Department of Family Medicine

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    Electronic Laboratory Data Quality and the Value of a Health Information Exchange to Support Public Health Reporting Processes
    (2011-10) Dixon, Brian E.; McGowan, Julie J; Grannis, Shaun J
    There is increasing interest in leveraging electronic health data across disparate sources for a variety of uses. A fallacy often held by data consumers is that clinical data quality is homogeneous across sources. We examined one attribute of data quality, completeness, in the context of electronic laboratory reporting of notifiable disease information. We evaluated 7.5 million laboratory reports from clinical information systems for their completeness with respect to data needed for public health reporting processes. We also examined the impact of health information exchange (HIE) enhancement methods that attempt to improve completeness. The laboratory data were heterogeneous in their completeness. Fields identifying the patient and test results were usually complete. Fields containing patient demographics, patient contact information, and provider contact information were suboptimal. Data processed by the HIE were often more complete, suggesting that HIEs can support improvements to existing public health reporting processes.
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    Measuring the impact of a health information exchange intervention on provider-based notifiable disease reporting using mixed methods: a study protocol
    (2013-10) Dixon, Brian E.; Grannis, Shaun J; Revere, Debra
    Background Health information exchange (HIE) is the electronic sharing of data and information between clinical care and public health entities. Previous research has shown that using HIE to electronically report laboratory results to public health can improve surveillance practice, yet there has been little utilization of HIE for improving provider-based disease reporting. This article describes a study protocol that uses mixed methods to evaluate an intervention to electronically pre-populate provider-based notifiable disease case reporting forms with clinical, laboratory and patient data available through an operational HIE. The evaluation seeks to: (1) identify barriers and facilitators to implementation, adoption and utilization of the intervention; (2) measure impacts on workflow, provider awareness, and end-user satisfaction; and (3) describe the contextual factors that impact the effectiveness of the intervention within heterogeneous clinical settings and the HIE. Methods/Design The intervention will be implemented over a staggered schedule in one of the largest and oldest HIE infrastructures in the U.S., the Indiana Network for Patient Care. Evaluation will be conducted utilizing a concurrent design mixed methods framework in which qualitative methods are embedded within the quantitative methods. Quantitative data will include reporting rates, timeliness and burden and report completeness and accuracy, analyzed using interrupted time-series and other pre-post comparisons. Qualitative data regarding pre-post provider perceptions of report completeness, accuracy, and timeliness, reporting burden, data quality, benefits, utility, adoption, utilization and impact on reporting workflow will be collected using semi-structured interviews and open-ended survey items. Data will be triangulated to find convergence or agreement by cross-validating results to produce a contextualized portrayal of the facilitators and barriers to implementation and use of the intervention. Discussion By applying mixed research methods and measuring context, facilitators and barriers, and individual, organizational and data quality factors that may impact adoption and utilization of the intervention, we will document whether and how the intervention streamlines provider-based manual reporting workflows, lowers barriers to reporting, increases data completeness, improves reporting timeliness and captures a greater portion of communicable disease burden in the community.
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    Development and Assessment of a Public Health Alert Delivered through a Community Health Information Exchange
    (2010-10) Gamache, Roland; Stevens, Kevin C; Merriwether, Rico; Dixon, Brian E.; Grannis, Shaun
    Timely communication of information to health care providers during a public health event can improve overall response to such events. However, current methods for sending information to providers are inefficient and costly. Local health departments have traditionally used labor-intensive, mail-based processes to send public health alerts to the provider community. This article describes a novel approach for delivering public health alerts to providers by leveraging an electronic clinical messaging system within the context of a health information exchange. Alerts included notifications related to the 2009 H1N1 flu epidemic, a syphilis outbreak, and local rabies exposure. We describe the process for sending electronic public health alerts and the estimated impact on efficiency and cost effectiveness.
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    Impact of Selective Mapping Strategies on Automated Laboratory Result Notification to Public Health Authorities
    (2012-11) Gamache, Roland E; Dixon, Brian E.; Grannis, Shaun; Vreeman, Daniel J
    Automated electronic laboratory reporting (ELR) for public health has many potential advantages, but requires mapping local laboratory test codes to a standard vocabulary such as LOINC. Mapping only the most frequently reported tests provides one way to prioritize the effort and mitigate the resource burden. We evaluated the implications of selective mapping on ELR for public health by comparing reportable conditions from an operational ELR system with the codes in the LOINC Top 2000. Laboratory result codes in the LOINC Top 2000 accounted for 65.3% of the reportable condition volume. However, by also including the 129 most frequent LOINC codes that identified reportable conditions in our system but were not present in the LOINC Top 2000, this set would cover 98% of the reportable condition volume. Our study highlights the ways that our approach to implementing vocabulary standards impacts secondary data uses such as public health reporting.
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    Why “What Data Are Necessary for This Project?” and Other Basic Questions are Important to Address in Public Health Informatics Practice and Research
    (2011-12) Dixon, Brian E.; Grannis, Shaun J
    Despite the likelihood of poor quality data flowing from clinical information systems to public health information systems, current policies and practices are pushing for the adoption and use of even greater numbers of electronic data feeds. However, using poor data can lead to poor decision-making outcomes in public health. Therefore public health informatics professionals need to assess, and periodically re-evaluate, the quality of electronic data and their sources. Unfortunately there is currently a paucity of tools and strategies in use across public health agencies. Our Center of Excellence in Public Health Informatics is working to develop and disseminate tools and strategies for supporting on-going assessment of data quality and solutions for overcoming data quality challenges. In this article, we outline the need for better data quality assessment and our approach to the development of new tools and strategies. In other words, public health informatics professionals need to ask questions about the electronic data received by public health agencies, and we hope to create tools and strategies to help informaticians ask questions that will lead to improved population health outcomes.
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    State and Local Health Agency Engagement in HIE: A Cross-Sectional Survey
    (2012) Dixon, Brian E.; Gamache, Roland E; Grannis, Shaun J
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    Towards Estimation of Electronic Laboratory Reporting Volumes in a Meaningful Use World
    (2012) Dixon, Brian E.; Gamache, Roland E; Grannis, Shaun J
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    Variation in Information Needs and Quality: Implications for Public Health Surveillance and Biomedical Informatics
    (2013-11) Dixon, Brian E.; Lai, Patrick T; Grannis, Shaun J
    Understanding variation among users’ information needs and the quality of information in an electronic system is important for informaticians to ensure data are fit-for-use in answering important questions in clinical and public health. To measure variation in satisfaction with currently reported data, as well as perceived importance and need with respect to completeness and timeliness, we surveyed epidemiologists and other public health professionals across multiple jurisdictions. We observed consensus for some data elements, such as county of residence, which respondents perceived as important and felt should always be reported. However information needs differed for many data elements, especially when comparing notifiable diseases such as chlamydia to seasonal (influenza) and chronic (diabetes) diseases. Given the trend towards greater volume and variety of data as inputs to surveillance systems, variation of information needs impacts system design and practice. Systems must be flexible and highly configurable to accommodate variation, and informaticians must measure and improve systems and business processes to accommodate for variation of both users and information.
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    Estimating Increased Electronic Laboratory Reporting Volumes for Meaningful Use: Implications for the Public Health Workforce
    (2014-02) Dixon, Brian E.; Gibson, P Joseph; Grannis, Shaun J
    Objective: To provide formulas for estimating notifiable disease reporting volume from ‘meaningful use’ electronic laboratory reporting (ELR). Methods: We analyzed two years of comprehensive ELR reporting data from 15 metropolitan hospitals and laboratories. Report volumes were divided by population counts to derive generalizable estimators. Results: Observed volume of notifiable disease reports in a metropolitan area were more than twice national averages. ELR volumes varied by institution type, bed count, and by the level of effort required of health department staff. Conclusions: Health departments may experience a significant increase in notifiable disease reporting following efforts to fulfill meaningful use requirements, resulting in increases in workload that may further strain public health resources. Volume estimators provide a method for predicting ELR transaction volumes, which may support administrative planning in health departments.
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    Using Information Entropy to Monitor Chief Complaint Characteristics and Quality
    (2013) Grannis, Shaun J; Dixon, Brian E.; Xia, Yuni; Wu, Jianmin
    As we enter the 'big medical data' era, a new core competency is to continuously monitor quality of data collected from electronic sources, including population surveillance data sources. We describe how entropy, a fundamental information measure, can help monitor the characteristics of chief complaints in an operational surveillance system.