Health Informatics Delivering innovative analytics using health data science

Sajan Khosla Head, Health Informatics - Advanced Analytics Centre

Disclaimer

These slides are my perspective and do not

represent the perspective of AZ.

I am employed by AZ ☺

Overview

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• The Advanced Analytics Center

• Health Informatics for Real World Evidence

• Tools & technologies

• Smart drug development

– Case study: Visualise & explore asthma treatment reality

• Closing remarks

Our mission

We are

responsible for

generating the

data that

enables the

business to:

Understand

where there

is unmet

medical need

Shape

Therapy Area

strategies

Make critical

pipeline and

investment

decisions

Ensure the

right molecules

are selected

for progression

Seize the

right lifecycle

management

opportunities

We transform innovative molecules into medicines that change lives

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What breed of skills do Health Informaticians have?

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Bespoke Innovation

Scaling of research to scientific community

through training, applications, tool

development & support

Health Data Science

Health data techies from a Medical, Epi,

Outcomes Research, Public Health background

Data Driven Decisions

Scientists versed in statistical approaches and

methodologies, harnessing Real World Evidence

Smart drug development accelerated with Real World insight

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Containing heterogeneous patient population reflecting realistic scenario, RWE

has the potential to fill in the evidence gap at various stages of product cycle.

Source: SVMPharma Ltd.

Tools & Technologies

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US -EMR

US – Patient Registries

UK Primary

CareInternational EMR/EHR

AP

I Layers

Advanced analytics

Visual analytics

Observational studies

IBM Netezza - MPP (massively parallel processing) environment

Case study: Visualise & explore asthma treatment reality

Research Question: Does an asthma patient adhere to appropriate level of

treatment or only take medications to alleviate symptoms?

Does the treatment pattern have seasonality?

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Real World Data Analysed

Integrated Data from commercial, Medicare supplemental & Medicaid claims

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136 Million Unique Patients3

50 US States4

Coverage 1995-20145

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MarketScan Research Database1

Best suited for examining health economics and treatment outcomes

Multiple dimensions to mapping a patient journeyComorbidities and historic

conditions – developed by

detailing the diagnoses

observed prior to the incident

recording of the patients

diseaseOutpatient diagnostics –

understanding the care of a

patient within the clinic and

ambulatory setting

Inpatient procedures &

diagnostics – developing

knowledge of the hospital

interactions and disease

presentation

Laboratory observations –

understanding laboratory

events within the patients

journey pulling in key vital signs

for risk stratification

Clinical Note Extraction –

extraction of key molecular

observations and disease

presentation characteristics

from clinical notes which are

often missing from structured

forms of EMR data

Medications – hospital prescriptions

administered to patients throughout their

treatment, longitudinally identifying gaps

in treatment regimens including

switching and supplementing treatments

Survival – linkage back to

Social Security Death Index

measures to define mortality

outcomes

Visualizing treatment pathway using temporal pattern discovery

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Visually interrogate pathways for treatment reality

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Treatment 1 Treatment 2

• Treatment paths color on the modeled

exacerbation outcomes

• Red - Higher the risk of exacerbation

• Green – Lower risk of exacerbation

• Patient level sampling can be

generated at chosen cohort or

treatment switch group level

• Similar to EventFlow1

approach, patient level

sampling visualization allows

detail understanding of

treatment adherence and

exacerbation patterns

• Adding frequency plot and time

dimension, we can explore the

associations among

exacerbation events, treatment

adherence and seasonality.

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Patient level sampling allows detail interrogation

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Findings

�Usage of Short Acting Beta Agonist’s

(the rescue meds) higher among severe

asthma patients (58-69%) than medium

or mild patients (23-34%)

�Treatment adherence observed to be

lower among severe and medium

severity asthma patients compared to

mild severity.

�Treatment step up does not have a

seasonality effect.

�Exacerbations are more frequent within

severe asthma patient group with

subsequent step down treatment

switches.

Closing remarks

• Access to an international suite of health related data

• Insurance claims

• EHR/EMR’s

• Patient registries

• Linked data assets from health markets• Technology enabled analytics accelerating the path for insight and

evidence generation for therapeutic clinical development

• Increasing knowledge of disease

• Defining the real unmet medical needs• Innovating and accelerating the pace of getting science to patients

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Confidentiality Notice

This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove

it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the

contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 2 Kingdom Street, London, W2 6BD, UK, T: +44(0)20 7604 8000,

F: +44 (0)20 7604 8151, www.astrazeneca.com

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Want to know more?

t: @sajan_khosla

e: Sajan.Khosla@astrazeneca.com