Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency

Abstract

BackgroundPast and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.ObjectivesThe study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.MethodsThe process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.ResultsAll DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals.ConclusionWith increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

Error evaluation in the laboratory testing process and laboratory information systems

“…System development highly contributed to error occurrence in the LIS and HIS use in terms of introduction of new technology, heterogeneous software, human-computer interaction, and communication issues within the system developer team. These factors are consistent with other findings [3] [5] [6] . These latent failures hinder the optimized potentials of the LIS.…”

“…Many errors identified in laboratory test results were caused by a complex, error prone, unreliable, and poorly designed LIS (4,5). These outcomes are aggravated when the LIS linked patient and test data to other units and institutions and involved data exchange because of complex inter system interaction (6). Errors were also attributed to human factors, including patient misidentification and an erroneous test request (7).…”

Digital Health Data Quality Issues: Systematic Review

“…In total, 30 subthemes were identified and grouped into 6 DQ dimensions: accuracy, consistency, completeness, contextual validity, accessibility, and currency ( Table 1 ; Multimedia Appendix 6 [ 8 - 12 , 14 - 16 , 18 - 22 , 31 - 62 , 67 , 69 , 71 , 72 , 76 , 79 - 168 ]). Consistency (164/227, 72.2%), completeness (137/227, 60.4%), and accuracy (123/227, 54.2%) were the main DQ dimensions.…”

“…Multiple definitions of DQ were discussed in the literature (Multimedia Appendix 5 [17,18,[20][21][22]31,54,[67][68][69][70][71][72][73][74][75][76][77]). There was no consensus on a single definition of DQ; however, an analysis of the definitions revealed two perspectives, which we labeled as the (1) context-agnostic perspective and (2) context-aware perspective.…”

Automating Case Reporting of Chlamydia and Gonorrhea to Public Health Authorities in Oregon Clinics

“…2 Electronic laboratory reporting (ELR) has been shown to improve the timeliness and accuracy of case confirmation and reporting when compared with traditional manual mechanisms (eg, paper and Web-based forms). [3][4][5] Since March 2010, ELR for CT and GC to state and local public health authorities has been legally mandated for most commercial, public health, and hospital laboratories in Oregon by the state. 6 However, ELRs are limited to the information available on laboratory test results and have typically incomplete demographics (patient race, sex, ethnicity, etc.)…”