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Mr Leigh Donoghue - GP CME North/Fri_Plenary_1200-Big... · Consumer (credit card, internet)...
Transcript of 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?
Copyright © 2017 Accenture All rights reserved. 4
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%
Copyright © 2017 Accenture All rights reserved. 5
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
Copyright © 2017 Accenture All rights reserved. 6
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
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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
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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
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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
Me
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Fin
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Cli
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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
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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
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And yet …
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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