Medical Research Data Management - Architectural Viewpoints - Part 3

In my previous blogs, I focussed on detailing the motivation and the strategy adopted to deliver a singular data repository.

The approach was to deliver a data repository that would satisfy the twin objective of

1. Evolving the organisation into a data-driven healthcare organisation.
2. Modernizing and integrating new platforms rather than replacing old ones.

Central to the success of the architectural design is the understanding and acumen required in modelling the research domain. It becomes critical that we understand how medical research is classified and managed into different themes and research speciality groups. 

Below diagram shows how the research data management process is associated with different research functions and speciality groups. Although not complete, I have tried to include those research services of the NHIR entity that was necessary to deliver an exemplar ( diabetes data product).

The singular repository ought to have the capability to enable the complex and intricate research data lifecycle. 

The course of action taken to meet the data required for research are as follows

1. Make it easy to access source data
2. Make data solutions fast to deploy and easy to manage for researchers
3. Make research tools easy to use
4. Make research results easy to consume and enhance

The above set of guidelines requires an agile and self-service approach to research data management.

One of the steps to achieving self-service research capability is to conceptually separate research data management into three different layers. 

1. Pre-Research Data Management
2. Peri-Research Data Management
3. Post-Research Data Management

Below diagram shows the processes involved in medical research and how different sub-processes collaborate to provide enhanced access to knowledge and also increase research capability.

The different stages of the research data management align with the different stages of the standard operating procedures of the research management entity.

Standard Data Analysis Process

The standard research data analysis captures data science activities into different stages of the data science workflow.  The research function carries out one or more of the advanced analytics services to produce datasets and eventually a research data product.

The research data product composition is detailed below. The research product is a deliverable consisting of multiple pieces of research. Each of the research will produce at least one study, data-level, metadata and analytical documentation.

In my next blog, I will be sharpening the data science workflow bit more by including the actors and their role. The viewpoints will help us understand how data is managed to enable and service different functions of research data management. 

References

https://www.nihr.ac.uk/02-documents/policy-and-standards/Faster-easier-clinical-research/Research-Support-Service/NIHR%20Research%20Support%20Services%20Framework%20May%202011.pdf

https://anesthesia.mcmaster.ca/docs/librariesprovider8/research/methodology/study-design-and-methodological-issues/types-of-study-in-medical-research.pdf?sfvrsn=6fa7ca18_2