The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature
Open accessAbstract
Background: The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools. Objective: The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems. Methods: We conducted a systematic review of early COVID-19–related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas. Results: The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)–powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement. Conclusions: In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period
Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020) · Science · doi:10.1126/science.abb5793 · Cited by 2,611
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D., & Fraser, C. (2020) · Science · doi:10.1126/science.abb6936 · Cited by 2,512
Telehealth transformation: COVID-19 and the rise of virtual care
Wosik, J., Fudim, M., Cameron, B., Gellad, Z. F., Cho, A., Phinney, D., Curtis, S., Roman, M., Poon, E. G., Ferranti, J., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa067 · Cited by 1,581
Artificial Intelligence (AI) applications for COVID-19 pandemic
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,497
Digital technology and COVID-19
Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020) · Nature Medicine · doi:10.1038/s41591-020-0824-5 · Cited by 1,413
Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing
Wang, C. J., Ng, C. Y., & Brook, R. H. (2020) · JAMA · doi:10.1001/jama.2020.3151 · Cited by 1,391
An interpretable mortality prediction model for COVID-19 patients
Yan, L., Zhang, H.-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0180-7 · Cited by 1,301
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Zheng, C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,243
Covid-19 and Health Care’s Digital Revolution
Keesara, S., Jonas, A., & Schulman, K. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2005835 · Cited by 1,126
Cited by
Use of Telemedicine and Virtual Care for Remote Treatment in Response to COVID-19 Pandemic
Jnr., B. A. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01596-5 · Cited by 800
Digital technology use during COVID ‐19 pandemic: A rapid review
Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.242 · Cited by 459
Idrees, S. M., Nowostawski, M., & Jameel, R. (2021) · JMIR Medical Informatics · doi:10.2196/25245 · Cited by 80
Tomczyk, S., Barth, S., Schmidt, S., & Muehlan, H. (2021) · Journal of Medical Internet Research · doi:10.2196/25447 · Cited by 69
Ghaddaripouri, K., Mousavi Baigi, S. F., Abbaszadeh, A., & Mazaheri Habibi, M. R. (2023) · Health Science Reports · doi:10.1002/hsr2.1156 · Cited by 54
Metadata
- Authors
- Golinelli, D., Boetto, E., Carullo, G., Nuzzolese, A. G., Landini, M. P., & Fantini, M. P.
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-11-06
- Volume
- 22
- Issue
- 11
- Pages
- e22280
- ISSN
- 1438-8871 (ISSN-L)
- OpenAlex
- W3093542778
- Cited by
- 574
- References
- 136
- FWCI
- 19.04
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature. https://app.cochat.ai/papers/10.2196/22280
- The paper
- Golinelli, D., Boetto, E., Carullo, G., Nuzzolese, A. G., Landini, M. P., & Fantini, M. P. (2020). Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature. Journal of Medical Internet Research. https://doi.org/10.2196/22280
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.