Big Data Little Disease Using Big Data Techniques to Predict Health Outcomes Dr Rupert Dunbar-Rees MRCGP CEO & Founder, Outcomes Based Healthcare Mike Merritt-Holmes CSO & Co-Founder, Big Data Partnership

What We Do

Big Data Partnership helps organisations become more data driven through data science and the adoption of new generation big data technologies, rapidly and at low risk. Outcomes Based Healthcare are health outcomes data specialists. We offer specialist advice, tools and technology to help commissioners and providers make a reality of value-based healthcare strategies and outcomes-based contracts.

What We Do We help you Discover why and how to become data driven; we work with you to Develop and prove the value of this approach; we Deliver cost effective solutions which exploit faster and more scalable technology. We reduce risk by Training your staff in the necessary new skills and by providing Support. We call this the Big Data Journey.

Partnerships

BigDataPartnershiphaveformedstrategicandtechnologicalallianceswithbest-of-breedpartnerstodeliveraworld-classserviceofferingtotrailblazingclients.

Technologies

Big Data Partnership have deep expertise in the latest emerging big data technologies and platforms including but not limited to the following:

Who We Are Working With Our current client portfolio consists of some of the most well known names in their sectors

FinancialServices Betting&Gaming

Others

PublicSector Retail&CPG

What is Big Data?

1.  New technology ▪  Volume ▪  Variety ▪  Velocity

2.  New philosophy ▪  Value of data ▪  Taming Voracity ▪  Becoming data-driven ▪  Empirical approach: Data Science

3.  1 + 2 = Business Transformation

Volume: Why can’t I just make it bigger?

$ / GB

$$ / GB

$$$ / GB

Large Application Database or Data Warehouse

$$$$ / GB TB ???

Data Volume

Performance

Cost

Scal

e U

P

Velocity: Why can’t I capture everything?

▪  All single-server information systems have limits on throughput.

▪  The only question is whether you hit that limit or not.

▪  If you do, your options are limited unless you have a distributed system to capture the data as it arrives.

▪  Distributed systems which are designed in an appropriate way can scale linearly to accept increasing data throughput rates, effectively lifting the cap on capture throughput.

▪  In today’s high data intensity applications, this is becoming ever more important.

Variety: Why won’t it load my data?

▪  Business are increasingly moving beyond relational data – 80% of enterprise data is unstructured.

▪  The rise of social media data integrated with other enterprise data leaves us with the problem of handling complex graph data.

▪  Machine-generated data such as log data is often semi-structured.

▪  Often as datasets get much larger, it is more efficient to leave them in their original format and store them that way, than to transform everything into a normalised relational schema.

What’s Data Science all about?

▪  Before: analysts used intuition and domain knowledge to draw conclusions from statistics.

▪  Unfortunately, statistics can be easily manipulated, as we often see in the media. “There are lies, damned lies, and statistics” – Mark Twain

▪  Critical evaluation of data empirically is key to avoiding bias.

▪  More modern techniques such as Bayesian statistics can help to remove subjective bias.

▪  Machine Learning methods can remove the human element almost entirely.

Data Science + Big Data

More data + limited compute resource

More aggressive sampling

Less accurate results

Improve accuracy of results + limited compute

resource More complex

models Less accountable

results

✓ All data + scalable compute resource No sampling More accurate

results

All data + scalable compute resource

Less complex models

More accountable results

✗

✗

✓ Often quoted as “more data trumps smarter algorithms” (Google)

Big Data and Healthcare

Hadoop : The Data Lake • Hadoopprovidesastorageandprocessinglayerthatallowsallofthekeyprinciplesdiscussedandprovidestheperfectpla@ormforalltypesofanalysis.

Predicting Complications in People with Type 2 Diabetes

▪  UsingHadoopandavarietyofmachinelearningtechniqueswecanstarttoexplorethepossibiliDesoflinkingandpredicDngcomplicaDons.

▪  Ascalableandflexiblepla@ormfortruedataexploraDon

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healthcare.

arewedoinganygood?

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February2014Adapted from Alliance Scotland: We’ve Got to Talk about Outcomes, June 2013

OUTPUTS Cake Blood results, scan results, weight measurement, examination results

PROCESSES Following the recipe, adding in ingredients, mixing Blood pressure check, blood sugar test, X-Ray, weight, assessments

INPUTS Ingredients, mixing bowl, recipe book Staff, training, buildings

OUTCOMES Happy child Cake tastes good No food poisoning Good quality of life Getting back to work Able to self-manage Prevent complications Less pain

• NEWSLIDE

Segmentation In Other Industries

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Hum

an Characteristics D

isea

se C

hara

cter

istic

s

Segmentation

By Disease, Diagnosis

Hyper-Segmentation

By Age, Sex, Disease, Blood Marker, Gene Mutation

Micro-Segmentation

By Income, Educational Status, Preferences, Eating Habits, Sleep, Exercise, IQ, Browsing Habits…

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PROMs&Implementa/on&

An individual, their data, and potential impact on health outcomes

PERSON&

Characteris/cs&

Environment&

Health&characteris/cs&

Disease&

Medica*on&

Interven*on&

Procedure&

Behaviour&

Change&

Employment&

Educa*on&

Loca*on&

Gender&

Age&

Ethnicity&

Gene/cs&

Physiology&

Psychology&/&Emo/onal&&

IQ&

Behaviour&

Heart&Rate&

Blood&Pressure&

Weather&

Air&Quality&

Other&people&

Living&Condi*ons&

Personality&

Temperature&

Medical&Records&

/&HSCIC&

SelfHMonitoring&

(Wearable&/&App)&

Sensing,&con*nuous&

Medical&Record&

Ac*ve,&retrospec*ve&

MetOffice&

Census&data&

&

3

rd

&party&e.g.&

Experian&(Mosaic)&

&

Data.gov.uk&

Social&Media&

Through&analysis&of&datapoints&from&

selfHmonitoring,&social&media&etc&

BM&(Blood&Sugar)&

Ac*vely&collected&

data&

Medical&record&&

Ac*vely&collected&data&

Person&Category&

Datapoint&Examples&

Poten*al&Data&Source&&

Gene*c&Sequence&/&

Muta*ons&

Census&data&

Movement&

Decision&

Making&

Mood&

SelfHMonitoring&(Wearable&/&

App)&e.g.&tone&of&voice&

Sleep&

SelfHMonitoring&

(Wearable&/&App)&

Sensing,&con*nuous&

Sensing&data&/&facial&recogni*on&

Purchasing&

behaviour&

Exercise&

Travel&

Food&intake&3

rd

&party&

Key

Ini*al&Focus&

There’smoretoyouthanjustyourhealthdata…

LINKING

NEW SLIDE – 1:1 1:50 1:5000

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Linking Hierarchy: Health and Non-Health Data

Ayasdi: Topological Data Analysis, Mount Sinai

Source:Lietal,ScienceandTranslaDonalMedicine,Oct2015

Outcomes Data Lab: Vision

“Through advanced analytics and machine learning on health-related and non-health datasets, we will develop a model which learns the characteristics, behaviours, interventions and lifestyle features that directly impact the outcomes and disease progression of people with

diabetes, specifically, their clinical complications.“

April 2015 – December 2016

Part-funded by Innovate UK

What outcomes are we trying to predict?

Clinical complications which can occur in people with diabetes:

•  Lower limb amputation •  Preventable blindness •  End stage renal failure •  Stroke •  Heart attack

Potential for cost savings Through pre-treating rather than treating the complication when it has arisen ▪  NHS diabetes spend is £9.8bn pa (increasing 2% per annum) ▪  80% of these costs are incurred in managing complications ▪  Expenditure on metformin has increased from £37m to £81m

between 2005 and 2013 in the UK Potential to save a total of £1.2bn in avoided complications in the UK

Finding accurate measures for these complications

…. Not as easy as it looks Considerations relating to healthcare data:

•  Undercoding by health care providers •  Linking of multiple datasets from GPs, healthcare providers,

HSCIC (such as SUS and HES) to improve data quality •  Understanding data inclusions and exclusions relating to the

complication, using clinical coding •  Defining the population segment using clinical coding

Lets take Metformin as an example All-cause mortality in people taking metformin has an NNT of 14

14 people with diabetes must take the drug for 1 person’s death to be prevented

Myocardial infarction in people taking metformin has a NNT is 16 16 people with diabetes must take the drug for 1 person not to have a myocardial infarction

Health data availability: interval bias

Survival Probability

The survival function gives the probability that a subject will ‘survive’ past time t.

Split by gender, we see different survival probabilities. NB: Survival here is not meant literally, but means avoiding the events: MI, Stroke, Amputation, End-stage Renal Failure, Angina OR TIA

Source:MtSinai,Ayasdi

“It is more important to know what sort of person has a disease, than to know what sort of disease a person has.”

Hippocrates

Disease, or Person?

Outcomes Data Lab | Contact Us

Mike Merritt-Holmes CSO, Big Data Partnership mike@bigdatapartnership.com @BigDataExperts Dr Rupert Dunbar-Rees CEO, Outcomes Based Healthcare rupsdr@outcomesbasedhealthcare.com @obh_uk

www.outcomesdatalab.com @outcomesdatalab