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Benefits of AI and ML in healthcare

artificial intelligence in healthcare

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Benefits of AI and ML in healthcare

  1. 1. Benefits and opportunities of using AI & ML in Healthcare Dr. Saurabh Bhatia
  2. 2. Introduction  As a domain expert in both healthcare and AI, I am going to layout the following topics in this presentation  The business case for using AI in healthcare  Data- sourcing, cleaning, enrichment  Analytics- maturity model, business impact  Implementation and operations for maximizing ROI
  3. 3. Benefits  The benefits of using AI and ML in healthcare can be seen in following areas • Better care • Lesser complications/ recurrence • Cost effective co-pay models Benefits to patients • Lesser readmissions/ workload • Higher performance of providers • Cost saving interventions increase the profits Benefits to Care providers
  4. 4. Improving bottom-line for providers  2/3rd of clinical outcomes depend on patients’ situations outside the hospital  Living conditions  Need of Logistic support like transport, cooking food etc.  Financial condition  Using AI/ML, the hospital can gain access to the above factors and have the AI recommend interventions in high risk cases.  When above interventions are done, the readmission rate, complications and costly investigations can be brought down significantly, improving the revenue, bottom-line and clinical outcomes for the provider. Clinical factors
  5. 5. Data Requirements • Diagnosis • Prognostic data Clinical data • Ease of access of medical facility • Availability of care givers Logistic data • Affordability of treatment • Access to better performing providers Financial Data • Non-modifiable genetic data • Modifiable living situations Family data
  6. 6. Benefits of data collection  AI/ML systems can help in the following:  Identification of High risk patients  Early identification of intervention points  Recommendation of actions needed for intervention  Collation of results when intervention was done vs. not done  Learning for future optimization of interventions and recommendations based on previous results Ability to improve clinical outcomes using both clinical as well as non-clinical data improves providers’ performance
  7. 7. Types of AI Models Descriptive models • use data aggregation and data mining to provide insight into the past and answer: “What has happened?” • Learning from past Predictive models • use statistical models and forecasts techniques to understand the future and answer: “What could happen?” • Predicting the future possibility Prescriptive models • use optimization and simulation algorithms to advise on possible outcomes and answer: “What should we do?” • Recommending an intervention
  8. 8. Extending these models to healthcare Descriptive models • Learning from past • Utilize existing data to correlate with • Find out deficiencies in data models Predictive models • Predicting the future possibility • Using prognostic data with statistical models Prescriptive models • Recommending an intervention • Optimizing the statistical models to include high risk factor reduction
  9. 9. Bring stakeholders together  Improved healthcare outcomes benefits all stakeholders like patient, provider, PCP, insurance provider and caregiver  Using AI, we can identify points where an intervention would alleviate the current problem, as well as a potential problem  Each stakeholder must be made aware of the actions she needs to take and its impact in the overall picture of patient’s clinical outcome  Well defined benefit in terms of ROI as well as clinical endpoints help the stakeholders work towards a pre- determined definitive goal
  10. 10. Challenges  Disengaged patients  Need to make patient aware of potential benefits of engagement with more efficient PCP/ Provider for a better outcome  In person contact may be necessary  Incomplete data  Improvement of EMRs/ software systems to include social and financial data to make predictive and interventional data modelling possible  Technological upgrade barrier  Cost of upgrading to newer hardware/ software can be brought down by easing out rules of engagement with software providers and cloud providers  Skilled personnel need to be hired or existing workforce may need re-skilling to optimize the cost of AI interventions
  11. 11. “ ” Thank you
  • allbhatias

    Jul. 5, 2019

artificial intelligence in healthcare

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