A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

Background COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

[1]  S. Lindstrom,et al.  First Case of 2019 Novel Coronavirus in the United States , 2020, The New England journal of medicine.

[2]  Suzanne Bakken,et al.  Informatics is a critical strategy in combating the COVID-19 pandemic , 2020, J. Am. Medical Informatics Assoc..

[3]  Yunpeng Ji,et al.  Potential association between COVID-19 mortality and health-care resource availability , 2020, The Lancet Global Health.

[4]  Dinesh A. Mirchandani,et al.  How hospitals in mainland China responded to the outbreak of COVID-19 using information technology–enabled services: An analysis of hospital news webpages , 2020, J. Am. Medical Informatics Assoc..

[5]  K. Braun,et al.  Fathers' thoughts on breastfeeding and implications for a theory-based intervention. , 2012, Journal of obstetric, gynecologic, and neonatal nursing : JOGNN.

[6]  C. Yen,et al.  COVID-19-Related Information Sources and the Relationship With Confidence in People Coping with COVID-19: Facebook Survey Study in Taiwan , 2020, Journal of medical Internet research.

[7]  Shan Nan,et al.  Development of an openEHR Template for COVID-19 Based on Clinical Guidelines , 2020, Journal of medical Internet research.

[8]  Arie Hasman,et al.  A parallel guideline development and formalization strategy to improve the quality of clinical practice guidelines , 2009, Int. J. Medical Informatics.

[9]  George Michel,et al.  GEM at 10: a decade's experience with the Guideline Elements Model. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[10]  Qiangsheng Huang,et al.  Mathematical Modeling of COVID-19 Control and Prevention Based on Immigration Population Data in China: Model Development and Validation , 2020, JMIR Public Health and Surveillance.

[11]  Pieter Van Gorp,et al.  Towards Generic MDE Support for Extracting Purpose-Specific Healthcare Models from Annotated, Unstructured Texts , 2012, FHIES.

[12]  Marlene Millen,et al.  Rapid response to COVID-19: health informatics support for outbreak management in an academic health system , 2020, J. Am. Medical Informatics Assoc..

[13]  Jingwen Zhang,et al.  Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study , 2020, Journal of Medical Internet Research.

[14]  Jeffrey Ferranti,et al.  Telehealth transformation: COVID-19 and the rise of virtual care , 2020, J. Am. Medical Informatics Assoc..

[15]  Nadim Anani,et al.  Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language , 2014, BMC Medical Informatics and Decision Making.

[16]  Michael Marschollek,et al.  An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR , 2018, Artif. Intell. Medicine.

[17]  Uzay Kaymak,et al.  Design and implementation of a platform for configuring clinical dynamic safety checklist applications , 2018, Frontiers of Information Technology & Electronic Engineering.

[18]  Stephane M Meystre,et al.  An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report , 2020, J. Am. Medical Informatics Assoc..

[19]  D. Rajgor,et al.  The many estimates of the COVID-19 case fatality rate , 2020, The Lancet Infectious Diseases.

[20]  Ralph Gonzales,et al.  Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19 , 2020, J. Am. Medical Informatics Assoc..

[21]  Tonya Hongsermeier,et al.  A study of diverse clinical decision support rule authoring environments and requirements for integration , 2012, BMC Medical Informatics and Decision Making.

[22]  Stefan Störk,et al.  Translating openEHR Models to FHIR , 2020, MIE.

[23]  C. Zheng,et al.  Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) , 2020 .

[24]  Alessandro Verde,et al.  Clarification of Misleading Perceptions of COVID-19 Fatality and Testing Rates in Italy: Data Analysis , 2020, Journal of Medical Internet Research.

[25]  Jingwen Zhang,et al.  Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China. , 2020, Journal of medical Internet research.

[26]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[27]  Huilan Xu,et al.  Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study (Preprint) , 2020 .

[28]  Akshaya Srikanth Bhagavathula,et al.  Knowledge and Perceptions of COVID-19 Among Health Care Workers: Cross-Sectional Study , 2020, JMIR Public Health and Surveillance.

[29]  Yaqing Fang,et al.  Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis , 2020, Journal of medical virology.

[30]  R. Walls,et al.  Supporting the Health Care Workforce During the COVID-19 Global Epidemic. , 2020, JAMA.