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R E S E A R C H 
2014 OCT 27 
PREDICTIVE ANALYTICS 
THE FUTURE OF PERSONALIZED HEALTH CARE
A R O C K R E P O R T B Y 
E DISTINCTLY REMEMBER THE MOMENT THAT SCIENTISTS CLAIMED 
Wvictory against all nature of future...
PRESENTATION © 2014 ROCK HEALTH 
Contents 
SECTION 
4 Background Definition of predictive analytics and personalized healt...
[Genome science] will 
revolutionize the diagnosis, 
prevention and treatment of 
most, if not all, human 
diseases.” PRES...
Nearly fifteen years later, it is obvious that health care is far more 
complex than simply understanding our DNA 
PERSONA...
Landscape
Predictive analytics is not reinventing the wheel. It’s 
applying what doctors have been doing on a larger 
scale. What's ...
PREDICTIVE ANALYTICS is the process of learning 
from historical data in order to make predictions 
about the future (or a...
Our report focuses on how predictive analytics is directly 
impacting patient care 
PRESENTATION © 2014 ROCK HEALTH 
THIS ...
The goal of predictive analytics in any field is to reliably predict 
the unknown 
WHEN WILL I DIE? 
PRESENTATION © 2014 R...
In fact, “predictive analytics” underlies most of traditional 
medicine and health care, whether technology-enabled or not...
The overabundance of data and widespread availability of tools 
has catalyzed predictive analytics in health care 
PRESENT...
Investors certainly believe in the promise, pouring $1.9B into 
companies that purport to use predictive analytics 
MOST A...
Funded companies claiming to use predictive analytics are highly 
focused on providers, practically ignoring patients 
ENT...
New data streams, including those direct from patients, are 
beginning to be used by companies for predictive analytics 
6...
Direction
Familiar methods of predictive analytics with a long history in 
other technology services are also appearing in health ca...
Symptom calculators are the “recommendation engines” of 
health care, helping millions of consumers diagnose themselves 
P...
Lacking appropriate context, clinical indicators—including vital 
signs—can generate false positives or negatives in alert...
Genetic screening companies similarly know the inherent risks 
before a child is conceived, allowing decisive action 
PRES...
Building models that break the curve of uncertainty will lead to 
personalized care, but it is not without significant cha...
Challenges
“The keystone of any successful predictive analytics 
model is the ability to improve the prediction based on 
a feedback ...
Startup companies are attacking the key challenges in predictive 
analytics, advancing the space and creating differentiat...
Aggregating, cleansing, and labeling data from disparate sources 
is the building block for developing non-obvious predict...
Using new data sources creates an opportunity to surface better 
(i.e., more accurate, timely, or cheaper to collect) pred...
Real-time data collection reduces traditional intervention 
response time 
CASE STUDY: HIGH-RISK PREGNANCY EXAMPLE: 
CHALL...
Improving the transparency of methodologies and the data 
behind analytics better supports physicians in decision-making 
...
By tailoring both recommendations and timing, companies can 
motivate consumers via a personalized toolset 
CASE STUDY: 10...
Companies that are able to quickly improve algorithms through 
closed loop models build significant long-term defensibilit...
Considerations
“Healthcare providers don’t just want predictive 
analytics to output graphs and statistics. They need 
something that’s a...
Personalizing care through predictive analytics represents a 
significant opportunity to reduce costs in the healthcare sy...
It will largely fall onto the healthcare industry to recognize the 
value of predictive analytics and implement critical u...
The industry might be waiting to implement predictive analytics 
as the FDA decides how best to regulate clinical decision...
Beyond regulation, the biggest risk to predictive analytics being 
used in health care is adoption as power dynamics shift...
We are underestimating the potential impact of 
predictive analytics in process tools to help physicians 
make better deci...
ACKNOWLEDGEMENTS 
We are indebted to our industry partners who not only support 
our work every day but provided invaluabl...
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Companies that are able to quickly improve algorithms through
closed loop models build significant long-term defensibility
CASE STUDY: POPULATION HEALTH EXAMPLE:
CHALLENGES
BASIC ADVANCED
Relevant data is accessible but split
across multiple entities
ACCESS All relevant financial, clinical, and
customer data is stored within a
single structure or warehouse
Predictive capability restricted by
dated relationships between
attributes and recommendations
TESTING Patients are randomized at point of
intervention to allow rapid testing of
population health interventions
Retrospective observational reviews
are conducted to assess effectiveness
of interventions
TIMING Performance is measured in near real-time
to link patient predictive
attributes to recommended
interventions to outcomes (e.g.,
engagement, health, financial)
RELATIONSHIP SEARCH DATA COLLECTION PERFORMANCE CAPTURE
PRESENTATION © 2014 ROCK HEALTH
Source: Company website, interviews 30
• Control of data collection along the
continuum from predictor data to
treatment/intervention to health outcome
• Ability to aggregate historical data and
patient information at point-of-care for
real-time performance measures
• Integration into clinical workflow for
intervention testing and performance
capture
• Health outcomes are inherently lagged,
limiting timely assessment of effectiveness
DATA AGGREGATION INDIVIDUAL CASE CONTEXTUALIZATION

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