Data Management Policies and Issues with Life Science Data

Data management and sharing are relatively new concepts in the health and life sciences fields. This presentation will cover some basic policies as well as the impediments to data sharing unique to health and life sciences data.

Overview of Data Management Planning Tools

These Data Management Plans are more comprehensive and complex than in the past. Libraries around the nation are trying to put together tools to help researchers write plans that conform to the new requirements. This session will look at some of these tools.

A MANAGEMENT INFORMATION SYSTEM AND EVALUATION SCHEME FOR THE UNIVERSITY OF TEXAS EMPLOYEE ASSISTANCE PROGRAMS

Early Employee Assistance Programs (EAPs) had their origin in humanitarian motives, and there was little concern for their cost/benefit ratios; however, as some programs began accumulating data and analyzing it over time, even with single variables such as absenteeism, it became apparent that the humanitarian reasons for a program could be reinforced by cost savings particularly when the existence of the program was subject to justification.^ Today there is general agreement that cost/benefit analyses of EAPs are desirable, but the specific models for such analyses, particularly those making use of sophisticated but simple computer based data management systems, are few.^ The purpose of this research and development project was to develop a method, a design, and a prototype for gathering managing and presenting information about EAPS. This scheme provides information retrieval and analyses relevant to such aspects of EAP operations as: (1) EAP personnel activities, (2) Supervisory training effectiveness, (3) Client population demographics, (4) Assessment and Referral Effectiveness, (5) Treatment network efficacy, (6) Economic worth of the EAP.^ This scheme has been implemented and made operational at The University of Texas Employee Assistance Programs for more than three years.^ Application of the scheme in the various programs has defined certain variables which remained necessary in all programs. Depending on the degree of aggressiveness for data acquisition maintained by program personnel, other program specific variables are also defined. ^

Standardization of data collection for the Lifetime Surveillance of Astronaut Health (LSAH)

Problems due to the lack of data standardization and data management have lead to work inefficiencies for the staff working with the vision data for the Lifetime Surveillance of Astronaut Health. Data has been collected over 50 years in a variety of manners and then entered into a software. The lack of communication between the electronic health record (EHR) form designer, epidemiologists, and optometrists has led to some level to confusion on the capability of the EHR system and how its forms can be designed to fit all the needs of the relevant parties. EHR form customizations or form redesigns were found to be critical for using NASA's EHR system in the most beneficial way for its patients, optometrists, and epidemiologists. In order to implement a protocol, data being collected was examined to find the differences in data collection methods. Changes were implemented through the establishment of a process improvement team (PIT). Based on the findings of the PIT, suggestions have been made to improve the current EHR system. If the suggestions are implemented correctly, this will not only improve efficiency of the staff at NASA and its contractors, but set guidelines for changes in other forms such as the vision exam forms. Because NASA is at the forefront of such research and health surveillance the impact of this management change could have a drastic improvement on the collection of and adaptability of the EHR. Accurate data collection from this 50+ year study is ongoing and is going to help current and future generations understand the implications of space flight on human health. It is imperative that the vast amount of information is documented correctly.^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Can prospective usability evaluation predict data errors?

Increasing amounts of clinical research data are collected by manual data entry into electronic source systems and directly from research subjects. For this manual entered source data, common methods of data cleaning such as post-entry identification and resolution of discrepancies and double data entry are not feasible. However data accuracy rates achieved without these mechanisms may be higher than desired for a particular research use. We evaluated a heuristic usability method for utility as a tool to independently and prospectively identify data collection form questions associated with data errors. The method evaluated had a promising sensitivity of 64% and a specificity of 67%. The method was used as described in the literature for usability with no further adaptations or specialization for predicting data errors. We conclude that usability evaluation methodology should be further investigated for use in data quality assurance.

A systematic review of the methods used to assess race and racial disparities in uterine fibroid research

Context: Black women are reported to have a higher prevalence of uterine fibroids, and a threefold higher incidence rate and relative risk for clinical uterine fibroid development as compared to women of other races. Uterine fibroid research has reported that black women experience greater uterine fibroid morbidity and disproportionate uterine fibroid disease burden. With increased interest in understanding uterine fibroid development, and race being a critical component of uterine fibroid assessment, it is imperative that the methods used to determine the race of research participants is defined and the operational definition of the use of race as a variable is reported for methodological guidance, and to enable the research community to compare statistical data and replicate studies. ^ Objectives: To systematically review and evaluate the methods used to assess race and racial disparities in uterine fibroid research. ^ Data Sources: Databases searched for this review include: OVID Medline, NML PubMed, Ebscohost Cumulative Index to Nursing and Allied Health Plus with Full Text, and Elsevier Scopus. ^ Review Methods: Articles published in English were retrieved from data sources between January 2011 and March 2011. Broad search terms, uterine fibroids and race, were employed to retrieve a comprehensive list of citations for review screening. The initial database yield included 947 articles, after duplicate extraction 485 articles remained. In addition, 771 bibliographic citations were reviewed to identify additional articles not found through the primary database search, of which 17 new articles were included. In the first screening, 502 titles and abstracts were screened against eligibility questions to determine citations of exclusion and to retrieve full text articles for review. In the second screening, 197 full texted articles were screened against eligibility questions to determine whether or not they met full inclusion/exclusion criteria. ^ Results: 100 articles met inclusion criteria and were used in the results of this systematic review. The evidence suggested that black women have a higher prevalence of uterine fibroids when compared to white women. None of the 14 studies reporting data on prevalence reported an operational definition or conceptual framework for the use of race. There were a limited number of studies reporting on the prevalence of risk factors among racial subgroups. Of the 3 studies, 2 studies reported prevalence of risk factors lower for black women than other races, which was contrary to hypothesis. And, of the three studies reporting on prevalence of risk factors among racial subgroups, none of them reported a conceptual framework for the use of race. ^ Conclusion: In the 100 uterine fibroid studies included in this review over half, 66%, reported a specific objective to assess and recruit study participants based upon their race and/or ethnicity, but most, 51%, failed to report a method of determining the actual race of the participants, and far fewer, 4% (only four South American studies), reported a conceptual framework and/or operational definition of race as a variable. However, most, 95%, of all studies reported race-based health outcomes. The inadequate methodological guidance on the use of race in uterine fibroid studies, purporting to assess race and racial disparities, may be a primary reason that uterine fibroid research continues to report racial disparities, but fails to understand the high prevalence and increased exposures among African-American women. A standardized method of assessing race throughout uterine fibroid research would appear to be helpful in elucidating what race is actually measuring, and the risk of exposures for that measurement. ^