Building a Next Generation Clinical and Scientific Data Management Solution

Copyright © 2019, Saama Technologies
Next Generation Clinical/Scientific Data
Management Solution

2
Srini Anandakumar
Senior Director of Clinical Analytics Innovation, Saama Technologies
– 18+ years of life sciences experience, mostly in Clinical Analytics
– Deep expertise in solution development and application of data and AI
– Leads Saama’s patient data strategy

3
Building a Next-Generation Clinical/Scientific Data Management Solution
What will I learn?
– What a patient data analytics solution roadmap should look like
– Which building components should be included
– How it works, as demonstrated by an implementation case study
Why should I care?
– EDC and third-party clinical/scientific data can be used to manage and control:
• Trial effectiveness
• Submission pathway

4
Clinical Trial Cost
Category
Percentage
Range
Patient recruitment and
retention
20% -25%
Clinical procedure cost 15% - 22%
Administrative site cost 11% - 29%
Site monitoring 15% - 20%
Physician 6% -8%
Data services 2% -8%
KPI Range
Data acquisition and
cleansing
20 to 40 days
Enabling data access 2 to 5 days
LPLV to DB Lock 20 to 40 days
DB Lock to TLG (final
submission datasets)
15 to 45 days
VALUE
INNOVATION
CORE
SERVICES
VALUE
INNOVATION
CORE
SERVICES
TIME
COST
CURRENT STATE FUTURE STATE
• 20% reduction in overall cost of
patient data services (5M to 20M
savings)
• 25% reduction in time (25 to 45
days in submission pathway
reduction)*
COST
TIME
COST
TIME
Where is the Impact of Clinical Data Management Solution?
Sources:
https://www.centerpointclinicalservices.com/blog-posts/driving-drive-drug-innovation-and-market-access-part-2-utilizing-technology-and-maximizing-resources/
http://journals.sagepub.com/doi/abs/10.1177/1740774515625964?ssource=mfr&rss=1
http://www.appliedclinicaltrialsonline.com/why-are-cancer-clinical-trials-increasing-duration
https://pharmaphorum.com/r-d/views-analysis-r-d/impact-data-management-clinical-trials-new-study/
* Assuming 100+ ongoing trials with data
services per year 25 Mn to 100 Mn

5
Sponsor’s Requirements and Current Process
1. Patient Data
Collection
2. DQ and Data
Management
3. Standardized Data
Data
4. Analyzed Data 5. Tables, Listings and
and Graphs
Third Party Data
(Lab, Biomarker, PK/PD,
Randomization)
Subject Level
Reporting
Edit
Checks
Domain
Checks
Project Level
Analysis
Statistical
Monitoring
Medical Monitoring
TA Level Analysis
Risk Monitoring
Study Level
Analysis
Cross Domain
Checks
CRF/Visit Checks
SDTM Datasets
Define.XML
ADaM Datasets
TLG

6
Sponsor’s Requirements and Current Process
Data Acquisition
Use Cases
1. Collected Data Management (data quality and review, queries management)
management)
2. Clinical Data Standardization (SDTM conversion and verification, submission datasets
submission datasets creation)
3. Clinical Analysis Datasets Creation (ADaM conversion)
4. Tables, Figures and Listing (TFL creation)
5. Medical Monitoring and Trial Safety (patient level, trial level)
6. Study Risk Management (patient data KPI/KRI, study data integrity)
7. Cross Trial Analysis (integrated summary – safety, efficacy)
8. Adaptive Trials–Design and Execution (sub-population research, treatment arms
treatment arms reduction, etc.)
9. Trial Data Transparency and Sharing
Business Challenges
Data Acquisition Standardization and Submission Analytics and Insights
• High Lead Time – EDC set up, third party data set up for
data set up for data acquisition (low data refresh)
• Manual Process for third party data acquisition & quality
acquisition & quality check
• Duplicate and Inconsistent Process in data acquisition
acquisition (blinded and non-blinded data)
• Study Specific Process – high lead time & effort in
effort in process repetition and duplication
• Manual and Legacy-Based Study Specific siloes
siloes standardization and submission deliverables process
• Gaps in transformation & Data Traceability
• Manual Process in providing data access (high lead time)
(high lead time)
• Manual process in data aggregation, Source Code
(Programs) management
• Gaps in Analytics Use Cases – trial search, cross trial
cross trial analysis, trial transparency

7
Representation of a Modern Patient Data Platform
Patient Data Pipeline
Sponsor
Downstream Systems
Sponsor
Tools
Sponsor
Security
EDC
External Lab
Data
Source Data
Analytics &
Submissions
Key Components to Meet Sponsor Objectives
Intelligent
Adapters
Library
Management
ML Workflows Metadata
Management
Data Review & MM
- ML Models
Interactive
Visualizations
Virtual
Assistant
Data
Acquisition
Dropzone/
Raw Layer
Patient Data
Lake
DQ
Rules
Raw
Data
Rules
AnalyticsPre-Conformance/
Conformance

8
Patient Data Applications

9
DEMO

10

11
Thank You
Srini Anandakumar
Senior Director of Clinical Analytics Innovation
Saama Technologies
srinivasan.anandakumar@saama.com
www.saama.com

12
Summary of Solution Benefits
1. Study Start Up 2. Study Conduct 3. Study Insights
Efficiency • Reduce effort and time to set up studies (CRF, edit
checks associated metadata) in EDC
• Optimize effort and time involved in creating transfer
specifications for third party data acquisition
• Faster and standardized process to create data quality and
medical review packages
• Access to collected data real-time (EDC data and Increased
frequency of third party data)
• Improve collaboration with site/third party for data
discrepancy and queries resolution
• Optimize effort and process involved in creation of
standardized data, analysis data sets and tables, figures
tables, figures and listing (TFL)
• Automation in performing cross trial analysis
• Ability to search/drill on clinical trial data for exploratory
analysis
• Improved support to design and execute adaptive trials
Quality • Increase quality of the inbound data (EDC and Lab) with
consistent and standard process
• Enable easier maintenance of data standards (collection,
submission and analysis)
• Improve quality of submission deliverables, process and
data traceability
• Enable consistent and reliable process for all business
teams for data access
• Improved quality of responses to queries from regulatory
agencies
• Derive better real time insights – trial/program/site/patient
Scalability
for the
Future
• Automated set up for data acquisition of wearables and
OMICs data
• Easier set up for acquiring unstructured data (documents,
images)
• Automate submission pathway and enable continuous
improvement from historical data
• Shift from descriptive patient data analytics to predictive
analytics
• Enable multi-channel communication experience with
data

13
PDAVA

14
Life Science Analytics Cloud (LSAC) – Patient Data Analytics (PDA)
LSAC - Patient Data Analytics
Sponsor
Downstream Systems
Sponsor
Tools
Sponsor
Security
EDC
External Lab
Data
Source Data
Analytics &
Submissions
Key Components to Meet Sponsor Objectives
Intelligent
Adapters
Library
Management
ML Workflows Metadata
Management
Data Review & MM
- ML models
Configurable
Workflows
Interactive
Visualizations
Virtual
Assistant
Data
Acquisition
Dropzone/
Raw Layer
Patient Data
Lake
DQ
Rules
Raw
Data
Rules
AnalyticsPre-Conformance/
Conformance

15
10
• Pre-built conversational experience on key intents (topics) for a scope of safety and efficacy domains
• Enables users to view graphs on demand (on known intents) to provide details on an intent conversation
• Supports continuous training of virtual assistant for accuracy improvement with response
• Trained on standardized data (SDTM) so can be plugged in to any SDTM based trial data without any set up
• Will support voice based conversations in future
S.No Intent Intent Definition Mandatory Entities
1 StudiesInSystem Shows studies configured NA
2 AdverseEventRatio To find out count of AE vs. count of patients for a site or a site with the lowest or highest
value.
Study
3 InvestigatorName To find out the investigator name. If a site is provided, investigator name is provided. If no
site is provided, investigator name is shown for all sites.
Study
4 PatientDemogDetail Provide patient details by age. If site is provided, show the details for that site. If site is not
provided show for the entire study.
Study
5 AEdetailsbyRaceAgeandSex Provide adverse event, sex, age and race details. If the site is given, provide details at a site
level, if not show at the study level.
Study
6 Medicalhistoryofpatient Provide medical history for a patient. Study, Subject
7 PatientDrugdetails Provide drug exposure details for a patient. Study, Subject
8 ConcomitantDetailsofpatient Provide concomitant details for a patient. Study, Subject
9 LabTestDistributionforvisitsandSites Show lab test distribution for a lab test. If a visit is provided, show the values graphically for
that visit. If a visit and site is shown, display them for the visit and site. If only a site is given,
show all visits only for that site.
Study, Lab Test
PDA Virtual Assistant

Building a Next Generation Clinical and Scientific Data Management Solution

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Building a Next Generation Clinical and Scientific Data Management Solution

Similar to Building a Next Generation Clinical and Scientific Data Management Solution (20)

More from Saama

More from Saama (20)

Recently uploaded

Recently uploaded (20)

Building a Next Generation Clinical and Scientific Data Management Solution