eHealth Literacy of German Physicians in the Pre–COVID-19 Era: Questionnaire Study

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Abstract

Background

Digitalization is a disruptive technology that changes the way we deliver diagnostic procedures and treatments in medicine. Different stakeholders have varying interests in and expectations of the digitalization of modern medicine. Many recent digital advances in the medical field, such as the implementation of electronic health records, telemedical services, and mobile health apps, are increasingly used by medical professionals and patients. During the current pandemic outbreak of a novel coronavirus-caused respiratory disease (COVID-19), many modern information and communication technologies (ICT) have been used to overcome the physical barriers and limitations caused by government-issued curfews and workforce shortages. Therefore, the COVID-19 pandemic has led to a surge in the usage of modern ICT in medicine. At the same time, the eHealth literacy of physicians working with these technologies has probably not improved since our study.

Objective

This paper describes a representative cohort of German physicians before the COVID-19 pandemic and their eHealth literacy and attitude towards modern ICT.

Methods

A structured, self-developed questionnaire about user behavior and attitudes towards eHealth applications was administered to a representative cohort of 93 German physicians.

Results

Of the 93 German physicians who participated in the study, 97% (90/93) use a mobile phone. Medical apps are used by 42% (39/93). Half of the surveyed physicians (47/93, 50%) use their private mobile phones for official purposes on a daily basis. Telemedicine is part of the daily routine for more than one-third (31/93, 33%) of all participants. More than 80% (76/93, 82%) of the trial participants state that their knowledge regarding the legal aspects and data safety of medical apps and cloud computing is insufficient.

Conclusions

Modern ICT is frequently used and mostly welcomed by German physicians. However, there is a tremendous lack of eHealth literacy and knowledge about the safe and secure implementation of these technologies in routine clinical practice.

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Most cited references 46

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Virtually Perfect? Telemedicine for Covid-19

Judd Hollander, Brendan Carr (2020)

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Artificial intelligence in healthcare: past, present and future

Fei Jiang, Yong Jiang, Hui Zhi … (2017)

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

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Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps

Stoyan Stoyanov, Leanne Hides, David J Kavanagh … (2015)

Background The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond “star”-ratings. Objective The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. Methods A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. Results There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). Conclusions The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.

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Author and article information

Contributors

Ulrich Bork:

ORCID: https://orcid.org/0000-0003-0813-0136

Department of Visceral, Thoracic, and Vascular SurgeryUniversity Hospital Carl Gustav CarusTechnische Universität DresdenFetscherstr 74Dresden, 01307Germany49 3514585400ulrich.bork@ukdd.de

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: October 2020

Publication date (Electronic): 16 October 2020

Volume: 8

Issue: 10

Electronic Location Identifier: e20099

Affiliations

[1 ] Department of Visceral, Thoracic, and Vascular Surgery University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany

Author notes

Corresponding Author: Ulrich Bork ulrich.bork@ 123456ukdd.de

Author information

Johanna Kirchberg https://orcid.org/0000-0002-2786-4269

Johannes Fritzmann https://orcid.org/0000-0003-1869-3960

Jürgen Weitz https://orcid.org/0000-0002-5760-6391

Ulrich Bork https://orcid.org/0000-0003-0813-0136

Article

Publisher ID: v8i10e20099

DOI: 10.2196/20099

PMC ID: 7600010

PubMed ID: 33064102

SO-VID: 9cd0ccf8-ab94-46cb-99c3-29d8ad9d0281

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 11 May 2020

Date revision requested : 30 June 2020

Date revision received : 7 September 2020

Date accepted : 20 September 2020

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