Mr Leigh Donoghue - GP CME North/Fri_Plenary_1200-Big... · Consumer (credit card, internet)...

Mr Leigh DonoghueManaging Director Health (Au/NZ)

Accenture

Melbourne Supported by:

12:00 - 12:15 Big Data - Useful or Useless in Health

BIG DATA: USEFUL OR USELESS IN HEALTH?

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Copyright © 2017 Accenture All rights reserved. 3

Big Data: Useful or Useless?

The promise of Big Data.

Hope or Hype?

A case for and against.

Where it will lead: utopia vs.

dystopia? Who decides?


How well do you know your patient?

Clinical Care Social & Environmental Genetics Individual Behavior

Home & Family

Mental Wellness

Economic

Stability

Stress Mgmt.

Diet & Exercise

Care Plan

Adherence

Genomics &

Medical History

Factors in determining health outcomes

40%30%20%10%


Big Data in Health is Getting Really Big

Volume, variety and velocity of data for healthcare is

rapidly increasing. Traditional claims and EMR data

can be supplemented by new data sources.

Claims

+ EHR, Lab,

Pharmacy

+ PHR

+ Social Media &

Call Center Logs

+ Lifestyle / Behavioral

+ Socio-economic

+ Omics

+ Wearables, Sensors,

Biometrics

Increasing volume of healthcare relevant data on a given individual


The promise of better healthcare … attracting investment

Venture capital investment in health big data and analytics has grown over 200% since 2013, reflecting

the promise that big data / analytics hold in enabling better, smarter, more cost effective healthcare.

$70M

Platform links health-related

data from different systems

$175M

Cloud-based platform dedicated

to improving cancer care

$45M

Cloud-based platform for genomics

& biomedical information

VC Funding in

Digital Health

Companies

(2016)

SELECT INVESTMENTS 2016

$341M

BIG DATA / ANALYTICS

$410M

GENOMICS &

SEQUENCING

$312M

WEARABLES &

SENSORS

$287M

TELEMEDICINE

$198M

POP HEALTH

Source: RockHealth 2016 Year in Review

$10M

Big data-driven health care

optimization with patient data

$8.5M

Platform with machine learning for

proactive interventions


Vendors responding with leading-edge solutions

Leading-edge technologies, including machine learning, artificial intelligence and behavioral science

are gradually being employed to automate / accelerate the prediction and personalization of insights

Largest segment of health analytics; Range of

established players dominate market with new

product launches and inorganic growth

5.0 FINANCE & CLAIMS

2.0 CLINICAL 3.0 POPULATION HEALTH 4.0 PROVIDER NETWORK

1.0 MARKETING & MEMBER


Key Emerging Trends in Health Analytics

Exploding volume and variety of data is allowing companies to

leverage a broader set of member, community, and lifestyle data;

Pushing boundaries into genomics and precision medicine.

New Data

Shifting from “Single Dataset Analyses” to “Linked, Multiple

Dataset Analyses”, within & across organizations; Deploying

insight into operations to drive value is increasing in priority.

Interoperability

Driving more accurate predictions and actions through iteration –

analytics produce answers and predictions, answers drive actions,

actions then recorded and fed back into analytics to “learn”

Adaptive Learning

Removing data latency, time between when insight is revealed

and when the appropriate action is taken, to meet consumer

demands of 24/7 servicing and increasing clinical productivity

On Demand

Providing home based, self-management that draws on biometric

and behavioral data from wearables and remote devices to predict

health deterioration and enable timely interventions.

In the Home

Identifying patient risk and tailoring treatments and programs at the

sub-population or individual level; Personalizing sales and service

channels and experiences to boost loyalty and engagement.

Individualized


Health Analytics – Boundless Potential

EMOTION AICustomer service agent receives live in-call

guidance to enhance member engagement,

based on real-time speech analytics,

behavioral science and pattern recognition

ADAPTIVE LEARNINGHealth plan witnesses marked behavior

change by prescribing personalized

“nudges” that are predicted (via

continuously learning and adapting

systems) to be most likely to respond to

PERSONALISED MEDICINEPhysician leverages the power of cognitive computing to

accurately and rapidly diagnose a rare form of leukemia

and prioritize potential treatment options based on patient’s

genetic and personal health data

BEHAVIOUR MODIFICATIONHealthcare provider can proactively outreach when

alerted of significant changes in behaviour through

real-time monitoring of patient activity (screen time,

mobility, social connectivity, etc.)

SMART STEERAGEPatient is matched with the most appropriate (in-network)

specialist based upon a variety of factors such as clinical

need, preference, location, wait time, likelihood of patient

satisfaction, etc.

INSTANT RESPONSEPowered by natural language processing,

member receives instant response to a benefit

or claim-related question via SMS

CRACKING DOWN ON FRAUDHealth insurer identifies fraudulent claims at an early

stage, thus improving reimbursement accuracy and

preventing inappropriate payments

“ALWAYS ON” CONNECTIVITYAn asthma patient that hasn’t used his inhaler or is in a

location with high pollen count receives alert

recommending optimal path of therapy

CONTEXT AWAREBy understanding a patient’s transportation options, income

and financial obligations, a care manager is able to determine

her likelihood of follow-up, as well as care plan adherence,

prompting individualized actions such as transportation

assistance and longer-lasting medication supply

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The future of analytics is prescriptive and personalized for insights on a single individual, enabling

healthcare organizations to proactively intervene, in real time


Claims

EHR

User Generated

Socioeconomic

CASE STUDY: Mayo Clinic

• Combined risk cohorts from claims

data with vitals and labs to ID

missed diagnosis and proactively

intervene for segments identified as

likely for hospital visits

• Used Clinical Text Analysis and

Knowledge Extraction System with

IBM’s NLP to create meaningful info

from unstructured notes in EHR

Unlocking Value from Data Augmentation Claims + EHR

Claims data accomplishes the goal of providing episodic care, but known limitations (vitals, histories) are

gaps that integrated EHR data can fill to “complete the picture” to fully manage a patient’s health status

Use Case

Billed Services (ICD codes)

Patient History/Assessments

Clinical Notes

Care Provider/Setting

Medication Fills/Refills

Vital Signs/Lab Results

Consumer (credit card, internet)

Demographics

Public Data (zip code)

Interactions (CRM, Telehealth)

Self-Reported/Social Media

Wearable/Sensor

Data SubjectsData Types

20% Reduction in AvoidableReadmissions

BENEFITS

• Fill in diagnostic gaps, capture over-

the counter medication use, and

other lifestyle habits (e.g. smoking)

to get robust picture of patient

• Data about patient much more

actionable due to timelier nature of

EMR data

EXAMPLES

• Personalized care plans due to

clinical data filling gaps in claims

data (e.g. 20+% diabetic patients

not reflected in claims)

• Longitudinal patient histories due to

supplemented view from lab and

clinical notes and can help care

teams prioritize interventions

• Real-time care alerts due to timely

incorporation of lab results data

Augmentation Benefits


And yet …


Dealing with the data deluge

Solid foundations required: ‘data in

motion’, not ‘islands of information’

Be selective: not all health data is

equally relevant and useful

Developing new ways to process

information (equipping the digital doctor)

Trust: maintaining the social license

Equity in a digital age

Mr Leigh Donoghue - GP CME North/Fri_Plenary_1200-Big... · Consumer (credit card, internet)...

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