CC BY-NC-ND 4.0 · Yearb Med Inform 2021; 30(01): 176-184
DOI: 10.1055/s-0041-1726503
Section 6: Knowledge Representation and Management
Survey

The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

Karin Verspoor
1   School of Computing Technologies, RMIT University, Melbourne VIC 3000 Australia
2   Centre for Digital Transformation of Health, The University of Melbourne, Melbourne VIC 3010 Australia
3   School of Computing and Information Systems, The University of Melbourne, Melbourne VIC 3010 Australia
› Author Affiliations

Summary

Objectives: We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases.

Methods and Results: We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system.

Conclusions: The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.



Publication History

Article published online:
03 September 2021

© 2021. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Friedman CP, Wong AK, Blumenthal D. Achieving a Nationwide Learning Health System. Science Translational Medicine 2010; 2: 57cm29-57cm29 DOI: 10.1126/scitranslmed.3001456.
  • 2 Grossman C, Powers B, McGinnis JM. Digital infrastructure for the learning health system: The foundation for continuous improvement in health and health care workshop series summary. National Academies Press Washington; DC: 2011: 311
  • 3 Rojek AM, Horby PW. Modernising epidemic science: enabling patient-centred research during epidemics. BMC Med. 2016; 14: 212 DOI: 10.1186/s12916-016-0760-x.
  • 4 Devadoss PR, Shan Ling P, Singh S. Managing knowledge integration in a national health-care crisis: lessons learned from combating SARS in Singapore. IEEE Transactions on Information Technology in Biomedicine 2005; 9: 266-75 DOI: 10.1109/TITB.2005.847160.
  • 5 Collins R, Bowman L, Landray M, Peto R. The Magic of Randomization versus the Myth of Real-World Evidence. New England Journal of Medicine 2020; 382: 674-8 DOI: 10.1056/NEJMsb1901642.
  • 6 Ewers M, Ioannidis JPA, Plesnila N. Access to data from clinical trials in the COVID-19 crisis: open, flexible, and time-sensitive. Journal of Clinical Epidemiology 2020; DOI: 10.1016/j.jclinepi.2020.10.008.
  • 7 Lee JJ, Haupt JP. Scientific globalism during a global crisis: research collaboration and open access publications on COVID-19. High Educ (Dordr) 2020; 1-18 DOI: 10.1007/s10734-020-00589-0.
  • 8 The RECOVERY Collaborative Group. Dexamethasone in Hospitalized Patients with Covid-19 ’ Preliminary Report. New England Journal of Medicine 2020; DOI: 10.1056/NEJMoa2021436.
  • 9 The RECOVERY Collaborative Group. Effect of Hydroxychloroquine in Hospitalized Patients with Covid-19. New England Journal of Medicine. 2020; 383: 2030-40 DOI: 10.1056/NEJMoa2022926.
  • 10 Horby PW, Mafham M, Bell JL, Linsell L, Staplin N, Emberson J. et al. Lopinavir-ritonavir in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. The Lancet 2020; 396: 1345-52 DOI: 10.1016/S0140-6736(20)32013-4.
  • 11 Pan H, Peto R, Karim QA, Alejandria M, Henao-Restrepo AM, García CH. et al. Repurposed antiviral drugs for COVID-19 -interim WHO SOLIDARITY trial results. medRxiv . 2020:2020.10.15.20209817 DOI: 10.1101/2020.10.15.20209817.
  • 12 Tikkinen KAO, Malekzadeh R, Schlegel M, Rutanen J, Glasziou P. COVID-19 clinical trials: learning from exceptions in the research chaos. Nature Medicine 2020; 26: 1671-2 DOI: 10.1038/s41591-020-1077-z.
  • 13 Glasziou PP, Sanders S, Hoffmann T. Waste in COVID-19 research. BMJ 2020; 369: m1847 DOI: 10.1136/bmj.m1847.
  • 14 Tovstiga N, Tovstiga G. COVID-19: a knowledge and learning perspective. Knowledge Management Research & Practice. 2020; 1-6 DOI: 10.1080/14778238.2020.1806749.
  • 15 Tendal B, Vogel JP, McDonald S, Norris S, Cumpston M, White H. et al. Weekly updates of national living evidence-based guidelines: Methods for the Australian living guidelines for care of people with COVID-19. J Clin Epidemiol 2020; 131: 11-21 DOI: 10.1016/j.jclinepi.2020.11.005.
  • 16 Lomotan EA, Meadows G, Michaels M, Michel JJ, Miller K. To Share is Human! Advancing Evidence into Practice through a National Repository of Interoperable Clinical Decision Support. Appl Clin Inform 2020; 11: 112-21 DOI: 10.1055/s-0040-1701253.
  • 17 Rochwerg B, Agoritsas T, Lamontagne F, Leo Y-S, Macdonald H, Agarwal A. et al. A living WHO guideline on drugs for covid-19. BMJ 2020; 370: m3379 DOI: 10.1136/bmj.m3379.
  • 18 Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J. et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020; 323: 1061-9 DOI: 10.1001/jama.2020.1585.
  • 19 Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020; 395: 497-506 DOI: 10.1016/S0140-6736(20)30183-5.
  • 20 Callahan A, Steinberg E, Fries JA, Gombar S, Patel B, Corbin CK. et al. Estimating the efficacy of symptom-based screening for COVID-19. npj Digital Medicine 2020; 3: 95 DOI: 10.1038/s41746-020-0300-0.
  • 21 Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger RP. et al. Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Comput Biol Med. 2020; 124: 103949 DOI: 10.1016/j.compbiomed.2020.103949.
  • 22 Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE. et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020; 584: 430-6 DOI: 10.1038/s41586-020-2521-4.
  • 23 Tartof SY, Qian L, Hong V, Wei R, Nadjafi RF, Fischer H. et al. Obesity and Mortality Among Patients Diagnosed With COVID-19: Results From an Integrated Health Care Organization. Annals of internal medicine 2020; 173: 773-81 DOI: 10.7326/M20-3742.
  • 24 Haimovich AD, Ravindra NG, Stoytchev S, Young HP, Wilson FP, van Dijk D. et al. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Annals of Emergency Medicine 2020; 76: 442-53 DOI: 10.1016/j.annemergmed.2020.07.022.
  • 25 Geleris J, Sun Y, Platt J, Zucker J, Baldwin M, Hripcsak G. et al. Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19. New England Journal of Medicine 2020; 382: 2411-8 DOI: 10.1056/NEJMoa2012410.
  • 26 Madhavan S, Bastarache L, Brown JS, Butte A, Dorr D, Embi PJ. et al. Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers. Journal of the American Medical Informatics Association 2020; DOI: 10.1093/jamia/ocaa287.
  • 27 Burn E, You SC, Sena AG, Kostka K, Abedtash H, Abrahão MTF. et al. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nature Communications 2020; 11: 5009 DOI: 10.1038/s41467-020-18849-z.
  • 28 Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L. et al. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. npj Digital Medicine 2020; 3: 109 DOI: 10.1038/s41746-020-00308-0.
  • 29 Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte Salles T. et al. Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study. medRxiv . 2020:2020.06.11.20125849. DOI: 10.1101/2020.06.11.20125849.
  • 30 Hripcsak G, Shang N, Peissig PL, Rasmussen LV, Liu C, Benoit B. et al. Facilitating phenotype transfer using a common data model. Journal of Biomedical Informatics 2019; 96: 103253 DOI: 10.1016/j.jbi.2019.103253.
  • 31 Lee J, Kim JH, Liu C, Hripcsak G, Ta C, Weng C. COHD-COVID: Columbia Open Health Data for COVID-19 Research. medRxiv . 2020:2020.11.17.20232983. DOI: 10.1101/2020.11.17.20232983.
  • 32 Deep A, Upadhyay G, du Pré P, Lillie J, Pan D, Mudalige N. et al. Acute Kidney Injury in Pediatric Inflammatory Multisystem Syndrome Temporally Associated With Severe Acute Respiratory Syndrome Coronavirus-2 Pandemic: Experience From PICUs Across United Kingdom. Crit Care Med. 2020; 48: 1809-18 DOI: 10.1097/ccm.0000000000004662.
  • 33 Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML. et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Internal Medicine 2020; 180: 1436-46 DOI: 10.1001/jamainternmed.2020.3596.
  • 34 Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X. et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Eng J Med. 2020; 382: 1708-20 DOI: 10.1056/NEJMoa2002032.
  • 35 Liang W, Liang H, Ou L, Chen B, Chen A, Li C. et al. Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19. JAMA Internal Medicine 2020; 180: 1081-9 DOI: 10.1001/jamainternmed.2020.2033.
  • 36 Vaid A, Somani S, Russak AJ, De Freitas JK, Chaudhry FF, Paranjpe I. et al. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. J Med Internet Res. 2020; 22: e24018 DOI: 10.2196/24018.
  • 37 Neuraz A, Lerner I, Digan W, Paris N, Tsopra R, Rogier A. et al. Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic. J Med Internet Res. 2020; 22: e20773 DOI: 10.2196/20773.
  • 38 Walkey AJ, Kumar VK, Harhay MO, Bolesta S, Bansal V, Gajic O. et al. The Viral Infection and Respiratory Illness Universal Study (VIRUS): An International Registry of Coronavirus 2019-Related Critical Illness. Crit Care Explor 2020; 2: e0113 DOI: 10.1097/cce.0000000000000113.
  • 39 Walkey AJ, Sheldrick RC, Kashyap R, Kumar VK, Boman K, Bolesta S. et al. Guiding Principles for the Conduct of Observational Critical Care Research for Coronavirus Disease 2019 Pandemics and Beyond: The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study Registry. Crit Care Med. 2020; 48: e1038-e44 DOI: 10.1097/ccm.0000000000004572.
  • 40 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)’a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of biomedical informatics 2009; 42: 377-81
  • 41 Verma SS, Chung WK, Dudek S, Williamson JL, Verma A, Robinson S. et al. Research on COVID-19 through patient-reported data: a survey for observational studies in the COVID-19 pandemic. Journal of Clinical and Translational Science 2020; 1-5 DOI: 10.1017/cts.2020.509.
  • 42 Research Data Alliance COVID-19 Working Group. RDA COVID-19 Recommendations and Guidelines on Data Sharing. Zenodo 2020
  • 43 Leaman R, Lu Z. editors. A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-22020 dec; Online: Association for Computational Linguistics.
  • 44 Vorisek CN, Klopfenstein SAI, Sass J, Lehne M, Schmidt CO, Thun S. Evaluating Suitability of SNOMED CT in Structured Searches for COVID-19 Studies. Stud Health Technol Inform. 2021; 281: 88-92 DOI: 10.3233/shti210126.
  • 45 Haendel MA, Chute CG, Gersing K. The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment. Journal of the American Medical Informatics Association 2020; DOI: 10.1093/jamia/ocaa196.
  • 46 Moore JH, Barnett I, Boland MR, Chen Y, Demiris G, Gonzalez-Hernandez G. et al. Ideas for how informaticians can get involved with COVID-19 research. BioData Mining 2020; 13: 3 DOI: 10.1186/s13040-020-00213-y.
  • 47 Scott IA, Coiera EW. Can AI help in the fight against COVID-19?. Medical Journal of Australia 2020; 213: 439-41.e2 DOI: 10.5694/mja2.50821.
  • 48 Ammirato S, Linzalone R, Felicetti AM. Knowledge management in pandemics. A critical literature review. Knowledge Management Research & Practice. 2020; 1-12 DOI: 10.1080/14778238.2020.1801364.
  • 49 Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z. et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering 2020; 01 DOI: 10.1109/RBME.2020.2987975.
  • 50 Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:200611988. 2020
  • 51 Reps JM, Kim C, Williams RD, Markus AF, Yang C, Salles TD. et al. Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets. medRxiv 2020:2020.06.15.20130328 DOI: 10.1101/2020.06.15.20130328.
  • 52 Barish M, Bolourani S, Lau LF, Shah S, Zanos TP. External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19. Nature Machine Intelligence 2020; DOI: 10.1038/s42256-020-00254-2.
  • 53 Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E. et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369: m1328 DOI: 10.1136/bmj.m1328.
  • 54 Wang L, Schnall J, Small A, Hubbard RA, Moore JH, Damrauer SM. et al. Case contamination in electronic health records based case-control studies. Biometrics 2020; 1-11 DOI: 10.1111/biom.13264.
  • 55 Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Nature 2020; 579: 193
  • 56 Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA 2020; 324: 782-93 DOI: 10.1001/jama.2020.12839.
  • 57 Boutron I, Chaimani A, Meerpohl JJ, Hróbjartsson A, Devane D, Rada G. et al. The COVID-NMA Project: Building an Evidence Ecosystem for the COVID-19 Pandemic. Ann Intern Med. 2020; 173: 1015-7 DOI: 10.7326/m20-5261.
  • 58 Rada G, Verdugo-Paiva F, Ávila C, Morel-Marambio M, Bravo-Jeria R, Pesce F. et al. Evidence synthesis relevant to COVID-19: a protocol for multiple systematic reviews and overviews of systematic reviews. Medwave 2020; 20: e7868 DOI: 10.5867/medwave.2020.03.7867.
  • 59 Verspoor K, Suster S, Otmakhova Y, Mendis S, Zhai Z, Fang B. et al. Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research. 2021; 559-64
  • 60 Wang LL, Lo K. Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Briefings in Bioinformatics 2020; DOI: 10.1093/bib/bbaa296.
  • 61 Verspoor K, Cohen KB, Dredze M, Ferrara E, May J, Munro R. et al., editors. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020. ACL2020; 2020: Association for Computational Linguistics
  • 62 Verspoor K, Cohen KB, Conway M, de Bruijn B, Dredze M, Mihalcea R. et al., editors Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. EMNLP2020; 2020: Association for Computational Linguistics
  • 63 Wang LL, Lo K, Chandrasekhar Y, Reas R, Yang J, Burdick D. et al. CORD-19: The COVID-19 Open Research Dataset. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020: Association for Computational Linguistics 2020
  • 64 Clark J, Glasziou P, Del Mar C, Bannach-Brown A, Stehlik P, Scott AM. A full systematic review was completed in 2 weeks using automation tools: a case study. Journal of Clinical Epidemiology 2020; 121: 81-90 DOI: 10.1016/j.jclinepi.2020.01.008.
  • 65 Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: early experience from the COVID-19 pandemic. Anaesthesia 2020; 75: 1579-82 DOI: 10.1111/anae.15057.
  • 66 Dorasamy M, Raman M, Kaliannan M. Knowledge management systems in support of disasters management: A two decade review. Technological Forecasting and Social Change 2013; 80: 1834-53 DOI: 10.1016/j.techfore.2012.12.008.
  • 67 Richesson RL, Bray BE, Dymek C, Greenes RA, McIntosh LD, Middleton B. et al. Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge’A movement to accelerate translation of knowledge into action. Learning Health Systems 2020; 4: e10222 DOI: 10.1002/lrh2.10222.
  • 68 Kawamoto K, Hongsermeier T, Wright A, Lewis J, Bell DS, Middleton B. Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts. J Am Med Inform Assoc. 2013; 20: 199-207 DOI: 10.1136/amiajnl-2012-000887.
  • 69 Ostaszewski M, Mazein A, Gillespie ME, Kuperstein I, Niarakis A, Hermjakob H. et al. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Scientific Data 2020; 7: 136 DOI: 10.1038/s41597-020-0477-8.
  • 70 COVID-19 Disease Map. 2020 [17 December 2020]. Available from: 10.17881/covid19-disease-map
  • 71 Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S. et al. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. Patterns DOI: 10.1016/j.patter.2020.100155.
  • 72 Wang Q, Li M, Wang X, Parulian N, Han G, Ma J. et al. COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation. arXiv:2007.00576; 2020
  • 73 Dunn AG, Bourgeois FT. Is it time for computable evidence synthesis?. Journal of the American Medical Informatics Association. 2020; 27: 972-5 DOI: 10.1093/jamia/ocaa035.
  • 74 Alper BS, Richardson JE, Lehmann HP, Subbian V. It is time for computable evidence synthesis: The COVID-19 Knowledge Accelerator initiative. Journal of the American Medical Informatics Association 2020; 27: 1338-9 DOI: 10.1093/jamia/ocaa114.
  • 75 Friedman C, Rubin J, Brown J, Buntin M, Corn M, Etheredge L. et al. Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. Journal of the American Medical Informatics Association 2014; 22: 43-50 DOI: 10.1136/amiajnl-2014-002977.
  • 76 Surma V, Kudchadkar S, Bembea M, Fackler JC. The Critical Care Learning Healthcare System: Time to Walk the Walk. Crit Care Med. 2020; 48: 1907-9 DOI: 10.1097/ccm.0000000000004700.
  • 77 Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. Hastings Cent Rep. 2013; Spec No:S16-27. DOI: 10.1002/hast.134.
  • 78 Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M. et al. Digital technologies in the public-health response to COVID-19. Nature Medicine 2020; 26: 1183-92 DOI: 10.1038/s41591-020-1011-4.
  • 79 Alper B, Mayer M, Shahin K, Richardson J, Schilling L, Tristan M. et al. Achieving evidence interoperability in the computer age: setting evidence on FHIR. BMJ Evidence-Based Medicine 2019; 24: A15-A DOI: 10.1136/bmjebm-2019-EBMLive.28.
  • 80 Iglesias N, Juarez JM, Campos M. Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines. Artif Intell Med. 2020; 103: 101741 DOI: 10.1016/j.artmed.2019.101741.
  • 81 Foraker RE, Lai AM, Kannampallil TG, Woeltje KF, Trolard AM, Payne PRO. Transmission dynamics: Data sharing in the COVID-19 era. Learn Health Syst. 2020; e10235 DOI: 10.1002/lrh2.10235.
  • 82 Schulz WL, Kvedar JC, Krumholz HM. Agile analytics to support rapid knowledge pipelines. npj Digital Medicine 2020; 3: 108 DOI: 10.1038/s41746-020-00309-z.
  • 83 Broadwell MM. Teaching For Learning (XVI.). The Gospel Guardian 1969; 1-3a
  • 84 Flynn AJ, Friedman CP, Boisvert P, Landis-Lewis Z, Lagoze C. The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems. Learning Health Systems 2018; 2: e10054 DOI: 10.1002/lrh2.10054.