The document outlines the benefits and opportunities of using artificial intelligence (AI) and machine learning (ML) in healthcare. It discusses how AI can help improve clinical outcomes by using both clinical and non-clinical patient data to identify high-risk patients, recommend interventions, and learn from previous results. This allows providers to reduce readmissions, complications, and costs while improving the bottom line. The document also covers the types of AI models that can be used, from descriptive models that learn from past data to predictive and prescriptive models, as well as challenges to implementation.
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. 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. 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. 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. 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. 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. 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. 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. 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
