Update background.Rmd

Moved Goals and notable challenges to the Development effort and made few editorial changes
OHDSI · Jan 15, 2024 · 05042bc · 05042bc
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diff --git a/rmd/background.Rmd b/rmd/background.Rmd
@@ -16,81 +16,9 @@ In a typical observational study, the definition of the study population (cohort
 
 Appropriate characterization of cancer requires details such as anatomical site, morphology, local penetration, affected lymph nodes, metastatic spread, biomarkers, and disease staging and grading. In typical observational data sources, this necessary level of detail is not regularly present. Patient results from diagnostic procedures are collected but may not be available within the given data source or what is collected cannot appropriately serve as a surrogate for the above attributes. Correct identification of cancer treatment regimens also tends to be more complex compared to other disease modalities within observational data. Most cancer treatments are administered in chemotherapy regimens with complex dosing and scheduling in multiple cycles and are often combined with targeted therapies, immunotherapies, surgery or radiotherapy. None of these attributes follow standard definition to be applied to observational data, as most regimens are personalized to the individual patient need, making a priori standardized definitions more complex. Additionally, clinically relevant information on disease, treatment and outcomes that appropriately reflects a patient's journey including information on the time of diagnosis, response to treatments, time to treatment failure, disease progression, recurrence and (disease-free and overall) survival requires data abstraction and is rarely available in the source data and has not been traditionally supported in OMOP CDM.
 
-The Oncology CDM Extension of the OMOP CDM aims to provide a foundation for representing cancer data at the levels of granularity and abstraction required to support observational cancer research.
+The Oncology Module of the OMOP CDM aims to provide a foundation for representing cancer data at the levels of granularity and abstraction required to support observational cancer research.
 
-The extension has been tested in EHR and Cancer Registry data against a number of typical use cases.
-
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-
-# **Goals**
-
-The overarching goals of the OHDSI Oncology Working Group:
-
-<br>
-
-### 1) Oncology research
-
--   Enabling
--   Conducting
--   Promoting
-
-### 2) Maturing oncology standards
-
--   Data model, ontologies and conventions
--   A shared, international oncology data standard
--   Support of observational, claims and curated data sources
-
-### 3) Community growth
-
--   Model adoption and growth of network
--   Data holders, developers, and subject matter experts
--   Research use cases and applications
-
-### 4) Transparency
-
--   Clear processes and mechanisms for collaboration
--   Thorough documentation of conventions and approaches
--   Open-source development and project management
-
-<br>
-
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-
-# **Notable Challenges**
-
-<br> 
-
-### 1) Oncology data in OMOP
-
--   OMOP typically a person-centric model
-    -   Oncology data often requires additional levels of detail
-    -   e.g., "Observations about observations"
--   Novel entity relationships in source data
-
-
-<br>
-
-
-
-### 2) Source data representations
-
--   Differing granularity and detail between data sources
--   Goal of maximum accommodation while maintaing FAIR principles
--   Sources include observational, claims and curated data
-    -   Sources often overlap and can provide complementary data for same patient population
-
-<br>
-
-
-<figure>
-
-<img src="https://user-images.githubusercontent.com/57408355/76053847-9c1c5380-5f3c-11ea-8ac2-4efbcc4ee66e.png" alt="Condition Map"/>
-
-<figcaption>Diagram illustrating condition modifier representation</figcaption>
-
-</figure>
-
-<br>
+The Oncology Module has been tested in EHR and Cancer Registry data against a number of typical use cases.
 
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