Core Datasets and OCL Improving Collaboration in Developing High Quality M&E Systems - OpenConceptLab/ocl_user_wiki GitHub Wiki

####Core Dataset Engineering: How OCL Enables Core Dataset Development and Collaboration ######Core Datasets A core dataset represents the central data elements, quality measures and indicators used within a particular health service or domain. Core datasets are used to guide program design, development of job aids, specify reporting requirements, facilitate data analysis and quality improvement, and simplify information exchange. Core datasets already exist for many areas of health service delivery. The trend towards community-based health care and the increased utilization of mobile and electronic health systems among LMICs necessitates the development of core datasets for these new care models.

Core dataset engineering is the process of analyzing the relationships between sets of terms, measures and indicators to infer those that are most salient to a specified use case. The relationships are defined by mappings between concepts in the CIEL dictionary and one or more of the following international reference vocabularies: Systematized Nomenclature for Medicine: Clinical Terms (SNOMED CT); International Classification for Diseases version 10 (ICD-10-WHO); and Logical Observation Identifiers Names and Codes (LOINC).

OCL leverages these mappings to support core dataset development by providing various views of the relationships between two or more concept collections. NEEDS MORE, BROKEN DOWN- IS THIS GETTING TO TECH DOCUMENTATION AND I SHOULD CHILL OUT?

NEEDS A TRANSITION The CIEL dictionary is an “interface” dictionary, which means that it puts a semantic layer in between tools and the reference dictionaries, such as SNOMED CT, LOINC, and ICD-10-WHO. The interface layer offers many advantages to using the reference dictionaries directly that make it ideally suited for the core dataset engineering process just described. A few of these advantages are:

• Map a single interface concept to all of its related reference terms, each with the appropriate relationship type (e.g. “is a”, “narrower than”, “located on”, etc.) rather than having to maintain separate mappings for each term and synonym.
• Capture variations in how a term is phrased in a data collection instrument while retaining interoperability with related terms.
• Define terms for domains that are not represented in the reference vocabularies, especially community- based care and unique aspects to health care delivery in LMICs.