# Transitioning towards fit-for-purpose Public Health Surveillance Systems

Maria N. Anastasiadou1, Philippos Isaia1, Panayiotis Kolios1, Christos
Charalambous4  1KIOS Center of Excellence for Research and Innovation,
University of Cyprus,
4Unit for Surveillance and Control of Communicable Diseases, Ministry of
Health

###### Abstract

The COVID-19 pandemic has exposed several weaknesses in the public health
infrastructure, including supply chain mechanisms and public health ICT
systems. The expansion of testing and contact tracing has been key to
identifying and isolating infected individuals, as well as tracking and
containing the spread of the virus. Digital technologies, such as telemedicine
and virtual consultations, have experienced a surge in demand to provide
medical support while minimizing the risk of transmission and infection. The
pandemic has made it clear that cooperation, information sharing, and
communication among stakeholders are crucial in making the right decisions and
preventing future outbreaks. Redesigning public health systems for effective
management of outbreaks should include five key elements: disease surveillance
and early warning systems, contact tracing and case management, data analytics
and visualization, communication and education, and telemedicine. As the world
navigates the COVID-19 pandemic, healthcare ICT systems will play an
increasingly important role in the future of healthcare delivery. In a post
COVID-19 world, several ICT strategies should be implemented to improve the
quality, efficiency, and accessibility of healthcare services, including the
expansion of telemedicine, data analytics and population health management,
interoperability, and cybersecurity.

Overall, this report summarises the importance of early detection and rapid
response, international cooperation and coordination, clear and consistent
communication, investing in public health systems and emergency preparedness,
digital technology and telemedicine, and equity and social determinants of
health. These lessons demonstrate the need for better preparedness and
planning for future crises and the importance of addressing underlying issues
to create a more resilient and accessible digital infrastructure.

##  I Introduction

The COVID-19 pandemic has had a significant impact on healthcare systems
worldwide. The novel virus, which caused a highly contagious respiratory
illness, has led to a surge in demand for medical care, including testing,
treatment, and hospitalisation. In response, many countries have implemented
measures to increase capacity and minimise the spread of the virus. One of the
key measures has been the expansion of testing and contact tracing, which
helps to identify and isolate infected individuals, as well as to track and
contain the spread of the virus. Quarantine and isolation protocols have also
been put in place to separate those who are infected or have been exposed to
the virus. Of course personal protective equipment (PPE) especially for
healthcare workers has also played an important role in the response. However,
digital technologies have been the ones experiencing a sugre in demand, with
digital public health tools and virtual consultations being the most prevalent
healthcare services to continue providing medical support while also
minimising the risk of transmission and infection.

Hence, a key outcome of the COVID-19 pandemic was that it has highlighted
numerous weaknesses in the existing public health infrastructure, including
its supply chain mechanisms and public health ICT systems. This has made it
difficult for health officials and health workers to effectively make informed
decisions and implement measures to control the spread of the virus. Many
healthcare facilities have underestimated and found under prepared to handle
the surge and lacked adequate tools to properly address the evolving
challenges.

A second key outcome is that it has become evident that cooperation and
information sharing among stakeholders, including governments and healthcare
institutions, as well as clear and timely communication with the public, is
crucial in making the right decisions and communicating the right information
for smooth society functioning [1]. However, it is still uncertain whether
public health ICT systems in any one country have adapted to cope with future
outbreaks [2].

Evidently, there is an urgent need to redesign public health systems for
effective digital management of these outbreaks that will include the
following five key elements [3]:

  1. 1.

Disease Surveillance and Early Warning Systems: Real-time monitoring and
surveillance of diseases can help to quickly identify new outbreaks and track
their spread. This includes the use of tools such as web-based reporting
systems, mobile health (mHealth) applications, and social media monitoring.

  2. 2.

Contact Tracing and Case Management: Contact tracing is a critical component
of pandemic management, and it can be greatly facilitated through the use of
ICT. This includes digital contact tracing apps, case management systems for
healthcare providers, and digital tools for self-reporting and self-isolation
monitoring.

  3. 3.

Data Analytics and Visualization: Data analytics and visualization tools are
essential for understanding the spread of the pandemic, identifying trends,
and making informed decisions. This includes the use of dashboards, data
visualizations, and predictive analytics.

  4. 4.

Communication and Education: Effective communication is crucial during a
pandemic, and ICT can be used to disseminate information to the public,
healthcare providers, and other stakeholders. This includes the use of digital
communication channels such as social media, chatbots, and online forums, as
well as digital education and training resources.

  5. 5.

Telemedicine and Remote Care: The use of telemedicine and remote care can help
to reduce the burden on healthcare systems, minimize exposure to the virus,
and improve access to care. This includes the use of teleconsultation
platforms, remote monitoring tools, and digital health records.

The transitioning of these key functionalities towards digitization,
automation and intelligences are crucial to improve patient care quality and
capacity during outbreaks; which were severely impacted during the pandemic.
Given the current shortcomings in healthcare systems, steps must be taken to
modernise and adapt healthcare systems in order to better prepare for future
pandemics. As the world continues to navigate the COVID-19 pandemic, it has
become clear that healthcare ICT systems will play an increasingly important
role in the future of healthcare delivery. In a post COVID-19 world, there are
several ICT strategies that should be implemented to improve the quality,
efficiency, and accessibility of healthcare services. These include [3]:

  1. 1.

Expansion of Telemedicine: Telemedicine has become an essential tool during
the pandemic for remote consultations and monitoring of patients. In a post-
COVID-19 world, healthcare providers should continue to expand telemedicine
services to provide remote care and support to patients in underserved areas,
as well as those who are unable to travel to healthcare facilities.

  2. 2.

Data Analytics and Population Health Management: Healthcare ICT systems should
prioritize data analytics and population health management to identify high-
risk populations and provide targeted interventions. This includes the use of
predictive analytics to identify patients who are at risk of developing
certain conditions, as well as the implementation of population health
management tools to support chronic disease management and preventive care.

  3. 3.

Remote Patient Monitoring: Remote patient monitoring tools can help healthcare
providers to manage chronic conditions and monitor patients remotely. In a
post-COVID-19 world, healthcare providers should expand the use of remote
patient monitoring tools to reduce the need for in-person appointments and
support patients in managing their health outside of the clinical setting.

  4. 4.

Interoperability: Interoperability is key to improving the efficiency and
effectiveness of healthcare delivery. In a post COVID-19 world, healthcare ICT
systems should prioritize the development of interoperable systems to enable
the seamless sharing of data between healthcare providers, patients, and
different healthcare systems.

  5. 5.

Cybersecurity: With the increased use of healthcare ICT systems, cybersecurity
will become even more critical. Healthcare organizations should prioritize
cybersecurity to ensure that patient data is protected and secure, and to
prevent cyber attacks that could disrupt healthcare delivery.

During the early stages of the pandemic, protocols for treating COVID-19 cases
were frequently changing due to a lack of information, insufficient supplies
and capabilities of healthcare facilities, limited availability of effective
prophylactics, and disagreements over treatment methods. For example, the use
of the antiparasitic drugs as a treatment for COVID-19 was widespread in
various countries, but its effectiveness was not supported by clinical
evidence [4], [5]. Additionally, there were disruptions in the supply chain of
certain drugs [5], leading to shortages and the use of alternative
medications. These issues highlight the need for better preparedness in
healthcare systems to manage pandemics and ensure access to necessary medical
supplies and treatments, especially in areas of lower economic status.
Effective planning is essential to protect against the spread of pathogens and
minimize lockdowns and restrictions [6]. Preventive measures are equality
important with the establishment of streamlined processes for the development
of vaccines and innovative treatments through understanding of molecular and
genomic aspects [7]. This process necessitates cross-boarder collaboration for
spatio-temporal tracking of viruses across large geographical regions and
constant assessment of genetic variations [8].

The ongoing impact of shutdowns on healthcare systems, particularly in
underdeveloped countries, is still apparent. The last two years have shown the
urgent need for reviving and restructuring medical services worldwide. This
includes improving ICU triage, flexible increase in ICU capacity and staffing,
ensuring safety in facility design, having enough supplies and equipment,
focusing on the well-being of healthcare workers, and incorporating better
end-of-life care in hospital management [9]. The pandemic also highlighted the
significance of digital transformation and streamlining of epidemiological
registries in healthcare. Reorganising healthcare systems should be a priority
to improve efficiency and availability of drugs, preventive medicine, tele-
medicine, and reduce emergency and hospital visits. The SARS-CoV-2 pandemic
has demonstrated that this is crucial for global health, regardless of
location [10], [11], [12].

It also becomes increasingly clear that the development and distribution of
vaccines worldwide was never enough to end the COVID-19 pandemic, even though
considerable resources have been invested in design, production, and
distribution of vaccines [13]. Evidently, people in countries with high
vaccination rates have less severe symptoms and reduced hospitalisation rates,
but vaccines do not prevent infection and transmission [12]. Effective control
of the virus will depend on the creation of vaccination platforms with
priority groups, deployment of vaccination centres, and trust in medical
professionals. Transparency of government authorities, research experts, and
healthcare professionals is also crucial to inform the public about the safety
and side effects of vaccines compared to the risk of COVID-19 [14]. To make
informed decisions, a system to report potential side effects and other issues
concerning vaccination is necessary. Despite these challenges, the future of
healthcare has gone online, with many advantages for patients and healthcare
providers.

Overall, this survey aims to provide a holistic review of the key components
around public health surveillance systems and the necessary transformations
that need to take place in order to be better prepared for future incidents
and emergencies. The survey is organised as follows. Section II describes
three categories of Healthcare ICT systems, the Electronic Health Records
(EHR), Hospital Management Systems (HMS) and Health Tracking & Monitoring
Systems and their challenges. Section III provides the Public Healthcare needs
and its challenges as experienced during the COVID-19 pandemic. Specifically,
we emphasised on Epidemiological Surveillance, Case Identification and Public
Communication Platforms. Section IV reveals the emergency response ICT
challenges in the public Health sector during COVID-19. Section V discusses
key findings and highlights important outcomes and future developments.
Section VI provides concluding remarks and highlights a number of important
lessons learned during the COVID-19 pandemic in order to be better prepared to
future pandemics.

##  II Healthcare ICT systems

Healthcare may be one of the most complex industries to digitalize as it
involves many stakeholders: from hospitals and medical centers, through
doctors, pharmaceutical and research companies, up to patients themselves.
Moreover, Healthcare Information and Communication Technology (ICT) systems
refer to the use of technology in healthcare to manage and process
information, as well as to improve communication and collaboration among
healthcare professionals [15]. These systems can include electronic health
records (EHRs), telemedicine, mHealth, and health information exchange (HIE).
One of the main benefits of healthcare ICT systems is the ability to improve
the quality and safety of care [16]. Electronic health records (EHRs), for
example, allow for the storage and sharing of patient information in a secure
and efficient manner, which can improve the accuracy of diagnoses and
treatment plans. Telemedicine and mHealth can also improve access to care,
particularly for those in remote or underserved areas [16]. Another benefit of
healthcare ICT systems is the ability to improve efficiency and reduce costs.
Electronic health records (EHRs) can reduce the need for paper records and
reduce administrative tasks, while telemedicine and mHealth can improve the
coordination of care and reduce the need for in-person visits. Health
information exchange (HIE) allows for the sharing of patient information
across different healthcare organizations, which can improve continuity of
care, reduce errors and support population health management. One of the main
challenges of healthcare ICT systems is the need for robust and secure systems
to protect patient data. This includes issues such as data privacy and
security, as well as the need for systems that are reliable and can withstand
cyber attacks. Another challenge is the need for interoperability, which means
that different systems need to be able to communicate and share information
with each other in a seamless and efficient manner. Finally, there is a need
to ensure that healthcare ICT systems are user-friendly and easy to use,
particularly for healthcare professionals who may not have a lot of experience
with technology [17]. In conclusion, healthcare ICT systems are an important
aspect of modern healthcare. They can improve the quality and safety of care,
increase efficiency, and reduce costs. However, it is important to address
challenges such as data security, interoperability, and user-friendliness in
order to ensure that these systems are effective and beneficial for both
patients and healthcare professionals. Below we describe three categories that
are more relevant with the subject matter of this survey.

###  II-A Electronic Health Records (EHR)

The electronic health record (EHR) has been gaining popularity as a source of
patient data for both clinical and research purposes [18]. EHRs allow for the
digitalization and centralization of patient health information, making it
more accessible and usable for healthcare providers, which can improve
continuity of care and coordination between different providers, as well as
enable population health management and analytics [19]. EHRs were initially
designed for clinical care and billing purposes [20]. However, there has been
an effort to optimize the types of data and methods of storing this data to
support research initiatives. The primary types of information available from
EHRs include billing data, laboratory results and vital signs, provider
documentation, documentation from reports and tests, and medication records
[20]. This data can be used for a variety of secondary purposes [18] such as
decision-making, managing patient conditions, data exchange, building
predictive models and discovering new medical knowledge.

EHRs are widely used in epidemiological research, but the validity of the
results is dependent upon the assumptions made about the healthcare system,
the patient, and the provider [21].

Figure 1: AdvancedMD EHR Software [22]

####  II-A1 Data Available in EHRs

  * •

Billing data: They typically consists of codes derived from the International
Classification of Diseases (ICD) and Current Procedural Terminology (CPT). ICD
is a terminology system for diseases, symptoms, and procedures that is
maintained by the World Health Organization (WHO). It is widely used across
the world and most countries use version 11 [23]. These codes are commonly
used in Electronic Medical Record (EMR) systems and are also used for research
purposes [24, 25, 26, 27, 28, 29], they are often used to bill insurance
companies. However, previous research [30, 31] has shown that these codes can
have low accuracy and specificity. Despite this, they are still used as part
of more complex algorithms that have a high level of performance [32, 33, 34].
CPT codes are created and maintained by the American Medical Association and
are used for billing clinical services, they are often paired with ICD codes
to satisfy the requirements of insurance companies. For example, insurance
companies will pay for a brain MRI scan if it is ordered for certain symptoms
or diseases but not for others [35].

  * •

Laboratory and Vital Signs: The medical record contains a long-term record of
mostly structured data in the form of laboratory data and vital signs. These
data can be stored as name-value pairs, and can also be encoded using standard
terminologies. The most commonly used controlled vocabulary for laboratory
tests and vital signs is the Logical Observation Identifiers Names and Codes
(LOINC), which is a Consolidated Health Informatics standard for the
representation of laboratory and test names and is part of Health Language 7
(HL7) [36, 37]. Despite the increasing use of LOINC, many hospital lab systems
still use local dictionaries to encode laboratory results internally. This can
cause issues as hospital laboratory systems or testing companies may change
over time, resulting in different internal codes for the same test result, so
care must be taken to implement selection logic based on laboratory results
[38].

  * •

Provider Documentation: Clinical documentation is a valuable source of
information for identifying patients with specific characteristics or
conditions, known as phenotyping. This documentation, which is required for
billing and can be found in electronic health record systems, must be in an
electronic format for it to be useful for phenotyping efforts [39]. This can
include unstructured narrative text or structured documentation, which can be
processed using text searches or natural language processing techniques.

  * •

Documentation from Reports and Tests: The provider-generated reports and test
results include things like radiology and pathology reports and some procedure
results such as echocardiograms. They often come in the form of narrative text
results [40], which can include both structured and unstructured data. For
example, an electrocardiogram report may have structured data like interval
durations, as well as a narrative text interpretation provided by the
cardiologist. These structured data are generated by automated algorithms and
may vary in accuracy [41].

  * •

Medication Records: Medication records play a crucial role in identifying and
characterizing a patient’s condition. They can be used to improve the accuracy
of identifying a particular case, and to confirm that patients believed to be
healthy do not have the disease. Medications received by a patient serve as
evidence that the treating physician believed that the disease was present and
required treatment. Medication records can be in different forms within an
electronic record. With the widespread use of computerized provider order
entry systems in hospitals, inpatient medication records are often available
in highly structured records that can be mapped to controlled vocabularies.
Many hospitals are also using automated barcode medication administration [42]
records, which allow for accurate drug exposure and timing to be recorded for
each inpatient. Even without these systems, research has shown that
medications ordered through CPOE systems are administered with high
reliability [43].

In early 90s EHR systems were mostly developed and used at academic centers as
a hybrid of paper and electronic data. These systems are based on hierarchical
and relational databases and are mainly focused on billing and scheduling
systems, as well as some clinical systems. They were mostly running on large
mainframe and minicomputers with limited data storage capacity, but with the
advancement of technology, personal computers with graphics were used as
monitors. Data entry was mainly done through keyboards and mouse. EHR systems
were used in both inpatient and outpatient facilities and were connected
through Local area networks and the Internet, with many systems being web-
based. They included features such as image scanning and paper printouts,
Clinical Decision Support, Computerized Provider Order Entry, drug references,
clinical manuals, textbooks of medicine, literature searching, physician
documentation, and electronic signatures. They also complied with Health Level
7 and IEEE P1157 standards and Universal Medical Language System. There were
also interfaces for medical devices and Picture Archiving and Communication
Systems (PACS) were used to store and retrieve images. However, there were
also ethical issues that need to be considered such as data ownership, data
liability, informed consent, security, and privacy.

In 2022 EHR systems are widely used in health care, nursing homes, insurance
companies, and relevant governmental departments. They are mostly vendor EHRs
which are interfaced with Personal Health Records. Intra-facility data sharing
with standards is becoming more common and data mapping to SMOMED & LOINC [44]
is also used. They are also used in digital pathology, mental health, external
labs, e-prescribing, order-sets, family history, genetics, biobanks,
biosurveillance, and public health. With the increase of EHRs, Clinical
Decision Support has also increased and it is now incorporating Natural
Language Processing and Big Data. They are also being used in mobile devices
and open-source EHRs are also becoming more prevalent. The use of digital
identities and enterprise data warehouses for example to monitor birth to
death information, are also increasingly supported by theses systems. Cloud
computing is also being used more often and notifications/alerts are
increasingly prevailing (for instance via email, pagers and cell phone sms) to
inform relevant users of changes. However, there are also ongoing challenges
for introducing further information and new features that have significantly
increased interest in EHR related publications.

###  II-B Hospital Management Systems (HMS)

A hospital management system (HMS) [45] is a computerized system that is used
to improve patient care by increasing knowledge and reducing uncertainty. It
allows for rational decisions to be made based on the most recent information
provided. The system streamlines operations, improves administration and
control, and enhances patient care while also controlling costs and improving
profitability. HMS is designed to be powerful, flexible, and easy to use [46,
47]. It is made up of various subsystems that are integrated to capture data
in different sections of a hospital, including patient registration, billing,
laboratory, pharmacy, and radiology. These subsystems include [48, 49]:

Figure 2: HMS High Level Architecture [50]

  * •

Appointment Management[51]: Hospital appointment management platforms (see
Figure 3) are digital tools that help hospitals and medical clinics to
streamline their patient scheduling and appointment booking processes. They
provide a centralized system that allows patients to easily book appointments
online, while also giving healthcare providers access to real-time patient
schedules and appointment information. Some of the key features of these
platforms include:

    1. 1.

Online appointment booking: Patients can easily book appointments online,
choosing a date and time that works for them.

    2. 2.

Real-time schedules: Healthcare providers can view real-time schedules and
appointment information, making it easy to manage patient flow and avoid
overbooking.

    3. 3.

Automated reminders: Patients can be automatically reminded of upcoming
appointments via text or email, reducing the risk of missed appointments.

    4. 4.

Patient self-scheduling: Patients can schedule their own appointments and view
available slots in real-time, reducing the need for staff to manage the
process.

    5. 5.

Integrations: These platforms can be integrated with electronic health records
(EHRs) or other hospital systems, allowing for seamless transfer of patient
data.

    6. 6.

Reporting: Platforms can generate reports on patient scheduling and
appointment information, allowing hospitals to analyze data and improve
patient flow.

Overall, hospital appointment management platforms can help to improve patient
satisfaction and reduce administrative burdens on staff, by allowing patients
to easily schedule appointments, and providing real-time scheduling data to
healthcare providers.

Figure 3: Hospital Appointment Management Platform Architecture [52]

  * •

Patient Management[53]: Hospital Patient Management Platforms (HPMP) are
computer-based systems used by healthcare organizations to manage and track
patient information. These systems typically include features such as:

    1. 1.

Patient registration and demographic information management: This feature
allows for the capturing and storage of patient personal and contact
information, insurance details, and other demographic data.

    2. 2.

Appointment scheduling and management: By using this function, patients you
will be able to schedule and manage appointments online, view upcoming
appointments, and receive reminders. It also enables the healthcare provider
to manage their schedules and ensure the availability of resources.

    3. 3.

Electronic Medical Records (EMR) and charting: With the implementation of this
tool, healthcare providers can document and access patient medical history,
treatments, and lab results electronically. It also enables the sharing of
patient information among different healthcare providers.

    4. 4.

Clinical decision support and treatment planning: This new functionality
offers healthcare providers the opportunity to access patient data, guidelines
and best practices to make informed decisions about patient care, and develop
personalized treatment plans.

    5. 5.

Medication management and prescription writing: Healthcare providers are now
able to electronically prescribe medications, track dosages and refill
requests, and alert providers to potential drug interactions.

    6. 6.

Laboratory and diagnostic test ordering and results management: This tools
enables healthcare providers to electronically order and review laboratory and
diagnostic test results.

    7. 7.

Patient billing and insurance management: This tool can easily allows the
management of patient billing, insurance claims, and payments. It also enables
the tracking of outstanding balances and insurance reimbursements.

    8. 8.

Patient education and self-management tools: This feature ensures that the
patients with educational resources, self-management tools, and access to
their medical information to improve their understanding of their health
conditions and treatment options.

    9. 9.

Care coordination and communication with other healthcare providers: By
utilizing this feature, healthcare providers can easily communicate and
collaborate with other providers, such as specialists and primary care
physicians, to coordinate patient care.

    10. 10.

Population health management and analytics:By utilizing this feature,
healthcare providers can easily to track and manage patient populations,
identify trends and patterns, and monitor the effectiveness of treatment
plans.

    11. 11.

Secure messaging and telemedicine capabilities: This feature allows patients
and providers to communicate securely via messaging and videoconferencing,
enabling remote consultations and follow-up.

    12. 12.

Reporting and data analytics for patient outcomes and performance metrics: The
introduction of this feature means that healthcare providers can now access
patient data and analytics to track outcomes, monitor performance, and
identify areas for improvement.

    13. 13.

Mobile access for patients and providers: This tool enables patients and
providers to access the system and patient data on their mobile devices.

    14. 14.

Compliance with regulatory standards such as HIPAA and Meaningful Use: This
functionality ensures that the system is compliant with regulations such as
the Health Insurance Portability and Accountability Act (HIPAA) and the
Meaningful Use (MU) program.

Overall, HPMPs are computer-based systems that allow healthcare organizations
to manage and track patient information, including electronic health records,
appointment scheduling, medication management, lab results tracking,
analytics, and reporting. The goal of HPMP is to improve the quality of care
for patients by providing healthcare providers with quick and easy access to
patient information, which can help to reduce errors, improve communication,
and make data-driven decisions.

Figure 4: Healthcare Facility Management Model [54]

  * •

Facility Management[55]: Hospital facility management platforms are digital
tools that help hospitals and medical clinics to manage their physical
facilities and infrastructure. They provide a centralized system for tracking
and monitoring various aspects of a hospital’s physical operations, such as
maintenance, safety, and energy efficiency. Some of the key features of these
platforms include:

    1. 1.

Room Management: Provides a comprehensive solution for managing the allocation
and availability of rooms, as well as scheduling regular maintenance tasks.

    2. 2.

Equipment Management: Enables tracking and maintenance of medical equipment,
ensuring that all equipment is functioning properly, and maintenance tasks are
scheduled as needed.

    3. 3.

Inventory Management: Medical supplies can be tracked, reordered, and usage
can be monitored to ensure that inventory levels are optimized.

    4. 4.

Maintenance Management: Facility managers can schedule and track maintenance
tasks, ensuring that all systems are functioning optimally and that any issues
are addressed in a timely manner.

    5. 5.

Energy Management: Enables tracking and management of energy usage, which can
lead to cost savings and more efficient energy use.

    6. 6.

Security Management: Provides tools for monitoring and managing security
systems, including access control and surveillance cameras, ensuring that the
facility is secure at all times.

    7. 7.

Asset Management: All assets within the facility, including furniture and
equipment, can be tracked and managed, ensuring that they are properly
maintained and accounted for.

    8. 8.

Compliance Management: Enables monitoring and management of facility
compliance with local and national regulations, including HIPAA and OSHA
compliance, ensuring that the facility is operating in a safe and compliant
manner.

    9. 9.

Reporting and Analytics: Provides reporting and analytics tools for facility
usage, maintenance, and energy usage, enabling data-driven decision-making and
more effective management.

    10. 10.

Mobile Access: Remote monitoring and management of the facility through mobile
devices it is allowed by this platform, ensuring that managers can stay up-to-
date on facility operations even when they are not on-site.

Overall, hospital facility management platforms can help to improve the
efficiency and effectiveness of a hospital’s physical operations, by providing
a centralized system for tracking and monitoring various aspects of facility
management and by providing real-time visibility into the hospital’s physical
operations.

  * •

Staff Management[56]: Hospital staff management platforms are digital tools
that help hospitals and medical clinics to manage and organize their staff.
They provide a centralized system for tracking and monitoring staff schedules,
time-off, and performance, as well as providing tools for communication and
collaboration among staff members. Some of the key features of these platforms
include:

    1. 1.

Employee Management: Facilitates the tracking and management of employee
information, including personal details, job titles, and schedules.

    2. 2.

Shift Management: By using shift management tools, managers can schedule and
manage employee shifts, allowing for shift swapping and effective overtime
management.

    3. 3.

Attendance Management: Attendance management software enables the tracking and
management of employee attendance, including leave requests and time-off
management.

    4. 4.

Payroll Management: With payroll management software, managers can efficiently
handle employee payroll, including calculating pay, taxes, and deductions, and
generating paystubs and reports.

    5. 5.

Performance Management: Performance management tools enable managers to track
and manage employee performance, including setting goals, tracking progress,
and providing feedback.

    6. 6.

Training Management: Training management software allows managers to track and
manage employee training, including scheduling and tracking progress.

    7. 7.

Recruitment Management: Recruitment management software streamlines
recruitment processes by enabling posting of job openings, tracking resumes,
and scheduling interviews.

    8. 8.

Reporting and Analytics: Reporting and analytics software generates insightful
reports and analytics on employee data, including attendance, performance, and
turnover rate.

    9. 9.

Mobile Access: Mobile access to employee management platforms allows managers
to remotely monitor and manage staff using their mobile devices.

    10. 10.

Communication: By facilitating internal communication through messaging,
discussion boards, and shared documents, communication tools on employee
management platforms foster collaboration and engagement among staff.

Overall, hospital staff management platforms can help to improve the
efficiency and effectiveness of a hospital’s staff, by providing a centralized
system for tracking and monitoring staff schedules, time-off, and performance,
and by providing tools for communication and collaboration among staff
members.

Figure 5: Healthcare Supply Chain Structure [57]

  * •

Supply Management[58]: Hospital supply management platforms are digital tools
that help hospitals and medical clinics to manage their inventory of medical
supplies, equipment and drugs. They provide a centralized system for tracking
and monitoring the inventory levels, ordering, receiving, and distribution of
these items. Some of the key features of these platforms include:

    1. 1.

Inventory management: Offers a comprehensive solution for tracking inventory
levels, managing stock levels, and preventing stockouts.

    2. 2.

Ordering and receiving Hospitals can utilize the tools provided by these
platforms to create purchase orders, monitor deliveries, and receive items
into inventory efficiently.

    3. 3.

Distribution and allocation: The distribution and allocation of medical
supplies, equipment, and drugs to different departments and clinics within the
hospital can managed by this platforms.

    4. 4.

Automated reordering: By automatically reordering items when stock levels drop
below a certain threshold, inventory management platforms minimize the risk of
stockouts and help hospitals maintain optimal inventory levels.

    5. 5.

Reporting: Inventory management platforms generate a variety of reports on
inventory levels, costs, usage, and other metrics, enabling managers to
identify trends and make informed decisions.

    6. 6.

Supply Chain Visibility: Supply chain visibility is greatly enhanced by
inventory management platforms, which provide real-time visibility of the
entire supply chain process, from order placement to delivery.

    7. 7.

Electronic Data Interchange (EDI): By leveraging electronic data interchange
(EDI), inventory management platforms streamline the purchase order process
and ensure timely delivery of items by communicating seamlessly with
suppliers, distributors, and other partners.

Overall, hospital supply management platforms can help to improve the
efficiency and effectiveness of a hospital’s supply chain by providing a
centralized system for tracking and monitoring inventory levels, ordering,
receiving, and distribution of medical supplies, equipment and drugs, and
providing real-time visibility of the entire supply chain process.

  * •

Financial Management[59]: Hospital financial management platforms are digital
tools that help hospitals and medical clinics to manage their financial
operations. They provide a centralized system for tracking and monitoring
revenue, expenses, billing, and other financial activities. Some of the key
features of these platforms include:

    1. 1.

Billing and Invoicing: Generation and management of patient bills, including
insurance claims and payment processing, are enabled by the Billing and
Invoicing feature

    2. 2.

Accounts Payable: Vendor invoices, purchase orders, and payments can be
managed with ease thanks to the Accounts Payable feature.

    3. 3.

Accounts Receivable: The management of patient payments, including patient
statements and payment collections, is made possible by the Accounts
Receivable feature.

    4. 4.

Revenue Cycle Management: The Revenue Cycle Management feature provides a
comprehensive solution for managing the entire revenue cycle, from patient
registration to insurance claims and payments.

    5. 5.

Budgeting and Forecasting: Creation and management of budgets, including
forecasting future revenue and expenses, is made possible by the Budgeting and
Forecasting feature.

    6. 6.

General Ledger: Financial transactions, including journal entries and account
balances, can be managed easily using the General Ledger feature.

    7. 7.

Financial Reporting: Financial Reporting feature allows for the generation of
financial reports, including income statements, balance sheets, and cash flow
statements, providing valuable insights into the hospital’s financial health.

    8. 8.

Cost Analysis: The Cost Analysis feature enables analysis of costs, including
resource utilization, supply chain, and labor costs, helping hospitals to
identify areas for improvement and cost savings.

    9. 9.

Auditing and Compliance: The Auditing and Compliance feature provides a tool
for managing audit and compliance processes, including HIPAA, CMS, and other
regulatory requirements, ensuring that hospitals comply with all necessary
regulations.

    10. 10.

Integration with other systems: The integration with other hospital systems,
such as EHRs and supply chain management systems, to streamline financial
processes and ensure accurate data is utilised with this feature.

Overall, hospital financial management platforms can help to improve the
efficiency and effectiveness of a hospital’s financial operations by providing
a centralized system for tracking and monitoring revenue, expenses, billing,
and other financial activities. They can also provide tools for budgeting,
forecasting, and cost accounting, which can help managers to make data-driven
decisions and optimize the financial performance of the hospital.

  * •

Insurance Management[60]: Hospital insurance management platforms are digital
tools that help hospitals and medical clinics to manage their interactions
with insurance providers and payers. They provide a centralized system for
tracking and monitoring insurance claims, payments, and other insurance-
related activities. Some of the key features of these platforms include:

    1. 1.

Claims management: process and manage insurance claims, including submission,
tracking, and approval processes.

    2. 2.

Patient enrollment and eligibility verification: verify patient insurance
coverage and enrollment status.

    3. 3.

Billing and revenue cycle management: generate and submit bills to insurance
companies, track payments and denials, and manage the revenue cycle.

    4. 4.

Contract management: manage contracts with insurance companies, including rate
negotiations and compliance with contract terms.

    5. 5.

Fraud detection and prevention: identify and prevent fraudulent claims and
billing practices.

    6. 6.

Reporting and analytics: generate reports and analyze data on insurance
claims, payments, denials, and other key metrics.

    7. 7.

Integration with other systems: integrate with other hospital systems, such as
electronic health records and scheduling systems, to streamline processes and
improve data accuracy.

    8. 8.

Policy management: manage and track insurance policies and ensure compliance
with regulations.

    9. 9.

Communication with insurance companies: communicate with insurance companies
via electronic data interchange (EDI) or other methods to ensure timely and
accurate claim processing.

    10. 10.

Security and compliance: ensure compliance with regulatory requirements for
patient data protection and security.

Overall, hospital insurance management platforms can help to improve the
efficiency and effectiveness of a hospital’s interactions with insurance
providers and payers. They provide a centralized system for tracking and
monitoring insurance claims, payments, and other insurance-related activities,
and can help to ensure that hospitals and clinics receive the reimbursement
they are entitled to. The platform’s functionality can also help to automate
the process and reduce administrative burdens on staff.

  * •

Laboratory Management[61]: Hospital laboratory management platforms are
digital tools that help hospitals and medical clinics to manage their
laboratory operations. They provide a centralized system for tracking and
monitoring lab results, test ordering, and other laboratory-related
activities. Some of the key features of these platforms include:

    1. 1.

Patient Information Management: The storage and retrieval of patient data,
such as demographics, medical history, and test results, is made possible by
the Managing Patient Information feature.

    2. 2.

Test Ordering and Result Reporting: This feature is a tool that enables
tracking and management of laboratory test orders, as well as the generation
and distribution of test results.

    3. 3.

Inventory Management: With the Inventory Control feature, laboratory supplies
and reagents can be managed and tracked more efficiently.

    4. 4.

Billing and Insurance: Facilitates the process of billing patients and
insurance companies for laboratory services.

    5. 5.

Electronic Medical Records (EMR) Integration: This tool is responsible for
tracking and monitoring quality control measures and compliance with
regulatory standards, ensuring a high level of quality in laboratory testing.

    6. 6.

Quality Control and Compliance: For tracking and monitoring quality control
measures and compliance with regulatory standards, ensuring a high level of
quality in laboratory testing.

    7. 7.

Reporting and Analytics: Generates various reports and analytics, including
patient statistics and test volume, providing valuable insights into
laboratory performance.

    8. 8.

Security and Access Control: Ensures secure access to patient information and
laboratory data, with roles-based access control.

    9. 9.

Mobile and Remote Access: Allows laboratory information to be accessed
remotely from different locations and devices, providing greater flexibility
and convenience.

    10. 10.

Cloud-based Deployment: The deployment of the platform is allowed in the
cloud, which enables scalability and easy access to the system from anywhere.

Overall, hospital laboratory management platforms can help to improve the
efficiency and effectiveness of a hospital’s laboratory operations by
providing a centralized system for tracking and monitoring lab results, test
ordering, and other laboratory-related activities. The platform’s
functionality can also help to automate processes, ensure compliance and
improve the quality of lab results and patient care.

  * •

Radiology Management[62]: Hospital radiology management platforms are digital
tools that help hospitals and medical clinics to manage their radiology
operations. They provide a centralized system for tracking and monitoring
radiology exams, images, and other radiology-related activities. Some of the
key features of these platforms include:

    1. 1.

Radiology imaging management: Allows for the storage, retrieval, and viewing
of radiology images such as X-rays, CT scans, and MRI images.

    2. 2.

Order management: Allows for the creation and tracking of radiology orders and
exams, including scheduling, patient information, and exam results.

    3. 3.

Reporting and analysis: Generates reports and provides tools for analyzing
radiology data, including patient statistics, exam trends, and equipment
usage.

    4. 4.

Electronic Health Records (EHR) integration: Integrates with the hospital’s
EHR system to share patient information and radiology images with other
healthcare providers.

    5. 5.

Workflow management: Allows for the management of radiology workflow and
tasks, including the assignment of tasks to radiologists and technologists,
and the tracking of progress.

    6. 6.

Image annotation and interpretation: Allows radiologists to annotate and
interpret images, providing detailed information about the patient’s
condition.

    7. 7.

Quality assurance and compliance: Provides tools for ensuring compliance with
regulatory standards, such as the HIPAA and DICOM, and for quality control of
radiology images and reports.

    8. 8.

Advanced visualization tools: Includes advanced visualization tools such as 3D
imaging, multi-planar reconstruction, and image fusion.

    9. 9.

PACS (Picture Archiving and Communication Systems) integration: Integrates
with the hospital’s PACS system to enable the storage, retrieval, and
distribution of radiology images and reports.

    10. 10.

Remote access and collaboration: Allows remote access to radiology images and
reports, and enables collaboration between radiologists and other healthcare
providers.

Overall, hospital radiology management platforms can help to improve the
efficiency and effectiveness of a hospital’s radiology operations by providing
a centralized system for tracking and monitoring radiology exams, images, and
other radiology-related activities. The platform’s functionality can also help
to automate processes, ensure compliance, improve patient care and streamline
the radiology workflow.

  * •

Report Management[63]: Hospital report management platforms are digital tools
that help hospitals and medical clinics to manage their medical reports. They
provide a centralized system for generating, storing, and sharing medical
reports, such as radiology, lab, and pathology reports. Some of the key
features of these platforms include:

    1. 1.

Report generation: The ability to generate and format medical reports, such as
radiology, lab, and pathology reports, is provided by these platforms.

    2. 2.

Report storage and sharing: These platforms offer tools for storing and
sharing medical reports, such as integration with electronic medical records
(EMR), making it easy for authorized providers and patients to access reports.

    3. 3.

Report tracking: Tools for monitoring the status of medical reports, including
report generation, review, and approval, are provided by these platforms.

    4. 4.

Compliance: Can help hospitals and clinics to comply with regulatory
requirements, such as HIPAA and DICOM, by providing tools for tracking and
monitoring report management operations.

    5. 5.

Reporting: Provides various reports on medical reports, such as report
generation, review, and approval, which can help managers to identify trends
and make data-driven decisions.

    6. 6.

Automation: Automates many report management-related processes, such as report
generation and sharing, to reduce administrative burdens on staff.

Overall, hospital report management platforms can help to improve the
efficiency and effectiveness of a hospital’s report management operations by
providing a centralized system for generating, storing, and sharing medical
reports. The platform’s functionality can also help to automate processes,
ensure compliance, and improve patient care by providing easy access to
medical reports for authorized providers and patients.

  * •

Support Management[64]: Hospital support management platforms are digital
tools that help hospitals and medical clinics to manage their administrative
and support operations. They provide a centralized system for managing and
organizing non-clinical activities such as human resources, payroll, and
procurement. Some of the key features of these platforms include:

    1. 1.

Human resources management: Provides tools for managing human resources
activities such as employee records, payroll, benefits, and compliance with
labor laws.

    2. 2.

Payroll management: This is for managing payroll processes, including
calculating, tracking and distributing employee salaries and benefits.

    3. 3.

Procurement management: Managing the procurement process, including creating
purchase orders, tracking deliveries and managing inventory.

    4. 4.

Facility management: Platforms can provide tools for managing the hospital’s
facility, such as tracking and scheduling maintenance, monitoring energy
consumption and compliance with safety regulations.

    5. 5.

Reporting: Elavorates various reports on support operations, such as payroll,
procurement, and facility management, which can help managers to identify
trends and make data-driven decisions.

    6. 6.

Automation: Automates many administrative and support-related processes, such
as payroll and procurement, to reduce administrative burdens on staff.

Overall, hospital support management platforms can help to improve the
efficiency and effectiveness of a hospital’s administrative and support
operations by providing a centralized system for managing and organizing non-
clinical activities. The platform’s functionality can also help to automate
processes, ensure compliance with regulations, and improve overall hospital
operations.

To achieve these services it is mandatory to have high quality data storage,
data speed, data exchange and networking. This is due to the variety as well
as the amount of data that is collected and retrieved [65, 66].

In order to model, validate and create HMS systems, several tools and
technologies have been proposed through the years. For modelling, one of the
dominant tools is Three-layer Graph based metal model ($3LGM^{2}$)[67],
successor of $3LGM$[68] which is a structured and validated approach [69]. In
[70] using $3LGM^{2}$ HMS approach was revised into a layer architecture.

In order to standardise HMS, American National Standards Institute (ANSI)
introduced Health Level Seven (HL7) [71]. HL7 vision is to create an
infrastructure for interoperability in the healthcare domain.

###  II-C Health Tracking & Monitoring Systems

Health tracking and monitoring is a crucial aspect of the healthcare industry
as it allows medical doctors and researchers to gain insight into the health
conditions of patients as well as the general population. Health Tracking &
Monitoring Systems (HTMS) play a vital role in proactively identifying and
providing individuals with the assistance and services they need. This is made
possible through the collection and analysis of contextual information. The
success of these systems is dependent on their ability to collect and process
data to understand a subject’s environment and provide targeted services.

Contextual services in healthcare refer to a set of continuous processes that
automatically acquire information about a subject, including behavioral,
physiological, and environmental data. This information is then used to
provide and adapt services accordingly, resulting in a better understanding of
the subject’s health conditions and improved care. This is achieved through
the identification of behavior patterns and the ability to make more precise
inferences about the subject’s situation and environment. Numerous survey
studies [72, 73, 74, 75] have been carried out in the literature, mainly
focusing on smart home perspectives, health smart homes, wearable sensor-based
systems, and healthcare applications for the elderly. These studies provide
valuable information on the design and implementation of health tracking and
monitoring systems, as well as the challenges and limitations of these
systems.

Data collection is a crucial aspect of health tracking and monitoring systems,
and the frequency of data collection plays an important role in the overall
system performance. Data can be generated through three types of events:
constant, instant, or interval. Constant events ensure continuous data
transmission, instant events send data immediately upon the occurrence of a
specific event, and interval events involve data being sent periodically at
uniform time intervals. Physical sensors are commonly used to collect raw
sensor data in HTMS, while virtual sources include existing health records and
data from social media networks. These sensors are used to monitor various
aspects of a subject’s health and environment, including physiological data
such as heart rate and blood pressure, environmental data such as temperature
and humidity, and behavioral data such as activity levels and sleep patterns.
The use of virtual sources can provide additional information on the subject’s
health history, behavior patterns, and social network, which can be used to
improve the accuracy of the system’s predictions and recommendations.
Additionally, with the advent of IoT and connected devices, the use of
wireless sensors and networked devices is becoming more prevalent in health
tracking and monitoring systems. This allows for more accurate and real-time
data collection, and enables remote monitoring of patients, which is
particularly beneficial for elderly and disabled individuals who may have
difficulty traveling to a healthcare facility for regular check-ups.

Figure 6: Patient Monitoring System Structure [76]

HTMS play a vital role in proactively identifying and providing individuals
with the assistance and services they need. The success of these systems is
dependent on their ability to collect and process data to understand a
subject’s environment and provide targeted services. The use of physical
sensors and virtual sources, and wireless and networked devices, is becoming
increasingly important in health tracking and monitoring systems, allowing for
more accurate and real-time data collection, and enabling remote monitoring of
patients.

Sensor data can come in various formats, such as numerical, categorical,
graphics, video, etc. Based on these formats and sensor types, health
monitoring can be divided into two approaches: vision-based and sensor-based
approaches. Vision-based approaches rely on visual sensors, such as video
cameras, for movement and gesture recognition, while sensor-based approaches
use a wide range of emerging sensors and technologies for health and
biomedical monitoring [77, 78]. Recent systems and projects for healthcare
monitoring commonly use standard or commercial sensors to gather raw data to
monitor individuals and their environment. According to research projects
surveyed in this paper, there are three main classes of interconnected
networks that are often used in this field: Personal Sensor Networks (PSN),
Body Sensor Networks (BSN), and Multimedia Devices (MD) [79, 80, 81, 82, 83,
84]. These sensors and devices are integrated into home objects and
infrastructure and connected using network technologies. Each sensor is
responsible for one or more task at the same time.

PSNs are used to detect human daily activities and measure conditions in the
subject’s environment. They can be placed in a living environment or attached
to different home objects to detect the subject’s activities such as a sofa,
table, bed, chair, or floor containing pressure sensors. BSNs, on the other
hand, are used to monitor vital signs and health conditions by measuring
physiological parameters and detecting ambulatory activities. Finally, more
contextual information related to human activity is collected via MD to
monitor movement, environmental changes, and to increase the interaction
between the monitored subject and the e-health application. Nowadays, advances
in technology have made it possible to use wireless sensors and networked
devices, which allow for more accurate and real-time data collection, enabling
remote monitoring of patients. This is particularly beneficial for elderly and
disabled individuals who may have difficulty traveling to a healthcare
facility for regular check-ups. An overview of sensors used in HTMS is
depicted in Table I.

Category | Name | Purpose | Data Format
---|---|---|---
PSN | PIR | Motion detection | Categorical
| RFID | Persons and objects identification | Categorical
| Pressure | Identify location | Numerical
| Ultrasonic | Tracking location and posture | Numerical
| Contact switches | Open/close door detection | Categorical
| Light | Use of light and its intensity | Time series
| Temperature | Measure room temperature | Time series
| Weight | Elderly weight | Numerical
| Humidity | Measure room humidity | Time series
| Power | On/off and measure power consumption | Numerical
BSN | Accelerometer | Measure acceleration, fall detection, location and posture | Time series
| Gyroscopes | Measure orientation, motion detection | Time series
| GPS | Motion detection and location tracking | Categorical
| ECG | Monitor cardiac activity | Analog signal
| EEG | Measure of brain waves | Analog signal
| EOG | Monitor eye movement | Analog signal
| EMG | Monitor muscle activity | Analog signal
| PPG | Heart rate and blood velocity | Analog signal
| Pulse oximeter | Measure blood oxygen saturation | Analog signal
| Blood pressure | Measure blood pressure | Numerical
| SKT | Skin temperature | Numerical
MD | Cameras | Monitoring and tracking | Image, video
| Microphone | Voice detection | Audio
| Speakers | Alerts and instructions | Audio
| TV | Visual information | Audio, video
TABLE I: Health monitoring devices and sensors

PSNs or environmental sensors play a crucial role in capturing and retrieving
contextual data about a subject and their surroundings. These sensors can be
placed in a living environment or attached to different household objects to
detect the subject’s activities. For example, a pressure sensor placed on a
sofa, table, bed, chair, or floor can detect the subject’s movements and daily
living activities (ADL). By observing the subject in their environment and
their interactions with household objects, a performance level can be
measured. For instance, if a motion sensor detects that the user is in the
kitchen and a sensor on a cooking object such as a gas stove is turned on,
along with water usage or the refrigerator’s door being open, it can be
inferred that the subject is preparing a meal.

Environmental sensors can provide rich contextual information to detect daily
activities and observe human behavior in Health Smart Homes (HSH). Passive
infrared (PIR) [85] sensors are commonly used for detecting a person’s
presence and ADL [86]. Additionally, radio-frequency identification (RFID)
technology, which includes both active and passive tags attached to objects
and readers worn by the subject, can be used to identify users and objects
within the HSH environment [87]. Pressure [88] and ultrasonic [89] sensors can
also be attached to home objects to track their location and therefore the
movements of the user. Contact switches can be used to detect the subject’s
interactions with other objects in the space, such as a door, window or
refrigerator. Environmental sensors can also be used to monitor additional
dimensions such as light, temperature, and humidity. They are deployed in
different locations to monitor environmental conditions and identify daily
activities. Power sensors are used to measure and manage energy consumption
and to detect the usage of electrical devices using On/Off events. The usage
of electrical appliances such as microwaves, water kettles, toasters, room
heaters, and televisions can also be used to detect activity and further
refine behavior information. PSNs play a vital role in capturing and
retrieving contextual data in health monitoring and smart homes. They can
detect daily activities, observe human behavior, and track the movements of
individuals. These sensors can also monitor environmental conditions and
identify daily activities, measure energy consumption, and detect the usage of
electrical devices. Advances in technology have made it possible to use these
sensors and networked devices to provide accurate, real-time data collection,
enabling remote monitoring of patients.

BSNs utilize wearable sensors that are worn by individuals being monitored,
such as the elderly and patients. These sensors are used in Health Monitoring
Systems (HMS) to provide continuous information about the individual’s real-
time health conditions. These sensors are often embedded into everyday
accessories such as clothing, belts, watches, or glasses. BSNs often use
inertial measurement units, such as accelerometers, to detect ambulatory
activities or vital sign devices, such as heart rate sensors, to monitor the
individual’s health condition. The most commonly used inertial sensors for
monitoring movements and body postures are accelerometers and gyroscopes.
Accelerometer sensors measure acceleration values and are typically based on
three-axis accelerometers positioned on specific locations on the human body.
In a study [90], the authors used four accelerometers attached to the chest,
left under-arm, waist, and thigh to monitor and recognize five activities,
such as standing, sitting, lying, walking, and transition.

A study [91] proposed a method for recognizing human activities using four
wearable accelerometers placed on the forearms and shins of subjects, which
monitored ten different daily activities and gym exercises such as standing,
sitting, walking, jogging, and weightlifting. Another study [92] used a single
accelerometer and gyroscope sensor to monitor 26 events and activities.
Gyroscopes are often used in conjunction with accelerometers for movement
detection. In [93], a system was proposed that used accelerometer and
gyroscope sensors placed on the wrist and foot of the subject to collect
linear accelerations and angular rates of motion, which could recognize
activities like walking, standing, writing, smoking, jacks, and jogging.
Additionally, a fall detection system for the elderly was proposed using
accelerometers and gyroscopes [94]. Global positioning systems (GPS) can also
be used as wearable sensors to monitor location-based activities in open or
mobile environments, such as tracking locations or predicting movement across
multiple subjects [95], or determining mode of transportation based on GPS
data logs [96].

In HTMS, various biosensors are used to monitor the vital signs of patients
and the elderly, such as heart rate, oxygen saturation, blood pressure, blood
glucose, body temperature, and weight. These sensors can be embedded into
everyday accessories such as watches [97], shirts [98], and belts [99], and
provide real-time physiological information related to the health of the
monitored subject. Other biosensors used in HMS include electrocardiography
sensors (ECG) for monitoring cardiac activity, electroencephalography sensors
(EEG) for monitoring brain activity, electromyography sensors (EMG) for
monitoring muscle activity, and electrooculography sensors (EOG) for
monitoring eye movements. Pulse oximeters are used to measure the oxygen
saturation levels of the blood, while photoplethysmography sensors (EPG) are
used to monitor blood flow.

Additional biomedical parameters can also be measured, such as using CO2 gas
sensors to evaluate the levels of carbon dioxide in the air to monitor
respiration. However, many devices currently available do not have the
capability for data relay. Many existing studies and projects have not focused
on improving the link between these devices and their infrastructure. The goal
is often to transform these sensors into context data sources that can be
connected to a network and automatically collect data. While there are various
protocols and algorithms that have been proposed for wireless sensor networks,
there are currently no features or requirements that are well-suited for body
area networks (BANs) [73]. Factors such as the number of sensors, data rate,
mobility, latency, communication, and transmission are determined based on the
application and the needs of the subject. Additionally, energy consumption and
battery life are still significant challenges for devices in these networks.

The use of home appliances equipped with sensors can help to create an
interactive healthcare environment. These appliances can include electrical or
electronic devices that serve multiple purposes at home. Examples include
cameras, microphones, telephones, speakers, and televisions, which provide a
platform for exchanging data between the individual and the system. This
increases the individual’s interaction with health applications and can serve
as new sources for gathering contextual information or as a means for
providing guidance and counseling [100].

Multimedia-based approaches involve the use of visual and audio sensing
devices such as cameras and microphones. Sound-based detection methods can be
used to monitor certain activities of daily living [101], while vision-based
methods have a broader scope and can be used for posture recognition [102],
detecting human presence [103], movement and fall detection [104], and
monitoring complex activities [105]. However, there is debate over the use of
multimedia-based applications for healthcare. While these approaches provide
rich contextual information, they also have limitations such as computational
costs and privacy concerns. The data acquired by these sensors is basic and
raw, often imperfect and uncertain in nature, which requires further
development to create a high-level approach for healthcare services.

##  III Public Healthcare needs: in the COVID-19 case

A fundamental aspect of managing outbreaks is comprehending how the infection
spreads in terms of time, place, and individuals, and identifying risk factors
for the disease to guide effective interventions. Digital data sources are
being used to enhance and interpret key epidemiological data gathered by
public health authorities for COVID-19 [106].

The COVID-19 pandemic exposed a number of flaws in various systems, such as
infrastructure, supply chains, government readiness, human resources, and
public health systems. In addition, officials responsible for public health
and healthcare system management faced difficulties in conveying a consistent
message on public health measures to control the spread of the virus.
Healthcare facilities were not properly equipped or prepared to handle the
large influx of patients, and medical and epidemiological training was
inadequate to provide proper care [107]. Overall, public health systems were
unprepared to deal with a rapidly spreading viral pathogen, and containment
measures were not effectively implemented during the critical period [107].

After more than two years since the emergence of the SARS-CoV-2 virus, it is
clear that cooperation in sharing information between governments and
healthcare institutions, as well as clear and timely communication with the
public, is essential to slow the spread of the virus and prevent a resurgence
of the pandemic. Nevertheless, it is uncertain whether healthcare measures in
any country have adapted sufficiently to cope with future outbreaks [108].

###  III-A Epidemiological Surveillance

Traditionally, population-surveillance systems are based on health data from
laboratories, notifications of cases diagnosed by doctors, and syndromic
surveillance networks. Syndromic surveillance relies on identifying
”influenza-like illness” from hospitals and selected primary and secondary
healthcare facilities that agree to provide regular surveillance data of all
cases. However, these sources often miss cases where individuals do not seek
healthcare. For example, in the UK, until recently only hospitalized patients
and healthcare workers were routinely tested for COVID-19, meaning that
confirmed cases only represent an estimated 4.7% of symptomatic COVID-19 cases
[109]. Identifying undetected cases would help provide a clearer understanding
of the outbreak and reduce transmission [110].

In recent years, data from online news sites, news-aggregation services,
social networks, web searches, and participatory longitudinal community
cohorts have been used to address this gap. Data-aggregation systems,
including ProMED-mail [111], GPHIN [112], HealthMap [113] and EIOS [114],
which use natural language processing and machine learning to process and
filter online data, have been developed to provide epidemiological insight.
These data sources are increasingly being incorporated into the formal
surveillance landscape [115] and have a role in COVID-19 surveillance. The
World Health Organization’s (WHO) platform, EPI-BRAIN [116], brings together a
variety of datasets for infectious-disease emergency preparedness and
response, including environmental and meteorological data.

Some systems have claimed to detect early reports of COVID-19 through the use
of crowdsourced data and news reports, before the World Health Organization
(WHO) officially announced the outbreak [117],[111], [118]. For example, the
UK’s automatic syndromic surveillance system scans National Health Service
digital records [119] to detect clusters of a respiratory syndrome that could
indicate COVID-19. There is also interest in using online data to estimate the
true spread of infectious diseases in the community [120], [121].

Research on the epidemiological analysis of COVID-19-related social media
content has been conducted for example see [122], [123] and [124]. Models for
COVID-19 [125], which build on previously established internet search
algorithms for influenza, are included in Public Health England’s weekly
reports [126]. Crowdsourcing systems used to understand the true burden of the
disease are also supporting syndromic surveillance. For instance, InfluenzaNet
gathers information about symptoms and compliance with social distancing from
volunteers in several European countries through a weekly survey [127].
Similar efforts exist in other countries, such as COVID Near You [128] in the
USA, Canada, and Mexico. The COVID-19 symptom-tracker app has been downloaded
by 3.9 million people in the UK and USA [129] and is feeding into national
surveillance.

####  III-A1 Positive Reporting

The World Health Organization (WHO) has stated that the SARS-CoV-2 virus can
be spread through various bodily fluids and respiratory droplets, making
personal protective equipment for healthcare workers essential for both
treating patients and protecting workers. In the general population, the virus
has spread quickly, making testing labs crucial for identifying and tracking
the spread of COVID-19. While testing is a reactive measure, it can provide
important information about the prevalence of infection and aid in
coordinating healthcare for infected individuals [130], [131]. Standard
protocols for dealing with COVID-19 cases often involve transferring patients
to designated containment areas, but these protocols vary and there is no
cohesive global protocol. This has made it difficult to trace and track
patients’ contacts and implement population-level containment measures. Even
patients who do not require hospitalisation can still pose a risk as they may
come into contact with others. Asymptomatic individuals who are not tested can
also contribute to the spread of the virus, which is why comprehensive testing
strategies are necessary to curb the pandemic.

Figure 7: Chicago Department of Public Health COVID-19 Management Architecture
[132]

The ability of the virus to spread even when the infected person shows no
symptoms is a major obstacle in controlling the COVID-19 pandemic [133],
[134], and so regular testing of staff working with vulnerable populations is
crucial. Rapid antigen tests and at-home testing kits have made it easier to
test people, but they do have the potential for false negatives. Nevertheless,
these tests and kits are important tools in tracking the spread of the virus.

According to regulations worldwide, laboratories involved in detecting and
sequencing the SARS-CoV-2 virus must be managed by trained staff or experts,
who must follow strict protocols and use appropriate equipment, including
nucleic acid extractors, RT-PCR devices, ultra-low freezers, UV lamps for
decontamination, and other disinfection equipment, robots, and contamination-
free consumables [135], [136].

####  III-A2 Data analytics

During the pandemic, data dashboards have been widely utilized to collect and
display real-time information on public health, including the number of
confirmed cases, deaths, and testing results, in order to inform the public
and aid decision-makers in adjusting their responses [137], [138], [139].
These COVID-19 dashboards often present the data in the form of timelines and
geographic maps, showing statistics at both the regional and individual level
[138], [140]. Some dashboards also include information on other aspects of the
pandemic, such as clinical trials [141], government policies, economic
measures [142], and compliance with social distancing guidelines [143].

There are few data dashboards that include information on contact tracing or
monitoring through apps, and their effectiveness is not well-documented.
Problems with the quality and consistency of data collection remain a concern.
The absence of official standards and variations in how governments report
statistics across different countries make it hard to compare data globally.
Additionally, up-to-date and accurate statistics from governments are not
always readily available. New ways of visualising data, such as the NextStrain
open repository [139], which uses viral sequence data to create a worldwide
map of the spread of the disease, are emerging. This is made possible by open
data sharing and the use of open-source code, which allows for faster sharing
of information compared to previous global outbreaks [144].

####  III-A3 Vaccination platforms

A vaccination platform [145] is a system that is used to manage the
administration, distribution, and tracking of vaccines. This can include
features such as:

  * •

Scheduling: Allows individuals to schedule appointments for vaccine doses.
This feature allows individuals to select a date, time, and location for their
vaccine appointment, and can also include options for rescheduling or
canceling appointments.

  * •

Registration: Allows individuals to register and create a profile with their
personal information. This can include contact information, medical history,
and insurance information. This feature is useful for tracking individuals’
vaccination status and ensuring that they receive the correct vaccine.

  * •

Eligibility: Determines whether an individual is eligible to receive a vaccine
and provides information on the vaccines that are available. This feature can
be based on factors such as age, health condition, and occupation. It can also
provide information on the different types of vaccines available and the
schedule of doses.

  * •

Administration: Tracks the administration of vaccine doses, including the date
and location of the vaccine, and the vaccine type. This feature can also
include the option for healthcare providers to enter notes about the vaccine
administration, such as any adverse reactions that occurred.

  * •

Reminders: Sends reminders to individuals about upcoming vaccine appointments.
This feature can include text messages, emails, or phone calls to remind
individuals of their appointment and can also include information on how to
reschedule or cancel.

  * •

Reporting: Generates reports on vaccine administration and inventory
management. This feature can provide information such as the number of doses
administered, the number of doses remaining, and the number of individuals who
have received the vaccine.

  * •

Data Management: Stores and manages data related to vaccine administration and
inventory. This feature can include options for exporting data to other
systems, such as electronic health records (EHRs), and can also include data
analytics tools to track the progress of the vaccine distribution.

  * •

Communication: Allows for communication between healthcare providers,
government agencies, and individuals. This feature can include options for
messaging, email, or phone calls to facilitate communication and ensure that
everyone is on the same page.

  * •

Integration: Integrate with other healthcare systems like EHR(Electronic
Health Record) to provide a more complete and accurate picture of an
individual’s health history.

  * •

Security: Ensures the security and privacy of personal information and vaccine
data. This feature can include options for encrypting data and using secure
protocols to protect sensitive information.

  * •

Analytics: Provide analytics and insights to monitor the vaccine distribution.
This feature can include dashboards and reports to track the progress of the
vaccine distribution, such as the number of doses administered, the number of
individuals vaccinated, and the number of doses remaining.

  * •

Multi-lingual support: Platform is available in multiple languages to reach
more people. This feature can help to ensure that individuals who speak
different languages can access the platform and understand the information
provided.

Figure 8: Vaccine ecosystem, Platform Application and Data Architecture [146]

The platform can be accessed through a website or a mobile application, and
can be used by healthcare providers, government agencies, and individuals. The
aim of this platform is to simplify the process of managing and tracking
vaccines and help to ensure that individuals are able to receive their
vaccines as quickly and efficiently as possible.

A decentralized vaccination platform [147] is a system that uses blockchain
[148] technology to manage the administration, distribution, and tracking of
vaccines. In contrast to traditional centralized systems, where a single
entity controls and manages the data, a decentralized platform distributes the
control and management of the data among multiple participants. It uses a
distributed ledger technology (DLT) like blockchain to create a tamper-proof
record of vaccine administration and inventory. This allows for secure and
transparent tracking of vaccines and ensures that the data is accurate and up-
to-date. Some of the features of a decentralized vaccine electronic platform
include:

  * •

Immutable record keeping: all the data is stored in a decentralized ledger,
making it tamper-proof and resistant to unauthorized modification.

  * •

Smart contracts: the platform can use smart contracts to automate certain
actions, such as scheduling appointments or tracking inventory.

  * •

Anonymized data: individuals’ personal information can be encrypted and stored
on the blockchain, preserving their privacy.

  * •

Interoperability: the platform can be integrated with other healthcare systems
like EHR.

  * •

Access control: the platform can be built in a way that allows only authorized
parties to access the data.

  * •

Traceability: the platform can provide a complete record of the vaccine
administration and inventory, allowing for better traceability and
transparency.

  * •

Public accessibility: the platform can be accessible to the public and provide
real-time information about the vaccine distribution.

The vaccination process spans into three phases [147]:

  * •

Pre-Vaccination phase includes activities in which the knowledge about
available vaccines is acquired, the availability of a certain number of
vaccines ensured, and patients are selected in terms of scheduling a
vaccination appointment.

  * •

Vaccination phase includes activities in which the patient is prepared for
vaccination, informed about potential side effects and adverse events, and the
vaccination process, including injection, is executed.

  * •

Post-Vaccination phase includes activities in which post-injection monitoring
is conducted, and post-procedure administrative tasks are completed. This
involves recording vaccination-related data and any side effects that have
been occurred during the vaccination procedure.

####  III-A4 Clinical Testing platforms

Clinical testing platforms existed before the COVID-19 pandemic, but the
outbreak put a strain on these platforms, largely due to the high demand and
the large number of users accessing them concurrently [149]. These platforms
typically have four main user groups [150]: individuals who will take the
test, lab personnel who process the test and record the results, medical
doctors who use the results to monitor patients, and government medical
services who use the results to track the epidemiological status of a region.
The pandemic led to the creation of several new clinical testing platforms
(some of them show in TABLE II), or existing platforms being adapted to
include clinical testing capabilities. Some platforms were even repurposed as
data collection platforms due to the urgent need during the pandemic.

Platform |  Features
---|---
DHIS2 [151] |  • Enrolls & tracks suspected cases, captures symptoms, demographics, risk factors & exposures, creates lab requests and captures laboratory data about the case, links confirmed cases with contacts and monitors patient outcomes. • Strengthens active case detection through contact tracing activities, such as identification and follow-up of contacts of a suspected or confirmed COVID-19 case. • Enrolls travelers who have visited high-risk locations at Ports of Entry for 14-day monitoring and follow-up. • Simplified line-list that captures a subset of minimum critical data points to facilitate rapid analysis & response. • Aggregate reporting dataset that captures minimum necessary data points for daily or weekly reporting.
SORMAS [152] |  • Enrolls and tracks suspected cases, contact tracing & follow-up visit, laboratory sample management, event tracking (early warning), patient information system for tracking symptoms. • Reporting which includes case count, contact count, lab confirmation and deaths. • Statistical analysis based on reports (Maps, charts, graphs, dashboards, etc.). Network transmission chain visualization. • Importing files from other entities or datasets and exporting files to transfer data to other entities or datasets.
Go.Data [153] |  • WHO case reporting forms (COVID-MART / X-MART on daily and weekly basis) • Generates streamlined Integrated Disease Surveillance and Response (IDSR) reporting • implements COVID-19 First Few Hundred (FFX) Cases Protocol • Case line lists, case investigation protocol, contact line lists, contact follow-up, event & exposure registration, tracking of relationship & viewing dynamically chains of transmission
Epi Info [116] |  • Case Surveillance Forms, custom development to meet local, regional, and country requirements.
Open Data Kit (ODK) [154] |  • Disease surveillance, rapid diagnostics, vaccine trials
CommCare [155] |  • Fully-functioning application on desktop or mobile browsers and as standalone, offline-capable to carry out disease surveillance and educational activities based on protocol from WHO, CDC, and other leading public health organizations. • Contact tracing using WHO First Few Hundred (FFX) Cases Protocol, Port of entry surveillance, facility readiness and stock tracking, Sample tracking and Lab testing • Health Care Provider Training & Monitoring
TABLE II: Clinical Testing Platforms Features

####  III-A5 Decision making

The COVID-19 pandemic has revealed shortcomings in decision-making processes.
The rapid spread of the virus within and between countries led to a strain on
governments and healthcare organisations. To aid decision-making, various
tools, techniques, and strategies have been suggested and implemented. In
[156] they compared static versus interactive decision support tools for
COVID-19. They concluded that due to the limited decision time and space
during COVID-19, static dicision support tools are more efficient in providing
guidance to the interested parties. In [157] they proposed a Clinical Decision
Support System (CDSS) and a mobile app to support home care, primary care,
urgent care clinics, emergency departments, hospitals and intensive care
units. In [158] they proposed the SMART COVID Navigator which allows
physicians to predict the fatality and severity of COVID-19 progression given
a particular patient’s medical conditions. This allows physicians to determine
how aggressively to treat patients infected with COVID-19 and to prioritize
different patients for treatment considering their prior medical conditions.
In [159] they proposed an accessible, flexible tool for local health care
professionals for a more systematic examination of down-stream effects of
decisions under varying and interdependent conditions, to detect a shortage of
finite resources such as staff, medication and medical equipment early on. In
[160] they proposed an interactive and adaptive clinical decision support tool
which had a web-based user-interface used to help nurses and clinicians from
emergency departments with the triage of patients during the COVID-19
outbrake. In [161] they proposed a toolkit for designers to select design
interventions and modelling tools to improve resilience against COVID or
future pandemics. To achieve that they had to figure out the pathogen types,
transmission modes and phases, and map the design interventions as well as
modelling methodologies used. In addition they examined the relationship
between different types of infectious diseases. In [162] they describe the
adaptation, development and operation of epidemiological surveillance and
modelling systems in South Africa in response to the COVID-19 epidemic. They
used data from laboratory-confirmed COVID-19 cases, hospitalisations,
mortality and recoveries in order to evaluate how these date is used to inform
modelling projections and public health decisions. In [163] they modeled and
compared different exit scenarios from the lock-down for the COVID-19 UK
epidemic. This allowed them to create a modelling basis for laying out the
strategy options for the decision-makers. In [164] they used reinforcement
learning to demonstrated that it can be efficient at solving epidemic control
problems. They showed that this learning-based approach given the difficulties
facing policymakers in defining optimal control policies with different
constraints can be useful.

####  III-A6 Contact Tracing

Contact tracing is crucial in controlling the spread of COVID-19 as it can
identify individuals who have been in close contact with confirmed cases, and
help enforce quarantine and social distancing measures. By using digital
technology for contact tracing, governments can be more efficient and
effective in terms of time and resources. However, these digital contact
tracing methods raise concerns about data privacy and security, particularly
when it comes to location data. While people may be willing to temporarily
give up some privacy to fight the pandemic, these concerns may continue in the
long term and need to be addressed through technology and policy changes.
Failure to address these concerns could lead to a decline in data governance
and make it more difficult for governments to gain public cooperation.
Additionally, it could cause tension between different groups [165].

Figure 9: Contact Tracing Impact [166]

The incorporation of mobile apps and online platforms in healthcare has
greatly expanded patients’ access to medical providers. With the widespread
availability of healthcare interface applications that connect patients to
medical facilities providing real-time results for diagnostic tests and
virtual consultations with care providers, the COVID-19 pandemic has
highlighted the importance of digital solutions for basic health care issues.
The advancement of digital health solutions has significantly transformed the
healthcare system by simplifying the process of consulting with medical
professionals through telemedicine. Telemedicine offers several benefits such
as reducing the risk of infection, eliminating the need for waiting rooms and
reducing travel costs, travel time, and lost work time. However, it is not
able to replace in-person medical care for certain conditions that require
clinical observations, specialized interventions, and exams. [167].

Digital health applications have revolutionized the healthcare industry by
allowing patients to easily access personal health information, diagnostic
results, and have virtual consultations with medical professionals. The
widespread use of mobile devices has led to an increase in the development and
adoption of mobile health care apps, which are becoming more specialized for
specific diagnostic tests and can be used with external devices such as
inhalers, digital stethoscopes, and blood pressure monitors [168], [169].

The growth of digital health has resulted in over 318,000 medical health apps
developed as of 2017 [170], and the mobile health (mHealth) market is expected
to reach a value of 111 billion dollars by 2025 [171], [172]. These
technologies are also supported by the U.S. Food and Drug Administration,
which has issued the Digital Health Innovation Action Plan in 2017 [170] to
encourage innovation and regulate digital health.

The Digital Health Innovation Action Plan encourages innovation in digital
health and includes three goals:

  1. 1.

providing new guidance for regulating digital health,

  2. 2.

developing new methods for oversight,

  3. 3.

and increasing expertise within the agency.

To ensure the safety of medical care, mobile health applications should be
regulated and monitored by government health agencies worldwide. In the United
States, for instance, mobile health apps are regulated by three federal
agencies:

  * •

The Office for Civil Rights (OCR) within the Department of Health and Human
Services (HHS) enforces the Health Insurance Portability and Accountability
Act (HIPAA) rules to protect the privacy and security of health information.

  * •

The FDA enforces the Federal Food, Drug, and Cosmetic Act (FD&C Act) to
regulate the safety of using medical devices, and eliminate the risk when
health app does not work properly.

  * •

The Federal Trade Commission (FTC) enforces the Federal Trade Commission Act
(FTC Act) to prohibit or create alerts of unfair acts or practices and
monitors app’s safety or performance.

During the COVID-19 pandemic, many governments around the world developed
contact tracing mobile alert apps to help control the spread of the virus. The
National Health Service in the United Kingdom, for example, developed an app
that could warn people when they were in violation of physical distancing
guidelines or had been in close contact with a confirmed case. The app was
downloaded over 10 million times within ten days of its release. Additionally,
the World Health Organization launched a health alert messaging service on
WhatsApp that provided the latest information on COVID-19, including symptoms
and precautions. This service also provided up-to-date situation reports to
aid government officials and health care agencies in taking effective measures
to protect public health. As of now, there are about 300 apps that focus on
controlling the spread of COVID-19, but their effectiveness varies widely and
requires further evaluation [173]. As the pandemic continues, more mHealth
apps that focus on COVID-19 will be developed and optimized, as they have
proven to be useful tools. Examples of mHealth apps used to control the spread
of COVID-19 are shown in Table III.

Country/Region | App | Type of App | Impact
---|---|---|---
Australia [174], [175] | COVIDSafe | Tracing |
|  |  | Positive – epidemiological, surveillance,
|  |  | date, time and duration of contact
France [174], [175] | Stop Covid |  | Negative – user data privacy concerns, lack of trust
| TousAntiCovid |  | ethical issues, security vulnerabilities, technical constraints
Germany [174], [175] | Corona-Warn-App |  |
Globally [176] | TraceTogether |  |
| Covid Trace |  |
| NOVID Share Trace Safe2 |  | Positive – The user receives a unique ID, and at
|  |  | infection, he is redirected to medical applications
Italy [174], [175] | Immuni |  | Positive–Epidemiological surveillance, date, time
|  |  | duration of contact
|  |  | Negative– user data privacy conscerns, lack of trust, ethical
Spain [174], [175] | AsistenciaCOVID-19 |  | issues security vulnerabilities, technical constraints
Switzerland [174], [175] | SwissCovid |  |
Cyprus [177] | COVTRACER-EN |  |
Globally [176] | HowWeFeel | Medical | Positive – for health care professionals and researchers; a useful
|  |  | tool for patients, quarantine advice, and measures to ensure
|  |  | well being. The authorised access was
|  |  | ensured by firewalls, anti-viruses, and cryptographic
|  |  | algorithms
Mexico [178] | Sofia |  | Positive–video consultations:internal medicine and
|  |  | pediatric consultations, prescriptions, follow–up indications;
|  |  | high levels of patient satisfaction, versatile and convenient
|  |  | tool to manage the situation
USA [179] | Teladoc |  | Positive–access to low–cost and high–quality doctors, clinical
|  |  | expertise, virtual care for consumers and clinicians
TABLE III: Examples of m-Health apps developed by government agencies for
contact tracing and information delivery for the spread of COVID–19

###  III-B Case identification

During a pandemic, identifying cases and their contacts quickly is essential
for controlling the spread of the virus and understanding how it is
transmitted [106]. The World Health Organisation has stated that while the
pandemic has rapidly progressed, the rate of decrease has been much slower
[180]. Therefore, early detection and isolation of those carrying the virus is
crucial in preventing further spread of the outbreak [181], [182]. Early
identification of COVID-19 patients can decrease the extent of the virus and
reduce the infection and death rate [183], [184]. Studies have demonstrated
that NPIs such as monitoring epidemics, investigating epidemiology and
tracking close contacts can aid in detecting and reporting potential and
confirmed cases promptly [185], [186]. Big data analysis and artificial
intelligence assist in screening for epidemics by collecting personal
information, tracking movements and measuring body temperature [187], [188],
[189], [190].

Figure 10: COVID-19 Case Identification [191]

In the initial stages of the outbreak, information reporting was the only
source of big data, leading to a heavy workload for staff collecting data
manually and the possibility of missing important information [192]. In China,
government departments and internet companies have collaborated to develop
various applications to monitor the public’s health and individual behavior in
real time with the goal of accurately tracking and managing the movement of
people [192]. Residents report personal information through digital tools,
including identification information, daily body temperature, location, and
traffic data [193], [194]. These tools are based on real data and use advanced
algorithms and big data technology to determine if a user is a confirmed case
or a potential source of infection [195], [196].

Digital technologies can aid in identifying COVID-19 cases by supplementing
clinical and laboratory notifications through the use of symptom-based case
identification and providing widespread access to community testing and self-
testing. This can also automate and speed up reporting to public health
databases. Case identification through online symptom reporting, as seen in
Singapore [197] and the UK [198], can provide advice on isolation and
referrals to further healthcare services such as video assessments and
testing. However, this approach must be linked to ongoing public health
surveillance and actions such as isolating cases and quarantining contacts.
Widespread testing and contact tracing also play a crucial role in identifying
cases, as an estimated 80% of COVID-19 cases are mild or asymptomatic [110].
Technologies such as sensors, thermal imaging cameras, and wearable devices
are also being explored for monitoring COVID-19 in populations [199].

There has been growing interest in the use of digital rapid diagnostic tests
for COVID-19 in order to increase access to testing, boost capacity, and ease
the burden on healthcare systems and diagnostic laboratories [200], [201],
[202]. At the early and mid stage of the pandemic, some point-of-care PCR
tests for COVID-19 have been developed [203], but their use was currently
limited to healthcare settings. Drive-through testing facilities and self-swab
kits have expanded access to testing, but it was observed delays between
sampling, sending samples to centralised labs, waiting for results, and
follow-up. On the other hand, rapid diagnostic antibody tests that can be used
in home or community settings and provide results in minutes have the
potential to improve this process. By linking these tests to smartphones and
using image processing and machine-learning methods [204], [205], mass testing
can be linked with geospatial and patient information and quickly reported to
both clinical systems and public health systems. However, for this to be
successful, standardisation of data and integration of data into electronic
patient records are necessary.

Curbing the spread of COVID-19 requires identifying individuals who have the
virus, isolating and treating them, and quarantining their close contacts. To
accomplish this, monitoring the health data and activity range of the
population in public areas, such as body temperature records, travel
trajectories, and close contact records, is crucial [206], [207], [208].
Hence, temperature monitoring in crowded areas such as train stations and
subway stations has become an important method for detecting potential cases.
However, traditional body temperature detection methods have limitations. In
dense scenarios for instance, it requires a lot of resources to use the
traditional forehead temperature measurement tools, and large-scale body
temperature detection can cause congestion and increase the risk of cross-
infection. Infrared thermal imaging temperature measurement systems have
therefore been used in public places with high traffic such as airports,
stations, and hotels to try and estimate the body temperature at group level
[209], [210]. However, legacy telecom network capacity is not sufficient for
real-time transmission and storage of large amounts of HD image data and
dynamic trajectory data. To address this issue, integration of several
different tools using 5G technology combined with artificial intelligence,
biometrics, and thermal imaging algorithms were employed to quickly measure
the temperatures of large populations at multiple railway stations and to
identify individuals who were not wearing masks [211], [212]. This intelligent
temperature measurement system not only aimed to prevent cross-infection but
also improve detection efficiency.

Artificial intelligence technologies such as face recognition can aid in
identifying suspected COVID-19 cases in a timely manner [213]. Traditional
access controls such as using turnstiles and punching cards or fingerprints
poses a risk of infection through indirect contact. To eliminate this risk,
during the pandemic, face recognition access control was widely deployed.
Smart cameras with face recognition capabilities were installed at entrances,
not only to open and restricted areas but also to track the movement of crowds
[214].

###  III-C Public Communication Platforms

The key to successfully carrying out interventions during a pandemic is to
educate the public and gain their cooperation through a well-designed
communication plan that involves active community involvement to establish
trust. With a large number of people having access to the Internet [215] and
mobile devices [216], utilising digital platforms for communication can
quickly reach a vast audience and mobilise communities. However, there are
ongoing challenges such as the spread of potentially false information [217],
[218] and digital disparities [219].

The use of online data and social media has been crucial in the way
information about the COVID-19 pandemic is being shared with the public [220].
Efforts are being made to counter the spread of false information [180], [221]
and promote credible sources. For example, Google’s SOS alert feature [222]
highlights trusted sources such as the World Health Organization in search
results. However, there is limited information on the effectiveness of these
interventions [223], [224], and the definition of misinformation is not always
clear [225]. Many governments have put COVID-19 information on their websites
and are using text messaging to reach people without internet access [226].
Chatbots are also being used to provide information and alleviate pressure on
non-emergency health advice lines [227]. Telemedicine, or the use of
technology for remote healthcare [228], is becoming more prevalent,
particularly in primary care. Digital communication platforms are also helping
with social distancing efforts, enabling remote work and classes [229],
supporting mental health [230], and facilitating community mobilization [231].
However, there are concerns about the security and privacy of these platforms,
particularly in regards to the handling of confidential healthcare
information.

##  IV Public Health Emergency response ICT Challenges

The COVID-19 pandemic has shown that current public health systems need to be
revisited and the existing use of technology to fight the pandemic presents
numerous challenges. To effectively combat pandemics, it’s important to have
strong coordination between data, people, and systems at different scales
[232]. However, public health agencies and healthcare stakeholders often use
different systems and data formats, making it difficult to identify trends and
create interventions. To improve the response to future pandemics, it’s
crucial for public health researchers, epidemiologists, and government
officials to be connected through integrated systems with shared data. It’s
also essential to involve people in the fight against COVID-19 by connecting,
coordinating, and supporting them through innovative technology. Digital
technologies have been a part of public health strategies for a long time, but
have not been adopted as quickly in the public health sector as in other
industries. For instance, WHO release guidelines on digital health
interventions in 2019 [233], [234]. However, the COVID-19 pandemic has
accelerated the development and implementation of new digital technologies.
These technologies have the potential to improve epidemic intelligence through
online data, detect and track cases and clusters of infections, trace
contacts, monitor travel during lockdowns, and disseminate public health
messages on a large scale. Despite these potential benefits, there are still
barriers to the widespread use of digital solutions in public health.

The effectiveness of big data and AI methods depend on the quality of the data
that is inputted into them. However, public health and private data sets that
are necessary for research are often not easily accessible due to privacy and
security concerns [235], [236], [237]. These datasets also lack
standardization and completeness. Researchers are urging technology and
telecommunications companies to share their data in an ethical and privacy-
respectful way, citing a moral responsibility for these companies to
contribute when there is a valid cause for using the data [238], [222], [239],
[240]. Some companies are already making aggregated data available, but
further steps need to be taken in order to make this data consistent and
timely, and there is no standard format or long-term commitment.

Researchers have been working together globally to gather data from various
sources that have been voluntarily reported [241]. Additionally, governments
should be more transparent and make their data, including information on
epidemiology and how the disease spreads, easily accessible to researchers in
downloadable formats. Some governments have made individual-level data
available for research [242], [243], but this raises concerns about privacy.
The use of open-source data, code, and scientific methods is becoming more
common, with many being shared online, including the use of pre-prints which
makes data available faster but hasn’t undergone peer review [244].
Nevertheless solid foundations have been set in the
EU111https://covid19scenariohub.eu/ and in the
USA222https://covid19scenariomodelinghub.org/, respectively, to facilitate the
collective gathering of relevant epidimiological data and collaborating in
developing models for analysis and prediction of future patterns.

The impact of COVID-19 has been widely discussed in various papers.
Chakraborty and Maity [245] looked at the impact of COVID-19 on society and
the environment and suggested preventative measures such as controlling
population growth, avoiding large gatherings, and developing a safe vaccine.
They did not mention the use of IoT as a preventative strategy. Oyeniyi et al.
[246] focused specifically on the use of IoT in the medical field to treat
COVID-19 patients remotely by collecting data through smartphone apps.
Nasajpour et al. [247] discussed the various applications of IoT in different
phases of COVID-19, such as early diagnosis, quarantine, and recovery. Mondal
et al. [248] presented a survey on the role of IoT in fighting COVID-19 and
proposed an IoT-based framework for early detection. Kumar et al. [249]
proposed a smart healthcare system using IoT to track quarantine patients and
provide remote health monitoring. Malliga et al. [250] explored the use of
technology to maintain social distancing. Bassam et al. [251] proposed a
three-layer IoT infrastructure for remote health monitoring. Singh et al.
[252] explored the use of IoT in orthopedic treatment for patients in remote
locations. The pandemic has also impacted business and supply chains, leading
researchers to focus on supply chain optimization. Chowdhury et al. [253]
reviewed the effectiveness of supply chain measures during the pandemic.
Shahed et al.[254] developed a three-layer optimization model to improve
inventory and reduce supply chain disruptions. De Sousa Jabbour et al. [255]
discussed the role of supply chain managers in building a smarter and more
resilient supply chain management system. Karmaker et al. [256] proposed a
methodology to improve supply chain sustainability in the context of COVID-19.
The impact of COVID-19 on the food and beverage industry was discussed by
Chowdhury et al. [257], who analyzed the short and long-term effects and
suggested strategies for recovery such as reducing operational expenses and
restructuring the supply chain.

Overall, during and after the pandemic, public health emergency response teams
face a number of challenges when it comes to using information and
communication technologies (ICTs) to respond to the aftermath. Some of the key
challenges posed by the pandemic COVID-19 [258]:

  1. 1.

Overwhelming demand for healthcare services: The pandemic has resulted in a
sudden surge in demand for healthcare services, with hospitals and clinics
facing an enormous burden to provide care to COVID-19 patients while also
attending to other patients with non-COVID related conditions.

  2. 2.

Limited availability of medical resources: The pandemic has exposed existing
shortages in the medical resource sector, including a lack of medical
supplies, personal protective equipment (PPE), and healthcare workers.

  3. 3.

Telehealth and remote monitoring: The need for social distancing measures has
led to a rapid increase in tele-health and remote monitoring solutions, but
these solutions require access to reliable internet and appropriate equipment,
which may not be available for all patients.

  4. 4.

Integration and interoperability of health data systems: The need for real-
time and accurate health data sharing has exposed the limitations of existing
health data systems, particularly in terms of integrating and exchanging data
between different systems.

  5. 5.

Cybersecurity and data privacy: The increased use of digital health
technologies has heightened concerns over cybersecurity and data privacy,
particularly in the context of sensitive personal and medical information.

  6. 6.

Digital divide and access to technology: The pandemic has amplified existing
disparities in access to technology and digital health services, with
marginalized and low-income communities at higher risk of being left behind.

Those challenges were mostly addressed by the integration of new technologies
like the IoT, big data analytics, AI, and blockchain. Moreover, this kind of
technologies is being used to find solutions to problems caused by the
coronavirus. For instance, Facebook has used AI and big data to map population
density, demographics, and travel patterns to determine where to send supplies
and minimize the spread of the virus [259]. Big data analysis of GIS and IoT
data from infected patients can assist in tracing the source of the virus and
identifying close contacts [260]. The U.S. National Science Foundation has
funded a project that explores the use of social media big data, geospatial
data, and AI for spatial epidemiology research and risk communication. The
combination of blockchain, IoT, and AI has potential in addressing trust and
security in public health, by storing medical device data and non-personal
sensor data on the blockchain while keeping patients’ personal data private
[261], [262]. AI and big data technologies can be utilized to analyze both on-
chain and off-chain data and provide real-time analytics and recommendations
through customized dashboards.

The COVID-19 pandemic has shown the necessity of transforming the public
health system from being reactive to proactive and creating innovations that
can give timely information for proactive decision-making in local, state, and
national public health systems [263]. Therefore, pandemic has brought about
significant changes to the way we live, work, and interact with one another.
In the post-pandemic world, many sectors are likely to see a greater reliance
on technology to continue with business as usual. IoT (Internet of Things)
technologies can play a crucial role in facilitating the transition to the new
normal by enabling remote work, telehealth services, and online learning. By
connecting devices and collecting data, IoT can help monitor and manage the
spread of the virus, improve access to healthcare services, and provide
efficient and effective remote work solutions. Additionally, IoT technologies
can drive efficiency, productivity and innovation across various industries,
thus boosting the economy. The post-COVID world presents an opportunity to
embrace and harness the power of IoT to create a more connected, resilient and
sustainable society.Therefore, some of the key challenges that we are facing
after the pandemic and need to be addressed includes [264]:

  1. 1.

Sustaining telehealth services: The pandemic has led to a significant increase
in the use of telehealth services, but maintaining these services in the long
term will require addressing issues of reimbursement, interoperability, and
data privacy.

  2. 2.

Addressing the digital divide: Ensuring equitable access to technology and
digital health services for all communities will be a critical challenge in
the post-pandemic period, particularly in light of the existing disparities
that have been exacerbated by the pandemic.

  3. 3.

Integration of digital health solutions into existing healthcare systems: The
rapid adoption of digital health solutions during the pandemic has highlighted
the need for these solutions to be integrated into existing healthcare systems
in a seamless and efficient manner.

  4. 4.

Data privacy and security: Ensuring the security and privacy of patient health
data will remain a critical challenge in the post-pandemic period,
particularly given the increasing amount of sensitive medical information
being generated and stored digitally.

  5. 5.

Personalized and predictive healthcare: The increased use of digital health
technologies has the potential to drive personalized and predictive healthcare
solutions, but this will require overcoming challenges related to data sharing
and integration, privacy, and cybersecurity.

  6. 6.

Addressing workforce needs: The post-pandemic period will require addressing
the needs of the healthcare workforce, including ensuring the appropriate
training and reskilling to make effective use of new digital health
technologies.

  7. 7.

Return to normal: As society returns to normal, ICTs will be important in
tracking and responding to any resurgence of the disease, as well as in
providing information and support to individuals and communities as they
adjust to the new normal.

Overall, effective public health emergency response during a pandemic requires
a coordinated and comprehensive approach to addressing these ICT challenges,
including improving access to technology, providing clear and accurate
information, protecting personal privacy, promoting interoperability, and
strengthening cybersecurity. Moreover, addressing these ICT challenges after a
pandemic requires a continued coordinated and comprehensive approach,
including effective data management, telemedicine, vaccination programs,
mental health support and contingency plans for future outbreaks.

##  V Discussion

###  V-A Electronic Health Records (EHR)

EHRs are digital versions of the traditional paper-based medical records that
healthcare providers use to store patient information. EHRs have several
advantages over traditional records, including improved data security,
increased efficiency, and better patient care. One of the main benefits of
EHRs is improved data security. Electronic records are stored on secure
servers, which can be accessed only by authorized personnel. This eliminates
the risk of lost or stolen paper records, and ensures that patient information
is kept confidential.

Another advantage of EHRs is increased efficiency. With electronic records,
healthcare providers can access patient information quickly and easily, which
can lead to faster diagnoses and treatment. Electronic records also make it
easier for healthcare providers to share patient information with other
providers, which can improve communication and coordination of care. EHRs can
also improve patient care by providing healthcare providers with more accurate
and complete patient information. Electronic records can include a wide range
of data, such as lab results, X-ray images, and medication lists. This can
help providers make more informed decisions about patient care and treatment.
In conclusion, Electronic Health Records (EHRs) have many benefits over
traditional paper records. They improve data security, increase efficiency,
and better patient care. EHRs are becoming more prevalent in the healthcare
industry, as more and more healthcare providers are adopting them to improve
the quality of care they provide to patients.

###  V-B Hospital Management Systems (HMS)

HMS are a complex and vital aspect of healthcare, but they come with a number
of challenges [265, 266, 267, 268]. High cost is one of the main challenges,
as both the development and deployment of the system can be expensive.
Additionally, the vast number of features in HMS can make the design of the
system complex, which can lead to difficulties with the user interface and
user experience. Data security is another major challenge, as healthcare
organizations must ensure that patient information is kept confidential and in
compliance with changing regulations.

Another challenge is migrating from existing manual processes to an HMS. This
can be difficult for both staff and patients, especially individuals who may
not be familiar with computer technology. Additionally, patients can become
impatient during the transition period as they may not be used to the new
system.

Despite these challenges, there are several advantages to using an HMS. One of
the main advantages is time-saving, as technology can automate many manual
tasks. Additionally, HMS can improve efficiency. Furthermore, HMS can be cost-
effective and easily manageable, providing easy access to patient data and
history. It can also improve patient care, monitor inventory and reduce the
workload of documentation. Lastly, it can provide better audit controls and
policy compliance.

###  V-C Health Tracking & Monitoring Systems

There are several limitations to the use of wearable technology in healthcare.
High device cost is a concern that may hinder the accessibility of this
consumer healthcare technology to benefit only those who can afford the
devices. Furthermore, many devices are not compatible across platforms (e.g.,
iOS and Android smart phone operating systems) and there is a lack of data
standards which limits their broad use and ubiquity. The time needed to choose
and properly set up a device, and remembering to charge the device are also
potential barriers to proper use. The acceptability of these devices and
potential stigma associated with them varies by community and may also be a
factor in dividing who benefits from these technologies [269].

Most importantly, the accuracy of the devices and evidence that they do in
fact improve health outcomes must be established with large clinical and field
trials. It is common to find unsubstantiated scientific claims with no
reference to peer-reviewed studies on commercial wearable device company
websites and advertising materials that can set a dangerous precedent if
inaccurate or misinterpreted data is factored into medical decision making.
False negatives can cause a potentially fatal condition to be missed while
false positives can lead to over-treatment and/or anxiety. Inaccuracy of
activity trackers may also lead individuals to overestimate their level of
physical activity, limiting their effectiveness for lifestyle interventions
[270, 271]. [272] demonstrated that most wrist-worn devices adequately measure
HR in laboratory-based activities, but poorly estimate energy expenditure and
caution the use of those measurements as part of health improvement programs.
[273] demonstrated that step counts are most accurate at slow walking speeds.
While useful for just-in-time interventions, real-time delivery of results
should be carefully considered to ensure that accurate data is presented at
the right way and at the appropriate time to avoid potentially harmful
interventions and patient confusion and anxiety. Training should be provided
to health practitioners to increase awareness of the utilities and limitations
of wearable technologies [274]. New tools should be developed to assist with
the interpretation of wearable device data in a clinical setting. Finally, it
is important to further evaluate the emotional burden and increased
distraction and technology addiction that wearables can inflict, potentially
causing more harm than good.

New and extremely novel wearable sensors are being developed that build upon
movement, physiological and biochemical sensors in existence to improve
disease detection and prognostication through changes in physiology over time
or as a result of a specific treatment or intervention. Mechanical properties
of soft tissues were recently monitored via wearables to determine
spatiotemporal changes in viscoelasticity of basal cell carcinoma lesions
[275]. Wearable oxygen diffusion sensors have been used to track wound
healing, and moisture sensors can inform better timing of wound dressing
changes [276, 277].

###  V-D Epidemiological Surveillance

Epidemiological surveillance of infectious viruses including COVID-19 is the
process of identifying, tracking, and monitoring cases of the disease in order
to understand its spread and inform public health response efforts. There are
several key challenges to effective epidemiological surveillance [278],
including:

  1. 1.

Identification and tracking of cases: Identifying and tracking all cases can
be difficult, especially in areas with limited testing capacity or where
asymptomatic cases are prevalent.

  2. 2.

Data quality and completeness: Ensuring that data collected on cases is
accurate and complete is important for effective surveillance, but this can be
difficult in areas with limited resources or where data systems are not fully
developed.

  3. 3.

Data sharing and dissemination: Sharing data on positive cases in a timely and
transparent manner is important for effective surveillance and response, but
this can be challenging in situations where data sharing is hindered by legal
or political barriers.

  4. 4.

Privacy and confidentiality: Protecting the privacy and confidentiality of
individuals is an important consideration in surveillance efforts, but this
can be difficult to achieve in practice.

  5. 5.

Limited resources: Limited resources, including funding, staff, and equipment,
can make it difficult to conduct effective surveillance.

  6. 6.

Public trust: Building public trust in surveillance systems and ensuring that
surveillance data is perceived as reliable and trustworthy is crucial to
effectively track and respond to the pandemic.

Overall, epidemiological surveillance of infectious viruses is a critical tool
for understanding the spread of the disease and guiding public health response
efforts. However, effectively addressing the challenges to surveillance is
crucial for ensuring that surveillance data is accurate, complete, and
actionable.

###  V-E Case identification

Case identification is a critical component of the response to epidemics/
pandemics. It involves identifying individuals who test positive and isolating
and treating individuals, as well as quarantining their close contacts. This
is crucial in controlling the spread of the virus. There are several methods
used for case identification, including:

  1. 1.

Testing: Testing is the most widely used method for identifying cases. It
involves taking samples from individuals and analyzing them for the presence
of the virus.

  2. 2.

Symptom-based identification: Individuals who have symptoms consistent with
the virus signatures, such as fever, cough, and difficulty breathing, may be
considered as cases even if they have not been tested.

  3. 3.

Contact tracing: Close contacts of known positive cases may be considered as
cases even if they have not been tested or have no symptoms.

  4. 4.

Digital tools: Government departments and internet companies have jointly
developed digital tools in some countries that monitor the health of the
public, personal information and behavior in real-time, with the goal of
accurately tracking and managing the flow of people to help identify potential
cases.

  5. 5.

Artificial intelligence and biometrics: AI and biometrics such as face
recognition and thermal imaging technology can be used to identify potential
cases in crowded places such as airports and train stations.

Case identification is an essential part of controlling the spread of
infectious viruses, it can be done through a combination of different methods,
and different methods may be more effective in different situations and
settings.

###  V-F Public Communication Platforms

Public communication platforms play a critical role in providing accurate and
timely information to the public. They can be used to disseminate information,
the viral transmission pathways, and preventative measures, as well as to
provide updates on the number of cases and deaths, and the status of the
response efforts.

  1. 1.

Official websites: Governments and health organizations often have official
websites dedicated to providing information on the pandemic. These websites
can be a reliable source of information on the virus, and can be updated in
real-time.

  2. 2.

Social media: Social media platforms like Twitter, Facebook, and Instagram are
widely used to disseminate information on the pandemic. These platforms can be
used to share official information, as well as to share personal experiences
and perspectives on the virus.

  3. 3.

News outlets: News outlets such as television, radio and newspapers can
provide information on the pandemic, as well as updates on the response
efforts.

  4. 4.

Public health hotlines: Governments and health organizations may establish
public health hotlines that the public can call for information on the
pandemic.

  5. 5.

Mobile apps: Some governments have developed mobile apps that provide
information on the pandemic, including the number of cases, locations of
testing centers, and other relevant information.

  6. 6.

Public awareness campaigns: Public awareness campaigns can be used to educate