Browsing by Subject "Health Information Exchange"

Now showing 1 - 8 of 8
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    An Evaluation of Two Methods for Generating Synthetic HL7 Segments Reflecting Real-World Health Information Exchange Transactions
    (American Medical Informatics Association, 2014) Mwogi, Thomas S.; Biondich, Paul G.; Grannis, Shaun J.; Department of Pediatrics, IU School of Medicine
    Motivated by the need for readily available data for testing an open-source health information exchange platform, we developed and evaluated two methods for generating synthetic messages. The methods used HL7 version 2 messages obtained from the Indiana Network for Patient Care. Data from both methods were analyzed to assess how effectively the output reflected original 'real-world' data. The Markov Chain method (MCM) used an algorithm based on transitional probability matrix while the Music Box model (MBM) randomly selected messages of particular trigger type from the original data to generate new messages. The MBM was faster, generated shorter messages and exhibited less variation in message length. The MCM required more computational power, generated longer messages with more message length variability. Both methods exhibited adequate coverage, producing a high proportion of messages consistent with original messages. Both methods yielded similar rates of valid messages.
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    Event Notification in Support of Population Health: The Promise and Challenges from a Randomized Controlled Trial
    (IOS Press, 2017) Dixon, Brian E.; Boockvar, Kenneth S.; Epidemiology, School of Public Health
    Event notifications are real-time, electronic alerts that have the promise of improving population health by exchanging critical information to a patient's extended care team. In a trial of event noficiations in U.S. Veterans Affairs facilities, we seek to understand the impact of notifications on health care utilization within 30 and 90-days. Lessons from the trial have implications beyond the evidence by informing strategies to develop and implement event notifications in other health systems.
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    Generalization of Machine Learning Approaches to Identify Notifiable Diseases Reported from a Statewide Health Information Exchange
    (MEDINFO Conference proceedings, 2019-08-25) Dexter, Gregory; Kasthurirathne, Suranga; Dixon, Brian E.; Grannis, Shaun
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    Health information exchange use during dental visits
    (American Medical Informatics Association, 2020) Taylor, Heather; Apathy, Nate; Vest, Joshua R.
    Dental and medical providers require similar patient demographic and clinical information for the management of a mutual patient. Despite an overlap in information needs, medical and dental data are created and stored in multiple records and locations. Electronic health information exchange (HIE) bridge gaps in health data spread across various providers. Enabling exchange via query-based HIE may provide critical information at the point of care during a dental visit. The purpose of this study is to characterize query-based HIE use during dental visits at two Federally Qualified Health Centers (FQHCs) that provided on-site dental services. First, we determine the proportion of dental visits for which providers accessed the HIE. Next, site, patient and visit characteristics associated with query-based HIE use during dental visits are examined. Last, among dental visits with HIE use, the aspects of the HIE that are accessed most frequently are described. HIE use was low (0.17%) during dental visits, however our findings from this study extend the body of research examining HIE use by studying a less explored area of the care continuum.
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    Impact of document consolidation on healthcare providers’ perceived workload and information reconciliation tasks: a mixed methods study
    (Oxford University Press, 2019-02) Hosseini, Masoud; Faiola, Anthony; Jones, Josette; Vreeman, Daniel J.; Wu, Huanmei; Dixon, Brian E.; Medicine, School of Medicine
    Background Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive “outside information” about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers’ impression of the system and the challenges faced when reconciling information in practice. Results While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers’ task complexity and workload.
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    THE PERCEIVED AND REAL VALUE OF HEALTH INFORMATION EXCHANGE IN PUBLIC HEALTH SURVEILLANCE
    (2011-08-22) Dixon, Brian Edward; Jones, Josette F.; McGowan, Julie J.; Grannis, Shaun J.; Gamache, Roland E.
    Public health agencies protect the health and safety of populations. A key function of public health agencies is surveillance or the ongoing, systematic collection, analysis, interpretation, and dissemination of data about health-related events. Recent public health events, such as the H1N1 outbreak, have triggered increased funding for and attention towards the improvement and sustainability of public health agencies’ capacity for surveillance activities. For example, provisions in the final U.S. Centers for Medicare and Medicaid Services (CMS) “meaningful use” criteria ask that physicians and hospitals report surveillance data to public health agencies using electronic laboratory reporting (ELR) and syndromic surveillance functionalities within electronic health record (EHR) systems. Health information exchange (HIE), organized exchange of clinical and financial health data among a network of trusted entities, may be a path towards achieving meaningful use and enhancing the nation’s public health surveillance infrastructure. Yet the evidence on the value of HIE, especially in the context of public health surveillance, is sparse. In this research, the value of HIE to the process of public health surveillance is explored. Specifically, the study describes the real and perceived completeness and usefulness of HIE in public health surveillance activities. To explore the real value of HIE, the study examined ELR data from two states, comparing raw, unedited data sent from hospitals and laboratories to data enhanced by an HIE. To explore the perceived value of HIE, the study examined public health, infection control, and HIE professionals’ perceptions of public health surveillance data and information flows, comparing traditional flows to HIE-enabled ones. Together these methods, along with the existing literature, triangulate the value that HIE does and can provide public health surveillance processes. The study further describes remaining gaps that future research and development projects should explore. The data collected in the study show that public health surveillance activities vary dramatically, encompassing a wide range of paper and electronic methods for receiving and analyzing population health trends. Few public health agencies currently utilize HIE-enabled processes for performing surveillance activities, relying instead on direct reporting of information from hospitals, physicians, and laboratories. Generally HIE is perceived well among public health and infection control professionals, and many of these professionals feel that HIE can improve surveillance methods and population health. Human and financial resource constraints prevent additional public health agencies from participating in burgeoning HIE initiatives. For those agencies that do participate, real value is being added by HIEs. Specifically, HIEs are improving the completeness and semantic interoperability of ELR messages sent from clinical information systems. New investments, policies, and approaches will be necessary to increase public health utilization of HIEs while improving HIEs’ capacity to deliver greater value to public health surveillance processes.
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    Predicting COVID-19–Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study
    (JMIR, 2021-11) Kasturi, Suranga N.; Park, Jeremy; Wild, David; Khan, Babar; Haggstrom, David A.; Grannis, Shaun; Pediatrics, School of Medicine
    BACKGROUND: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health-driven decision-making by developing robust analytical models that predict COVID-19-related health care resource utilization across patients served by Indiana's statewide Health Information Exchange. METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). RESULTS: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). CONCLUSIONS: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.
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    Short-Term Medical Costs of a VHA Health Information Exchange: A CHEERS-Compliant Article.
    (Wolters Kluwer Health, 2016-01) French, Dustin D.; Dixon, Brian E.; Perkins, Susan M.; Myers, Laura J.; Weiner, Michael; Zillich, Allan J.; Haggstrom, David A.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    The Virtual Lifetime Electronic Record (VLER) Health program provides the Veterans Health Administration (VHA) a framework whereby VHA providers can access the veterans’ electronic health record information to coordinate healthcare across multiple sites of care. As an early adopter of VLER, the Indianapolis VHA and Regenstrief Institute implemented a regional demonstration program involving bi-directional health information exchange (HIE) between VHA and non-VHA providers.The aim of the study is to determine whether implementation of VLER HIE reduces 1 year VHA medical costs.A cohort evaluation with a concurrent control group compared VHA healthcare costs using propensity score adjustment. A CHEERs compliant checklist was used to conduct the cost evaluation.Patients were enrolled in the VLER program onsite at the Indianapolis VHA in outpatient clinics or through the release-of-information office.VHA cost data (in 2014 dollars) were obtained for both enrolled and nonenrolled (control) patients for 1 year prior to, and 1 year after, the index date of patient enrollment.There were 6104 patients enrolled in VLER and 45,700 patients in the control group. The annual adjusted total cost difference per patient was associated with a higher cost for VLER enrollees $1152 (95% CI: $807–1433) (P < 0.01) (in 2014 dollars) than VLER nonenrollees.Short-term evaluation of this demonstration project did not show immediate reductions in healthcare cost as might be expected if HIE decreased redundant medical tests and treatments. Cost reductions from shared health information may be realized with longer time horizons.