ai trends in health care - manuel salgado, mckesson

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AI trends in healthcare H2O enables value based care delivery

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Page 1: AI trends in health care - Manuel Salgado, Mckesson

AI trends in healthcare

H2O enables value based care delivery

Page 2: AI trends in health care - Manuel Salgado, Mckesson

Continuum of careStakeholders throughout lifecycle of care• Patient• Provider• Payer• Manufacturer• Connected Services

Page 3: AI trends in health care - Manuel Salgado, Mckesson

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.

Page 4: AI trends in health care - Manuel Salgado, Mckesson

Divergent models for paymentPayment for service• Traditional• Individual interactions• Loosely coupled

Payment for outcome• Emerging• Collective result• Tightly integrated

Page 5: AI trends in health care - Manuel Salgado, Mckesson

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

Page 6: AI trends in health care - Manuel Salgado, Mckesson

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

Page 7: AI trends in health care - Manuel Salgado, Mckesson

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

Page 8: AI trends in health care - Manuel Salgado, Mckesson

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

Page 9: AI trends in health care - Manuel Salgado, Mckesson

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

Page 10: AI trends in health care - Manuel Salgado, Mckesson

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

Page 11: AI trends in health care - Manuel Salgado, Mckesson

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

Manufacturer

Payer

Patient

Services

Provider Payer

Patient

Manufacturer

Services

Provider Provider

Payer

Manufacturer

Services

PatientPatient

Payer

Manufacturer

Services

Provider

Page 12: AI trends in health care - Manuel Salgado, Mckesson

Enabling the sphere view at warp speedH2O 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.

Page 13: AI trends in health care - Manuel Salgado, Mckesson

H20 as core engine of the sphere

Clinical

Financial

PracticeWorkflow

Supply chain

ClassificationRegression

Feature Engineering

Aggregation

Deep Learning

PCA, GLM

Random Forest / GBM Ensembles

Fast Modeling Engine

Streaming

Nano Fast Scoring

Matrix Factorization Clustering

Munging

Ingestion