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