Fundamentals of Implementing a National Clinical Registries Program.

A National registry program is a resource intensive initiative involving multiple stakeholders, multi-institutional/multi-role/multi-users collaborative effort, where various aspects starting from work culture, research culture, registry conceptualization, resource availability, data format, data storage/retrieval techniques, data sharing protocols, data/dataset standards, data quality etc. vary drastically between different institutions. The biggest challenge for a national program will be to map these aspects under a common umbrella to establish standards for operations/execution, policies and procedures, which means aligning the registry operations with the operative process of each institution at first, due to this only a handful initiatives are implemented with limited success, hence it is advisable to study such implementations in great details as a guideline to build a solid foundation for future national initiatives[1][2]. The idea goes around building a solid database for holding all clinical registries under a single repository, along with streamlining and generalizing the policies and procedures for any disease or medical device registry, in order to save infrastructure spending, streamlining, saving on management and operational costs and overheads.

Revisiting the Skills of a Healthcare Data Scientist as a Field Expert.

The buzz words 'Data Science' and 'Data Scientist' are trending high in this age of information. The boundaries are still undefined, the exact skill sets are unclear, and the job description is still murky. This is an attempt to identify some mandatory or desired skills based on what data science demands from a data scientist. A very generic job description for a data scientist is 'A person who can perform advanced analytics on the institutional data', this gives a very unclear picture to the decision maker to identify the right resources within their data science activity. Practically the data scientist should be the one who can understand and moreover be involved with the data life cycle starting from inception > collection > operation > extraction > observation > preparation > description > prediction > prescription > Archival. Each of these aspects of data has a science behind it. An old team 'Jack of all trades' briefly defines this job description. A good data scientist essentially needs to be a good programmer, a good business/system/data analyst, a good statistician, one who can seamlessly visualize data, and is empowered with a vision to use and apply the necessary tools, techniques and methodologies in a scientific and applicable realistic way. Healthcare/Research environment is a complicated vertical when it comes to data, hence having domain knowledge is almost critical, complying with aspects of data governance such as patient privacy, consent, ethics etc.

Introducing a Research Management System to Speed Up and Streamline Clinical Research Activities.

Clinical Research is a complicated process within a research institution or a tertiary care hospital, almost all research project proposal needs to get an institutional review board (IRB) approval before any research activity takes place. IRB approval involves various processes, in form of sub-committees through which the proposal is reviewed. It is of utmost importance to understand the complete functioning of an IRB, in order to automate the various processes using a management system. The Research office entity forms the central body managing the IRB functions. It provides all sorts of administrative support as far as guidelines, documentation, communication and co-ordination is concerned; hence the research office forms the administrative wing of the IRB. Further the IRB has several sub-committees such as the Ethics Committee, Basic Research committee and the Animal Care and Use Committee. Each committee has a chairperson and several members from different specialty to cover all the aspects of research. Each committee may have its own process/workflow of approval, but usually the process of each committee is somewhat similar to each other. Apart from these workflows process the things that needs to be digitized would include researcher's profile, pre-award and post-award management, publication management, graduate student management and research analytics for the organization.