Browsing by Subject "Consolidation"

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    Consolidation of CDA-based documents from multiple sources : a modular approach
    (2016-09) Hosseini Asanjan, Seyed Masoud; Jones, Josette F.; Dixon, Brian E.; Vreeman, Daniel J.; Faiola, Anthony; Wu, Huanmei
    Physicians receive multiple CCDs for a single patient encompassing various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes the steps towards a system that integrates multiple CCDs into one consolidated document for viewing or processing patient-level data. Also, the impact of the system on healthcare providers’ perceived workload is evaluated. A modular system is developed to consolidate and de-duplicate CDA-based documents. The system is engineered to be scalable, extensible and open source. The system’s performance and output has evaluated first based on synthesized data and later based on real-world CCDs obtained from INPC database. The accuracy of the consolidation system along with the gaps in identification of the duplications were assessed. Finally, the impact of the system on healthcare providers’ workload is evaluated using NASA TLX tool. All of the synthesized CCDs were successfully consolidated, and no data were lost. The de-duplication accuracy was 100% based on synthesized data and the processing time for each document was 1.12 seconds. For real-world CCDs, our system de-duplicated 99.1% of the problems, 87.0% of allergies, and 91.7% of medications. Although the accuracy of the system is still very promising, however, there is a minor inaccuracy. Due to system improvements, the processing time for each document is reduced to average 0.38 seconds for each CCD. The result of NASA TLX evaluation shows that the system significantly decreases healthcare providers’ perceived workload. Also, it is observed that information reconciliation reduces the medical errors. The time for review of medical documents review time is significantly reduced after CCD consolidation. Given increasing adoption and use of Health Information Exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.
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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.