost) test to prove its worth. But in an industry that has always lacked skilled manpower to manage everyone’s health, AI can do wonders in times to come.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)