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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.
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.
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.
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.
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.
The COVID-19 pandemic, like all global crises in human history, is causing unprecedented health and economic disruptions in many countries. However, at the same time, this new situation is favoring the transition to digital solutions in many industries and in society as a whole. One example of this transition is education [
The list of new digital solutions is rapidly growing [
Before the COVID-19 pandemic, it was expected that digital transformation in health care would be as disruptive as the transformations seen in other industries. However, as discussed by Hermann et al [
According to Hermann et al [
The aim of this study is therefore 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.
We conducted a systematic review of the early scientific literature, following the Preferred Reporting Items for Systematic Reviews (PRISMA) approach [
The initial search was implemented on May 11, 2020, and was limited to the timespan from January 1 to April 30, 2020. The search query consisted of terms considered adequate by the authors to review the literature on the use of digital technologies in response to COVID-19. Therefore, we searched the MEDLINE database using the following search terms and database-appropriate syntax:
(“COVID-19”[All Fields] OR “COVID-2019”[All Fields] OR “severe acute respiratory syndrome coronavirus 2”[Supplementary Concept] OR “severe acute respiratory syndrome coronavirus 2”[All Fields] OR “2019-nCoV”[All Fields] OR “SARS-CoV-2”[All Fields] OR “2019nCoV”[All Fields] OR ((“Wuhan”[All Fields] AND (“coronavirus”[MeSH Terms] OR “coronavirus”[All Fields])) AND (2019/12[PDAT] OR 2020[PDAT]))) AND (digital[Title/Abstract] OR technology[Title/Abstract])]
We also searched the COVID-19/SARS-CoV-2 section of medRxiv, a preprint server for health science papers that have yet to be peer-reviewed, for studies related to digital technologies, using the search string
The search strategies and eligibility criteria used are provided in
We included articles for review if they were studies with original data or results referring to digital tools or interventions for COVID-19 and if they addressed the needs of patients or health care systems in the evaluation.
An article was excluded if it was not a study with original results; it did not focus on digital solutions for COVID-19; the full text was not available; or it was not written in English.
A two-stage screening process was used to assess the relevance of the identified studies. For the first level of screening, only the title and abstract were reviewed to preclude waste of resources in procuring articles that did not meet the minimum inclusion criteria. The titles and abstracts of the initially identified studies were checked by two independent investigators (DG and EB). For the second level of screening, all citations deemed relevant after the title and abstract screening were procured for subsequent review of the full-text article.
A spreadsheet in Excel (Microsoft Corporation) was developed to extract study characteristics such as the paper title, journal, publication date, type of technology, and patient needs addressed. In particular, we categorized the retrieved papers according to patient needs (diagnosis, prevention, treatment, adherence, lifestyle, and patient engagement). For the categorization of patient needs, we adapted the definition by Hermann et al [
The definition of patient needs is reported in
Definitions of the patient needs addressed by digital technologies.
Patient need | Definition |
Diagnosis | The process of determining which disease or condition explains a person's symptoms and signs [ |
Prevention | Preventing the occurrence of a disease (eg, by reducing risk factors) or by halting a disease and averting resulting complications after its onset [ |
Adherence | The degree to which a patient correctly follows medical advice [ |
Treatment | The use of an agent, procedure, or regimen, such as a drug, surgery, or exercise, in an attempt to cure or mitigate a disease [ |
Lifestyle | Adoption and sustaining behaviors that can improve health and quality of life [ |
Patient engagement | Actively involving people in their health and health care [ |
Surveillance | The continuous, systematic collection, analysis, and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice [ |
Other | All patient needs, addressed by digital technology, which are not included in the previous categories |
We built a scoring rubric by cross-classifying the patient needs addressed by the technology (or technologies) reported in each article with the type of technology itself. We relied on the report “Assessing the impact of digital transformation of Health Services” by the Expert Panel on Effective Ways of Investigating in Health (EXPH) of the European Commission [
We also extracted information and classified each technology reported by the selected articles according to health care system targets, grade of innovation, and scalability to other geographical areas. To do this, we also relied on the classifications and definitions reported by the EXPH (
Classification of digital technologies and health services.
Classification category | Definition | |
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Clients/patients | Members of the public who are potential or current users of health services, including caregivers. |
|
Health care providers | Members of the health care workforce who deliver health services. |
|
Health systems/resource managers | Systems and managers involved in the administration and oversight of public health systems. Interventions within this category reflect managerial functions related to supply chain management, health financing, and human resource management. |
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Data services | Crosscutting functionality to support a wide range of activities related to data collection, management, use, and exchange. |
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Supporting | Digital services or technologies that can be used to support old or established technologies for all or some health care system targets. These technologies may support or facilitate the performance of existing technologies. |
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Complementing | Digital services or technologies that can be used in addition to old or established technologies for all or some health care system targets. These technologies may strengthen or enhance the performance of existing technologies. |
|
Substituting | Digital services or technologies that may replace old or established technologies for all or some health care system targets. |
|
Innovating | New digital services or technologies that may offer new possibilities that previously were not available for all or some health care system targets. These disruptive technologies may represent a new entry into the market. |
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Not possible | Technologies strictly bonded to the context in which they were developed. |
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Local | Technologies whose scalability is limited to a local context (ie, regional or national context) for normative, legislative, ethical, or technical reasons. |
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Global | Technologies that do not present barriers to scalability that would prevent their possible global adoption. |
Some of the analyzed articles described multiple technologies. For these articles, we reported all the health care system targets addressed by the proposed technologies. However, we found it impractical to assign different grades of innovation and scalability for each technology reported. Therefore, we chose to report only the highest grade of innovation or scalability assigned to the technologies within each article (eg, innovating>substituting>complementing>supporting).
Two of the authors (DG and EB) independently classified all identified articles in the predefined categories. Any disagreements were resolved through discussion and consensus between the two reviewers. If disagreement persisted, another reviewer (GC) was called as a tiebreaker.
Given the characteristics of this literature review, which aims to describe proposed digital solutions, and the nature and design of the included studies, assessments of the risk of bias and the study quality were not possible and therefore were not performed.
The search identified 269 articles (174 from PubMed and 95 from medRxiv), of which 124 full-text articles were assessed and included in the review after screening (
Preferred Reporting Items for Systematic Reviews (PRISMA) literature review flowchart.
Out of the 124 selected articles, 65 (52.4%) addressed the use of digital technologies for diagnosis (
In
Frequency of appearance of each patient need within the 124 selected articles and the share of peer-reviewed articles for each need. The total percentage is higher than 100 because some articles include technologies used to address more than one patient need.
Articles included in the literature review with the main characteristics of each analyzed paper.
ID | Reference | Health care system targets | Grade of innovation | Scalability |
1 | Zhai et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
2 | Wang W et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
3 | Wang et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
4 | Yan et al [ |
Clients/patients, health care providers | Supporting | Global |
5 | Hou et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
6 | Feng et al [ |
Health care providers | Complementing | Global |
7 | Jin et al [ |
Health care providers | Complementing | Global |
8 | Torous et al [ |
Clients/patients, health care providers, health systems/resource managers | Innovating | Global |
9 | Wang et al [ |
Health care providers | Complementing | Global |
10 | Zheng et al [ |
Health care providers | Complementing | Global |
11 | Galbiati et al [ |
Health care providers, health systems/resource managers | Supporting | Global |
12 | Bai et al [ |
Health care providers | Complementing | Global |
13 | Ienca et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
14 | Ting et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
15 | Hua et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
16 | Fu et al [ |
Health care providers | Complementing | Global |
17 | Zhou et al [ |
Health care providers | Complementing | Global |
18 | Lin et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
19 | Ferretti et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
20 | Calton et al [ |
Clients/patients, health care providers | Innovating | Global |
21 | Lin et al [ |
Health care providers | Complementing | Global |
22 | Mashamba-Thompson et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
23 | Salako et al [ |
Clients/patients, health care providers | Innovating | Local |
24 | Hernández et al [ |
Clients/patients, health systems/resource managers | Innovating | Local |
25 | Ohannessian et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
26 | Shanlang et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
27 | Turer et al [ |
Clients/patients, health care providers | Innovating | Global |
28 | Keesara et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
29 | Her [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
30 | Klum et al [ |
Clients/patients, health care providers | Supporting | Global |
31 | Calvo et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
32 | Dandekar et al [ |
Health systems/resource managers, data services | Innovating | Global |
33 | Drew et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
34 | Segal et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
35 | Hassanien et al [ |
Health care providers | Complementing | Global |
36 | Martin et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
37 | Yasaka et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
38 | Medford et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
39 | Salg et al [ |
Health systems/resource managers, data services | Complementing | Local |
40 | Abhari et al [ |
Health systems/resource managers, data services | Innovating | Global |
41 | Jarynowski et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
42 | Stommel et al [ |
Clients/patients, health care providers | Complementing | Global |
43 | Judson et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
44 | Ćosić et al [ |
Clients/patients, health care providers | Innovating | Local |
45 | Grange et al [ |
Clients/patients, health care providers | Innovating | Global |
46 | Castiglioni et al [ |
Clinets/patients, health care providers | Complementing | Global |
47 | Serper et al [ |
Clients/patients, health care providers | Innovating | Global |
48 | Robbins et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
49 | Crump [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
50 | Punn et al [ |
Health systems/resource managers, data services | Innovating | Global |
51 | Myers et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
52 | Noonan et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
53 | Loebet et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
54 | Price et al [ |
Health care providers | Innovating | Local |
55 | Vaishya et al [ |
Health care providers | Complementing | Global |
56 | Stubblefield et al [ |
Health care providers | Complementing | Global |
57 | Yuan et al [ |
Clients/patients, data services | Innovating | Local |
58 | Pollock et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
59 | Mahmood et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
60 | Gallotti et al [ |
Clients/patients, data services | Innovating | Local |
61 | Ren et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
62 | Goldschmidt [ |
Clients/patients, health care providers | Innovating | Global |
63 | Al-karawi et al [ |
Health care providers | Complementing | Global |
64 | Kumar et al [ |
Health care providers | Complementing | Global |
65 | Garg et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
66 | Kuziemski et al [ |
Health systems/resource managers, data services | Innovating | Local |
67 | Jakhar et al [ |
Clients/patients, health care providers | Innovating | Local |
68 | Marasca et al [ |
Clients/patients, health care providers | Innovating | Local |
69 | Bulchandani et al [ |
Clients/patients, health systems/resource managers, data services | Innovating | Local |
70 | Green et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
71 | Nagra et al [ |
Clients/patients, health care providers | Innovating | Global |
72 | O'Connor et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
73 | Wittbold et al [ |
Clients/patients, health care providers | Innovating | Local |
74 | Hightow et al [ |
Clients/patients, health care providers | Innovating | Local |
75 | Bonavita et al [ |
Clients/patients, health care providers | Innovating | Local |
76 | Wosik et al [ |
Clients/patients, HO | Innovating | Global |
77 | Yan et al [ |
Health care providers, health systems/resource managers, data services | Supporting | Global |
78 | Lin et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Supporting | Not possible |
79 | Kummitha [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
80 | Abbas et al [ |
Health care providers, data services | Complementing | Global |
81 | Ardabili et al [ |
Health systems/resource managers, data services | Supporting | Global |
82 | Zhang et al [ |
Clients/patients, health care providers, | Supporting | Global |
83 | Hart et al [ |
Clients/patients, health care providers | Innovating | Global |
84 | Parikh et al [ |
Clients/patients, health care providers | Innovating | Global |
85 | Rahman et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
86 | Alwashmi Hart [ |
Clients/patients, health care providers, health systems/resource managers, data services | Complementing | Global |
87 | Sedov et al [ |
Health care providers, health systems/resource managers, data services | Complementing | Global |
88 | Mahapatra et al [ |
Health care providers; health systems/resource managers | Innovating | Global |
89 | Azizy et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
90 | Madurai et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Global |
91 | Park et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Innovating | Local |
92 | Negrini et al [ |
Clients/patients, health care providers | Complementing | Global |
93 | Tanaka et al [ |
Clients/patients, health care providers | Complementing | Global |
94 | Randhawa et al [ |
Data services | Innovating | Global |
95 | Javaid et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Supporting | Global |
96 | Kyhlstedt et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Supporting | Global |
97 | Barbosa et al [ |
Health systems/resource managers, data services | Innovating | Global |
98 | Blake et al [ |
Clients/patients, health systems/resource managers | Supporting | Global |
99 | Reeves et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Substituting | Global |
100 | Khan et al [ |
Clients/patients, | Substituting | Global |
101 | Whelan et al [ |
Clients/patients, health care providers, health systems/resource managers | Complementing | Global |
102 | Meinert et al [ |
Clients/patients, health care providers, data services | Innovating | Global |
103 | Ekong et al [ |
Clients/patients, health care providers, health systems/resource managers | Supporting | Local |
104 | Pérez Sust et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Complementing | Global |
105 | Kim et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Complementing | Global |
106 | Krukowski et al [ |
Clients/patients, health care providers, health systems/resource managers | Complementing | Global |
107 | Liu et al [ |
Health care providers, health systems/resource managers | Supporting | Global |
108 | Vaid et al [ |
Health care providers, health systems/resource managers | Complementing | Local |
109 | Lee [ |
Clients/patients, health care providers | Supporting | Global |
110 | Ramsetty et al [ |
Clients/patients, health care providers, health systems/resource managers | Complementing | Global |
111 | Tarek et al [ |
Health systems/resource managers | Complementing | Global |
112 | Awasthi et al [ |
Health care providers, health systems/resource managers | Supporting | Global |
113 | Khan et al [ |
Health care providers, health systems/resource managers | Supporting | Global |
114 | Husnayain et al [ |
Health care providers, health systems/resource managers | Supporting | Global |
115 | Weemaes et al [ |
Health care providers, health systems/resource managers, data services | Innovating | Local |
116 | Espinoza et al [ |
Clients/patients, health care providers, health systems/resource managers | Innovating | Global |
117 | Shweta et al [ |
Health care providers, health systems/resource managers, data services | Complementing | Global |
118 | Brat et al [ |
Health care providers, health systems/resource managers, data services | Supporting | Local |
119 | Hegde et al [ |
Clients/patients, health care providers | Innovating | Global |
120 | Tobias et al [ |
Clients/patients, health care providers, health systems/resource managers, data services | Supporting | Local |
121 | Compton et al [ |
Clients/patients, health care providers, health systems/resource managers | Complementing | Global |
122 | Smith et al [ |
Health care providers, health systems/resource managers | Complementing | Local |
123 | Kalteh et al [ |
Health care providers, health systems/resource managers, data services | Supporting | Global |
124 | Woo et al [ |
Clients/patients, health care providers | Complementing | Global |
In
Cross-classification of the published studies by the type of technology and the patient needs addressed by the technology.
Technology | Diagnosis | Surveillance | Prevention | Treatment | Adherence | Lifestyle | Patient engagement | Other | ||||||||
|
n | Refsa | n | Refs | n | Refs | n | Refs | n | Refs | n | Refs | n | Refs | n | Refs |
Artificial intelligence | 24 | [ |
12 | [ |
11 | [ |
2 | [ |
1 | [ |
1 | [ |
1 | [ |
4 | [ |
Big data analytics | 6 | [ |
11 | [ |
12 | [ |
2 | [ |
2 | [ |
3 | [ |
0 | N/Ab | 2 | [ |
Blockchain | 2 | [ |
2 | [ |
2 | [ |
0 | N/A | 0 | N/A | 0 | N/A | 0 | N/A | 0 | N/A |
Chatbot | 3 | [ |
0 | N/A | 1 | [ |
0 | N/A | 0 | N/A | 0 | N/A | 0 | N/A | 0 | N/A |
Electronic health records | 7 | [ |
5 | [ |
4 | [ |
5 | [ |
1 | [ |
1 | [ |
0 | N/A | 0 | N/A |
Internet of Things | 3 | [ |
5 | [ |
3 | [ |
2 | [ |
1 | [ |
1 | [ |
0 | N/A | 1 | [ |
Internet search engines, social media | 1 | [ |
8 | [ |
3 | [ |
1 | [ |
0 |
|
1 | [ |
0 | N/A | 1 | [ |
Mobile app | 8 | [ |
8 | [ |
9 | [ |
3 | [ |
3 | [ |
2 | [ |
3 | [ |
0 | N/A |
Mobile tracing | 6 | [ |
14 | [ |
6 | [ |
2 | [ |
1 | [ |
1 | [ |
0 | N/A | 0 | N/A |
Robotics, mechanical tools, drones, sensors, wearable devices | 5 | [ |
3 | [ |
3 | [ |
2 | [ |
3 | [ |
1 | [ |
1 | [ |
1 | [ |
Telehealth, tele-medicine | 29 | [ |
7 | [ |
18 | [ |
34 | [ |
11 | [ |
8 | [ |
8 | [ |
0 | N/A |
aRefs: references.
bN/A: not applicable.
Given the heterogeneity of the included technologies and solutions, we summarized the findings through a narrative synthesis. In fact, some patient needs share the same technology for health issues, which can be considered superimposable (eg, prevention and surveillance are often addressed by the same technology, such as mobile surveillance apps); this is also evident from
In the early scientific literature, digital solutions and innovative technologies were mainly proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions of the use of AI-powered tools for the diagnosis and screening of SARS-CoV-2 or COVID-19, as reported in
In addition to these studies, many authors proposed AI-powered diagnostic tools for COVID-19 that are not based on CT scan data [
A further innovative digital technology proposed to support the diagnosis of COVID-19 is blockchain (or distributed ledger) technology. In one study [
Another interesting digital tool proposed for the diagnosis and triage of patients is chatbots. Chatbots are applications that provide information through conversation-like interactions with users; they can be used for a broad range of purposes in health care (eg, patient triage, clinical decision support for providers, directing patients and staff to appropriate resources, and mental health applications). Chatbots can help evolve triage and screening processes in a scalable manner [
Our literature review suggests that digital technologies can be useful for COVID-19 diagnosis as well as for implementing prevention and surveillance measures.
Judson et al [
Another topic of paramount importance in the context of health care digitalization is epidemiological surveillance. Our review highlights that prevention and surveillance are often considered together in the scientific literature, given that “prevention of COVID-19” can be intended as “prevention of further spread,” which is mainly achieved through surveillance. For the COVID-19 pandemic, surveillance definitely overlaps with prevention, as the risk of infection can be reduced by applying a successful surveillance plan and controlling the interactions between infected persons and the healthy population.
A study by Ferretti et al [
An example of successful use of a mobile app for contact tracing is the app that the Chinese government implemented in Wuhan [
Our literature review suggests that another meaningful way to control the spread of an epidemic is through monitoring and surveillance of internet searches and social media usage. Wang et al [
Furthermore, a technology that can aid the automatic, decentralized, and remote collection of data for surveillance purposes is the IoT. In [
Although its potential is irrefutable, the technology behind surveillance and contact tracing apps raises many concerns; as discussed by Calvo et al [
Although the aforementioned articles addressed surveillance and prevention in outpatients and the general population, an interesting point of view on inpatient surveillance comes from the study by Lin et al [
In the field of prevention, other important digital technologies proposed in the literature include telemedicine and telehealth. Telemedicine is not always applicable in emergencies, and many patients with COVID-19 may need to go to the hospital to receive higher level care. For this purpose, Turer et al [
Telemedicine and telehealth technologies are also used to increase patient adherence and for treatment purposes. Torous et al [
In the early literature responding to the COVID-19 pandemic, fewer scientific contributions addressed the use of digital technologies for lifestyle empowerment or patient engagement. This is probably due to the current phase of the pandemic, which has conditioned scientific research to focus primarily on aspects related to more acute patient needs. However, some articles can be found. For instance, Krukowski et al [
Although SARS-CoV-2 is causing a pandemic worldwide, it is also favoring the rapid adoption of digital solutions and advanced technology tools in health care. On the one hand, physicians and health systems may need to track large populations of patients on a daily basis for surveillance purposes [
In this systematic review of the early scientific literature in response to COVID-19, we describe numerous digital solutions and technologies addressing several patient and health care needs. The constantly updated scientific literature is a source of important ideas and suggestions for finding innovative solutions that guarantee patient care during and possibly after the COVID-19 crisis.
In the field of diagnosis, digital solutions that integrate with the traditional methods of clinical, molecular or serological diagnosis, such as AI-based diagnostic algorithms based both on imaging and clinical data, seem promising.
For surveillance, digital apps have already proven their effectiveness [
The fact that the digital technologies proposed in the analyzed scientific literature mainly address the fields of diagnosis, prevention, and surveillance probably reflects the emergency phase of the COVID-19 pandemic. As time passes, well-known digital tools could be proposed for different purposes and patient needs, such as adherence, lifestyle, and patient engagement, which are considered to be important determinants of patient health [
In addition to the patient needs addressed by digital technologies, our review sheds light on the most used digital technology tools. Given the early phase of the pandemic and its reflection on the articles included in this review, the technologies that have shown to be more easily and quickly implementable can be also considered as the most scalable. In fact, the speed with which these technologies have been deployed demonstrates their ease of adoption and manageability in many different contexts, despite their deployment during the course of a pandemic. Many of these solutions have demonstrated a technical, economic, regulatory and usability weight that is sufficiently low to allow their rapid and effective use, at least during the emergency phase. Among these solutions, we report AI tools for diagnosis, big data analytics and mobile tracing for surveillance and prevention, and telemedicine and telehealth, which have proved to be transversal tools for diagnosis, prevention, and treatment.
We advocate that many of the digital technologies that have been quickly implemented in this emergency phase can also be adopted in the following phases of the pandemic, as also stated by Fagherazzi et al [
Many countries are facing these regulatory issues: the challenges for digital health have become a global issue in the public health response to COVID-19 and future outbreaks. Digital tools such as telemedicine should indeed be integrated into international and national guidelines for public health preparedness, alongside the definition of national regulations and funding frameworks in the context of public health emergencies. To switch to new digital models of care, increasing the digital expertise of health care professionals and educating the population are fundamental issues. Moreover, by implementing a data-sharing mechanism, digitally collected and stored data will be a precious tool for epidemiological surveillance that, as discussed earlier, is fundamental in controlling the epidemic spread. Lastly, to describe and assess the impact of digital tools during outbreaks, scientific evaluation frameworks should be defined [
This literature review presents some limitations. First, the research was conducted in a period of epidemiological emergency. Thus, the number of daily publications is high, and it is difficult to keep up to date. As a result, we have been forced to select articles in a reduced time span, potentially missing some studies and including studies that have yet to be peer-reviewed. Secondly, due to the design of the review, the search could not be fully comprehensive, as it was conducted exclusively on the MEDLINE database and medRxiv to preserve both time and resources; however, PubMed/MEDLINE is reported to be the primary database used by health science faculties [
The COVID-19 pandemic is favoring the implementation of digital solutions with unprecedented speed and impact. It is therefore recommended to keep track of the ideas and solutions being proposed today to implement best practices and models of care tomorrow and to be prepared for future national and international emergencies. It is worth taking advantage of the impetus provided by the crisis we are currently experiencing to implement at least some of the solutions proposed in the scientific literature, especially in national health systems, which in recent years have proved to be particularly resistant to the digital transition.
Eligibility criteria and search strategy.
Table S1. Articles included in the literature review.
artificial intelligence
computed tomography
electronic health record
electronic personal protective equipment
Expert Panel on Effective Ways of Investigating in Health
general data protection regulation
Internet of Things
influenza pneumonia
information technology
novel coronavirus pneumonia
Preferred Reporting Items for Systematic Reviews
quick response code
The authors declare that they have not received any specific funding.
DG and EB conceived and designed the work; reviewed the literature and independently classified all identified articles in the predefined categories; acquired, analyzed, and interpreted the data; and wrote the manuscript. GC acted as a tiebreaker in case of disagreement between DG and EB during the classification of the identified articles. GC and AGN helped with the analyses, interpreted the data, and reviewed the manuscript. MPL and MPF interpreted the data and reviewed and edited the final manuscript in collaboration with DG and EB for intellectual content. All authors have read and approved the final version of the manuscript.
None declared.