what’s the big picture for big health...

What’s the Big Picture

for Big Health Data? 25-May-2013

Tracy Allison Altman, PhD

PepperSlice

Big Data Science Meetup 25-May-2013




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Why study big health data?

• Correlation

• Association

• Inference

• Cause & effect

• Explanation



A little philosophy.

What does “cause” mean?

What’s a “satisfactory” explanation?



• Diagnosis

• Treatment

• Quality of care

• Cost of care

• Patient outcomes

It’s about outcomes.



Health data is the same.

• Rigorous scientific research

• Predictive analytics

• Social influences: Adherence, behavioral, marketing

• Business pressure: Accountability



• Human lives at stake

• Institutional barriers, incentives

• Public policy issues

• Findings are closely scrutinized

Health data is different.



• Diagnosis precision

• Treatment precision

• Quality, satisfaction

• Cost, operational efficiency

• Patient QOL, function, lifespan

It’s not easy measuring outcomes.



• Bird flu outbreaks

• Drug-resistant bacteria

• Communicable diseases

Public Health vs. Medicine.

• Population health

• Personalized medicine



Big data is changing Medicine.

Payment models are shifting:

• Away from fee-for-service

• Toward accountable care: How to define & measure success?



Population: Health by numbers.

Accountable Care Organizations (ACOs) manage population health.

• Predictive analytics to prioritize care

• Analysis showing what affects outcomes

• EHR and claims are crucial



We need a Single Patient Identifier.

Big data analytics are handcuffed.

• Need to follow a patient’s progress

• US has no single patient identifier

• Difficult to identify success



It’s all about me: Personalized medicine.

• Big $ investments now

• Individual profiles (genetic, behavioral, social, spatial, etc.)

• Pinpoint diseases, conditions, responses,



Traditional vs. Modern.

• Research question

• Deterministic

• Proprietary

• RCTs

• Structured

• Clinical

• Discovery

• Probabilistic

• Open

• Algorithms

• Unstructured

• Real-world



Sources of health data: Big & small.

• Genetic info

• Insurance claims, EHR, clinical notes

• Real-time monitoring (ICU)

• Patient-reported outcomes

• Peer-reviewed research, gray literature

• Order sets, knowledge assets



Analytical methods for health data.

• De-identifying records & claims

• Aggregating massive data sets

• Developing statistics, algorithms

• Applying machine learning, semantics

• Ingesting ontologies



Not all research is created equal.

• Prospective better than retrospective?

• Interventional vs. observational?

• Comparative effectiveness reliable?

• Are core outcome sets the future?



Example: Watson.

Answering questions:

• Precision of diagnosis and treatment

• Insurance pre-authorizations

• Ontologies, structured data, and unstructured evidence



Example: IBM is setting the pace.

AI, analytics, integration.



Example: Apixio.

Big data healthcare platform.

• Risk management & revenue cycle

• Analyzing text, scanned, coded data



Example: Treato.

Mining social networks for pharma info.

“See what millions of

patients are saying.”



• Taking trial-and-error out of RX

• The ‘Wanamaker’ problem: What works, and for whom?

Example: GNS Healthcare.

‘Big Data Is BS in Healthcare.

When Will It Become Real?’



• Develop breakthrough algorithm

• Use patient data to predict and prevent unnecessary hospitalizations (how measuring outcomes?)

Example: $3M Heritage Prize.



Example: 23andMe.

Precisely targeted, patient-centered.

• Estimate likelihood of disease

• Take preventive steps



Example: Ayasdi.



Ayasdi: Topological data analysis.

• Characterize patients based on gene expression levels, biological properties, clinical data

• Discover without asking research questions or running queries



• Aggregates patient-reported data, shares with R&D companies

• New Open Research Exchange for validating ways to measure health outcomes

Example: Patients Like Me.



Example: Text messaging.

• Rock Health: SF startup incubator

• Several focus on adherence & education

• Predicting who won’t seek care, who will be a no-show, who needs follow-up

It appears you are at 7-11. Don’t buy things that are bad for you!



Example: CMS $1B.

• US Centers for Medicare, Medicaid Services

• Up to $1B for innovations that could reduce costs and improve outcomes

• Non-hospital care



• Diagnosis

• Treatment

• Quality of care

• Cost of care

• Patient outcomes

It’s (still) all about outcomes.



Some practical advice.

• Don’t get lost in data

• Aggregate data that supports prioritized improvement efforts

• Measure specific outcomes

• Prepare for close scrutiny



Avoid random association syndrome.

• Big data means lots of correlation

• Random associations with health outcomes are a distraction



PepperSlice

Health analytics startup.

Visualize, analyze, and optimize information assets (medical evidence, outcomes research, clinical/EHR analytics, operational data).


email: [email protected] twitter: @EvidenceSoup

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