Literature DB >> 32527863

Digital crowdsourcing: unleashing its power in rheumatology.

Martin Krusche1, Gerd R Burmester2, Johannes Knitza3.   

Abstract

The COVID-19 pandemic forces the whole rheumatic and musculoskeletal diseases community to reassemble established treatment and research standards. Digital crowdsourcing is a key tool in this pandemic to create and distil desperately needed clinical evidence and exchange of knowledge for patients and physicians alike. This viewpoint explains the concept of digital crowdsourcing and discusses examples and opportunities in rheumatology. First experiences of digital crowdsourcing in rheumatology show transparent, accessible, accelerated research results empowering patients and rheumatologists. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  health services research; outcome and process assessment, health care; outcome assessment, health care; quality indicators, health care

Mesh:

Year:  2020        PMID: 32527863      PMCID: PMC7456558          DOI: 10.1136/annrheumdis-2020-217697

Source DB:  PubMed          Journal:  Ann Rheum Dis        ISSN: 0003-4967            Impact factor:   19.103


‘Scientia potestas est—Knowledge is power’—This quote, which is linked to Sir Francis Bacon (1561–1626), is particularly true in medicine. It is the knowledge about the patient and his disease that empowers the physician to provide the best possible care. An economic perspective refers to the information superiority of the physician in relation to the patient as the theory of ‘asymmetric information’, leading to a competitive advantage. Modern medicine naturally leads to a higher level of specialisation and, thus, an increase in asymmetric information. The global COVID-19 pandemic reduced information asymmetry to almost zero and this scenario was entirely new for patients and physicians alike. There are a lack of data concerning COVID-19 in general, the influence of immunosuppression on its course, the interaction with rheumatic diseases and therapeutic use of immunosuppressive drugs (eg, hydroxychloroquine, anti-interleukin (IL)-1 or anti-IL-6 medication). Digital crowdsourcing promises to clear the mist of current nescience. Crowdsourcing uses the power of many, using the collective wisdom and resources of the crowd, to complete human intelligence tasks.1 It is not a new concept. Historically, crowdsourcing has often been used in competitions for problem solving. It originated in England in 1714, where the British government proposed £20 000 to anyone who could find a solution for calculating the longitudinal position of a ship.2 Digitisation, the internet, global networks and simple and instant data transfer have amplified the idea into a game-changing concept in many scientific fields.

COVID-19 Global Rheumatology Alliance

A significant example of successful digital crowdsourcing in rheumatology is the ‘COVID-19 Global Rheumatology Alliance’ (https://rheum-covid.org). The international registry collects information pertinent to COVID-19 infection in patients with rheumatic and musculoskeletal diseases (RMD). The registry was set up in record time and is supported by virtually all international professional and patient organisations in the field of rheumatology. Rheumatologists and patients from around the globe are working together to collect data to improve the treatment of patients with RMD with COVID-19. So far, 526 patients have already been enrolled (status: 16 April 2020), and the first important results have been published.3–6 However, even more remarkable is that the initiative and results are not limited to medical professionals. Crowdsourcing directly involved patients with RMD and results are published constantly, accessible to everyone at any time via the homepage and Twitter feeds. So far, the response from patients was overwhelming. To date, over 9894 patients (status: 18 April 2020) have participated in the survey, actively joining the effort to create and distil clinical knowledge.

Open-access preprint platforms

Even before the COVID-19 pandemic spread around the globe, open-access digital platforms such as medRxiv and bioRxiv have gained increasing popularity in the scientific community.7 These platforms enable researchers to share preprinted data and manuscripts with the scientific community to discuss their research prior to journal publication. Via crowdsourcing, open-peer review can be carried out, allowing for collaboration among researchers and the acceleration of scientific progress. Therefore, these digital platforms improve the openness, accessibility and most of all quality of scientific research.

Social media

Social media in particular significantly facilitate crowdsourcing.8 9 Using platforms, like Twitter, Instagram or Facebook, it is easy and instantly possible to get in touch with people from all over the world and different disciplines, to network, discuss topics and issues or to initiate collaborative projects.10 Twitter journal clubs (ie, @RheumJC11 and @EULAR_JC12) are examples of educational digital crowdsourcing that allow the rheumatology community to discuss new scientific work location and time independent.

Data donation and artificial intelligence

Various studies have already shown the great willingness of patients to donate medical data for research purposes to advance scientific progress and thus potentially influence the disease positively for themselves and others.13 14 Especially for rare diseases, digital crowdsourcing has led to significant patient empowerment and clinical outcome improvements via registries15 and digital information exchange platforms.16 Robust datasets can be rapidly crowdfunded to answer complex and frequently asked questions.17 18 These datasets can be used as a basis for artificial intelligence (AI) and machine learning. Moreover, these methods are able to identify new disease subtypes19 20 and to predict therapy response21 or individual disease progression.22 AI also has the potential to empower the autonomy of patients, for example, by providing individual treatment propositions.23 New telemedicine approaches such as passive gait analysis24 and remote patient self-monitoring with teleguidance25 and AI-based real-live feedback26 will further encourage the patient empowerment and decrease current information loss in follow-up appointments. ‘Knowledge isn’t power until it is applied.’ Dale Carnegie (1888–1955, American writer and lecturer)

Opportunities for digital crowdsourcing in rheumatology

The COVID-19 pandemic marks a significant turning point in modern medicine. The global healthcare community is confronted with an infection for which almost no prior knowledge or guidelines pre-exist. Frequently, comparisons are made to the Spanish influenza. However, a fundamental difference to the past is the possibility to exchange data and experiences electronically in real time. Via crowdsourcing, the medical community creates a bowl of knowledge that can then be distilled and translated into practical and desperately needed clinical knowledge. In the long term, COVID pandemic improvisations born from despair could lead to a lasting paradigm shift and information asymmetry cutback also in rheumatology. Digital crowdsourcing and AI should be used to advance global medical collaborations, facilitate patient empowerment and decrease medical barriers. As a rheumatology community, we should embrace the principle of ‘connect and engage to target’.
  23 in total

1.  Will the pandemic permanently alter scientific publishing?

Authors:  Ewen Callaway
Journal:  Nature       Date:  2020-06       Impact factor: 49.962

2.  Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.

Authors:  Dana E Orange; Phaedra Agius; Edward F DiCarlo; Nicolas Robine; Heather Geiger; Jackie Szymonifka; Michael McNamara; Ryan Cummings; Kathleen M Andersen; Serene Mirza; Mark Figgie; Lionel B Ivashkiv; Alessandra B Pernis; Caroline S Jiang; Mayu O Frank; Robert B Darnell; Nithya Lingampali; William H Robinson; Ellen Gravallese; Vivian P Bykerk; Susan M Goodman; Laura T Donlin
Journal:  Arthritis Rheumatol       Date:  2018-04-02       Impact factor: 10.995

3.  Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers.

Authors:  Yuanfang Guan; Hongjiu Zhang; Daniel Quang; Ziyan Wang; Stephen C J Parker; Dimitrios A Pappas; Joel M Kremer; Fan Zhu
Journal:  Arthritis Rheumatol       Date:  2019-11-04       Impact factor: 10.995

Review 4.  Using Health Information Technology to Support Use of Patient-Reported Outcomes in Rheumatology.

Authors:  Julie Gandrup; Jinoos Yazdany
Journal:  Rheum Dis Clin North Am       Date:  2019-05       Impact factor: 2.670

5.  Need for online information and support of patients with systemic sclerosis.

Authors:  Rosalie van der Vaart; Han Repping-Wuts; Constance H C Drossaert; Erik Taal; Hanneke K A Knaapen-Hans; Mart A F J van de Laar
Journal:  Arthritis Care Res (Hoboken)       Date:  2013-04       Impact factor: 4.794

6.  Social media use among young rheumatologists and basic scientists: results of an international survey by the Emerging EULAR Network (EMEUNET).

Authors:  Elena Nikiphorou; Paul Studenic; Christian Gytz Ammitzbøll; Mary Canavan; Meghna Jani; Caroline Ospelt; Francis Berenbaum
Journal:  Ann Rheum Dis       Date:  2016-10-24       Impact factor: 19.103

7.  Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

Authors:  Marco V Perez; Kenneth W Mahaffey; Haley Hedlin; John S Rumsfeld; Ariadna Garcia; Todd Ferris; Vidhya Balasubramanian; Andrea M Russo; Amol Rajmane; Lauren Cheung; Grace Hung; Justin Lee; Peter Kowey; Nisha Talati; Divya Nag; Santosh E Gummidipundi; Alexis Beatty; Mellanie True Hills; Sumbul Desai; Christopher B Granger; Manisha Desai; Mintu P Turakhia
Journal:  N Engl J Med       Date:  2019-11-14       Impact factor: 176.079

8.  Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis.

Authors:  Beau Norgeot; Benjamin S Glicksberg; Laura Trupin; Dmytro Lituiev; Milena Gianfrancesco; Boris Oskotsky; Gabriela Schmajuk; Jinoos Yazdany; Atul J Butte
Journal:  JAMA Netw Open       Date:  2019-03-01

9.  'Twitterland': a brave new world?

Authors:  Elena Nikiphorou; Paul Studenic; Alessia Alunno; Mary Canavan; Meghna Jani; Francis Berenbaum
Journal:  Ann Rheum Dis       Date:  2017-12-04       Impact factor: 19.103

10.  Psychology of personal data donation.

Authors:  Anya Skatova; James Goulding
Journal:  PLoS One       Date:  2019-11-20       Impact factor: 3.240

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  5 in total

1.  [Acceptance of video consultation among patients with inflammatory rheumatic diseases depends on gender and location-Results of an online survey among patients and physicians].

Authors:  Diana Vossen; Johannes Knitza; Philipp Klemm; Isabell Haase; Johanna Mucke; Anna Kernder; Marco Meyer; Arnd Kleyer; Philipp Sewerin; Gerlinde Bendzuck; Sabine Eis; Martin Krusche; Harriet Morf
Journal:  Z Rheumatol       Date:  2021-08-27       Impact factor: 1.530

2.  Social media platforms: a primer for researchers.

Authors:  Olena Zimba; Armen Yuri Gasparyan
Journal:  Reumatologia       Date:  2021-01-16

Review 3.  [Digital diagnostic support in rheumatology].

Authors:  J Knitza; M Krusche; J Leipe
Journal:  Z Rheumatol       Date:  2021-10-04       Impact factor: 1.372

4.  A randomized controlled trial enhancing viral hepatitis testing in primary care via digital crowdsourced intervention.

Authors:  William C W Wong; Gifty Marley; Jingjing Li; Weihui Yan; Po-Lin Chan; Joseph D Tucker; Weiming Tang; Yuxin Ni; Dan Dan Cheng; Lou Cong; Wai-Kay Seto
Journal:  NPJ Digit Med       Date:  2022-07-19

5.  Digital Health Transition in Rheumatology: A Qualitative Study.

Authors:  Felix Mühlensiepen; Sandra Kurkowski; Martin Krusche; Johanna Mucke; Robert Prill; Martin Heinze; Martin Welcker; Hendrik Schulze-Koops; Nicolas Vuillerme; Georg Schett; Johannes Knitza
Journal:  Int J Environ Res Public Health       Date:  2021-03-05       Impact factor: 3.390

  5 in total

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