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Underwriting and Real Time BiofeedbackThe rise of mobile computing and its impact on life underwriting

Jordi Posthumus, Mortality Solutions

MUD New York

January 2017

Content

Underwriting Viewed as an Information Technology

The Mobile Revolution

Sensors, Sensors, Everywhere

Will this information be worth anything ?

Final Thoughts.

Underwriting as an Information Technology

3 The Future of Life Underwriting

“Security doesn't rob ambition; the illusion of security robs ambition.” – Jay Samit

• We tend to think of Life Underwriting primarily as a Medical

Science.

• Recent developments in Tech have enabled us to effortlessly

capture many correlates of health.

• These correlates will inevitably move Data Science onto our

traditional territory of Medical Science… double counting !?

• Entire business models rise and fall as the Mobile industry

evolves. Our industry will not be unscathed.

• In the slides that follow, we explore some aspects of the tech

industry encroaching on our traditional turf.

Public Domain, https://commons.wikimedia.org

The most successful consumer product in history


“Those who recognize the inevitability of change stand to benefit the most from it.” – Jay Samit

• Apple’s iPhone, launched in 2007, is easily the most

successful consumer product ever.

• They are on track to have had over $ 1 trillion in

cumulative revenue from iOS devices this year.

• They sold 1 billion iPhones in less than 10 years.

• If you put those iPhones head-to-toe, at about 5” each

that’s 5,000,000,000” (about 79k miles).

• The circumference of the earth is about 25k miles.

• You could wrap your iPhone tower around the earth

more than three times.

By Wikigraphists, Evan-Amos, World Super Cars – CCO, https://commons.wikimedia.org



“Every threat to the status quo is an opportunity in disguise.” – Jay Samit

• This is not just a story about iPhones. It’s about mobile computing.

• The themes around this seismic shift are well covered by Benedict

Evans of Andreessen Horowitz, a VC firm.

• The addressable market plays a significant role:

• Big companies bought mainframes.

• Middle class families bought Personal Computers.

• Almost every human on earth buys a Smartphone.

• The Smart Phone is technology’s first Universal Product

• By 2020, more than 4 billion people will be online using more than

4 billion Smartphones.

By Richard Hilber CCO, https://commons.wikimedia.org, By AJ from openclipart.org

https://commons.wikimedia.org/



“Disruptors don't have to discover something new; they just have to discover a practical use for new discoveries.” – Jay Samit

• Smart Phones dwarf PCs:

• 4 billion people buying a new phone every 2 years vs.

• 1.6 billion buying a new PC every 5 years.

• The Mobile Phone Supply Chain has become a force of nature.

Much is disguised as VR / AR / Drones / Wearables / IoT:

• Lego-like deluge of Smartphone components.

• Contract manufacturers who assemble anything.

• In the past for an amazing new tech product, you’d spend years

designing just the chip. Now you just choose from the massive

menu of mobile components and use a contract manufacturer.

By Martin23230, Arto Alanenpää CC commons.wikimedia.org

Just how fast are things changing ?


“If we want things to stay as they are, things will have to change.” – di Lampedusa

By Martin23230, Arto Alanenpää CC commons.wikimedia.org

Next big thing in the world of universal technology products?


“Corporate planning cycles are a classic example of generals fighting the last war over again instead of preparing for what might lie ahead.” – Jay Samit

• The gargantuan economies of scale behind mobile is driving a

Cambrian Explosion of new device form factors and sensor

innovation. What does the future hold ?

• It seems likely that wearable devices will be a large part of our

technological future. Imagine being a wizard with a wand on

your wrist.

• The Apple Watch is arguably the archetype of its genre.

• It is estimated that the Apple Watch outsold the iPhone 2 to 1 in

their respective launch years.

• What does the Apple Watch, possible heir to the throne of

“Biggest-Consumer-Product-Ever”, have to do with Life

Underwriting?

Source: Apple.com,

• The Apple Watch has accelerometers and a

photoplethysmograph.

• What is this photoplethysmography, of which you speak ?

• Photoplethysmography can measure:

• Heart Rate, O2 Saturation, Respiration Rate, Blood

Pressure, Peak Wave Amplitude, Peak Velocity.

• What about other things like Blood Glucose?

• Any early clues for all cause mortality ?

• Apple continues to aggressively hire Biomedical Engineers.

Consider that they have over 800 people working directly and

full time on only the iPhone’s camera.

Mobile Sensors: The Next Frontier


“Corporate planning cycles are a classic example of generals fighting the last war over again instead of preparing for what might lie ahead.” – Jay Samit

Source: Apple.com,

smallbiztrends.com

A Quick Look at the Value of Resting Heart Rates


“If you want something new, you have to stop doing something old.” – Peter Drucker

• 20,000 stroke patients: Mortality Hazard Ratios for those patients in the highest

two quintiles of resting heart rates (RHRs) were 142% & 174%.

• Copenhagen Male Study followed 2800 healthy men for 16 years. Adjusting fitness

(by measuring VO2Max) they concluded a RHR > 90 carried a 300% of the overall

mortality of a RHR < 50. A 10bpm increase in RHR conveyed increase in mortality

of 16%. They concluded that RHR predicted mortality, independent of fitness or

other cardiovascular risk factors.

• Paris Prospective Study – Looked at cancer risk. Involved 6101 French men aged

42-53. Free of clinically detectable cardiovascular disease and cancer. Did

standardized exercise tests between 1967 & 1972, measured heart rate etc.

Mortality, including 758 cancer deaths, was assessed over the 25 years of follow-

up. Adjusting for age and tobacco, those in the highest quartile of RHR had a

240% relative risk of cancer mortality, as compared to the lowest quartile.

• Meaningful differences in RHRs between smokers and non-smokers.

Case Study


“Data has no ego and makes an excellent co-pilot.” – Jay Samit

• In 2015 and 2016 my Apple Watch measured my heart rate about

92,000 times.

• Approximately 41,300 of those relate to a “non-workout” state.

Source: Apple.com

Going Beyond the Resting Heart Rate: Heart Rate Variability


“Data may disappoint, but it never lies.” – Jay Samit

• Heart rate variability (HRV) is the physiological

phenomenon of variation in the time interval between

heartbeats.

• Heart Rate Variability is strongly related to mortality,

even in people with no obvious heart disease !

• Perhaps counterintuitively, more regular heart beats

correlate with higher mortality. And less regular heart

beats correlate with lower mortality.

Heart Rate Variability: Powerful Prognostic


“An average idea enthusiastically embraced will go farther than a genius idea no one gets.” – Jay Samit

Power-Law Relationship of Heart Rate Variability as a Predictor of Mortality in

the Elderly (Circulation – 1998)

Background—The prognostic role of heart rate (HR) variability analyzed from 24-hour ECG

recordings in the general population is not well known. We studied whether analysis of 24-hour

HR behavior is able to predict mortality in a random population of elderly subjects.

Methods and Results—A random sample of 347 subjects (mean age 73±6 years) underwent a

comprehensive clinical evaluation, laboratory tests, and 24-hour ECG recordings and were

subsequently followed up for 10 years.

Risk factors for all-cause, cardiac, cerebrovascular, cancer, and other causes of death were

assessed. By the end of 10-year follow-up, 184 subjects (53%) had died.

Among all analyzed variables, HR variability was the best univariate predictor of all-cause

mortality (odds ratio, 7.9; 95% confidence interval [CI], 3.7 to 17.0; P<.0001).



“The best big idea is only going to be as good as its implementation.” – Jay Samit

Conclusion:

HR variability is a more powerful predictor of death than the traditional risk

markers in elderly subjects.

The slope of the power-law regression line of HR variability showed weak correlations

with age and blood fasting glucose concentration but no significant correlations with

the other risk factors.

http://circ.ahajournals.org/content/97/20/2031.full

Oxygen Saturation and All Cause Mortality



Low oxygen saturation and mortality in an adult cohort: the Tromsø study

Background

Oxygen saturation has been shown in risk score models to predict mortality in emergency

medicine. The aim of this study was to determine whether low oxygen saturation measured by a

single-point measurement by pulse oximetry (SpO2) is associated with increased mortality in the

general adult population.

Method

Pulse oximetry was performed in 5,152 participants in a cross-sectional survey in Tromsø,

Norway, in 2001–2002 . Ten-year follow-up data for all-cause mortality and cause of death were

obtained . SpO2 categories were assessed as predictors for all-cause mortality and death using

Cox proportional-hazards regression models after correcting for age, sex, smoking history, body

mass index (BMI), C-reactive protein level, self-reported diseases, respiratory symptoms, and

spirometry results.

Conclusion

We observed that lower values from pulse oximetry were associated with increased all-cause

mortality in the general adult population. The association was weakened and no longer

statistically significant after adjusting for FEV1% predicted but remained significant for death

caused by pulmonary diseases.

Blood Glucose and Mortality



Blood glucose: A strong risk factor for mortality in nondiabetic patients with cardiovascular disease

Background

The prognostic significance of blood glucose (BG) for nondiabetic patients in a stable chronic phase of

cardiovascular disease (CVD) has been sparsely investigated, especially for glucose within the normal range.

Methods

We used the Framingham Heart Study 30-year data to determine 2-year all-cause, cardiovascular mortality

(CVM), and non-CVM risk adjusted for age, sex, and typical cardiovascular risk factors (systolic blood

pressure, total cholesterol, body mass index, cigarette smoking, and use of anti hypertensive drugs).

Results

There were steep graded relations of 2-year all-cause, CVM, and non-CVM to BG throughout the normal and

subdiabetic range with no evidence of a lower threshold. Two-year mortality continuously increased from

2.99% at the bottom of the normal range to 7.23% at the top of the normal range (a 2.42-fold increase) .There

were analogous steep increases for CVM and non-CVM.

Conclusions Blood glucose, even within the normal range, is a strong independent predictor of 2-year all-

cause, CVM, and non-CVM in nondiabetic subjects with CVD and therefore of prognostic significance for these

high-risk patients.

Agile Underwriting


“Speed to fail should be every entrepreneur's motto. When you finally find the one idea that can't be killed, go with it.” – Jay Samit

• Wearable Data is just one of many specialized types of

information now available that we will need to factor into the

Underwriting Process.

• Dealing with correlations will be our biggest challenge.

• Evidence of Absence is not the same as Absence of Evidence.

• Not embracing these new risk selection factors may leave one

writing aggregate rates in a smoker distinct world.

• Bringing this all together will require an appetite for iteration that

is a hallmark of technology companies. It’s better to make lots

of small mistakes than a few big ones !

Source: Pixabay.com

Final Thoughts



• The Cambrian Explosion in sensor technology is upon us.

Imagine the insights we’ll draw when continuous non-invasive

blood glucose monitoring comes on line.

• Interesting insurance models of the future include getting

access to people’s HealthKit data (possibly in exchange for

providing personal analytics) and forming the right

partnerships to understand that data.

• Imagine how the industry would change if we got to the point

where very effective underwriting could be done:

• Instantly,

• Continuously, and

• With zero/low marginal cost !

• Even if we only get close, very different models for both

Product Design and Distribution would arise.

Closing Points



Think for a moment about how the other information

technologies in your life have changed over the last 10

years…

Thanks for your attention !

Disclaimer

VI

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