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.

Race and discourse analysis: Building a dialogue in health service research and health care settings

Using a framework for discourse analysis developed by Van Dijk, the investigator will pinpoint the pathological forms of discourse on race, defined as 'race talk' in three professional domains: health services research, public health provider organizations, and literature on multiculturalism. Attention will then turn to developing an analytical strategy for building more meaningful dialogue on race. The retrieval of potential resources for dialogue will be drawn from the third domain. Analysis will focus on enhancing the prospects of converting 'race talk' into dialogue. This will be accomplished by characterizing the normative preconditions as formal procedural requirements for dialogue and then supplementing these conditions with others related specifically to race. From here, the practical implications of combining procedural requirements and resources in each of the domains will be considered. Finally, the author will attempt to determine how these selected resources might be employed to transform 'race talk' in practice and lay the groundwork for a dialogue of understanding. ^

Formalizing a Conceptual Framework of Work Domain Knowledge

Background: The failure rate of health information systems is high, partially due to fragmented, incomplete, or incorrect identification and description of specific and critical domain requirements. In order to systematically transform the requirements of work into real information system, an explicit conceptual framework is essential to summarize the work requirements and guide system design. Recently, Butler, Zhang, and colleagues proposed a conceptual framework called Work Domain Ontology (WDO) to formally represent users’ work. This WDO approach has been successfully demonstrated in a real world design project on aircraft scheduling. However, as a top level conceptual framework, this WDO has not defined an explicit and well specified schema (WDOS) , and it does not have a generalizable and operationalized procedure that can be easily applied to develop WDO. Moreover, WDO has not been developed for any concrete healthcare domain. These limitations hinder the utility of WDO in real world information system in general and in health information system in particular. Objective: The objective of this research is to formalize the WDOS, operationalize a procedure to develop WDO, and evaluate WDO approach using Self-Nutrition Management (SNM) work domain. Method: Concept analysis was implemented to formalize WDOS. Focus group interview was conducted to capture concepts in SNM work domain. Ontology engineering methods were adopted to model SNM WDO. Part of the concepts under the primary goal “staying healthy” for SNM were selected and transformed into a semi-structured survey to evaluate the acceptance, explicitness, completeness, consistency, experience dependency of SNM WDO. Result: Four concepts, “goal, operation, object and constraint”, were identified and formally modeled in WDOS with definitions and attributes. 72 SNM WDO concepts under primary goal were selected and transformed into semi-structured survey questions. The evaluation indicated that the major concepts of SNM WDO were accepted by 41 overweight subjects. SNM WDO is generally independent of user domain experience but partially dependent on SNM application experience. 23 of 41 paired concepts had significant correlations. Two concepts were identified as ambiguous concepts. 8 extra concepts were recommended towards the completeness of SNM WDO. Conclusion: The preliminary WDOS is ready with an operationalized procedure. SNM WDO has been developed to guide future SNM application design. This research is an essential step towards Work-Centered Design (WCD).

The role of the conserved N -terminal domain of STAT3 in STAT3 dephosphorylation and nuclear export

Rapid redistribution of STAT subcellular localization is an essential feature of cytokine signaling. To elucidate the molecular basis of STAT3 function, which plays a critical role in controlling innate immune responses in vivo, we initiated studies to determine the mechanisms controlling STAT3 nuclear trafficking. We found that STAT3 is transported to the nucleus in the absence of cytokine treatment, as judged by indirect immunofluorescence studies in the presence of leptomycin B, an inhibitor of CRM1-dependent nuclear export, suggesting that the non-phosphorylated STAT3 protein contains a functional nuclear import signal. An isoform lacking the STAT3 N-terminal domain (Δ133STAT3) retains the ability to undergo constitutive nuclear localization, indicating that this region is not essential for cytokine-independent nuclear import. Δ133STAT3 is also transported to the nucleus following stimulation with interleukin-6 (IL-6). Interestingly, IL-6-dependent tyrosine phosphorylation of Δ133STAT3 appears to be prolonged and the nuclear export of the protein delayed in cells expressing endogenous STAT3, consistent with defective Δ133STAT3 dephosphorylation. Endogenous STAT3 does not promote the nuclear export of Δ133STAT3, although dimerization between endogenous Stat3 and Δ133STAT3 is detected readily. Thus, the STAT3 N-terminal domain is not required for dimerization with full-length STAT3, yet appears to play a role in proper export of Stat3 from the nucleus following cytokine stimulation. STAT3-deficient cells reconstituted with Δ133STAT3 show enhanced and prolonged Stat1 signaling in response to IL-6, suggesting that induction of the STAT3-dependent negative regulator SOCS3 is impaired. In fact, Δ133STAT3 fails to induce SOCS3 mRNA efficiently. These studies collectively indicate that the STAT3 N-terminal region may be important for IL-6-dependent target gene activation and nuclear dephosphorylation, while dispensable for nuclear import. STAT3 is an oncogene. STAT3 is constitutively activated in primary tumors of many types. Thus far, research in the design of STAT3 protein inhibitors has focused on the SH2 and DNA-binding domains of STAT3. Interference with these domains eliminates all signaling through STAT3. If the N-terminal domain is involved in tetramerization on a subset of target genes, inhibition of this region may lead to a more selective inhibition of some STAT3 functions while leaving others intact. ^