Developing a K-CDA Implementation Guide for Applying Health Information Exchange Service in South Korea (Preprint)

2020 ◽  
Author(s):  
Sung Won Jung ◽  
Sungchul Bae ◽  
Donghyeong Seong ◽  
Byoung-Kee Yi

BACKGROUND Through several years of the healthcare information exchange based on the HIE project, some problems were found in the CDA documents generated. OBJECTIVE To fix some problems, we developed the K-CDA Implementation Guide (K means S. Korea) that conforms to the HL7 CDA, and suits the domestic conditions regarding the healthcare information. METHODS We achieved by analyzing HIE guideline and the U.S. C-CDA, and comparing each item. The items that required further discussion were reviewed by the expert committee. Based on the reviews, the previously developed templates were revised. RESULTS A total of 35 CDA templates were developed: five document-level templates, fourteen section-level templates, and sixteen entry-level templates. The 28 value sets used in the templates have been improved and the OIDs for HIE have been redefined CONCLUSIONS The K-CDA IG allows management in the form of a template library based on the definition of the General K-Header and the structured templates. This enables the K-CDA IG to respond to the expansion of national HIE templates with flexibility. For the K-CDA IG, the CDA template in current use was incorporated to the greatest extent possible, to minimize the scope of modifications. It enables the national HIE and the HIE with countries abroad.

2021 ◽  
Vol 60 (S 02) ◽  
pp. e111-e119
Author(s):  
Linyi Li ◽  
Adela Grando ◽  
Abeed Sarker

Abstract Background Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors. Objectives The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets. Methods We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors. Results Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets—while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients. Conclusion The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.


2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 12-12
Author(s):  
Jeremy Warner ◽  
Kevin S. Hughes ◽  
John C. Krauss ◽  
Suzanne Maddux ◽  
Peter Paul Yu ◽  
...  

12 Background: Cancer care is by nature interdisciplinary and increasingly depends on seamless electronic transmission of clinical data. Health information exchange and semantic understanding are critical for improved outcomes, personalized medicine, comparative effectiveness research, and cost control. While there is a growing focus on this, sharing patient information remains difficult due to a lack of standardization and general incompatibility between electronic health record products. There is a need for well-designed, oncology-specific interoperability standards. Thus ASCO is developing standards to improve the quality and insight of cancer care. Methods: ASCO volunteers formed a Standards Work Group (Standards WG) in 2012, and ASCO engaged an independent consulting firm to perform the technical work. The Standards WG first developed an interoperable standard with broad application that would also be a foundation for future standards work. They adapted ASCO’s Breast Cancer Adjuvant Treatment Plan and Summary (Breast TPS), which was originally developed as a paper-based form. This adaptation required extensive work involving input from medical and surgical oncologists, ASCO staff, and the consultants. This preparatory work was vital to define and disambiguate clinical concepts. Some value sets in the original Breast TPS were replaced with National Cancer Institute value sets. Multiple oncology and standards stakeholders reviewed the draft to ensure accurate representation of the data and harmonization with related standards. Results: The standard was developed using the Health Level Seven International (HL7) Clinical Document Architecture, a widely used XML-based markup standard with national and international recognition. The draft Breast Cancer Adjuvant Treatment Plan and Summary Standard was successfully balloted through HL7 in May 2013 and will subsequently be published for trial use in late 2013. Conclusions: The Breast Cancer Adjuvant Treatment Plan and Summary Standard will improve quality by allowing providers to efficiently transmit clinical data with semantic meaning to health professionals, patients, quality improvement initiatives, and registries.


2021 ◽  
Vol 28 (1) ◽  
pp. e100241
Author(s):  
Job Nyangena ◽  
Rohini Rajgopal ◽  
Elizabeth Adhiambo Ombech ◽  
Enock Oloo ◽  
Humphrey Luchetu ◽  
...  

BackgroundThe use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS).ObjectiveTo determine the maturity readiness of the interoperability capacity of Kenya’s HIS.MethodsWe used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative’s Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health’s Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology.ResultsMost domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms.ConclusionNone of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.


2014 ◽  
Vol 33 (9) ◽  
pp. 1672-1679 ◽  
Author(s):  
Michael F. Furukawa ◽  
Jennifer King ◽  
Vaishali Patel ◽  
Chun-Ju Hsiao ◽  
Julia Adler-Milstein ◽  
...  

2010 ◽  
Vol 01 (01) ◽  
pp. 1-10 ◽  
Author(s):  
S. E. Ross ◽  
B. K. Mellis ◽  
B. L. Beaty ◽  
L. M. Schilling ◽  
A. J. Davidson ◽  
...  

SummaryObjective: Assess the interest in and preferences of ambulatory practitioners in HIE.Background: Health information exchange (HIE) may improve the quality and efficiency of care. Identifying the value proposition for smaller ambulatory practices may help those practices engage in HIE.Methods: Survey of primary care and specialist practitioners in the State of Colorado.Results: Clinical data were commonly (always [2%], often [29%] or sometimes [49%]) missing during clinic visits. Of 12 data types proposed as available through HIE, ten were considered “extremely useful” by most practitioners. “Clinical notes/consultation reports,” “diagnosis or problem lists,” and “hospital discharge summaries” were considered the three most useful data types. Interest in EKG reports, diagnosis/problem lists, childhood immunizations, and discharge summaries differed among ambulatory practitioner groups (primary care, obstetrics-gynecology, and internal medicine subspecialties).Conclusion: Practitioners express strong interest in most of the data types, but opinions differed by specialties on what types were most important. All providers felt that a system that provided all data types would be useful. These results support the potential benefit of HIE in ambulatory practices.


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