arcadia flufender - surescripts technology challenge final presentation

1CONFIDENTIAL and PROPRIETARY. | ©2013 Arcadia Solutions.www.arcadiasolutions.com

Arcadia FluFenderPrepared for the SureScripts Technology Challenge


Viral infectious disease

Spreads via direct transmission, airborne, hand-to-eye/nose/mouth

A healthy adult can be contagious beginning one day before symptoms develop and up to seven days after becoming sick


5-20% of the population get the flu

in a typical year

The flu tends to be seasonal

In the U.S., Oct. – May, peaking in Jan. or Feb.


Between 1976-2006, estimates of flu-associated deaths in the U.S. range from 3,000 – 49,000 people

Vaccination and early treatment are effective prevention strategies


Why track with prescription antivirals?Indicates the presence of influenza but also of its intensity and the involved population

Antiviral prescriptions are a reliable indicator

Unlike OTC and ILI estimates, ePrescriptions contain verified data about the patient that reliably inform models

Antiviral prescriptions are a precise indicator

FDA requires that AVs be prescribed within 48h of symptoms, and only aggressive (non-complicated) cases

Symptoms generally commence 24-48h after contagion


FluFender Epidemiological ModelFluFender performs short-term forecasting based on known clinician and patient behavior

Rx Rx

𝑅𝑥 𝑡 =

𝑖≠𝑘

𝛾

𝛿𝑖𝑏

𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

+𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

𝑅𝑥 𝑡, 𝑖 = Prescriptions at time 𝑡 in area 𝑖𝛾 = effect of close proximity𝜑 = likelihood of transmission

𝛿𝑖 = spatial separation between 𝑖 and 𝑘𝑏 = strength of distance effect

1.01d

1.01d

2 3 4

1

t=0

1-4d

3 4

𝜑

2

𝜑

2

1

1 Patient is infected with the flu virus.



Rx Rx

𝑅𝑥 𝑡 =

𝑖≠𝑘

𝛾

𝛿𝑖𝑏

𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

+𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖



1.01d

1.01d

2 3 4

1

t=0

1-4d

3 4

𝜑

2

𝜑

2

1


2

2

Patient becomes highly symptomatic 1-4 days later.



Rx Rx

𝑅𝑥 𝑡 =

𝑖≠𝑘

𝛾

𝛿𝑖𝑏

𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

+𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖



1.01d

1.01d

2 3 4

1

t=0

1-4d

3 4

𝜑

2

𝜑

2

1


2

2


3 Patient seeks medical attention.3



Rx Rx

𝑅𝑥 𝑡 =

𝑖≠𝑘

𝛾

𝛿𝑖𝑏

𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

+𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖



1.01d

1.01d

2 3 4

1

t=0

1-4d

3 4

𝜑

2

𝜑

2

1


2

2



4 Patient can be prescribed Tamiflu up to 48 hours following onset of symptoms.

4



Rx Rx

𝑅𝑥 𝑡 =

𝑖≠𝑘

𝛾

𝛿𝑖𝑏

𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖

+𝜑

2𝑅𝑥 𝑡 − 2, 𝑖 + 𝑅𝑥 𝑡 − 3, 𝑖



1.01d

1.01d

2 3 4

1

t=0

1-4d

3 4

𝜑

2

𝜑

2

1


2

2



4 Patient can be prescribed Tamiflu up to 48 hours following onset of symptoms.

5 What no one noticed was that the patient was already contagious one day before onset of symptoms.

Therefore, the most likely window for transmission is 2-3 days prior to the day

a Tamiflu prescription is transmitted.

4

5


Why FluFender?Arcadia Analytics FluFender offers a platform for users and healthcare professionals to both track and forecast flu and antiviral requirements


FluFender AnalysisPrepared for SureScripts Technology Challenge

Based on dataset 11/13/2013


516,032Number of SCRIPT

10.6 Records


16,033


10.6 Records

Number of Antiviral Rx

(Tamiflu, Relenza, etc.)


2

16,033


10.6 Records



Number of Temporal Clusters


2

16,033

516,032

15/4

Number of SCRIPT 10.6 Records




Number of Spatial Clusters


2

16,033

516,032

15/4

Age/Sex

Number of SCRIPT 10.6 Records




Number of Spatial Clusters

Geo-temporally Relevant Flu

Cases


Starting In North Carolina


Moving to South Carolina


Growth and Peak in South Carolina


Peaks in Florida


Outbreak A – FloridaFluFender detected an outbreak with narrow geospatial movement in Orlando, FL with an outbreak radius of 51mi

Demographics Male (50%), Female (50%)

2-15yo - 25% 65+yo -70%

Population Status: Vulnerable Large percentage elderly (65+) and young (2-15)

Outbreak Status : Ongoing Date Range – 12/07/2013 – 12/30/2013

Predictions (3 Day Forecast) Likely continuation: 23 Rx in existing area Likely addition: 10 Rx in new areas (see report for

new Rx)


What can FluFender tell us?Forecasts antiviral requirements over a short timespan, assisting with potential drug distribution

15 clusters in November

– Upper New England• 274 prescriptions in November

• Almost exclusively 45 years of age and older, mostly in 65+

– Anchorage, Alaska• 231 prescriptions in November

• Almost exclusively ages 2-15, with large percentage in 0-2

4 Clusters in December

– Southeast United States (centered in Florida)• Large percentage elderly (65+) and young (2-15)


WHY US?

“Dreaming, after all, is a form of planning.” –Gloria Steinem


APPENDIX


Future Potential for FluFender

• Integrate Flu Vaccine provider locations• User Auto-Location• Local flu outbreak email updates• Add “Did You Feel It?” style survey that

relates directly to health outcomes

• Share flu tracking over social networks• Enable user-embedded FluFender maps

on blogs and websites• Timescale sliders for easier user

experienceUse

r F

ea

ture

sT

ech

no

log

y &

S

ecu

rity

Pu

bli

c H

ea

lth


• Enhanced cell-blocking on low Rx counts for improved Rx privacy

• Improved predictive geocoding• Improve graph options and layouts• Use of Rx ICD-9 codes to identify

vulnerable populations

• Improved predictive algorithm through additional “training” datasets from previous outbreaks

• Integration with Health Information Exchanges for improved population management

• E-mail prescription alerts to vulnerable populations

• Additional Rx and OTR medications in predictive model

• Alignment with existing public health data sources for improved forecasting

• Standardized public health reporting modules for local authorities and Federal agencies (e.g. HHS, CDC)


Future Potential for FluFender

• Integrate Flu Vaccine provider locations• User Auto-Location• Local flu outbreak email updates• Add “Did You Feel It?” style survey that

relates directly to health outcomes

• Share flu tracking over social networks• Enable user-embedded FluFender maps

on blogs and websites• Timescale sliders for easier user

experienceUse

r F

ea

ture

sT

ech

no

log

y &

S

ecu

rity

Pu

bli

c H

ea

lth


• Enhanced cell-blocking on low Rx counts for improved Rx privacy

• Improved predictive geocoding• Improve graph options and layouts• Use of Rx ICD-9 codes to identify

vulnerable populations

• Improved predictive algorithm through additional “training” datasets from previous outbreaks

• Integration with Health Information Exchanges for improved population management

• E-mail prescription alerts to vulnerable populations

• Additional Rx and OTR medications in predictive model

• Alignment with existing public health data sources for improved forecasting

• Standardized public health reporting modules for local authorities and Federal agencies (e.g. HHS, CDC,


FluFender Enhancements

Distinguish between palliative and prophylactic use

Distinguish between vulnerable and regular populations

– Determined based on ages and genders

– Increased accuracy if ICD-9 codes in Rx data were reliable

A small, intense outbreak could look the same as a large, relatively mild outbreak

Currently uses a simple predictive model

– Requires larger data sets and additional testing to determine whether the model extends from one outbreak to another, between years, and between populations.

– Need to build interaction between user-generated data and predictions


FluFender Epidemiological Model

Developing the model We considered the factors surrounding an individual getting a Rx for Tamiflu:

– Prophylactic: Vulnerable (0-2yo, 65+yo, pregnant ♀, ↓immune)– Palliative: Intense symptoms, non-complicated (e.g. no comorbidities)– Patient can only get Rx for Tamiflu within 48h of onset of symptoms– Since contagion starts about 1d before symptoms, that 1d represents a window for transmission of high-intensity flu;

because of the FDA regs, that 1 day is almost exactly 3d before the Rx is written– Also, transmission should be most likely very close to existing cases, and for children and high-density populations (that

have greater group contact)

These facts and assumptions directly form the model for occurrence and transmission used here.

Testing the model We tested the three-day conclusion on SureScripts test data

– The outbreak intensity auto-correlated at 2-3 days, supporting the contagion window model– The results rolled off for smaller outbreaks– Geospatial and learning models contributed very little in initial testing, although the number of cases available were

too small to make any strong conclusions.

The model currently primarily uses Rx data for forecasting; other prediction models have been tested but are not as reliable as the Rx data.

Improving the model The following could help improve the forecasting model: More training data; more information about the

prescriptions; more patient information in the prescription (e.g. ICD-9 codes); incorporation of information about populations into the model; reliable historical context


FluFender Epidemiological ModelHow a typical meeting starts at Arcadia Healthcare Solutions

arcadia flufender - surescripts technology challenge final presentation

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