Manuel Salgado, Sr. Data & Analytics Manager at McKesson, talks about the AI trends in healthcare. #h2ony - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. AI trends in healthcare
H2O enables value based care
delivery

2. Continuum of care
Stakeholders throughout lifecycle of care
• Patient
• Provider
• Payer
• Manufacturer
• Connected Services

3. Value Based Care
• Value-Based Care (VBC) is a strategy used by
purchasers to promote quality and value of
health care services. The goal of any VBC
program is to shift from pure volume-based
payment, as exemplified by fee-for-
service payments to payments that are more
closely related to outcomes.

4. Divergent models for payment
Payment for service
• Traditional
• Individual interactions
• Loosely coupled
Payment for outcome
• Emerging
• Collective result
• Tightly integrated

5. VBC needs advanced data & analytics
• Arriving at the best value requires optimizing cost and
benefit across all links in the treatment value chain
• This necessitates each link to analyze the data from
their own perspective in relation to all others
• Having a framework for advanced analytics that
enables fast & agile development of machine learning
models to answer the multitude of questions over
large amounts of data is necessary to thrive in this
payment environment

6. 360° view of stakeholder
• In Healthcare there isn’t a single customer
• At any point during the delivery of care each
of these stakeholders becomes the client in
need of a 360° view
• Each with different but related questions that
involve the other stakeholders

7. 360° view of the patient
• Project length of recovery and
success rate given the different
treatment options
• Which option will be the most
effective at the lowest cost across
providers and treatments
• Estimate cost throughout life of
treatment amongst different
payers
• Predict additional services based
on other patients that have
undergone similar treatment
Patient
Payer
Manufacturer
Services
Provider

8. 360° view of the provider
• Develop tailored treatment
recommendations based on
empirical outcome evidence
across all patients
• Predict profitability across
treatments and actual payer
fee schedules
• Optimize services portfolio
to maximize clinical and
financial success
Provider
Payer
Manufacturer
Services
Patient

9. 360° view of the payer
• Analyze patient characteristics
and the cost and outcomes of
treatments to identify the most
clinically effective and cost-
effective treatments to apply
• Profile disease on a broad scale to
identify predictive events and
support prevention initiatives
• Detect fraud and check claims for
accuracy and consistency
Payer
Patient
Manufacturer
Services
Provider

10. 360° view of the manufacturer
• Optimize profitability of product
supply chain (manufacture,
distribution, and delivery) to
current and future demand
• Tailor R&D expense to conditions
and treatments with highest
future demand, positive
outcomes and need across
patient populations
• Focus marketing efforts with
better segmentation across
geographies, payer response, and
disease types
Manufacturer
Payer
Patient
Services
Provider

11. Converge all 360° views = Sphere view
• Aggregating each 360°
perspective results in a sphere
view of knowledge
• Necessary to obtain a holistic
view across the continuum of
care that will derive the most
value for holistic treatment
• Machine learning and
advanced analytics underpin
this information model

12. Enabling the sphere view at warp
speed
H2O provides:
• Data science in a box. Easily apply math and
predictive analytics to solve your most
challenging business problems
• Multiple interfaces (from no code UI to
advanced integration R, Java, Scala, Python,
JSON)
• Supports data in any form. Connect to data
from HDFS, S3, SQL and NoSQL data sources
• Massively Scalable Big Data Analysis. Train a
model on complete data sets, not just small
samples, and iterate and develop models in real-
time with H2O’s rapid in-memory distributed
parallel processing
• Nano-fast Prediction Engine Score data against
models for accurate predictions in nanoseconds.
H2O enables:
• Speeds up data analysis, model building,
deployment and scoring
• Derive analytic models using either supervised
(classification/regression) or unsupervised
(clustering) on existing data to derive new
insights from data
• Turn the insights into a working predictive
model that can then be used on new data cases
to forecast outcomes
• Model can be integrated and used in real-time
as part of the regular operational flow of an
application. It can also be used in batch mode
to score millions of cases at once.

13. H20 as core engine of the sphere
Clinical
Financial
Practice
Workflow
Supply chain
Classification
Regression
Feature
Engineering
Aggregation
Deep Learning
PCA, GLM
Random Forest /
GBM Ensembles
Fast Modeling Engine
Streaming
Nano Fast Scoring
Matrix
Factorization Clustering
Munging
Ingestion