Healthcare News & Insights

AI and predictive analytics lead to improved delivery of healthcare services

Despite healthcare professionals’ purest intentions and dogged efforts to heal their patients, they’re limited by being human. They have only so much time and energy available for finding, processing and remembering information related to the medical conditions they manage, not to mention correlating that vast trove of information with their patients’ personal medical histories. That’s where artificial intelligence (AI) and predictive analytics come in. In this guest post, Anil Patil, a physician and health-IT professional experienced in inventing clinical IT tools for the healthcare industries, explains how together AI and predictive analytics have the potential to turbocharge modern medicine, helping to create better outcomes and reduced costs.


AI is the field of study that attempts to replicate human abilities, but without human limitations of time, energy and power. By using advanced algorithms, data processing capabilities and IT systems can produce data driven predictions within seconds – with little to no human intervention. Predictive analytics backed by real time and historic data processing can identify risky medical conditions ahead of time.

Predictive analytics uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. In medicine, predictions can range from responses to medications, to hospital readmission rates. Examples include predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even calculating future wellness.

Win-win for patients and healthcare professionals

Today, health care needs intelligent systems that can make good use of the massive reasoning abilities of AI and predictive analytics. Theoretically, there’s no upper limit to the amount of information that AI can learn, store and remember on every conceivable medical topic. AI can also learn, store and analyze every single detail of the real time and historical medical records of thousands of patients and correlate that information with its vast knowledge of medicine. This capability generates more accurate diagnoses, prescribes optimal medications at optimal doses, or suggests lifestyle changes that might forestall the need for expensive procedures.

Imagine the situation when a patient comes to an emergency room with complex symptoms and the ER doctor uses the patient’s vitals and past medical records to reach a diagnosis in a short period of time. Or, healthcare entities need an accurate estimate of future medical expenses and to design customized care plans for patients being managed under risk-based contracts. Without tapping into AI and predictive analytics, they’re at a disadvantage.

But with its massive computing power and vast data on every disease under the sun, AI can find patterns and correlations much more rapidly, without bias, fatigue or a paycheck. It can reduce healthcare professionals’ workloads and enable them to spend more time face to face with patients: advising, comforting and doing the things only humans can do.

Benefits of AI and predictive analytics

AI and predictive analytics provide the following benefits to healthcare professionals and the individuals they serve:

  • create better outcomes, i.e., healthier patients and communities
  • avoid the need for some procedures by intervening earlier in the disease process to recommend lifestyle changes to patients
  • manage resources better by predicting future demands for emergency rooms, specialty services, vaccinations and diagnostic procedures
  • help actuarial professionals in the insurance industry anticipate future healthcare costs, and
  • assist communities in dealing with community diseases, such as flu outbreaks.

In addition to increased efficiency, these benefits result in significant cost reductions across the board.

Obstacles to adopting AI and predictive analytics

Given the huge potential upside, why are these technologies still not being widely used? Here are some of the common reasons:

  • Health professionals are afraid that AI and predictive analytics will replace them.
  • There are privacy and ethical issues in gathering, storing and sharing large amounts of healthcare data between different, and sometimes competing, healthcare entities.
  • Healthcare data may not be available in the quantity and quality necessary to provide optimal performance.
  • Data received in a variety of formats needs to be standardized before meaningful insights can be gleaned.
  • AI has yet to prove itself in a medical setting.
  • The investment in training and equipment required to implement AI with predictive analytics may not pay off in the short term.

Healthcare costs currently consume 18% of the U.S. gross domestic product and climb higher every year. Since predictive analytics and AI have the potential to pare back expenditures and create healthier patients and communities, it’s time for healthcare leaders in both the government and private sectors to take the lead in capitalizing on the vast potential of AI and predictive analytics.

Anil Patil is a physician and health-IT professional experienced in inventing clinical IT tools for the healthcare industries in the United States, Canada and India. He can be reached at



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