Harnessing the Power of Digital Health in Responding to ... · new cases, prompt results sharing...

Harnessing the Power of Digital Health in Responding to COVID-19 and Beyond

K2P COVID-19 SERIES

K2P Harnessing the Power of Digital Health

Authors Mathilda Jabbour, Linda Matar, Nour Hemadi, Clara Abou Samra, Fadi El-Jardali*

Citation Jabbour, M., Matar, L., Hemadi, N., Abou Samra, C., El-Jardali, F. K2P COVID-19 Series: Harnessing the Power of Digital Health in Responding to COVID-19 and Beyond, Knowledge to Policy (K2P) Center, Beirut, Lebanon, May 8, 2020

*senior author


2

Key Messages

The rapid spread of the COVID-19 pandemic has currently infected more than three million person globally and has surged the need and demand for healthcare services beyond the capacity of some healthcare systems. Digital health, that includes big data, e-health and technologies such as artificial intelligence, has emerged as a fundamental tool for governments, healthcare providers and healthcare institutions to effectively respond to this public health crisis.

Technologies leveraged upon in the COVID-19 Response → Online platforms were leveraged to perform various functions including, raising awareness, educating personnel, sorting and monitoring patients, enabling virtual care, and informing decision-makers and the public.

→ Machine learning and artificial intelligence played an important role in contact tracing, effective design of diagnostic technologies, identification of drugs that can be re-purposed for COVID-19 treatment, and enhacement of epidemiological forecasts.

→ Blockchain technology was used to allow immediate payments for hospitals and connect healthcare providers to new vendors.

→ Robots and drones were utilized for preventing disease transmission, screening, diagnosis, and minimizing human-to-human contact.

Advancing the Agenda of Digital Health in the Eastern Mediterranean Region → Leverage on digital health to respond to public health emergencies such as the COVID-19

→ Promote the utilization of big data to advance and support digital health technologies in informing and supporting the response to COVID-19 and beyond

→ Invest in robust national health information systems (HIS) → Harness the utilization of virtual technology to respond to the continuous increase in demand for health services that is not limited to public health emergencies but also extends to everyday practice.

→ Enable public engagement and enrollment in digital health interventions


3

Preamble

On March 11, 2020 the World Health Organization (WHO) declared the novel coronavirus as a pandemic. Since then the number of confirmed COVID-19 cases reached over three million overwhelming healthcare systems around the globe and challenging economies. In addition to strict measures that escalated from social distancing to complete lockdowns, governments that have succeeded so far in containing the virus, heavily invested in digital health that includes technologies and big data.

Digital health encompass the utilization of electronic health, advancing computing science such as big data and leveraging on artificial intelligence (AI). Digital health draws on various technologies including phones, applications, telemedicine, wearable devices, robotics, drones, virtual reality and AI to support tasks and inform decision making. Digital technologies provide opportunities to address health system challenges, and enhance the coverage and quality of health practices and services, particularly in outbreaks (WHO, 2019; WHO, 2020).

Governments and international organizations mobilized digital technology during the Ebola crisis for numerous functions including awareness, education, outreach, coordination, surveillance, and planning. For instance, mobile technologies were leveraged to raise public awareness on disease prevention, educate healthcare workers on best practice, and provide infected individuals and those living in hard-to-reach regions with essential needs and financial support (Dahiya et al., 2016; Oza et al., 2017; Tambo et al., 2016). Mobile-based applications in addition to dashboards also helped front-line healthcare workers and management carry out various tasks such as information coordination, surveillance and reporting, real-time notifications on new cases, prompt results sharing and strategic planning (Tambo et al., 2017).

As the COVID-19 pandemic spreads to nearly every country around the world including the most developed, the role and scope of digital health stretched beyond that observed in the Ebola epidemic.


4

While this document does not evaluate the impact of the components of digital health, it provides insights based on evidence and experience during the COVID-19 on how digital health can harness and enable health systems to respond to the COVID-19 pandemic and other public health emergencies in the Eastern Mediterranean Region.

Selection Process

A search of the literature was undertaken to identify articles -systematic reviews and single studies- addressing the use of technology in COVID-19 response. A combination of free word and controlled vocabulary to combine the following concepts: “Pandemic” AND “Technology”; “COVID-19” AND “Technology”; “Epidemic” AND “Technology”; “Digital” AND “Health” AND “COVID-19”; “Digital” AND “Health” AND “Technology” were used to search the following databases: Medline/Pubmed, Health Systems Evidence, and Cochrane Library. Google Scholar and grey literature were also reviewed. Last search was done on the 4th of May, 2020.


It is essential to assess the state of E-health in the EMR to determine the region’s preparedness to leverage advanced technological applications. The socioeconomic and cultural variations among countries in the EMR reflect a wide array of challenges related to e-health preparedness especially in low-to-middle income countries (Al Shorbaji, 2008). Countries in this region vary in terms of wealth which results in a huge disparity in the level of networked readiness (Al Shorbaji, 2008). For instance, whereby countries such as UAE, Qatar and Bahrain are the top three leading countries in the EMR in terms of the networked readiness index, Yemen and Algeria have scored much less (Portulans Institute, 2019).

Other challenges include lack of standardized policies for health data management, lack of sustainable funding, poor E-health public-private partnerships, lack of skilled human resources, lack of political commitment, underdeveloped health information systems (HIS), poor policy and legal frameworks and cultural and multilingual challenges (Al Shorbaji, 2008, Alwan et al., 2016).

E-health which is “the use of information and communications technology in support of health and health-related fields” (WHO, 2019), covers a broad range of tools including health information systems (HIS), Electronic Health Records (EHR) and telemedicine (Kierkegaard, 2013). Robust HIS are particularly important E-health tools as they are pre-requisites for the provision of information essential for evidence-informed decision making, proper management and planning, and health system monitoring and evaluation (Alwan et al., 2016).

5

E-health preparedness depends on four key pillars including (1) network readiness, (2) standardization policies, protocols, and procedures, (3) user access and accessibility, and (4) government regulations and roles (Wickramasinghe et al.,2005)

E-health


Big data describes a large, complex and fast data that is difficult to be analyzed using traditional methods. Big data can be used for information extraction, machine learning and artificial intelligence, predictive modeling, and advanced analytics (Rouse, 2020). The opportunities revealed from big data include quality improvement, population and health management, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction (Kruse et al., 2016; Mehta et al., 2018).

Evidence showed that big data approaches that include analyzing social media data, such as posts, likes and comments, can be used to detect outbreaks. (Xie, et al., 2013). A Canadian artificial intelligence start-up called “Bluedot” was able to alert the world of the novel coronavirus outbreak 9 days before WHO released information. The start-up used big data and artificial intelligence to process information from different sources including statements from public health organizations, digital media, airline ticketing, livestock health reports and population demographics to detect and forecast the size of the outbreak (CNBC, 2020).

Lessons learned from previous epidemics showed that active case finding and prompt treatments are crucial in the reduction of disease incidence (Yuen et al., 2015). The Taiwanese government leveraged big data to actively find and test high-risk individuals. This was done by integrating the travel and customs database with the national health insurance database and patients’ travel histories and incorporating this information into the individuals’ medical records. This allowed clinicians to receive real-time alerts to investigate COVID-19 infection for those who report with flu-like symptoms and have recently visited endemic regions (Wang et al., 2020a).

Nonetheless, the use of big data should consider the data structure, security, data standardization, storage and transfers, and data governance to mitigate the negative impact of big data and enhance its benefits (Kruse et al., 2016).

6

Big Data


RAISING PUBLIC AWARENESS

Authorities are leveraging on social media to efficiently reach the public with accurate and comprehensive information. For instance, the Singaporean authorities established chatbots and a national WhatsApp channel to raise awareness on COVID-19 (UNDP, 2020). Nevertheless, social media has also been the platform for the fast and overwhelming spread of information, which created an infodemic, making it harder for people to distinguish credible from fake information (Wong et al., 2020). To mitigate the spread of misinformation, WHO collaborated with search companies and social media giants to track fake-news and ensure that the information published by WHO appears on the top of the search results when people search for information on COVID-19 (Bullock et al., 2020).

EDUCATING AND TRAINING HEALTHCARE WORKERS

WHO launched a list of online courses to educate healthcare workers on how to protect themselves and take better care of COVID-19 patients (WHO, 2020). China also trained healthcare workers through government-supported online courses and live teleconferencing sessions presented on mobile platforms (Li et al., 2020).

7

Digital Health Technologies Utilized for COVID-19 Response

Online Platforms01


TRIAGE, MONITORING AND PROVISION OF ROUTINE CARE

Virtual healthcare emerged as a key strategy to manage demand on hospital care as well as reduce the exposure and potential spread of the virus among healthcare professionals, patients and their communities (Greenhalgh et al., 2020). Several governments, such as the Chinese and the British governments, resorted to virtual consultations to provide initial patient screening and “forward triage” (Dorsey and Topel, 2020; Greenhalgh et al., 2020; Hollander and Carr, 2020). Additionally, virtual consultations and COVID-19 specific mobile applications were used to support and monitor suspected cases and patients with mild symptoms, therefore, decreasing the demand on healthcare (Ohannessian et al., 2020). Several countries such as the United States and Australia have also shifted non-COVID-19 related consultations online to ensure the continuous provision of routine care without the risk of exposure in busy and contaminated hospitals (Smith et al., 2020; Leite et al., 2020).

INFORMING DECISION MAKERS AND THE PUBLIC

National, regional and international live dashboards were established to provide decision-makers and the public with up-to-date information. For instance, John Hopkins developed an interactive dashboard that allows people to track cases around the globe in real-time (Dong et al., 2020).On the national level, numerous countries including Lebanon and the United Kingdom established country-specific dashboards. Some dashboards expanded to include key information that can help coordinate efforts, assess the impact of interventions and direct the flow of resources (Digital Health, 2020).

8

Online Platforms were leveraged to raise public awareness, educate healthcare workers, sort and monitor patients, enable virtual care, and inform decision-makers and the public during the COVID-19 pandemic.


CONTACT-TRACING

Several countries resorted to technology to automate the contact-tracing process whilst encountering the fast spread of the COVID-19 disease (Ferretti et al., 2020). For instance, the Chinese public health authority developed an application that collects real-time data on people’s geolocation and latest COVID-19 diagnosis to generate a green or a red QR code that is scanned at the entrance of public spaces, transportation systems, malls and hotels. A red code would deny entry to its holder and would indicate that the individual either tested positive for COVID-19 or came into proximity to another person who has recently tested positive (Ferretti et al., 2020). Singapore, traced the digital signature of people who tested positive with COVID-19 to identify the people they came in contact with, to test and isolate them accordingly (UNDP, 2020).

DIAGNOSIS

Evidence suggests that machine learning can be potentially used for COVID-19 diagnosis as machines can be trained to identify particular radiological signatures associated with COVID-19 on CT-Scans (Ai et al., 2020; Wang et al. 2020b) or identify an abnormally rapid breathing pattern associated with COVID-19 through footage from kinetic depth cameras (Cascella et al., 2020; Wang et al., 2020c). Other approaches include using mobile phones to diagnose COVID-19 through embedded sensors or through a questionnaire that can filter high-risk patients based on their responses (Maghdid et al., 2020; Rao & Vazquez, 2020).

9

Machine Learning and Artificial Intelligence

02


TREATMENT

Artificial intelligence can be used to identify the structure of the proteins associated with viruses, identify drugs that can be re-purposed for the treatment of this infection, and propose new drugs that provide the potential for treatment (Zhavoronkov, 2018). For instance, Richardson et al. (2020) used an artificial intelligence technique to identify Baricitinib, a drug which is commonly used to treat arthritis, as a potential drug that can be used for the treatment of COVID-19. Beck et al. (2020) used a machine learning approach to identify Atazanavir as a potentially effective drug for the treatment of COVID-19.

EPIDEMIOLOGICAL RESEARCH

Explaining the spread of the virus, and its effect on different demographics and geographic locations is crucial to inform policy decisions. Given the rapid progression of COVID-19, researchers leveraged models with machine learning capabilities to provide real-time forecasts (Bullock et al., 2020). For instance, Hu et al. (2020) used data compiled by WHO to develop a dataset of cases in China. This dataset was then used to train a modified auto-encoder to provide real-time forecasting of new cases.

10

Machine learning and artificial intelligence played an important role in contact tracing, effective design of diagnostic technologies, identification of drugs that can be re-purposed for COVID-19 treatment, and enhacement of epidemiological forecasts.


ADDRESSING SUPPLY CHAIN ISSUES

The rapid spread of COVID-19 resulted in a drastic increase in demand for medical supplies (i.e. PPE). Many non-traditional suppliers, such as apparel companies, shifted their activity to address this shortage, yet connecting these suppliers to health authorities and health providers was still a time-consuming task. In the United States, IBM launched a block-chain based network to connect health authorities and healthcare providers with non-traditional suppliers which allowed them to quickly identify and verify new vendors (Landi, 2020).

STREAMLINING INSURANCE OPERATIONS

Blockchain technology can be used to securely store patients’ medical records (Chen et al., 2019). Using this technology, an insurance company in China was able to get data directly from hospitals, track history of patient treatment, the tests they received, and the physicians that treated them in a secure manner ensuring no data tampering. The company also used smart contracts to automate insurance payments to issue immediate payments upon discharge (Field, 2020).

11

Blockchain03


12

Blockchain technology was used to allow immediate payments for hospitals and connect healthcare providers to new vendors.


PREVENTING DISEASE TRANSMISSION

Autonomous disinfection using UV Robots can be more efficient and effective as compared to manual disinfection which requires workforce mobilization and increases the exposure risk of cleaning workers (Yang et al., 2020). Similarly, China and Spain adapted drones that were originally designed to spray pesticides for agricultural applications to spray disinfecting chemicals in public spaces. Drone spray can be fifty times more efficient than hand spray and can help reduce the transmission mechanisms of the novel coronavirus (Pan, 2020; Yang & Reuter, 2020).

SCREENING

Mobile robots that incorporate thermal sensors and vision algorithms technology can increase the efficiency and coverage of screening in public areas and ports of entry (assisting with border controls). China deployed mobile robots to thermally scan large crowds and detect suspected coronavirus carriers; these robots live-stream temperature readings to control centers and send alerts when necessary (Ng, 2020; Yang et al., 2020).

13

Robots and Drones04


DIAGNOSIS

Robots can come in handy in performing nasopharyngeal and oropharyngeal swabbing to test for COVID-19, thus speeding up the diagnostic testing process, reducing the risk of infection and freeing up staff for other tasks (Yang et al., 2020; Caixiong, 2020).

MINIMIZING HUMAN-TO-HUMAN CONTACT

Robots have been used for the delivery of medications and meals for people in hospitals and quarantine zones in China to minimize human-to-human interaction and prevent the spread of COVID-19 (Okyere et al., 2020). Also, China has been utilizing drones to deliver medical testing samples which significantly reduced unnecessary human contact throughout the transport cycle and speeded the feedback for critical tests by cutting delivery time by more than half (Yang & Reuter, 2020). Drones are being used in China and Spain to patrol public spaces and track non-compliance to quarantine directives (Onyango, 2020; Pan 2020).

14

Robots and drones contributed to the COVID-19 response in areas related to preventing disease transmission, screening, diagnosis, and minimizing human-to-human contact.

K2P Harnessing the Power of Digital HealthK2P Harnessing the Power of Digital Health

Various technology applications described throughout require the collection and analysis of a large amount of citizens’ data. For instance, some contact tracing solutions require that the geolocation of every citizen at any point in time be stored in a central server. This raises significant concerns around privacy, cybersecurity and data misuse, especially when the utilization of the collected data shifts from its original purpose (Cho & Yu, 2020). Failing to address these concerns in public health emergencies may undermine public trust and result in public resistance to follow government recommendations (Ienca & Vayena, 2020).

The use of technology in the provision of healthcare raises a concern of social inequity. Barriers such as poor digital literacy, financial constraints, and poor infrastructure may result in inequitable access to digital healthcare (O’Connor et al., 2016). For instance, the younger generations are expected to have good digital literacy, older adults still struggle with the use of technology (Levine et al., 2016). Additionally, the cost of acquiring technology and poor internet access may prohibit certain groups from accessing digital healthcare especially those living in low-to-middle income countries (O’Connor et al., 2016; Yamin et al., 2011).

15

Ethical Considerations

Privacy, cybersecurity and data misuse

Social inequity


16


17

Advancing the Agenda of Digital Health in the Eastern Mediterranean Region

The COVID-19 pandemic provides an opportunity for countries in the EMR to advance digital health to proactively develop and implement evidence-informed decisions for effective and efficient response to the pandemic and beyond.

Insights derived from the global response to the COVID-19 pandemic highlight the importance of advancing the digital health agenda for better response. Key strategies were developed based on international best practices and specific examples from the response to COVID-19.

LEVERAGE ON DIGITAL HEALTH TO RESPOND TO PUBLIC HEALTH EMERGENCIES SUCH AS THE COVID-19 (MAHMOOD, 2020; SCOTT, 2013; WHO, 2019)

01

→ Assess the current state of digital health in the country and identify the national needs to integrate the utilization of digital health modalities within the system

→ Develop a national partnership across multiple sectors to align investments, resources and efforts; therefore ensuring sustainability of digital health efforts and enabling growth

→ Ensure data interoperability by complying with health data standards. This can allow the seamless flow of data among different stakeholders

→ Develop and implement a national and context-specific E-health vision and strategy based on local needs

→ Ensure the availability of adequate and sustainable financial resources → Develop a digital infrastructure that can support digital transformation → Build health workforce capacity including skills related to leadership and governance as well as technology-related skills

→ Develop laws and policies to ensure compliance with ethical standards


18

PROMOTE THE UTILIZATION OF BIG DATA TO ADVANCE AND SUPPORT DIGITAL HEALTH TECHNOLOGIES IN INFORMING AND SUPPORTING THE RESPONSE TO COVID-19 AND BEYOND (VAYENA ET AL., 2018; GUPTA ET AL., 2018; KRUSE ET AL., 2016; SAMUEL & DERRICK, 2020).

02

→ Identify big data sources that can be used to support digital health technologies and information mining

→ Promote interoperability of data sources → Develop an intricate governing framework to address concerns related to (1) data minimization, (2) privacy and security, (3) transparency, and (4) consent

→ Ensure that big data is leveraged for the public good


19

INVEST IN ROBUST NATIONAL HEALTH INFORMATION SYSTEMS (HIS) (WHO, 2007; WHO, 2008; WHO, 2014).

03

→ Updating HIS policies laws and regulations → Improving national coordination, planning and accountability and integrating both public and private providers.

→ Ensuring availability of adequate human, financial and technical resources → Optimizing data generation, quality assurance, monitoring and flow → Building capacity of users on the utilization of the health information systems → Strengthening data and information dissemination, such as translating data into information through dashboards and issuing alerts based on outbreak identification using the HIS

→ Conducting a periodic assessment of additional health information needs

HARNESS THE UTILIZATION OF VIRTUAL TECHNOLOGY TO RESPOND TO THE CONTINUOUS INCREASE IN DEMAND FOR HEALTH SERVICES THAT IS NOT LIMITED TO PUBLIC HEALTH EMERGENCIES BUT ALSO EXTENDS TO EVERYDAY PRACTICE (SMITH ET AL., 2020).

04

→ Ensure sustainable financing and appropriate remuneration for service provision → Train healthcare workforce on new methods required for virtual health consultations

→ Redesign the existing model of care by adopting a whole-system approach and embedding virtual health within the health system

→ Develop policies and procedures for virtual health → Ensure adequate infrastructure to support virtual technology (that can be scaled-up during emergencies)


20

ENABLE PUBLIC ENGAGEMENT AND ENROLLMENT IN DIGITAL HEALTH INTERVENTIONS (O’CONNOR ET AL., 2016)

05

→ Raise public awareness on the role of different digital health interventions, their benefits, and potential risks.

→ Consider incorporating two-way communication platforms where users can interact with healthcare providers or peers with similar health issues.

→ Obtaining endorsement from respected healthcare providers or instructions on the digital health technologies utilized to promote additional user sign up and engagement

→ Market the digital health interventions by targeting not just the patient but also his wider support network, such as his peers and his healthcare providers

→ Develop user-friendly and easy access and sign up digital interventions → Invest in upgrading network capacity and technical infrastructure → Invest in digital upskilling which would allow users to gain the skills needed to utilize the digital solutions

→ Provide funding models that can ensure equity → Inform the public of potential security threats and develop regulations to address security and user privacy and ensure the anonymity of information gathered


21

As the COVID-19 pandemic unfolds, technological applications are emerging as fundamental tools for governments, healthcare providers and healthcare institutions to effectively respond to public health crisis and to improve access to healthcare. There is no one-size-fits-all when it comes to digital health yet this advancement will require extensive assessments, localizing validated solutions and commitment to enable it.

While COVID-19 is a public health crisis, it is also an opportunity for the EMR governments, healthcare providers and healthcare institutions to harness, invest and capitalize on digital health technologies to strengthen evidence-informed decision-making during outbreaks and to address global and national health challenges.

Further Insights


22


23

REFERENCESAi, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., & Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus

disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 200642. Al Shorbaji, N. (2008). e-health in the Eastern Mediterranean Region: a decade of challenges and achievements. Eastern

Mediterranean Health Journal, 14(Special Issue), S157-S173. Alwan, A., Ali, M., Aly, E., Badr, A., Doctor, H., Mandil, A., Rashidian, A., & Shideed, O. (2016). Strengthening national health

information systems: challenges and response. EMHJ-Eastern Mediterranean Health Journal, 22(11), 840-850. Badr, W. (2019, April 22). Auto-Encoder: What Is It? And What Is It Used For? Retrieved from: https://towardsdatascience.

com/auto-encoder-what-is-it-and-what-is-it-used-for-part-1-3e5c6f017726Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on

the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal, 18, 784-790. doi:10.1016/j.csbj.2020.03.025

Bhattacharya, S., Singh, A., & Hossain, M. M. (2019). Strengthening public health surveillance through blockchain technology. AIMS public health, 6(3), 326.

Bullock, J., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the Landscape of Artificial Intelligence Applications against COVID-19. arXiv preprint arXiv:2003.11336.

Bustreo, F., & Tanner, M. (2020). How do we reimagine health in a digital age?. Bulletin of the World Health Organization, 98(4), 232.

Caixiong, Z. (2020, March9). Robots stand in for medical staff. China Daily. Retrieved from: https://www.chinadaily.com.cn/a/202003/09/WS5e661a2fa31012821727d954.html

Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Di Napoli, R. (2020). Features, evaluation and treatment coronavirus (COVID-19). In StatPearls [Internet]. StatPearls Publishing.

Chen, Y., Ding, S., Xu, Z., Zheng, H., & Yang, S. (2019). Blockchain-based medical records secure storage and medical service framework. Journal of medical systems, 43(1), 5.

China Daily (2020, February 12). Disinfection robots put to work fighting COVID-19. China Daily. Retrieved from: https://www.chinadaily.com.cn/a/202002/12/WS5e438906a310128217276fa7_1.html

Cho, H., Ippolito, D., & Yu, Y. W. (2020). Contact tracing mobile apps for covid-19: Privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511.

Christensson, P. (2006). GPS Definition. Retrieved 2020, Apr 21, from https://techterms.comCNBC (2020, March 3). How this Canadian start-up spotted coronavirus before everyone else knew about it. CNBC

Retrieved from: https://www.cnbc.com/2020/03/03/bluedot-used-artificial-intelligence-to-predict-coronavirus-spread.html

Dahiya, N., & Kakkar, A. K. (2016). Mobile health: Applications in tackling the Ebola challenge. Journal of family medicine and primary care, 5(1), 192.

Daniel, G., Cabot, J., Deruelle, L., & Derras, M. (2019, June). Multi-platform chatbot modeling and deployment with the Jarvis framework. In International Conference on Advanced Information Systems Engineering (pp. 177-193). Springer, Cham.

Deldar, K., Bahaadinbeigy, K., & Tara, S. M. (2016). Teleconsultation and clinical decision making: A systematic review. Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku BiH, 24(4), 286-292. doi:10.5455/aim.2016.24.286-292

Digital Health (2020, April 6). NHS partners with tech giants to develop Covid-19 data platform. Digital Health. Retrieved from: https://www.digitalhealth.net/2020/04/nhs-partners-with-tech-giants-to-develop-covid-19-data-platform/

Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet. Infectious Diseases, doi:10.1016/S1473-3099(20)30120-1

Dorsey, E. R., & Topol, E. J. (2020). Telemedicine 2020 and the next decade. The Lancet, 395(10227), 859.Eames, K. T., & Keeling, M. J. (2003). Contact tracing and disease control. Proceedings of the Royal Society of London.

Series B: Biological Sciences, 270(1533), 2565-2571.Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), E20. doi:10.2196/jmir.3.2.e20


24

Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., . . . Fraser, C. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science (New York, N.Y.), eabb6936. doi:10.1126/science.abb6936

Few, S. (2006). Information dashboard design: The effective visual communication of data. O’Reilly Media, Inc.Field, T. (2020, April 10). China Builds COVID-19 Recovery on Blockchain. Bank Info Security. Retrieved from: https://www.

bankinfosecurity.com/china-builds-covid-19-recovery-on-blockchain-a-14093Gartrell, J. W., & Mendenhall, N. M. (1975). Attitudes towards changes in communications technology: The introduction of

teleconferencing. STIN, 76, 14347.Gous, N., Boeras, D. I., Cheng, B., Takle, J., Cunningham, B., & Peeling, R. W. (2018). The impact of digital technologies on

point-of-care diagnostics in resource-limited settings. Expert review of molecular diagnostics, 18(4), 385-397.Greenhalgh, T., Wherton, J., Shaw, S., & Morrison, C. (2020). Video consultations for covid-19. Bmj, 368, m998. doi:10.1136/

bmj.m998Handforth, C. (2020, March 25) What Singapore can teach about an effective coronavirus response. UNDP. Retrieved

from: https://www.undp.org/content/undp/en/home/blog/2020/what-singapore-can-teach-about-an-effective-coronavirus-response.html

Hollander, J. E., & Carr, B. G. (2020). Virtually perfect? telemedicine for covid-19. The New England Journal of Medicine, doi:10.1056/NEJMp2003539

Hu, Z., Ge, Q., Jin, L., & Xiong, M. (2020). Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:2002.07112.

Ienca, M., & Vayena, E. (2020). On the responsible use of digital data to tackle the COVID-19 pandemic. Nature Medicine, 1-2.

Jing, S. (2020, February 2020). Robots, intelligent medical care respond to coronavirus outbreak. China Daily. Retrieved from: https://www.chinadaily.com.cn/a/202002/02/WS5e366cb4a3101282172742b8.html

Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., & Ketai, L. H. (2020). Essentials for radiologists on COVID-19: an update—radiology scientific expert panel.

Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and good practices. Paper presented at the 404-409. doi:10.1109/IC3.2013.6612229

Kierkegaard, P. (2013). eHealth in Denmark: a case study. Journal of medical systems, 37(6), 9991.Kovach, C. R., Taneli, Y., Neiman, T., Dyer, E. M., Arzaga, A. J., & Kelber, S. T. (2017). Evaluation of an ultraviolet room

disinfection protocol to decrease nursing home microbial burden, infection and hospitalization rates. BMC infectious diseases, 17(1), 186. https://doi.org/10.1186/s12879-017-2275-2

Kruse, C. S., Krowski, N., Rodriguez, B., Tran, L., Vela, J., & Brooks, M. (2017). Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ open, 7(8), e016242.

Landi, H (2020, April 27). IBM rolls out blockchain network to address supply-chain issues caused by COVID-19. Fierce Healthcare. Retrieved from: https://www.fiercehealthcare.com/tech/ibm-rolls-out-blockchain-network-to-match-healthcare-organizations-non-traditional-suppliers

Leite, H., Hogkinson I., & Gruber, T. (2020). “New development: ‘Healing at a distance’—telemedicine and COVID-19.” Public Money & Management: 1-3.

Levine, D. M., Lipsitz, S. R., & Linder, J. A. (2016). Trends in seniors’ use of digital health technology in the United States, 2011-2014. Jama, 316(5), 538-540.

Lexico. (n.d.a). Definition of Geolocation. Retrieved April 21, 2020, from https://www.lexico.com/en/definition/geolocationLi, L., Li, Y., Lin, M., Wang, X., & Bao, P. (2020). Preparing and responding to 2019 novel coronavirus with simulation and

technology-enhanced learning for healthcare professionals: Challenges and opportunities in china. BMJ Simulation and Technology Enhanced Learning, , bmjstel-2020-000609. doi:10.1136/bmjstel-2020-000609

Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., & Rabie, K. (2020). A novel ai-enabled framework to diagnose coronavirus covid 19 using smartphone embedded sensors: Design study. arXiv preprint arXiv:2003.07434.

Mahmood, S., Hasan, K., Carras, M. C., & Labrique, A. (2020). Global Preparedness Against COVID-19: We Must Leverage the Power of Digital Health. JMIR Public Health and Surveillance, 6(2), e18980.


25

REFERENCESMarston, H. D., Folkers, G. K., Morens, D. M., & Fauci, A. S. (2014). Emerging viral diseases: confronting threats with new

technologies. Science Translational Medicine, 6(253), 253ps10-253ps10.Mayo Clinic. (n.d.). CT Scan. Retrieved April 21, 2020, from https://www.mayoclinic.org/tests-procedures/ct-scan/about/

pac-20393675Ng, E. (2020, March 21). China’s big tech companies are using their roles in fighting world’s largest health crisis to

turbocharge HealthTech. South China Morning Post. Retrieved from: https://www.scmp.com/business/article/3076180/chinas-big-tech-companies-are-using-their-roles-fighting-worlds-largest

O’Connor, S., Hanlon, P., O’Donnell, C. A., Garcia, S., Glanville, J., & Mair, F. S. (2016). Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC medical informatics and decision making, 16(1), 120.

OECD (2019), “What is an “online platform”?”, in An Introduction to Online Platforms and Their Role in the Digital Transformation, OECD Publishing, Paris, https://doi.org/10.1787/19e6a0f0-en.

Ohannessian, R., Duong, T. A., & Odone, A. (2020). Global Telemedicine Implementation and Integration Within Health Systems to Fight the COVID-19 Pandemic: A Call to Action. JMIR Public Health and Surveillance, 6(2), e18810.

Okyere, M.A., Forson, R. Essel, Gaisey, F. (2020), Positive Externalities of an Epidemic: The Case of the Corona Virus (COVID-19) in China. J Med Virol. Accepted Author Manuscript. doi:10.1002/jmv.25830

Onyango, C. (2020, April 5). Technology key to effective response to curb contagion. Standard Digital. Retrieved from: https://www.standardmedia.co.ke/article/2001366944/technology-key-to-effective-response-to-curb-contagion

Oza, S., Jazayeri, D., Teich, J. M., Ball, E., Nankubuge, P. A., Rwebembera, J., & Walton, D. (2017). Development and deployment of the OpenMRS-Ebola electronic health record system for an Ebola treatment center in Sierra Leone. Journal of medical Internet research, 19(8), e294.

Pan, C. (2020, April 01). Spain’s military uses DJI agricultural drones to spray disinfectant in fight against Covid-19. South China Morning Post.

Portulans Institute. (2019). The Network Readiness Index 2019: Towards a Future-Ready. Retrieved from https://gbsn.org/wp-content/uploads/2020/02/QA-Report-The-Network-Readiness-Index-2019-Towards-a-Future-Ready-Society.pdf

Rao, A. S. S., & Vazquez, J. A. (2020). Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine. Infection Control & Hospital Epidemiology, 1-18.

Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., & Stebbing, J. (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet, 395(10223), e30-e31.

Rouse, M. (2019). What is Big Data and Why is it Important? Retrieved from https://searchdatamanagement.techtarget.com/definition/big-data

Samuel, G., & Derrick, G. (2020). Defining ethical standards for the application of digital tools to population health research. Bulletin of the World Health Organization, 98(4), 239.

Scott, R. E., & Mars, M. (2013). Principles and framework for eHealth strategy development. Journal of medical Internet research, 15(7), e155.

Silja, B., Dutta, S., & Lanvin, B. (2016). The Global Information Technology Report 2016: Innovating in the Digital Economy. In World Economic Forum and INSEAD. Geneva. Retrieved from: http://www3.weforum.org/docs/GITR2016/WEF_GITR_Full_Report.pdf

Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., Clemensen, J., & Caffery, L. J. (2020). Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). Journal of Telemedicine and Telecare, 1357633X20916567.

Tambo, E., Kazienga, A., Talla, M., Chengho, C. F., & Fotsing, C. (2017). Digital technology and mobile applications impact on Zika and Ebola epidemics data sharing and emergency response. J. Health Med. Informat, 8, 254.

Tambo, E., Khayeka-Wandabwa, C., Olalubi, O. A., & Ngogang, J. Y. (2016). Strengthening integrated Zika virus epidemics prevention and aedes mosquito management and containment programs innovations in Africa. J Mol Pathol Epidemiol, 1, 1.

Thompson, C. L., & Morgan, H. M. (2020). Ethical barriers to artificial intelligence in the national health service, United Kingdom of Great Britain and Northern Ireland. Bulletin of the World Health Organization, 98(4), 293.


26

Vayena, E., Dzenowagis, J., Brownstein, J. S., & Sheikh, A. (2018). Policy implications of big data in the health sector. Bulletin of the World Health Organization, 96(1), 66.

Wade, V. A., Karnon, J., Elshaug, A. G., & Hiller, J. E. (2010). A systematic review of economic analyses of telehealth services using real time video communication. BMC health services research, 10(1), 233.

Wallach, H., & O’Connell, D. N. (1953). The kinetic depth effect. Journal of Experimental Psychology, 45(4), 205–217. https://doi.org/10.1037/h0056880

Wang, C. J., Ng, C. Y., & Brook, R. H. (2020a). Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. Jama, doi:10.1001/jama.2020.3151

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., & Xu, B. (2020b). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv.

Wang, Y., Hu, M., Li, Q., Zhang, X. P., Zhai, G., & Yao, N. (2020c). Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534.

WHO (2020). Coronavirus disease (COVID-19) training: Online training. Retrieved from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/training/online-training

WHO. (2018). Managing epidemics: key facts about major deadly diseases. WH0.WHO. (2020). COVID-19 and digital health: What can digital health offer for COVID-19? Retrieved from https://www.who.

int/china/news/feature-stories/detail/covid-19-and-digital-health-what-can-digital-health-offer-for-covid-19Wickramasinghe, N. S., Fadlalla, A. M., Geisler, E., & Schaffer, J. L. (2005). A framework for assessing e-health preparedness.

International journal of electronic healthcare, 1(3), 316-334.Wong, J. E. L., Leo, Y. S., & Tan, C. C. (2020). COVID-19 in Singapore-current experience: Critical global issues that require

attention and action. Jama, 323(13), 1243. doi:10.1001/jama.2020.2467World Health Organization. (2019). Global strategy on digital health: 2020-2024World Health Organization. (2019). WHO guideline: recommendations on digital interventions for health system

strengthening: web supplement 2: summary of findings and GRADE tables (No. WHO/RHR/19.7). World Health Organization.

Xie, Y., Chen, Z., Cheng, Y., Zhang, K., Agrawal, A., Liao, W. K., & Choudhary, A. (2013, June). Detecting and tracking disease outbreaks by mining social media data. In Twenty-Third International Joint Conference on Artificial Intelligence.

Yamin CK, Emani S, Williams DH, Lipsitz SR, Karson AS, Wald JS, et al. The digital divide in adoption and use of a personal health record. Arch Intern Med. 2011;171:568–74.

Yang, G.-Z., Nelson, B. J., Murphy, R. R., Choset, H., Christensen, H., Collins, S. H., Dario, P., Goldberg, K., Ikuta, K., Jacobstein, N., Kragic, D., Taylor, R. H., & McNutt, M. (2020). Combating COVID-19—The role of robotics in managing public health and infectious diseases. Science Robotics, 5(40), eabb5589. DOI: 10.1126/scirobotics.abb5589

Yang, J., & Reuter, T. (2020, March16). 3 ways China is using drones to fight coronavirus. World Economic Forum. Retrieved from: https://www.weforum.org/agenda/2020/03/three-ways-china-is-using-drones-to-fight-coronavirus/

Yuen, C. M., Amanullah, F., Dharmadhikari, A., Nardell, E. A., Seddon, J. A., Vasilyeva, I. & Becerra, M. C. (2015). Turning off the tap: stopping tuberculosis transmission through active case-finding and prompt effective treatment. The Lancet, 386(10010), 2334-2343.

Zandi, D., Reis, A., Vayena, E., & Goodman, K. (2019). New ethical challenges of digital technologies, machine learning and artificial intelligence in public health: a call for papers. Bulletin of the World Health Organization, 97(1), 2-2.

Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395(10225), 676-676. doi:10.1016/S0140-6736(20)30461-XZayyad, M. A., & Toycan, M. (2018). Factors affecting sustainable adoption of e-health technology in developing countries:

an exploratory survey of Nigerian hospitals from the perspective of healthcare professionals. PeerJ, 6, e4436.Zhavoronkov, A. (2018). Artificial intelligence for drug discovery, biomarker development, and generation of novel

chemistry. Molecular Pharmaceutics, 15(10), 4311-4313. doi:10.1021/acs.molpharmaceut.8b00930


Knowledge to Policy (K2P) CenterFaculty of Health SciencesAmerican University of BeirutRiad El Solh, Beirut 1107 2020Beirut, Lebanon+961 1 350 000 ext. 2942-2943 www.aub.edu.lb/[email protected]

Follow usFacebook Knowledge-to-Policy-K2P-CenterTwitter @K2PCenter

Harnessing the Power of Digital Health in Responding to ... · new cases, prompt results sharing...

Documents

Transcript of Harnessing the Power of Digital Health in Responding to ... · new cases, prompt results sharing...