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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 disease after the human genome had successfully been decoded. However, over 
the ensuing decade-plus, it has become clear that our health is 
not quite that deterministic. Clinicians must weigh not just a 
string of nucleotides when making decisions about our care, but 
must also incorporate a growing set of health data that is 
generated and controlled by patients. Incorporating this data 
into health care to enable better decisions is at the heart of this 
report. The benefits of using predictive analytics are the same 
as many categories of digital health: better care and lower costs. 
The difference is that the path to realizing these benefits— 
through personalized care—is only possible by implementing 
these technologies. The concern that care will be reduced to a 
set of algorithmically-derived probabilities is important and 
real. But the promise is as well. 
AUTHORED BY WITH HELP FROM 
MALAY GANDHI 
@mgxtro 
TERESA WANG 
@teresawang6 
ROCK HEALTH is powering the future of the digital health ecosystem, 
bringing together the brightest minds across disciplines to build 
better solutions. Rock Health funds and supports startups building 
the next generation of technologies transforming healthcare. 
ROCK HEALTH partners include Abbott, Blue Shield of California, 
Boehringer Ingelheim, Deloitte, GE, Genentech, Harvard Medical 
School, Kaiser Permanente, Kleiner Perkins Caufield & Byers, Mayo 
Clinic, Mohr Davidow Ventures, Montreux Equity Partners, 
Qualcomm Life, UCSF and UnitedHealth Group. 
LEARN MORE AT rockhealth.com 
LAUREN DEVOS 
@lauren_devos
PRESENTATION © 2014 ROCK HEALTH 
Contents 
SECTION 
4 Background Definition of predictive analytics and personalized health care 
Scope of report 
6 Landscape Core technologies used in predictive analytics 
Venture funding of predictive analytics companies (2011-Q3 2014) 
Landscape of predictive analytics companies 
16 Direction Examples of predictive analytics in health care 
Requirements for personalized health care 
22 Challenges Key advancements in predictive analytics in health care 
Case studies of digital health companies 
31 Considerations Healthcare industry use cases 
Regulatory and adoption constraints 
38 Acknowledgements Contact information
[Genome science] will 
revolutionize the diagnosis, 
prevention and treatment of 
most, if not all, human 
diseases.” PRESIDENT BILL CLINTON 
“ 
Remarks on the completion of the first survey of 
the entire human genome (June 26, 2000)
Nearly fifteen years later, it is obvious that health care is far more 
complex than simply understanding our DNA 
PERSONALIZED MEDICINE PERSONALIZED HEALTH CARE 
Treatment (through drugs) FOCUS Prevention, intervention, and treatment 
Molecular DATA Demographic, social, administrative, clinical, 
“ If I wanted to be a doctor today 
PRESENTATION © 2014 ROCK HEALTH 
Right MANTRA Best 
Deterministic MODEL Probabilistic 
5 
molecular, patient-generated/reported 
Figuring out how to get the right 
drug to the right person at the 
right dose at the right time.” 
I’d go to math school not to 
medical school.” 
“ 
DR. FRANCIS COLLINS VINOD KHOSLA 
DIRECTOR, NATIONAL INSTITUTES OF HEALTH VENTURE CAPITALIST
Landscape
Predictive analytics is not reinventing the wheel. It’s 
applying what doctors have been doing on a larger 
scale. What's changed is our ability to better measure, 
aggregate, and make sense of previously hard-to-obtain 
or non-existent behavioral, psychosocial, and 
biometric data. Combining these new datasets with 
the existing sciences of epidemiology and clinical 
medicine allows us to accelerate progress in 
understanding the relationships between external 
factors and human biology—ultimately resulting in 
enhanced reengineering of clinical pathways and truly 
personalized care.” 
VINNIE RAMESH 
Chief Technology Officer 
Co-founder 
Wellframe enables 
health plans and 
healthcare providers to 
better manage clinical 
and financial risk, while 
augmenting the impact 
of their existing care 
resources 
“
PREDICTIVE ANALYTICS is the process of learning 
from historical data in order to make predictions 
about the future (or any unknown) 
FOR HEALTH CARE, predictive analytics will enable 
the best decisions to be made, allowing for care to 
be personalized to each individual
Our report focuses on how predictive analytics is directly 
impacting patient care 
PRESENTATION © 2014 ROCK HEALTH 
THIS NOT THIS 
• Clinical decision support 
• Readmission prevention 
• Adverse event avoidance 
• Chronic disease management 
• Patient matching 
9 
• Actuarial modeling for rate / 
premium setting 
• Advertising and purchasing 
• Customer satisfaction and retention 
• Business decision modeling 
• Fraud
The goal of predictive analytics in any field is to reliably predict 
the unknown 
WHEN WILL I DIE? 
PRESENTATION © 2014 ROCK HEALTH 
10 
PREDICTION 
CERTAINTY 
WHAT DID I EAT 
TODAY? 
HOW WILL MY 
BLOOD SUGAR 
CHANGE? 
HOW MUCH 
WEIGHT WILL I 
GAIN? 
WILL I GET 
DIABETES? 
WHAT COMPLICATIONS 
MIGHT I SUFFER FROM? 
KNOWN UNKNOWN
In fact, “predictive analytics” underlies most of traditional 
medicine and health care, whether technology-enabled or not 
PRESENTATION © 2014 ROCK HEALTH 
11 
TRAINING DATA 
AGGREGATION 
• Cleanse 
• Tag and/or label 
• Structure 
RELATIONSHIP 
SEARCH 
• Identify attributes that act as predictors 
• Develop algorithms 
Acute 
Chronic and 
preventive 
CASE DATA 
COLLECTION 
• Collect predictive attributes for specific case (e.g., a 
patient) Symptoms Risk factors 
INDIVIDUAL CASE 
CHARACTERIZATION 
• Apply algorithms derived from training data to 
case attributes of the patient 
• Describe an unknown Diagnosis Stratification 
RECOMMENDATION 
CONTEXTUALIZATION 
• Apply specific recommendations based on ‘who’, 
‘when’, ‘where’, etc. Treatment Intervention 
PERFORMANCE 
CAPTURE 
• Define success 
• Record results relative to recommendation 
• Improve algorithms for characterization and 
recommendations 
Outcome Outcome 
1 
2 
3 
4 
5 
6 
Note: Preventive care includes management of chronic diseases 
IN TRADITIONAL 
MEDICINE AND 
HEALTH CARE
The overabundance of data and widespread availability of tools 
has catalyzed predictive analytics in health care 
PRESENTATION © 2014 ROCK HEALTH 
BIG DATA 
Expected growth in healthcare 
data, 2012-2020 (petabytes) 
25,000 
500 
Source: American Medical Informatics Association 
DATA MINING 
DATABASES/WAREHOUSES 
BIG DATA PLATFORMS 
12 
2012 2020 
ALGORITHM PRODUCTION 
SERVICE PROVIDERS 
AGGREGATE SERVICE PROVIDER VENTURE FUNDING: $1.8B
Investors certainly believe in the promise, pouring $1.9B into 
companies that purport to use predictive analytics 
MOST ACTIVE INVESTORS 
PRESENTATION © 2014 ROCK HEALTH 
Venture funding for companies using predictive analytics (2011-Q3 2014) 
$902M 
13 
PREDICTING FUNDING 
$520M 
$300M 
$201M 
2011 2012 2013 Q3 2014 
NOTABLE 
DEALS 
• Khosla Ventures 
• Merck Global Health 
Innovation Fund 
• Norwest Venture Partners 
• Sequoia Capital 
• Social+Capital Partnership 
Source: Rock Health funding database 
Note: Only includes deals >$2M
Funded companies claiming to use predictive analytics are highly 
focused on providers, practically ignoring patients 
ENTERPRISE SHARED PATIENT 
PRESENTATION © 2014 ROCK HEALTH 
14 
KYRON 
USER OF ANALYTICS 
COMPANIES 
Source: Company websites 
Note: Only includes companies that received venture funding from 2011 to Q3 2014; 
companies are selected, not comprehensive
New data streams, including those direct from patients, are 
beginning to be used by companies for predictive analytics 
6% 
SO MUCH DATA 
Percentage of venture-backed predictive analytics companies using various types of data (2011-Q3 2014) 
CLINICAL CLAIMS PATIENT-GENERATED PATIENT-REPORTED RESEARCH MOLECULAR CLINICAL TRIALS 
PRESENTATION © 2014 ROCK HEALTH 
15% 14% 
26% 
42% 42% 
71% 
15 
Source: Company websites 
Note: Percentages do not sum to 100%; companies may collect multiple data types 
Current data sets generally revolve 
around claims but that’s going to be 
changing with lots of clinical data and 
transactional information with lifestyle 
becoming more readily accessible.” 
SAM HO, M.D. 
Chief Medical Officer, UnitedHealthcare
Direction
Familiar methods of predictive analytics with a long history in 
other technology services are also appearing in health care 
PRESENTATION © 2014 ROCK HEALTH 
CORRELATION CONTEXT ACTION 
Source: “Giving Viewers What They Want” The New York Times (February 24, 2013) 
17 
• Movie preferences (by rating, 
viewing history, etc.) are 
gathered across all users 
• Viewers who liked movie A also 
liked B, C and D and since you 
like A, so you’ll probably also 
like B, C, and D 
• Historical viewing is labeled 
and identified by individual 
viewers 
• You tend to watch movies on 
weekends and TV shows on 
weekdays, so a movie should 
be suggested on Saturday 
• Larger data sets on preferences 
that are based on real world 
viewing are collected 
• Audiences have a high likelihood 
of enjoying a type of TV show, so 
an entire season can be 
purchased instead of just a pilot 
Hom-Lay Harish Add Profile
Symptom calculators are the “recommendation engines” of 
health care, helping millions of consumers diagnose themselves 
PRESENTATION © 2014 ROCK HEALTH 
Source: Mayo.com 
Note: Other use cases are representative, not comprehensive 18 
HOW IT WORKS 
Consumers enter in their 
symptoms, and related factors, 
and in turn receive the diagnoses 
with the “most matches” 
OTHER USE CASES 
• Triage 
• Comorbidity identification 
• High cost patient identification 
• Physician-patient matching 
CORRELATION
Lacking appropriate context, clinical indicators—including vital 
signs—can generate false positives or negatives in alert systems 
HOW IT WORKS 
HOW HEART RATE RESPIRATORY RATE Lucile Packard Children’s Hospital 
CONTEXT 
Stanford adjusted its early warning 
algorithms to match actual vital 
signs from hospitalized children 
versus textbook definitions 
Using textbook definitions, 14% to 38% 
OTHER USE CASES 
of heart rate observations and 15% to 
• Decompensation 
30% of respiratory rate observations 
would have resulted in false alarms 
• Readmission prevention 
• Behavior change 
Source: “Development of Heart and Respiratory Rate Percentile Curves for Hospitalized Children” Pediatrics (2013), 
“A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care” Health Affairs (2014) 
Note: Other use cases are representative, not comprehensive 
19 
PRESENTATION © 2014 ROCK HEALTH
Genetic screening companies similarly know the inherent risks 
before a child is conceived, allowing decisive action 
PRESENTATION © 2014 ROCK HEALTH 
20 
ACTION 
HOW IT WORKS 
A couple planning its family submits 
DNA to Counsyl, which provides 
probabilities on 100+ health 
conditions that could be passed from 
parents to children 
OTHER USE CASES 
• Disease prevention 
• Population health management 
and early intervention 
• Treatment selection 
Source: Counsyl.com 
Note: Other use cases are representative, not comprehensive
Building models that break the curve of uncertainty will lead to 
personalized care, but it is not without significant challenges 
KEY REQUIREMENTS 
PRESENTATION © 2014 ROCK HEALTH 
21 
MOVING FORWARD 
PREDICTION 
CERTAINTY 
KNOWN 
UNKNOWN 
Using predictive analytics to personalize health care 
• Incorporation of new data 
types and sources 
• Reliability of predictive 
models 
• Timeliness of data 
• Transparency in prediction 
• Convenient (and in context) 
recommendations 
• Rapid learning and 
improvement 
Personalized care 
will emerge from high 
confidence algorithms that 
can predict actionable 
interventions that improve 
long-term health outcomes 
UNKNOWN
Challenges
“The keystone of any successful predictive analytics 
model is the ability to improve the prediction based on 
a feedback loop. 
Within seconds, Google knows whether its search 
engine prediction is correct. But in health care, the 
feedback loop—which is often measured in terms of 
impact on biometric or cost outcomes—can take 
years.” 
CHRISTINE LEMKE 
Co-founder and CEO 
The Activity Exchange is 
the connective tissue 
between healthcare 
companies and their 
populations to build and 
manage relationships to 
improve outcomes.
Startup companies are attacking the key challenges in predictive 
analytics, advancing the space and creating differentiation 
PRESENTATION © 2014 ROCK HEALTH 
1 
2 
3 
4 
5 
6 
TRAINING DATA 
AGGREGATION 
RELATIONSHIP 
SEARCH 
CASE DATA 
COLLECTION 
INDIVIDUAL CASE 
CHARACTERIZATION 
RECOMMENDATION 
CONTEXTUALIZATION 
PERFORMANCE 
CAPTURE 
BASIC ADVANCED EXAMPLES 
Limited Disparate 
Traditional data Novel data 
Lagged / point Real-time / continuous 
Obfuscated Transparent 
Generic Personalized 
Disjointed Closed loop 
There are a whole bunch of 
variables and very few 
observations. 
The number one thing holding 
predictive analytics back is the 
lack of data: the fact that things 
are not easily measured, 
collected, or accessible.” 
URI LASERSON 
DATA SCIENTIST, CLOUDERA 
PHD IN GENOMICS 
24 
“
Aggregating, cleansing, and labeling data from disparate sources 
is the building block for developing non-obvious predictions 
CASE STUDY: ONCOLOGY CARE EXAMPLE: CHALLENGES 
DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION INDIVIDUAL CASE CONTEXTUALIZATION PERFORMANCE CAPTURE 
PRESENTATION © 2014 ROCK HEALTH 
BASIC ADVANCED 
Single oncology data source (e.g., 
clinical trials, claims, or electronic 
health records) 
DATA TYPES Aggregating data from EHR, 
laboratory and billing systems 
Integrating and matching claims with 
patient trial data 
Avoids data that isn’t already 
cleansed, structured, and labeled 
(e.g., claims, pre-designated fields in 
EHRs, etc.) 
CLEANSING Identify high value data (e.g. EHR 
notes) and cleanse, structure, and 
label it as part of the aggregation 
process 
Data is historical with inherent bias 
from unintended use and lag 
associated with claims processing 
FREQUENCY Data is loaded on a nightly basis and 
processed continually, near real-time 
Source: Company website 25 
• Ability to access meaningful, historical 
data sets and normalize for inherent 
biases and validity concerns 
• Integrating with current clinical workflow 
to collect real-time, point of care patient 
data 
• Learning to manage and process new and 
existing forms of unstructured, siloed data 
• Addressing HIPAA and privacy related 
concerns to guarantee patient anonymity
Using new data sources creates an opportunity to surface better 
(i.e., more accurate, timely, or cheaper to collect) predictors 
CASE STUDY: CARDIAC REHAB EXAMPLE: 
CHALLENGES 
BASIC ADVANCED 
Printed packets of information and 
cardiac rehabilitation guidelines are 
handed to patients to follow 
DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION PERFORMANCE CAPTURE 
PRESENTATION © 2014 ROCK HEALTH 
26 
• Identifying existing and novel data points 
that can better predict outcomes 
• Identifying reliable and not spurious 
relationships 
• Rapid data collection and continual 
integration to train and iterate algorithms 
• Ability of predictive analytics to integrate 
and impact a clinician’s work flow 
• Limited data sources are analyzed by care 
providers 
INDIVIDUAL CASE CONTEXTUALIZATION 
Source: Company website, interviews 
ENGAGEMENT 
MODALITY 
Mobile app tracks patients’ 
interaction with the cardiac rehab 
program, which is linked in real-time 
to a care management dashboard 
Engagement and clinical data 
collected infrequently through office 
visits and in-person interactions 
ACQUISITION Collects additional data via activity 
trackers, meal logging, and non-diagnostic 
mental health questions 
Poor, incomplete data sets limits a 
clinician’s ability to identify patients 
likely to be readmitted or suffer 
adverse event 
PRIORITIZA-TION 
Algorithm predicts patients who need 
more attention and sends alerts to 
clinicians or care coordinators to take 
action
Real-time data collection reduces traditional intervention 
response time 
CASE STUDY: HIGH-RISK PREGNANCY EXAMPLE: 
CHALLENGES 
DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION PERFORMANCE CAPTURE 
PRESENTATION © 2014 ROCK HEALTH 
Source: Company website, interviews 27 
• Current care model predates that data 
collection happens in discrete intervals 
with an additional lag due to claims 
processing 
• Data collected may vary in reliability and 
accuracy if based solely on patient 
reporting or non-clinical devices 
• Using real-time data in a meaningful 
manner requires new infrastructure and 
workflow 
INDIVIDUAL CASE CONTEXTUALIZATION 
BASIC ADVANCED 
Regular check-ups generate claims 
data that get processed several weeks 
to months later 
PREDICTOR 
VARIABLE 
SOURCE 
Patient self-reports weight and mood 
data on a frequent basis, which is 
immediately accessible to care 
provider 
Infrequent, missed appointments 
results in missed data points 
RELIABILITY Decreased lag time between weight 
measurement and processed 
information 
Lagged and infrequent data results in 
late recognition and interventions 
TIMING Timely data allows for early 
stratification and intervention to 
avert high-risk complications
Improving the transparency of methodologies and the data 
behind analytics better supports physicians in decision-making 
CASE STUDY: CDS TOOLS CHALLENGES 
DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION INDIVIDUAL CASE PERFORMANCE CAPTURE 
PRESENTATION © 2014 ROCK HEALTH 
Source: Company website, interviews 28 
CONTEXTUALIZATION 
BASIC ADVANCED 
Clinical decision support based on 
limited set of protocols and 
guidelines 
BREADTH Incorporates the current scientific 
research and clinical practice data for 
analytics 
Guideline updates significantly lag 
clinical research and require approval 
through centralized bodies 
ADJUSTMENT Real-time analytics and continuous 
updates based on outcomes from 
observational data 
Medical practice highly paternalistic 
and substantiated through 
experience versus evidence 
VISIBILITY Transparency via medical knowledge 
graph to support physician decision-making 
regarding symptoms, 
medications, risk factors, and 
diagnoses 
• Visualization challenges in displaying all 
relevant data for time sensitive decision-making 
• Finding the balance between black box 
engines and information overload tools 
• Recency and accessibility of data to 
develop medical, evidence-based 
recommendations 
• Physician and patient adoption of 
“algorithms” dictating care 
EXAMPLE:
By tailoring both recommendations and timing, companies can 
motivate consumers via a personalized toolset 
CASE STUDY: 10,000 STEPS EXAMPLE: 
CHALLENGES 
DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION CONTEXTUALIZATION PERFORMANCE CAPTURE 
PRESENTATION © 2014 ROCK HEALTH 
Source: Company website, interviews 29 
• Using advanced algorithms and behavioral 
economics theory requires large 
individually tagged datasets 
• Determining the “right intervention” is 
challenging and requires trial and error 
• Consumer concerns around privacy and 
identity 
INDIVIDUAL CASE 
BASIC ADVANCED 
Tracking and visualization of gross 
progress or milestones against time 
(e.g., by day, week, month) 
MEASURE-MENT 
Tracking progress and challenges 
relative to consumer behavior and 
engagement patterns across all 
devices and services 
Focus on identifying trends at 
population level and applying 
learnings top down, demonstrating 
quick success for majority 
APPROACH Analyzing when and how individual 
consumers respond to incentives to 
allow for personalized notifications, 
or “interventions” 
Engagement and subsequent 
effectiveness weans and new 
population interventions are 
deployed 
EFFECT Results are sustainable as 
interventions continuously adapt to 
individuals, rolling up to significant 
population change
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
Considerations
“Healthcare providers don’t just want predictive 
analytics to output graphs and statistics. They need 
something that’s actionable. 
You have to distill it down to what matters and is 
actionable. Because there’s a hundred thousand 
things that come into play in health care, predictive 
analytics has to tell us what matters and how we can 
act on it.” 
ANIL JAIN 
Chief Medical Officer 
Explorys offers a 
software platform 
solution that helps 
healthcare systems 
aggregate, analyze, and 
manage their big data
Personalizing care through predictive analytics represents a 
significant opportunity to reduce costs in the healthcare system 
$192B $128B $35B 
OVERTREATMENT FAILURES OF CARE DELIVERY LACK OF CARE COORDINATION 
PRESENTATION © 2014 ROCK HEALTH 
• Eliminating care that cannot help 
patients—care that is outmoded, 
supply-driven, and eschews science 
• Restricting treatment and intervention 
to the patients who will benefit based 
on the individual and the context 
• Continuously studying care to identify 
what works for whom and in what 
context 
• Scaling best practices including 
preventive care and early warning 
systems that demonstrate effectiveness 
• Ensuring those at the highest risk of 
costly medical episodes are identified, 
monitored, and cared for between visits 
and following hospitalization 
Source: “Eliminating Waste in US Health Care” Journal of the American Medical Association (2012) 33
It will largely fall onto the healthcare industry to recognize the 
value of predictive analytics and implement critical use cases 
IDEALIZED USE CASE OVERTREATMENT CARE DELIVERY COORDINATION 
PRESENTATION © 2014 ROCK HEALTH 
34 
PAYERS Construct personalized medical policy (what is and isn’t 
covered) and benefits (how costs are shared by parties) 
Match interventions to individuals to scale behavior change 
programs (wellness, chronic disease management, etc.) 
PROVIDERS Provide point of care access to historical data in the context 
of a patient in ambiguous situations (“Green Button”) 
Reduce treatment variation and improve outcomes 
Manage risk of population health management programs 
under accountable care 
BIOPHARMA Predict individual responsiveness to treatment (within R&D 
and post-market contexts) 
Conduct pharmacovigilance
The industry might be waiting to implement predictive analytics 
as the FDA decides how best to regulate clinical decision support 
“ 
Any software that analyzes data and supports 
clinical decision making, including: 
• Computerized alerts, reminders and warnings 
• Computer-aided diagnosis 
• Treatment recommendations 
Regulation will be agnostic to information source 
(manual entry, automated, etc.) 
Our question: How will the FDA regulate the practice of medicine when algorithms prove more accurate than clinicians? 
PRESENTATION © 2014 ROCK HEALTH 
This guidance does not address the 
approach for software that performs 
patient-specific analysis to aid or 
support clinical decision-making.” 
LIKELY SCOPE OF FUTURE GUIDANCE POTENTIAL FRAMEWORKS 
Source: FDA.gov; 
“FDA regulation of clinical decision support software” Journal of Law and the Biosciences (2014) 35 
Bipartisan Policy Center (BPC) proposed CDS be 
subject to a new oversight framework: 
• Adherence to and implementation of designated 
standards 
• Participation in safety monitoring 
• Aggregation and analysis of trends to mitigate 
future risk 
Food and Drug Administration Safety and 
Innovation Act (FDASIA) working group advised: 
• Different frameworks dependent on risk, with 
low-risk categories exempt from pre-market 
approvals/clearances 
• Clarification amongst multiple agency regulation 
(e.g., FDA/ONC/FCC)
Beyond regulation, the biggest risk to predictive analytics being 
used in health care is adoption as power dynamics shift 
2 1 
PATHWAYS ADOPTION CHALLENGES 
1 Software-based clinical decision support 
Patient provides data to the doctor, who 
incorporates it into a decision support 
algorithm for diagnosis or treatment 
2 Patient-controlled 
Patient generates and submit their own 
data into the predictive algorithm, allowing 
them to directly receive clinical insights 
Our question: Can user experience and design influence decision making so deeply as to be regulated? 
PRESENTATION © 2014 ROCK HEALTH 
36 
0110001001 
1010010110 
1111011010 
0101101110 
0110011001 
PREDICTIVE 
ANALYTICS 
HEALTHCARE 
PROFESSIONAL 
PATIENT 
3 
1 
3 Traditional 
Patient provides the clinician with the data 
they need to diagnose and treat based on 
their own judgment 
• Loss of decision making power 
• Direct integration into clinical workflow 
• Transparency of complex algorithms 
• Management of liability 
• Convenience of accessing algorithms 
• Accuracy and reliability of recommendations 
• Management of privacy concerns 
• Regulatory burden
We are underestimating the potential impact of 
predictive analytics in process tools to help physicians 
make better decisions. 
Every week, at the airport, I get on an airplane, and I 
don’t worry about flying at all. There are so many tools 
deployed to assist the pilot. I was talking with a pilot 
about the new 787–and the pilot said he basically 
monitors the plane. We’re going to see more of that in 
health care. 
Physicians will be monitoring algorithms.” 
KEVIN FICKENSCHER 
President, AMC Health 
Former President, AMIA 
AMC Health provides 
customized, scalable 
telehealth solutions for 
organizations serving at-risk 
populations through 
remote patient 
monitoring programs. 
“
ACKNOWLEDGEMENTS 
We are indebted to our industry partners who not only support 
our work every day but provided invaluable feedback on an 
early draft of this report. 
A number of industry, startup and venture folks also offered 
their expertise. Special thanks to Karina Babock, Benjamin 
Berk, Archit Bhise, Joe Boyce, Matt Butner, Chris Coloian, David 
Crockett, Ash Damle, Asif Dhar, Bill Evans, Kevin Fickenscher, 
Luca Foschini, Ryan Goldman, Josh Gray, Sam Ho, Lucian 
Iancovici, Anil Jain, Donald Jones, Allen Kramer, Uri Laserson, 
Christine Lemke, Dave Levin, Dan Martich, Phil Okala, Trishan 
Panch, Vinnie Ramesh, Leah Sparks, David Tamburri, Euan 
Thomson, Abhimanyu Verma, Nate Weiner, and Jack Young for 
their time and insights. 
Finally, we are fortunate to work with the most encouraging and 
passionate team in digital health. We are certain that no one 
would even be reading this report if not for the tireless 
marketing efforts of Halle Tecco and Mollie McDowell. 
research@rockhealth.org 
@rock_health 
PRESENTATION © 2014 ROCK HEALTH

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The Future of Personalized Health Care: Predictive Analytics by @Rock_Health

  • 1. R E S E A R C H 2014 OCT 27 PREDICTIVE ANALYTICS THE FUTURE OF PERSONALIZED HEALTH CARE
  • 2. 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 disease after the human genome had successfully been decoded. However, over the ensuing decade-plus, it has become clear that our health is not quite that deterministic. Clinicians must weigh not just a string of nucleotides when making decisions about our care, but must also incorporate a growing set of health data that is generated and controlled by patients. Incorporating this data into health care to enable better decisions is at the heart of this report. The benefits of using predictive analytics are the same as many categories of digital health: better care and lower costs. The difference is that the path to realizing these benefits— through personalized care—is only possible by implementing these technologies. The concern that care will be reduced to a set of algorithmically-derived probabilities is important and real. But the promise is as well. AUTHORED BY WITH HELP FROM MALAY GANDHI @mgxtro TERESA WANG @teresawang6 ROCK HEALTH is powering the future of the digital health ecosystem, bringing together the brightest minds across disciplines to build better solutions. Rock Health funds and supports startups building the next generation of technologies transforming healthcare. ROCK HEALTH partners include Abbott, Blue Shield of California, Boehringer Ingelheim, Deloitte, GE, Genentech, Harvard Medical School, Kaiser Permanente, Kleiner Perkins Caufield & Byers, Mayo Clinic, Mohr Davidow Ventures, Montreux Equity Partners, Qualcomm Life, UCSF and UnitedHealth Group. LEARN MORE AT rockhealth.com LAUREN DEVOS @lauren_devos
  • 3. PRESENTATION © 2014 ROCK HEALTH Contents SECTION 4 Background Definition of predictive analytics and personalized health care Scope of report 6 Landscape Core technologies used in predictive analytics Venture funding of predictive analytics companies (2011-Q3 2014) Landscape of predictive analytics companies 16 Direction Examples of predictive analytics in health care Requirements for personalized health care 22 Challenges Key advancements in predictive analytics in health care Case studies of digital health companies 31 Considerations Healthcare industry use cases Regulatory and adoption constraints 38 Acknowledgements Contact information
  • 4. [Genome science] will revolutionize the diagnosis, prevention and treatment of most, if not all, human diseases.” PRESIDENT BILL CLINTON “ Remarks on the completion of the first survey of the entire human genome (June 26, 2000)
  • 5. Nearly fifteen years later, it is obvious that health care is far more complex than simply understanding our DNA PERSONALIZED MEDICINE PERSONALIZED HEALTH CARE Treatment (through drugs) FOCUS Prevention, intervention, and treatment Molecular DATA Demographic, social, administrative, clinical, “ If I wanted to be a doctor today PRESENTATION © 2014 ROCK HEALTH Right MANTRA Best Deterministic MODEL Probabilistic 5 molecular, patient-generated/reported Figuring out how to get the right drug to the right person at the right dose at the right time.” I’d go to math school not to medical school.” “ DR. FRANCIS COLLINS VINOD KHOSLA DIRECTOR, NATIONAL INSTITUTES OF HEALTH VENTURE CAPITALIST
  • 7. Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing on a larger scale. What's changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data. Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care.” VINNIE RAMESH Chief Technology Officer Co-founder Wellframe enables health plans and healthcare providers to better manage clinical and financial risk, while augmenting the impact of their existing care resources “
  • 8. PREDICTIVE ANALYTICS is the process of learning from historical data in order to make predictions about the future (or any unknown) FOR HEALTH CARE, predictive analytics will enable the best decisions to be made, allowing for care to be personalized to each individual
  • 9. Our report focuses on how predictive analytics is directly impacting patient care PRESENTATION © 2014 ROCK HEALTH THIS NOT THIS • Clinical decision support • Readmission prevention • Adverse event avoidance • Chronic disease management • Patient matching 9 • Actuarial modeling for rate / premium setting • Advertising and purchasing • Customer satisfaction and retention • Business decision modeling • Fraud
  • 10. The goal of predictive analytics in any field is to reliably predict the unknown WHEN WILL I DIE? PRESENTATION © 2014 ROCK HEALTH 10 PREDICTION CERTAINTY WHAT DID I EAT TODAY? HOW WILL MY BLOOD SUGAR CHANGE? HOW MUCH WEIGHT WILL I GAIN? WILL I GET DIABETES? WHAT COMPLICATIONS MIGHT I SUFFER FROM? KNOWN UNKNOWN
  • 11. In fact, “predictive analytics” underlies most of traditional medicine and health care, whether technology-enabled or not PRESENTATION © 2014 ROCK HEALTH 11 TRAINING DATA AGGREGATION • Cleanse • Tag and/or label • Structure RELATIONSHIP SEARCH • Identify attributes that act as predictors • Develop algorithms Acute Chronic and preventive CASE DATA COLLECTION • Collect predictive attributes for specific case (e.g., a patient) Symptoms Risk factors INDIVIDUAL CASE CHARACTERIZATION • Apply algorithms derived from training data to case attributes of the patient • Describe an unknown Diagnosis Stratification RECOMMENDATION CONTEXTUALIZATION • Apply specific recommendations based on ‘who’, ‘when’, ‘where’, etc. Treatment Intervention PERFORMANCE CAPTURE • Define success • Record results relative to recommendation • Improve algorithms for characterization and recommendations Outcome Outcome 1 2 3 4 5 6 Note: Preventive care includes management of chronic diseases IN TRADITIONAL MEDICINE AND HEALTH CARE
  • 12. The overabundance of data and widespread availability of tools has catalyzed predictive analytics in health care PRESENTATION © 2014 ROCK HEALTH BIG DATA Expected growth in healthcare data, 2012-2020 (petabytes) 25,000 500 Source: American Medical Informatics Association DATA MINING DATABASES/WAREHOUSES BIG DATA PLATFORMS 12 2012 2020 ALGORITHM PRODUCTION SERVICE PROVIDERS AGGREGATE SERVICE PROVIDER VENTURE FUNDING: $1.8B
  • 13. Investors certainly believe in the promise, pouring $1.9B into companies that purport to use predictive analytics MOST ACTIVE INVESTORS PRESENTATION © 2014 ROCK HEALTH Venture funding for companies using predictive analytics (2011-Q3 2014) $902M 13 PREDICTING FUNDING $520M $300M $201M 2011 2012 2013 Q3 2014 NOTABLE DEALS • Khosla Ventures • Merck Global Health Innovation Fund • Norwest Venture Partners • Sequoia Capital • Social+Capital Partnership Source: Rock Health funding database Note: Only includes deals >$2M
  • 14. Funded companies claiming to use predictive analytics are highly focused on providers, practically ignoring patients ENTERPRISE SHARED PATIENT PRESENTATION © 2014 ROCK HEALTH 14 KYRON USER OF ANALYTICS COMPANIES Source: Company websites Note: Only includes companies that received venture funding from 2011 to Q3 2014; companies are selected, not comprehensive
  • 15. New data streams, including those direct from patients, are beginning to be used by companies for predictive analytics 6% SO MUCH DATA Percentage of venture-backed predictive analytics companies using various types of data (2011-Q3 2014) CLINICAL CLAIMS PATIENT-GENERATED PATIENT-REPORTED RESEARCH MOLECULAR CLINICAL TRIALS PRESENTATION © 2014 ROCK HEALTH 15% 14% 26% 42% 42% 71% 15 Source: Company websites Note: Percentages do not sum to 100%; companies may collect multiple data types Current data sets generally revolve around claims but that’s going to be changing with lots of clinical data and transactional information with lifestyle becoming more readily accessible.” SAM HO, M.D. Chief Medical Officer, UnitedHealthcare
  • 17. Familiar methods of predictive analytics with a long history in other technology services are also appearing in health care PRESENTATION © 2014 ROCK HEALTH CORRELATION CONTEXT ACTION Source: “Giving Viewers What They Want” The New York Times (February 24, 2013) 17 • Movie preferences (by rating, viewing history, etc.) are gathered across all users • Viewers who liked movie A also liked B, C and D and since you like A, so you’ll probably also like B, C, and D • Historical viewing is labeled and identified by individual viewers • You tend to watch movies on weekends and TV shows on weekdays, so a movie should be suggested on Saturday • Larger data sets on preferences that are based on real world viewing are collected • Audiences have a high likelihood of enjoying a type of TV show, so an entire season can be purchased instead of just a pilot Hom-Lay Harish Add Profile
  • 18. Symptom calculators are the “recommendation engines” of health care, helping millions of consumers diagnose themselves PRESENTATION © 2014 ROCK HEALTH Source: Mayo.com Note: Other use cases are representative, not comprehensive 18 HOW IT WORKS Consumers enter in their symptoms, and related factors, and in turn receive the diagnoses with the “most matches” OTHER USE CASES • Triage • Comorbidity identification • High cost patient identification • Physician-patient matching CORRELATION
  • 19. Lacking appropriate context, clinical indicators—including vital signs—can generate false positives or negatives in alert systems HOW IT WORKS HOW HEART RATE RESPIRATORY RATE Lucile Packard Children’s Hospital CONTEXT Stanford adjusted its early warning algorithms to match actual vital signs from hospitalized children versus textbook definitions Using textbook definitions, 14% to 38% OTHER USE CASES of heart rate observations and 15% to • Decompensation 30% of respiratory rate observations would have resulted in false alarms • Readmission prevention • Behavior change Source: “Development of Heart and Respiratory Rate Percentile Curves for Hospitalized Children” Pediatrics (2013), “A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care” Health Affairs (2014) Note: Other use cases are representative, not comprehensive 19 PRESENTATION © 2014 ROCK HEALTH
  • 20. Genetic screening companies similarly know the inherent risks before a child is conceived, allowing decisive action PRESENTATION © 2014 ROCK HEALTH 20 ACTION HOW IT WORKS A couple planning its family submits DNA to Counsyl, which provides probabilities on 100+ health conditions that could be passed from parents to children OTHER USE CASES • Disease prevention • Population health management and early intervention • Treatment selection Source: Counsyl.com Note: Other use cases are representative, not comprehensive
  • 21. Building models that break the curve of uncertainty will lead to personalized care, but it is not without significant challenges KEY REQUIREMENTS PRESENTATION © 2014 ROCK HEALTH 21 MOVING FORWARD PREDICTION CERTAINTY KNOWN UNKNOWN Using predictive analytics to personalize health care • Incorporation of new data types and sources • Reliability of predictive models • Timeliness of data • Transparency in prediction • Convenient (and in context) recommendations • Rapid learning and improvement Personalized care will emerge from high confidence algorithms that can predict actionable interventions that improve long-term health outcomes UNKNOWN
  • 23. “The keystone of any successful predictive analytics model is the ability to improve the prediction based on a feedback loop. Within seconds, Google knows whether its search engine prediction is correct. But in health care, the feedback loop—which is often measured in terms of impact on biometric or cost outcomes—can take years.” CHRISTINE LEMKE Co-founder and CEO The Activity Exchange is the connective tissue between healthcare companies and their populations to build and manage relationships to improve outcomes.
  • 24. Startup companies are attacking the key challenges in predictive analytics, advancing the space and creating differentiation PRESENTATION © 2014 ROCK HEALTH 1 2 3 4 5 6 TRAINING DATA AGGREGATION RELATIONSHIP SEARCH CASE DATA COLLECTION INDIVIDUAL CASE CHARACTERIZATION RECOMMENDATION CONTEXTUALIZATION PERFORMANCE CAPTURE BASIC ADVANCED EXAMPLES Limited Disparate Traditional data Novel data Lagged / point Real-time / continuous Obfuscated Transparent Generic Personalized Disjointed Closed loop There are a whole bunch of variables and very few observations. The number one thing holding predictive analytics back is the lack of data: the fact that things are not easily measured, collected, or accessible.” URI LASERSON DATA SCIENTIST, CLOUDERA PHD IN GENOMICS 24 “
  • 25. Aggregating, cleansing, and labeling data from disparate sources is the building block for developing non-obvious predictions CASE STUDY: ONCOLOGY CARE EXAMPLE: CHALLENGES DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION INDIVIDUAL CASE CONTEXTUALIZATION PERFORMANCE CAPTURE PRESENTATION © 2014 ROCK HEALTH BASIC ADVANCED Single oncology data source (e.g., clinical trials, claims, or electronic health records) DATA TYPES Aggregating data from EHR, laboratory and billing systems Integrating and matching claims with patient trial data Avoids data that isn’t already cleansed, structured, and labeled (e.g., claims, pre-designated fields in EHRs, etc.) CLEANSING Identify high value data (e.g. EHR notes) and cleanse, structure, and label it as part of the aggregation process Data is historical with inherent bias from unintended use and lag associated with claims processing FREQUENCY Data is loaded on a nightly basis and processed continually, near real-time Source: Company website 25 • Ability to access meaningful, historical data sets and normalize for inherent biases and validity concerns • Integrating with current clinical workflow to collect real-time, point of care patient data • Learning to manage and process new and existing forms of unstructured, siloed data • Addressing HIPAA and privacy related concerns to guarantee patient anonymity
  • 26. Using new data sources creates an opportunity to surface better (i.e., more accurate, timely, or cheaper to collect) predictors CASE STUDY: CARDIAC REHAB EXAMPLE: CHALLENGES BASIC ADVANCED Printed packets of information and cardiac rehabilitation guidelines are handed to patients to follow DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION PERFORMANCE CAPTURE PRESENTATION © 2014 ROCK HEALTH 26 • Identifying existing and novel data points that can better predict outcomes • Identifying reliable and not spurious relationships • Rapid data collection and continual integration to train and iterate algorithms • Ability of predictive analytics to integrate and impact a clinician’s work flow • Limited data sources are analyzed by care providers INDIVIDUAL CASE CONTEXTUALIZATION Source: Company website, interviews ENGAGEMENT MODALITY Mobile app tracks patients’ interaction with the cardiac rehab program, which is linked in real-time to a care management dashboard Engagement and clinical data collected infrequently through office visits and in-person interactions ACQUISITION Collects additional data via activity trackers, meal logging, and non-diagnostic mental health questions Poor, incomplete data sets limits a clinician’s ability to identify patients likely to be readmitted or suffer adverse event PRIORITIZA-TION Algorithm predicts patients who need more attention and sends alerts to clinicians or care coordinators to take action
  • 27. Real-time data collection reduces traditional intervention response time CASE STUDY: HIGH-RISK PREGNANCY EXAMPLE: CHALLENGES DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION PERFORMANCE CAPTURE PRESENTATION © 2014 ROCK HEALTH Source: Company website, interviews 27 • Current care model predates that data collection happens in discrete intervals with an additional lag due to claims processing • Data collected may vary in reliability and accuracy if based solely on patient reporting or non-clinical devices • Using real-time data in a meaningful manner requires new infrastructure and workflow INDIVIDUAL CASE CONTEXTUALIZATION BASIC ADVANCED Regular check-ups generate claims data that get processed several weeks to months later PREDICTOR VARIABLE SOURCE Patient self-reports weight and mood data on a frequent basis, which is immediately accessible to care provider Infrequent, missed appointments results in missed data points RELIABILITY Decreased lag time between weight measurement and processed information Lagged and infrequent data results in late recognition and interventions TIMING Timely data allows for early stratification and intervention to avert high-risk complications
  • 28. Improving the transparency of methodologies and the data behind analytics better supports physicians in decision-making CASE STUDY: CDS TOOLS CHALLENGES DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION INDIVIDUAL CASE PERFORMANCE CAPTURE PRESENTATION © 2014 ROCK HEALTH Source: Company website, interviews 28 CONTEXTUALIZATION BASIC ADVANCED Clinical decision support based on limited set of protocols and guidelines BREADTH Incorporates the current scientific research and clinical practice data for analytics Guideline updates significantly lag clinical research and require approval through centralized bodies ADJUSTMENT Real-time analytics and continuous updates based on outcomes from observational data Medical practice highly paternalistic and substantiated through experience versus evidence VISIBILITY Transparency via medical knowledge graph to support physician decision-making regarding symptoms, medications, risk factors, and diagnoses • Visualization challenges in displaying all relevant data for time sensitive decision-making • Finding the balance between black box engines and information overload tools • Recency and accessibility of data to develop medical, evidence-based recommendations • Physician and patient adoption of “algorithms” dictating care EXAMPLE:
  • 29. By tailoring both recommendations and timing, companies can motivate consumers via a personalized toolset CASE STUDY: 10,000 STEPS EXAMPLE: CHALLENGES DATA AGGREGATION RELATIONSHIP SEARCH DATA COLLECTION CONTEXTUALIZATION PERFORMANCE CAPTURE PRESENTATION © 2014 ROCK HEALTH Source: Company website, interviews 29 • Using advanced algorithms and behavioral economics theory requires large individually tagged datasets • Determining the “right intervention” is challenging and requires trial and error • Consumer concerns around privacy and identity INDIVIDUAL CASE BASIC ADVANCED Tracking and visualization of gross progress or milestones against time (e.g., by day, week, month) MEASURE-MENT Tracking progress and challenges relative to consumer behavior and engagement patterns across all devices and services Focus on identifying trends at population level and applying learnings top down, demonstrating quick success for majority APPROACH Analyzing when and how individual consumers respond to incentives to allow for personalized notifications, or “interventions” Engagement and subsequent effectiveness weans and new population interventions are deployed EFFECT Results are sustainable as interventions continuously adapt to individuals, rolling up to significant population change
  • 30. 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
  • 32. “Healthcare providers don’t just want predictive analytics to output graphs and statistics. They need something that’s actionable. You have to distill it down to what matters and is actionable. Because there’s a hundred thousand things that come into play in health care, predictive analytics has to tell us what matters and how we can act on it.” ANIL JAIN Chief Medical Officer Explorys offers a software platform solution that helps healthcare systems aggregate, analyze, and manage their big data
  • 33. Personalizing care through predictive analytics represents a significant opportunity to reduce costs in the healthcare system $192B $128B $35B OVERTREATMENT FAILURES OF CARE DELIVERY LACK OF CARE COORDINATION PRESENTATION © 2014 ROCK HEALTH • Eliminating care that cannot help patients—care that is outmoded, supply-driven, and eschews science • Restricting treatment and intervention to the patients who will benefit based on the individual and the context • Continuously studying care to identify what works for whom and in what context • Scaling best practices including preventive care and early warning systems that demonstrate effectiveness • Ensuring those at the highest risk of costly medical episodes are identified, monitored, and cared for between visits and following hospitalization Source: “Eliminating Waste in US Health Care” Journal of the American Medical Association (2012) 33
  • 34. It will largely fall onto the healthcare industry to recognize the value of predictive analytics and implement critical use cases IDEALIZED USE CASE OVERTREATMENT CARE DELIVERY COORDINATION PRESENTATION © 2014 ROCK HEALTH 34 PAYERS Construct personalized medical policy (what is and isn’t covered) and benefits (how costs are shared by parties) Match interventions to individuals to scale behavior change programs (wellness, chronic disease management, etc.) PROVIDERS Provide point of care access to historical data in the context of a patient in ambiguous situations (“Green Button”) Reduce treatment variation and improve outcomes Manage risk of population health management programs under accountable care BIOPHARMA Predict individual responsiveness to treatment (within R&D and post-market contexts) Conduct pharmacovigilance
  • 35. The industry might be waiting to implement predictive analytics as the FDA decides how best to regulate clinical decision support “ Any software that analyzes data and supports clinical decision making, including: • Computerized alerts, reminders and warnings • Computer-aided diagnosis • Treatment recommendations Regulation will be agnostic to information source (manual entry, automated, etc.) Our question: How will the FDA regulate the practice of medicine when algorithms prove more accurate than clinicians? PRESENTATION © 2014 ROCK HEALTH This guidance does not address the approach for software that performs patient-specific analysis to aid or support clinical decision-making.” LIKELY SCOPE OF FUTURE GUIDANCE POTENTIAL FRAMEWORKS Source: FDA.gov; “FDA regulation of clinical decision support software” Journal of Law and the Biosciences (2014) 35 Bipartisan Policy Center (BPC) proposed CDS be subject to a new oversight framework: • Adherence to and implementation of designated standards • Participation in safety monitoring • Aggregation and analysis of trends to mitigate future risk Food and Drug Administration Safety and Innovation Act (FDASIA) working group advised: • Different frameworks dependent on risk, with low-risk categories exempt from pre-market approvals/clearances • Clarification amongst multiple agency regulation (e.g., FDA/ONC/FCC)
  • 36. Beyond regulation, the biggest risk to predictive analytics being used in health care is adoption as power dynamics shift 2 1 PATHWAYS ADOPTION CHALLENGES 1 Software-based clinical decision support Patient provides data to the doctor, who incorporates it into a decision support algorithm for diagnosis or treatment 2 Patient-controlled Patient generates and submit their own data into the predictive algorithm, allowing them to directly receive clinical insights Our question: Can user experience and design influence decision making so deeply as to be regulated? PRESENTATION © 2014 ROCK HEALTH 36 0110001001 1010010110 1111011010 0101101110 0110011001 PREDICTIVE ANALYTICS HEALTHCARE PROFESSIONAL PATIENT 3 1 3 Traditional Patient provides the clinician with the data they need to diagnose and treat based on their own judgment • Loss of decision making power • Direct integration into clinical workflow • Transparency of complex algorithms • Management of liability • Convenience of accessing algorithms • Accuracy and reliability of recommendations • Management of privacy concerns • Regulatory burden
  • 37. We are underestimating the potential impact of predictive analytics in process tools to help physicians make better decisions. Every week, at the airport, I get on an airplane, and I don’t worry about flying at all. There are so many tools deployed to assist the pilot. I was talking with a pilot about the new 787–and the pilot said he basically monitors the plane. We’re going to see more of that in health care. Physicians will be monitoring algorithms.” KEVIN FICKENSCHER President, AMC Health Former President, AMIA AMC Health provides customized, scalable telehealth solutions for organizations serving at-risk populations through remote patient monitoring programs. “
  • 38. ACKNOWLEDGEMENTS We are indebted to our industry partners who not only support our work every day but provided invaluable feedback on an early draft of this report. A number of industry, startup and venture folks also offered their expertise. Special thanks to Karina Babock, Benjamin Berk, Archit Bhise, Joe Boyce, Matt Butner, Chris Coloian, David Crockett, Ash Damle, Asif Dhar, Bill Evans, Kevin Fickenscher, Luca Foschini, Ryan Goldman, Josh Gray, Sam Ho, Lucian Iancovici, Anil Jain, Donald Jones, Allen Kramer, Uri Laserson, Christine Lemke, Dave Levin, Dan Martich, Phil Okala, Trishan Panch, Vinnie Ramesh, Leah Sparks, David Tamburri, Euan Thomson, Abhimanyu Verma, Nate Weiner, and Jack Young for their time and insights. Finally, we are fortunate to work with the most encouraging and passionate team in digital health. We are certain that no one would even be reading this report if not for the tireless marketing efforts of Halle Tecco and Mollie McDowell. research@rockhealth.org @rock_health PRESENTATION © 2014 ROCK HEALTH