Session 7 – Tech Trend-Watching for Health Insurance and How to React

Alain Peddle, FIA, FASSA

9/7/2016

Tech trend-watching for health insurance (and how to react)ALAIN PEDDLE (FIA)Ping An Health Deputy General Manager1 / 2 September 2016

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Advanced analytics

Hybrid cloud computing

Quantified self and wearables

Collaboration technologies

Telematics

User experience technologies

Right speed IT

Autonomic platforms

Augmented and virtual

reality

Internet of things

Industrialized analytics

Liquid workforce

Intelligent automation

Digital trust

Platform economy

Predictable disruption

Gartner, Top 10 Technology Trends Impacting Life and P&C Insurers in 2015, 13 April 2015; Juergen Weiss, Kimberly Harris-Ferrante, Jeff Haner, Laurie Shotton

Deloitte, Tech Trends 2016: Innovating in the digital era. Deloitte University PressBill Briggs

Accenture, Technology Vision for Insurance 2016: People First, 2016, Johan Cusano and Andrew Starrs

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Source: CDC

Source: WHO

Source: Oxford Alliance Group

Physical inactivity,

poor nutrition, smoking,

alcohol abuse

Cancer, diabetes, heart and lung diseases

A new type of life insurance

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460

Physical inactivity,

poor nutrition, smoking

Cancer, diabetes, heart and lung diseases

A new type of life insurance

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450Pneumonia

& Tuberculosis

Diarrhoeaand enteritisDiptheria

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2

3

Together

caused 30% of all deaths

1900

20032016

The nature of risk is changing

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SLEEP SENSORS

SMART GLASSES

HEADS-UP DISPLAYS

SMART CLOTHING

FITNESS & HEARTRATE MONITORS

ACTIVITY MONITORS

DRUG DELIVERY PRODUCTS

CONTINUOUS GLUCOSE MONITORING

PERS

SMART WATCHES

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PATCHES

ECG MONITORS

AUDIO EARBUDS

HEARING AIDS

DEFIBRILLATORS

FOOT POTS & PEDOMETERS

EMOTIONAL MEASUREMENT

INSULIN PUMPS

BLOOD PRESSURE MONITORS

PULSE OXIMETRY

Infographic created by Boston Technology Corporation (www.boston-technology.com ). | Accessed from http://hitconsultant.net/2015/02/13/infographic-wearables-in-healthcare/ on 25/08/2016.

Measuring Humans

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Number of steps per day for one client Number of steps per day for a population of clients using wearables

10,000 and 12,500 target values according to the activity program stand out in the image

Source: Discovery Vitality data

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Published peer-reviewed article: Fitness-Related Activities and Medical Claims Related to Hospital Admissions – South Africa, 2006. Estelle V. Lambert, PhD; Rosanne da Silva; Deepak Patel, MD, MSc; Libero Fatti, PhD; Tracy Kolbe-Alexander, PhD; Adam Noach; Craig Nossel, MBChB, MBA; Wayne Derman, MBChB, PhD; Thomas Gaziano, MD, MSc. Preventing Chronic Disease: Public Hea;th Research , Practice and Policy, Volume 6: No. 4, October 2009.

Difference in Hospital Admission Rates for highly active clients

Published article: The Association Between Medical Costs and Participation in the Vitality Health Promotion Program Among 948,974 Members of a South African Health Insurance Company. American Journal of Health Promotion, January/February 2010, Vol. 24, No. 3 | Published peer-reviewed article: Fitness-Related Activities and Medical Claims Related to Hospital Admissions – South Africa, 2006.. Preventing Chronic Disease: Public Health Research , Practice and Policy, Volume 6: No. 4, October 2009. | Published article: Participation in Fitness-Related Activities of an Incentive-Based Health Promotion Program and Hospital Costs: A Retrospective Longitudinal Study. American Journal of Health Promotion, May/June 2011, Vol. 25, No. 5.

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Verified health outcomes Applications

For engaged members versus non-engaged members:

Hospital costs 10% - 40% lower (non chronic)

Hospital costs 10% - 30% lower (chronic)

Admission rates 10% lower

Length of stay in hospital 25% lower

Hospital cost per patient 14% lower

1. Front end selection

2. Differentiated increases

3. Impaired lives

4. Funding incentives

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measuring things

Acceleration

Braking

Cornering

Speeding

Distance

Location

Time

Mobile usage

Data that can be collected

GPSGSM/Voice/ Data/SMS

Remote telematics transmitter

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Allows accurate measurement ofg-force during harsh acceleration

Raw data

Rotated data to allow for actual device positioning

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3.7g

1.6g

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part 2 changing outcomes

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On average, clients improve their driving behaviour by 15% within the first 30 days and maintain this improvement over time

Source: Discovery Insure telematics data from December 2014 to January 2016.

Discovery Insure app

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Complication reduction for each 1% point drop in A1c

From: National Diabetes Information Clearinghouse, NIH Publication No. 08–3874, May 2008, http://diabetes.niddk.nih.gov/dm/pubs/control/

- 21%

- 14%

- 40%- 43%

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Value

No.

of p

rovi

ders

Potential impact of managed

referrals

Network effect Differences in provider costs Impact of guidance & info

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70%Healthy

Frequency

Seve

rity

20%Well but at risk

8%Significant chronic condition

2%Complex condition

21% of claims

Source: South African experience from Discovery Health data.

Segmentation of the population into risk groups using the Johns Hopkins University ACG Case-mix system, then summarized into risk categories using Discovery Health’s Resource Utilization Band (RUB) classification system.

Paper Data tools Application

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2.8 x

Number of claimants per 10,000 claiming more than $65,000 (2010 money terms)

Expected market costs of new cancer drugs

Source: Oncology medicine products submitted to / discussed with the Centre for Clinical Excellence (CCE) for funding consideration between 2005 and 2015, based on South African experience

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Efficient

Inefficient

Outlier in terms of efficiency

Hospital Efficiency analysis

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part 3 conclusions

1. Skills

2. Juxtapositions

3. Actuary role

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