Browsing by Subject "Computerized"

Now showing 1 - 3 of 3
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    An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data
    (Oxford University Press, 2014-05) Dixon, Brian E.; Jabour, Abdulrahman M.; O’Kelly Phillips, Erin; Marrero, David G.; BioHealth Informatics, School of Informatics and Computing
    The aim of this study was to describe an integrated informatics approach to aggregating and displaying clinically relevant data that can identify problems with medication adherence and facilitate patient-provider communication about strategies to improve medication use. We developed a clinical dashboard within an electronic health record (EHR) system that uses data from three sources: the medical record, pharmacy claims, and a personal health record. The data are integrated to inform clinician-patient discussions about medication adherence. Whereas prior research on assessing patterns of medication adherence focused on a single approach using the EHR, pharmacy data, or patient-entered data, we present an approach that integrates multiple electronic data sources increasingly found in practice. Medication adherence is a complex challenge that requires patient and provider team input, necessitating an integrated approach using advanced EHR, clinical decision support, and patient-controlled technologies. Future research should focus on integrated strategies to provide patients and providers with the right combination of informatics tools to help them adequately address the challenge of adherence to complex medication therapies.
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    Natural language processing accurately categorizes findings from colonoscopy and pathology reports
    (Elsevier, 2013) Imler, Timothy D.; Morea, Justin; Kahi, Charles; Imperiale, Thomas F.; Medicine, School of Medicine
    Background & aims: Little is known about the ability of natural language processing (NLP) to extract meaningful information from free-text gastroenterology reports for secondary use. Methods: We randomly selected 500 linked colonoscopy and pathology reports from 10,798 nonsurveillance colonoscopies to train and test the NLP system. By using annotation by gastroenterologists as the reference standard, we assessed the accuracy of an open-source NLP engine that processed and extracted clinically relevant concepts. The primary outcome was the highest level of pathology. Secondary outcomes were location of the most advanced lesion, largest size of an adenoma removed, and number of adenomas removed. Results: The NLP system identified the highest level of pathology with 98% accuracy, compared with triplicate annotation by gastroenterologists (the standard). Accuracy values for location, size, and number were 97%, 96%, and 84%, respectively. Conclusions: The NLP can extract specific meaningful concepts with 98% accuracy. It might be developed as a method to further quantify specific quality metrics.
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    Real world performance of the 21st Century Cures Act population-level application programming interface
    (Oxford University Press, 2024) Jones, James R.; Gottlieb, Daniel; McMurry, Andrew J.; Atreja, Ashish; Desai, Pankaja M.; Dixon, Brian E.; Payne, Philip R. O.; Saldanha, Anil J.; Shankar, Prabhu; Solad, Yauheni; Wilcox, Adam B.; Ali, Momeena S.; Kang, Eugene; Martin, Andrew M.; Sprouse, Elizabeth; Taylor, David E.; Terry, Michael; Ignatov, Vladimir; Mandl, Kenneth D.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Objective: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. Materials and methods: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. Results: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. Discussion: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.