Steffen Frederiksen: DATA, DITA, DOCX
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Transcript of Steffen Frederiksen: DATA, DITA, DOCX
DATA -> DITA -> DOCXA Real Story of Efficiency from the Pharma Industry
LavaCon 2016 | Las Vegas | Steffen Frederiksen | [email protected] | www.ditaexchange.com
Background
SANOFI
• Sanofi is a global healthcare company focused on patient needs. Their business includes pharmaceuticals (notably prescription drugs for diabetes, rare diseases, multiple sclerosis and oncology, consumer healthcare products and generics), vaccines and animal health.
• Sales in 2015(Rx) of 34,896 million USD makes it the 5th largest pharma in the world
What Does a Pharma Company Produce?
?
4
What Does a Pharma Company Produce?
•The “old” answer was: Molecules and content
•The current answer is: Content
5
A Typical Timeline
And How Many Pages?
•Your Best Guess?
7
And How Many Pages?
•150,000 – 250,000 pages per molecule
8
The Situation
• Huge number of hours spent
• Documents finalized late
• Documents not e-compliant
• Lack of consistency
• No standard methodology or tools for data collection & aggregation
• Lack of structure
• Long review cycles
• Multiple review iterations
• Recurrent remarks (regression) at different stages of review
The SANOFI CRUISE Project: Business Drivers
ENSURE CONSISTENCY WITHIN THE ORGANIZATION AND BETWEEN PUBLIC DISCLOSURE AND REGULATED REPORTS (NDA, IND, CTA,
PSUR...) AGAINST DATABASES
QUICKLY ASSESS VALUE AND NON-VALUE ADD ACTIVITIES AND MANAGE THE SOURCING OF THOSE ACTIVITIES WITHIN THE
CAPABILITIES PORTFOLIOS BALANCING FIXED AND VARIABLE COSTS
REDUCE CYCLE TIMES, ENABLE EARLIER AND MORE EFFECTIVE DECISION-MAKING AROUND CLINICAL DEVELOPMENT PROGRAMS,
AND REDUCE TIME TO MARKET
SHARE KNOWLEDGE BY MOVING TOWARD REUSABLE COMPONENTS OF INFORMATION THAT CAN BE MANAGED AND REUSED ACROSS
PUBLICATIONS, DEPARTMENTS AND AUDIENCES.
Short Version
• Increase Quality, Consistency and Compliance
• Faster Time to Market for New Drugs
• Reduce Excessive Costs
The Idea
• Develop modular content re-use and structured authoring solution
• Leverage Microsoft platform (Word and SharePoint)
• Enable integration of other SANOFI applications
• Refine submission documents preparation
• Implement content reuse across lifecycle and processes
• Enable a more granular content management process
• Refine review process
• Improve traceability
Action: EnCORE
13
CASE STORY: PATIENT NARRATIVES
What Are ‘Patient Narratives’?
• As Per ICH E3 guidelines, a patient safety narrative should describe the following:• the nature, intensity, and outcome of the event
• the clinical course leading to the event
• an indication of timing relevant to study drug administration
• relevant laboratory measures
• action taken with the study drug (and timing) in relation to the event
• treatment or intervention
• post-mortem findings (if applicable)
• Investigator’s and Sponsor’s (if appropriate) opinion on causality
Content in Patient Narratives
• Specifically, narratives should include the following:• patient identifier
• age and sex of patient; general clinical condition of patient, if appropriate
• disease being treated (if this is the same for all patients, this information is not required) with duration (of current episode) of illness
• relevant concomitant/previous illnesses with details of occurrence/duration
• relevant concomitant/previous medication with details of dosage
• test drug administered, including dose, if this varied among patients, and length of time administered
BEFORE
Typical Process
• Preparation of first draft narrative from patient/subject data by the Medical Writer
• Scientific and editorial peer review by the CRO project lead to check the document is accurate, complete, and consistent with
requirements and across documents
• Clinical review of draft narrative: It is recommended that this be performed by the Sponsor or designate, although the CRO can
provide this service as necessary
• Medical Writer revision based on clinical review: If the writer does not agree with clinical review comments, for example, when
requested amendments conflict with the evidence or when changes would introduce inconsistencies between narratives, or when
review comments are unclear, these should be discussed with the Sponsor or designate, as appropriate, and responses retained on
file
• Quality control (QC) review based on final patient/subject data. Given the often large number of narratives required for individual
studies and small size of each document relative to the CSR, it is recommended that a single QC review be performed toward the
end of the process, rather than QC review of the first draft and final deliverable
• Medical Writer revision based on QC review findings. Note: when significant findings are identified during QC review, these should
be discussed with the Sponsor and clinical reviewer, as appropriate, and further updates should be checked for consistency and
accuracy
• Approval by the Sponsor after a final review
The Sources for Narratives
• A Medical Writer will use various sources of information when preparing patient safety narratives. These include • Council for International Organizations of Medical Sciences (CIOMS) forms
• Case Report Forms (CRFs)
• MedWatch forms
• Data Clarification Forms (DCFs)
• Clinical database listings
Structure of patient narratives (1/2)
HEADER
MEDICAL HISTORY
AE
PV
TABLES
Generally, one or more topics make up the content in each section. A single event is described per topic.
The output from a map is a publication in MS Word format
1 map = 1 patient narrative
Patient narrative maps are organized into sections
Structure of patient narratives (2/2)Header section
Medical history section
Adverse Event
section
Pharmacovigilance
summary section
21
Before Costs of Writing Narratives
• Per narrative with manual process:• 8hours/narrative * $100/hour = 800 USD
• Per study (example):• 625 narratives (average) * 800 USD = 500,000 USD
• Per year (example):• 100 studies * 0.5 million USD = 50 million USD
22
ROI for Narratives
23
Challenge #1:
“Can you design a robust,
extensible feature that
allows us to pull together
data from many different
sources and systems into
our topics?”
Map= Patient
Narrative
Topic SAE: Date: 1-Feb 2012
Topic AE leading to treat. Disc.:
Date: 10-March 2012
Topic Overdose: Date: 1-Feb 2012
Topic Cancer: Date: 5-Feb 2012
Topic PV: Date: 1-Feb 2012
24
Case Study - Patient Narrative CreationA single narrative will be provided for a given patient: The system associates all events(SAS and PV summary) related to same Patient (following business rules) in order to create the complete Patient Narrative. Patient Narratives will be grouped by the system following a configured priority: Deaths>SAEs>AE leading to treat. Disc.>AESIs>Other – files used in CSR
SAS data from system are retrieved
in Topics for each event
PV data from database
are retrieved in Topics
Generation of maps
Map= Patient Narrative 1
Topic SAE:
Topic Ae leading to treat. Disc.
Map= Patient Narrative 3 (id: 0003)
Topic Severe hypoglycemia
Topic pregnancy
Topic Cancer
Map= Patient Narrative 9 (id: 0009)
Topic overdose
Topic overdose
Map= Patient Narrative 2
Topic DeathFiles in repositoryFile Death:
Patient Narrative 2
File SAE:
Patient Narrative 1
File AE leading to treatment
Discontinuation:
Patient Narrative …..
File AESI:
Patient Narrative 3
Patient Narrative 9
File Other:
Patient Narrative …..
Improved Quality: Data, unstructured and structured information aggregated automatically for reuse
Improved productivity: automated generation of maps, style sheets to apply formats quickly
Response To Challenge #1
DITA’s content referencing (conref) mechanism
+
Microsoft SharePoint Business Connectivity Services(SharePoint BCS)
+
Our extension to conref resolving
(aka DxLiveData)
=
A general, robust and manageable mechanism for bringing in data from any LoB system into your topics
25
SharePoint BCS Overview
XXXXX Systems
“The nuts and bolts…”
OXML
DITAVAL
DITA XML
SharePoint
DitaExchange
Clinicaltrials.govFDA: 1572 FDA: Patient Narrative
Product wwwCountry wwwLanguage www
SubmissionsReview
Apps:ReviewApprove
IOSAndroidW8…
SubmissionsArchive
LoB
LoB
LoB
LoB
AFTER
After Costs of Writing Narratives
• Per narrative with automation:• 0.95 hours/narrative * $100/hour = 95 USD (before 800 USD)
• Per study (example):• 625 narratives (average) * 95 USD = 59,375 USD (before 500,000 USD)
• Per year (example):• 100 studies * 0.06 million USD = 6 million USD (before 50 million USD)
29
ROI for Narratives
Just One Small Detail…Say Hello To Reality!
30
Just One Small Detail…Say Hello To Reality!
31
To Hell with
DITA XML!
32
Challenge #2:
“OK, so we would like to
keep all that is good with
DITA XML, just give it to us
with normal Word
documents – and please
do not say DITA XML to
any of our writers in the
future…Can you do that,
please?”
Response To Challenge #2
DITA’s content architecture
+
ISO/IEC 29500 (aka Office Open XML, aka DOCX, aka Word)
+
Our authoring app for Word
(DxAuthor for Word)
+
Dx4 Publishing etc.
=
All that is good from DITA and much, much less of the bad…
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DITA: The 80% That Are Really Good?
• Modular, topic-based architecture
• Flexible metadata and data mining
• DITA maps for organizing modules
• Content reuse (topics, maps, conrefs, DxLiveData)
• Variables
• Semantic tagging of content:
• Folksonomy (for yourself)
• Taxonomy – using SharePoint Term Store to manage taxonomies
• Schema-based tagging (IDMP, Redaction, … and many more)
• Conditional publishing and filtering, including DxTimeLinePublishing
• XML-based format, allows automation (and XSLT is till an option)
• Publish to many layouts/formats/media, using Word templates for formatting
DITA: The 20% Bad And Ugly
• Unfamiliar XML tools : FIXED
• Writers, reviewers and approvers have to deal with XML: FIXED
• Suffocating rules: “Invalid element…”: FIXED
• Expensive and very complex XSLT/ publishing templates FIXED
• Expensive and complex DTD-specializations: FIXED
36
I can work with this!
Results Delivered
• Narratives @ Sanofi
• Delivered narratives for 16 studies
• Time reduction in QC process
• Use of industry standard data model as input for Narratives process
• Additional time benefits for on-demand narratives
• Partnering with FDA to develop standardized output (HL7)
• The Big Win
• Faster time to market
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Challenge #3:
“OK then. Our reviewers
and approvers will ONLY
work on complete
documents, not on topics.
But we need their
approved feedback
decomposed back to the
topics. Please?”
In Development…
Map & Topics Publish for Review Reviewers Use
Word
DxReview collates
feedback
Edits/Comments
back into Topics
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Challenge #4:
“OK then. We would like to
eliminate the writing
phase for patient
narratives all together –
maybe by using AI/NLP or
content rules - leaving us
with only review and
approval. Please?”
DxLiveData Conreffing Content Automation Example
Data import layer
Data preparation layer
Calculation layer
1st Language
Generation layer
2nd Language
Generation layer
Dynamically Integrating ML/AI Text into Topics
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The Vision of DitaExchange
"Bringing the Power of Structured Content Management to Everybody"
Data -> DITA -> Docx -> Thank You!A Real Story of Efficiency
Steffen Frederiksen | [email protected] | www.ditaexchange.com