Health Informatics. Delivering innovative analytics using health data science. Sajan Khosla Head, Health Informatics - Advanced Analytics Centre

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1 Health Informatics Delivering innovative analytics using health data science Sajan Khosla Head, Health Informatics - Advanced Analytics Centre

2 Disclaimer These slides are my perspective and do not represent the perspective of AZ. I am employed by AZ

3 Overview The Advanced Analytics Center Health Informatics for Real World Evidence Tools & technologies Smart drug development Case study: Visualise & explore asthma treatment reality Closing remarks 3

4 Our mission We transform innovative molecules into medicines that change lives 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

5 5

6 What breed of skills do Health Informaticians have? Health data techies from a Medical, Epi, Outcomes Research, Public Health background Health Data Science Data Driven Decisions Bespoke Innovation Scientists versed in statistical approaches and methodologies, harnessing Real World Evidence Scaling of research to scientific community through training, applications, tool development & support 6 6

7 Smart drug development accelerated with Real World insight Source: SVMPharma Ltd. 7 Containing heterogeneous patient population reflecting realistic scenario, RWE has the potential to fill in the evidence gap at various stages of product cycle.

8 Tools & Technologies Observational studies IBM Netezza - MPP (massively parallel processing) environment Visual analytics API Layers Advanced analytics US - EMR US Patient Registries UK Primary Care International EMR/EHR 8

9 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?

10 Real World Data Analysed MarketScan Research Database Integrated Data from commercial, Medicare supplemental & Medicaid claims 136 Million Unique Patients 4 50 US States Coverage Best suited for examining health economics and treatment outcomes

11 Multiple dimensions to mapping a patient journey Comorbidities and historic conditions developed by detailing the diagnoses observed prior to the incident recording of the patients disease Outpatient diagnostics understanding the care of a patient within the clinic and ambulatory setting 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 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

12 Visualizing treatment pathway using temporal pattern discovery 12

13 Visually interrogate pathways for treatment reality Treatment 1 Treatment 2 13 Treatment paths color on the modeled exacerbation outcomes Red - Higher the risk of exacerbation Green Lower risk of exacerbation

14 Patient level sampling allows detail interrogation Patient level sampling can be generated at chosen cohort or treatment switch group level Similar to EventFlow 1 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. 14

15 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. 15

16 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 16

17 Want to know more? e: 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) , F: +44 (0) , 17

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