Medical Research Data Management - Architectural Viewpoints - Part 1

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This is a series of blogs about research data management which I have been working on for the past few years. I thought of sharing my experience, thinking it would be useful to show the features of Archemate, a Togaf based enterprise architecture modelling tool.

Although the team I work with use a different tool, I have used Archemate.  I would explain my approach to designing a medical research data management system within the enterprise architectural context using Archemate modelling tool.

The research carried out is solely focussed on healthcare data. I would like to mention, whenever I refer to research data management, I intend to mean as healthcare research data management.

Situation Analysis

Effective Medicinal Research Facilitation 

The public health service in the UK has developed a research-oriented approach to patient care to advance its care objectives.  It has invested public funds on different entities responsible to meet different outcome. The outcome below marks the scope for analysing the requirements for architecture design.
  1. Improved Partnership and Engagement with Different Research Organisations
  2. Improved Research Data Reuse
  3. Improved Research Quality Management
  4. Research Data Management System Which Provides Reliable Evidence 
The overall goal of each of the different entities is to focus on research capacity strengthening and in turn facilitate data management modernization which has an innovation-focused design.

The first step to designing with Archemate is to analyse the motivations to develop or change the existing architecture. The process of understanding the stakeholder motivation maps to the preliminary and the vision phase of the Togaf architectural development framework.  
The figure below sets the context to streamline the strategic thinking to deliver the research data management architecture.



Modernising Data Management 

The plan was not just to develop an advanced research platform but also modernise the existing data warehouse. The current information management system faces a multitude of challenges.
  • DW platform limitations in the current information management solution
  • Research limitations due to poor quality on integrating data from different systems. 
  • Design challenges  associated with modelling for research
  • Missing ancillary tools for data integration, MDM & DQM
The below diagram sets the context for analysing the motivation and the lays the path to strategic management of data for clinical operations and research.


Strategy

The analysis involves understanding the drivers for research data management solutions. The health research entity would want to promote research by focusing on facilitating fellowships and doctoral training. This can only be accomplished by research capacity strengthening, a multi-faceted, multi-dimensional approach to developing a research data management system that would facilitate research programmes. 

Research Data Management

Below is the strategic course of actions taken to deliver a research platform.

The strategic course of actions are separated based on the context it is implemented and it also helps with separation of concerns. The conceptual layers will help to focus on each area of interest ultimately strengthening research capacity within the public health service.
The conceptual areas of interest are
  1. Pre-research data management
  2. Peri-research data management
  3. Post-research data management

Modern Healthcare Single Repository Data Strategy

Central to developing the data strategy is data management modernization & innovation focused design.
Data strategy manages data as a valuable resource by organising and aligning the two main disciplines, data lifecycle and data processing with information management and research entities motivations.

After having analysed the motivation and the goals of each of the stakeholders, it became evident that the architecture needs to address the below set of concerns.


  1. Development of a platform, that handle seamless integration of varied and high volume data in batches and in real time.
  2. To design and build a data processing platform that facilitates rapid data exploration, rapid testing and continuous integration.
  3. Implement extended data management functions, leveraging metadata along the data lifecycle to enable self-service analytics and improve medical research collaboration.
  4. Design effective solutions to address the scalability problem of healthcare data privacy and security concerns.
As the data architecture evolves from its defined stage to a controlled and optimised stage, initial architectural design will focus on below set of few of the data management functions as a priority.


  1. Data storage platform strategy
  2. Extended data management involving metadata management, MDM & data quality within self-service and standard BI context.
  3. Data provisioning  - self-service and standard data integration and analytics 
  4. Data security & privacy

The diagram below shows the prioritised data management functions and there associated motivational elements.



The below diagram will give us the overview of capabilities required to deliver a single repository. The composition of the architectural design elements helps us in handling the challenges of clinical data and deliver enhanced access to knowledge thereby increasing research capability.

In my next part of the blog, I would focus on how the strategy is refined to help design the domain and technical architectures.d

References

https://tdwi.org/research/2016/03/best-practices-report-data-warehouse-modernization.aspx
https://support.sas.com/documentation/onlinedoc/qkb/27/108753_0417.pdf





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