Analyzing Standards of Data

¶ … Standards

Technology Impact on Clinical Research and the Interrelationship of Uniform Data Format Standards

With increasing robustness of technology, clinical programmers began realizing inefficiencies in metadata and recreation from scratch each time, in addition to overlapping data elements over research works. Further, they saw the numerous inconsistent techniques for collection of specific, seemingly-simple data elements. The most straightforward example conveying the above challenge is: defining subject gender (Female or Male) in a clinical research work. At a swift glance, this appears to be a rather clearly defined data point. But, as is proven, even seemingly simple elements can prove challenging (McBride, 2014). An evolution is occurring in data management. By embracing emergent technologies once again, data managers are likely to be a lot more efficient than before. Under this article, only some aspects of innovation impact clinical managers of data are covered; however, it is evident that, over time, several more technologies will emerge and prove their impact (Etheredge, 2007).

Regulatory Controls and Legal and Ethical Frameworks

Clinical Data Management (CDM) has standards and guidelines, which have to be observed. As the pharmaceutical sector depends on electronic data for drug evaluation, they are required to observe good CDM practices and maintain electronic information capture standards. These electronic registers require 21 CFR- Part 11- compliance (CFR denotes Code of Federal Regulations). This regulation applies to electronic records, which are created, maintained, altered, transmitted, archived, or retrieved (Raptis, Mettler, Fischer, Patak, Lesurtel, Eshmuminov, De Rougemont, ... & Breitenstein, 2014), and calls for validated system application, for ensuring data accuracy, consistency and reliability, with use of time-stamped, secure, and computer-generated audit trails for independently recording time and date of actions and operator entries, which create, delete, or modify electronic records. SCDM (i.e., Society for CDM) publishes guidelines for good CDM practices (GCDMP), a record offering good practice standards for CDM. The guidelines were published initially in September of 2000, and have thereafter gone through numerous revisions. The latest version, followed at present, is that issued in July of 2009. GCDMP offers guidance on acceptable CDM practices in keeping with regulatory procedures. CDISC (Clinical Data Interchange Standards Consortium), a not-for-profit, multidisciplinary firm, has devised standards for supporting clinical metadata and research data acquisition, submission, exchange, and archival (Krishnankutty, Bellary, Kumar & Moodahadu, 2012).

System Parties Involved

Different responsibilities and roles are assigned to members of CDM teams. The minimum required educational qualification for CDM team members is a graduate degree in life sciences and computer applications knowledge. Preferably, medical coders must be graduates in the field of medicine. But graduates in the paramedical field are hired for the role of medical coders, as well. There are a few key roles vital to every CDM team; these are: A Data Manager, Medical Coder, Data Entry Associate, Quality Control Associate, Database Designer/Programmer, and Clinical Data Coordinator (Krishnankutty et al., 2012).

How Has the Growth of Technology and Data Standards Affected the Management of Clinical Data?

Forgoing repetitive error-prone tasks: The execution of identical tasks, repetitively, makes the process prone to mistakes, as well as being time-consuming and costly. Unfortunately, numerous everyday data managerial tasks necessitate manual creation and repeated checking of the same charts and reports. For instance, this may involve manual MS-Excel-based reviewing or filtering of listings (Cooper, 2009).

Information that isn't there: Finding out what information is absent is, at times, more crucial than information that is present. For instance, sometimes, missing and null data is needed and must be appropriately accounted for, while at other times, a missing case report form requires completion. A dozen other examples can be cited, but conventional tools haven't been adequately capable of addressing the problem (Leroux, McBride & Gibson, 2011).

Mobility: The advent of smartphones meant that nearly everyone is equipped 24/7 with a powerful personal computer. The world is witnessing several novel applications throughout the data management arena, which can dramatically improve data managers' jobs. Firstly, the ability to access reports and dashboards whenever they desire, accords immense power to data managers. They can conveniently and quickly see what's actionable, while also following up on assignments delegated to others. One fine example is greater real-time communication with field monitors, for following up (Cooper, 2009).

Collaboration: In the last decade, community sites enabling collaborated work by users are growing in popularity. This trend is seen increasing in the CDM arena too. Earlier, all studies and data systems possessed their corresponding query or discrepancy management system. Nowadays, systems are undergoing changes to facilitate collaboration, querying and discrepancy management across every relevant data system (including electronic data capture systems from diverse vendors) (McBride, 2014).

Big Data: This is the latest technological buzzword. In general, "big data" implies throwing large quantities of computer resources at extensive quantities of data (e.g., scrutinizing data terabytes in search of a proverbial needle). The emergence of this concept, for professionals in the CDM field, implies that they can now carry out previously non-feasible analyses (Leroux et al., 2011).

What are some of the Benefits of Using Data Standards? What are Some of the Challenges?