These days, lots of healthcare and pharmaceutical companies face a wide variety of data management challenges, along with the opportunities required to lower down operational expenses and improvise efficiency and performance. On the business front, these are the few opportunities and issues. Just for an example – cross trial examinations could be done only and only if the variables use the same protocols across different domains, which is a rare case to discuss. The potential to arbitrate trials becomes tedious in the absence of an acceptable reconciliation layer linking similar kind of variables together in a well-defined and systematic way. The mere truth is that Upper Arm Blood Pressure and Arm Diastolic Blood Pressure are related but not very well-known. If the outcomes could be concluded by any how or compared with other trials at any level, firms and enterprises can save a huge amount of money like in dollars and on the other front can save expensive and duplicate trials. The technical challenges faced and must be addressed by effective and sound master data management solutions in healthcare and pharmaceutical companies are mentioned in below.
- Automating and streamlining the acquisition, specification, analysis and integration of clinical data.
- Rendering end to end support for clinical procedures, right from protocol planning to specifications to post product launch analysis.
- Timely, precise and effective integration and deployment of master data management data in an organization with inconsistent systems, processes and procedures.
- Ability to seamlessly blend data standards including Meta data regulations into the enterprise SOA (Service Oriented Architecture) layer.
- Creating and sustaining a state-of-art MDM architecture, which supports not only the future growth but also the landscape changes without essential changes, overhead and that all in a determined and persistent manner.
In a shell, considering all above mentioned requirements, an efficient and good master data management solution needs to address the following components to be used in different domains including life sciences, pharmaceutical and healthcare industries.
- A standard form of Meta data layer that address the entire life cycle of a clinical program. Without the defined standard elements and components, there would be no central standard layer or point of reference to map to. This layer will support the standard deterioration of observations as required, as an integral part of the standard defined.
- The Meta data standard developed should be linked and associated to both, internal as well as external standards to make an immediate translation in between source and target links through defined standards. There should be availability of automated processes to find out linkages from both, internal and external Meta data assets to the standards.
- A standard data layer connected with external data domains including CDISC domains to be used internally by the enterprises that can be mapped or deployed within the solutions.
- The apt SDTM and CDISC elements and their usage contexts developed into the core solution.