Beginning October 1998, data analysts began tracking the amount of time spent on all of their work. The original rationale for keeping track of time emanated from the need to provide better estimates of time spent on various funded grants and contracts. Along with the amount of time spent, data analysts kept track of work activities, the rationale for work, and the personnel requesting that work, within weekly reports that were subsequently stored in an Access Database. Time was tracked to the nearest 15 minutes. For the purposes of this report, we compiled the time and work-related descriptions from January 1, 1999, through July 31, 2002. We extracted the amount of time spent by analysts on research retrieval activities and categorized the request type. We also determined the requester's profession and primary academic department. We tried to classify work into mutually exclusive categories based on the primary reason for the request. However, there often were secondary aims of interest that could have had some overlap among categories. We classified work as clinical epidemiology when the primary aim was focused on determining the frequencies of multiple diseases, predicting disease or events, preventing disease or habits such as smoking, ascertaining prognosis, or assessing risk. Otherwise, work was classified under a specific disease category.
During the three-and-a-half-year study period, 47,559 hours of work were categorized. Training exercises and classes aimed at improving knowledge and skills involved 9% of data analysts' time and were included within the 47,559 hours. The proportions of time spent by data analysts on various activities appear in . As expected, the greatest proportions of time were spent retrieving (36%) and analyzing (25%) data from the RMRS. After the extraction and preliminary analysis of data, data analysts reported (verbally and in writing) their summary data to key investigators working on the studies and performed other administrative and collaborative activities. Approximately three fourths of a data analyst's effort was spent working alone to extract, manage, analyze, and help interpret study data. However, data analysts also worked in coordination with others and frequently communicated with one another on data retrieval, analysis, and programming issues.
Activities of data analysts on projects requested by various investigators from January 1, 1999, to July 31, 2002.
Investigators had varied backgrounds (). Physicians requested the majority of data from the RMRS. Administrators and pharmacists were closely tied for second followed by smaller proportions of retrievals requested by PhD researchers and nurses. As shown in , most of the investigators were from the Department of Medicine at the Indiana University School of Medicine (39%) or Regenstrief Institute (24%). Faculty members from Riley Children's Hospital have been the fastest growing group of requesters, largely owing to a recently established Pediatric Health Services Research Department. Although geriatricians are academically affiliated with the Department of General Internal Medicine and Geriatrics, we chose to distinguish studies of older adults (as was done for pediatricians).
Professions of investigators for whom data analysts extracted data from the Regenstrief Medical Records System from January 1, 1999, to July 31, 2002.
Academic affiliations of investigators requesting data from the Regenstrief Medical Records System (January 1, 1999, to July 31, 2002).
shows the classification of data analysts' work. Work related to specific diseases took 35.4% of total time. The largest proportion of work was conducted on guideline implementation (13.1% of total time), followed by work on drug-related studies at (12.2% of total time). Analyses within the realm of clinical epidemiology required 5.7% effort, whereas work involving clinical trials required 3.7%. It should be noted that continuing education/training (alone and as a group) was a critical activity involving 8.5% of total data analyst time.
Classification of Data Analyst Work during 3.5 Years
We were able to estimate benefits of the section using funded grant applications and the number of research papers that included data analyst support. From July 1, 2000, to June 30, 2002, the research support section received $600,000 direct funding from research grants and contracts for requested work. However, because many other factors play a role in grant and contract award decisions, determining the number of grant awards that are directly attributable to this research support is not realistically possible. However, it is clear that many data would not be readily available without the assistance of these data analysts.
During December 2002, we surveyed Regenstrief Institute investigators to determine the number of grants written requesting such support, the number of papers written using data analyst support, and the qualitative value investigators put on the services provided by Regenstrief Institute's data analysts. The investigators were told that their responses would be anonymous and used only in aggregate in this report. Of 18 investigators who had used data from the RMRS as provided by a data analyst, 15 investigators (83%) responded to the survey. We excluded from analysis the results of one new investigator who had not yet conducted an independent study. The 14 investigators worked with data analysts a mean (± SD) of 10.0 ± 7.2 years (range, 1 to 23 years) during which time investigators wrote 117 grant applications (mean, 8.4 ± 9.0; range, 0 to 33 grants) and 139 peer-reviewed papers (mean, 9.9 ± 9.3; range, 0 to 33 papers) that specifically included data extracted by a data analyst. Overall, one of every three research papers written by these investigators included data extracted from the RMRS by a Regenstrief Institute data analyst.
We asked investigators to give their qualitative impression of the value of the data analysts' work using a five-point scale (extremely valuable to my research, moderately valuable, helpful but not critical, not helpful at all, or harmful to my research). Of 13 investigators who felt that they had worked a sufficient amount of time with data analysts to provide their assessment, all rated the data analysts' services as extremely valuable to their research. These results suggest that the support provided by the data analysts has a high overall value to researchers in many of their grants and research publications.