ai trends in health care - manuel salgado, mckesson

AI trends in healthcare

H2O enables value based care delivery

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

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.

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

Payment for outcome• Emerging• Collective result• Tightly integrated

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

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

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

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

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

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

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

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.

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