Content uploaded by J. Grey Faulkenberry
Author content
All content in this area was uploaded by J. Grey Faulkenberry on Nov 29, 2023
Content may be subject to copyright.
Title: Electronic Health Records, Mobile Health, and the Challenge of Improving
Global Health
Authors:
● J. Grey Faulkenberry MD MPH, Department of Biomedical and Health
Informatics, Children’s Hospital of Philadelphia
○ Corresponding author email: faulkenbej@chop.edu
● Anthony Luberti MD, Department of Biomedical and Health Informatics,
Children’s Hospital of Philadelphia
● Sansanee Craig MD, Department of Biomedical and Health Informatics,
Children’s Hospital of Philadelphia
Financial disclosure statement
Dr. Faulkenberry serves as Chief Technical Officer for MayJuun LLC.
Abstract: Technology continues to impact healthcare around the world. This provides
great opportunities, but also risks. These risks are compounded in low-resource settings
where errors in planning and implementation may be more difficult to overcome. Global
Health Informatics provides lessons in both opportunities and risks by building off of
general Global Health. Global Health Informatics also requires a thorough
understanding of the local environment and the needs of low-resource settings. Forming
effective partnerships and following the lead of local experts are necessary for
sustainability; it also ensures that the priorities of the local community come first.
There is an opportunity for partnerships between low-resource settings and high income
areas that can provide learning opportunities to avoid the pitfalls that plague many
digital health systems and learn how to properly implement technology that truly
improves healthcare.
Callouts:
1. Low- and middle-income countries (LMICs) and other low-resource settings have
the opportunity to pivot away from EHRs built to improve billing efficiency and
instead leverage modern technology to create a digital health ecosystem
designed to achieve the goal of improved healthcare.
2. True collaborations are partnerships with bidirectional sharing and respect.
3. The burnout and loss of healthcare workers is critical to consider in settings
where the healthcare workforce is already limited.
4. One final issue that has not yet been addressed in the literature is the possibility
of losing leaders, health workers and clinicians due to poorly designed electronic
health systems.
5. If the format of the data is aligned with already established international
standards, it could ease the sharing of data both within and between countries.
Introduction
Technology has become an intrinsic part of modern life around the world. The internet
has connected the world in ways previously only imagined in science fiction and cell
phones have put this connection in our pocket. Constantly having a computer at hand
and immediate access to the rest of the world has spurred the ability to collaborate and
share information on an unprecedented scale. Healthcare has begun to take advantage
of these changes, with some successes and important failures.1–4 While few clinicians
would go back to paper medical records, the introduction of electronic health records
(EHRs) to hospital systems has not yet improved the quality of care (i.e. equitable, safe,
patient-centered, efficient, effective and timely) as originally promised.5
The potential benefits of improved healthcare access and outcomes through electronic
data capture and shared patient information has led to increased use in clinical settings
in low- and middle-income countries (LMICs) since the early 2000s.6 However,
healthcare systems globally risk costly implementations of EHRs that appear to be
contributing to clinician burnout if the.7,8 Around the world this risk increases as EHR
vendors based in high-income countries seek international expansion of potentially
unsuitable technology to resource-constrained areas.9 The American healthcare system
is devoting research and operational resources to study and optimize workflow and
workarounds in EHR-supported work.10 On the other hand, low- and middle-income
countries (LMICs) and other low-resource settings have the opportunity to pivot away
from EHRs built to improve billing efficiency and leverage modern technology to create
a digital health ecosystem designed to achieve the goal of improved healthcare. To
accomplish this, those seeking to support the implementation of EHRs in LMICs
(clinicians, researchers, policymakers, and healthcare systems in the US and abroad)
must understand what the needs of LMICs are, what kind of technology is appropriate,
and what work has already been done in this field.
Global Health Clinical Informatics (GHI)
To understand the field of Global Health Clinical Informatics, a few definitions are
useful. The World Health Organization (WHO) defines health as “a state of complete
physical, mental and social well-being and not merely the absence of disease or
infirmity”.11 Global Health has been described as “an area for study, research, and
practice that places a priority on improving health and achieving equity in health for all
people worldwide”.12 Finally, the American Medical Informatics Association (AMIA)
states that Clinical Informatics is the “application of informatics and information
technology to deliver healthcare services”13 (sometimes referred to as operational
informatics). The combination of these fields of practice comprises the field of Global
Health Clinical Informatics. Global Health Clinical Informatics (also known as Global
Health Informatics or GHI) is thus the application of informatics and information
technology for study, research and practice that places a priority on improving complete
physical, mental, and social well-being, and of achieving equity in the same for all
people worldwide.6
Health Informatics in low-resource settings began to emerge following the new
millennium. While the US and other high-income countries have tended to focus almost
solely on electronic health record systems, LMICs have more effectively employed
mobile technology.14 This is often abbreviated as mHealth, defined by the WHO as
“medical and public health practice supported by mobile devices, such as mobile
phones, patient monitoring devices, personal digital assistants (PDAs), and other
wireless devices”.15 Worldwide, it is estimated that over 60% of all people have a cell
phone, and almost 50% have smart phones15–17 (some estimates put these figures at
over 80%)18. It is also estimated that wireless signals now cover 85% of the world’s
population.15 In 2012, one report estimated that mHealth has changed the doctor-patient
relationship for 27% of clinicians in LMICs compared to 16% in high-income countries,
and restructured the clinician workplace for 34% of LMIC clinicians vs 19% of high-
income counties’ clinicians.19 A similar report around the same time stated 59% of
patients in LMICs were using at least one mHealth application or service, while that
number in higher income countries at the time was only 35%.15
There are also large systems of EHRs operating in LMICs. OpenMRS is an electronic
health record that was launched in 2004.20 In the last 17 years, it has been
implemented in over 5,500 facilities in over 40 countries, reaching over 12.5 million
patients. District Health Information Software 2 (DHIS2) is a health management
information system used to analyze and display population health data to policy makers,
enabling them to access live-data when forming policy and making decisions. More
recently many of these resources have been collected together in the Global Goods
Guidebook.21 The resources listed are good examples of digital health interventions that
strengthen health systems through evidence-based, sustainable, harmonized digital
health systems. They align with the WHO global strategy on digital health that
established guidelines to ensure that innovation and technology help to improve health
and reduce inequities.22 This guidebook does not list any of the largest electronic health
record companies (Epic, Cerner, Allscripts, etc); a number of factors played into this
decision. These large systems are generally implemented at a system wide scale, they
do not have small modules that can be implemented independently. These large
systems are proprietary and closed-source, meaning that it is only the vendor that is
able to modify or make changes to their product to better adapt to the local
environment. Last, they are all comparatively expensive. Resources in the Global
Goods Guidebook are all free and open-source (i.e. they can be modified, edited or
adapted as desired). They are generally designed as small modules that can be run
independently or many at the same time. An institution can thus scale up and add
functionality only when and if desired. The emerging health IT monoculture in the US
should caution stakeholders to analyze the appropriateness of these vendors to meet
the needs of LMIC healthcare systems.23
A digital maturity model helps to clarify why mHealth is so widely used globally. There
are many models of digital maturity available24–27, but the ultimate goal is the same, to
evaluate effectively “organizational capacity related to governance, data management,
digital transformation, innovation and knowledge management.”27 A digital maturity
model (1) identifies the level of technology that an environment is prepared to
implement and use effectively, (2) whether the use can be sustained, and (3) assists
with setting realistic expectations for technology use. The digital maturity model can be
useful to evaluate the readiness of a clinical setting for technical devices and health
information software as well as other technological tools that support health care in
LMICs.28,29
Global Health, Public Health, Pediatrics & Informatics
Global Health and Public Health goals have long overlapped, particularly in the area of
child health outcomes.30–32 The first 3 of the WHO Sustainable Development Goals
significantly affect children.33 They include reducing global maternal mortality, reducing
childhood mortality, and ending the epidemics of AIDS, tuberculosis (TB), malaria and
other communicable diseases. Over half (57%) of the deaths from malaria are in
children under 5 years and over a million children contract TB yearly - and almost a
quarter of them die.34,35 Globally, 5.2 million children under 5 die every year; over 20%
of them from pneumonia and diarrhea. Almost all of these deaths could have been
prevented or treated.36 While maternal mortality isn’t precisely child health issue,
mothers are usually responsible for their children’s wellbeing, and worldwide 15% of
young women give birth before the age of 18.37,38
Demographic data also support the importance of children’s health within the broader
fields of Global Health and Global Health Informatics. Of the nearly 2 billion children in
the world, almost half live in poverty39. Most children (< 18 years old), live in LMICs.40 In
high income countries, 16% of the population is under the age of 15 years, compared to
25% in middle income countries and 42% in low income countries.41 Therefore, children
are more likely to live in lower-income countries than adults, and children are also more
likely to live in poverty.39
For all of these reasons, LMICs tend to have a strong focus on public health with an
emphasis on children’s health. This focus is also reflected in the priorities of the Global
Health Informatics community. As data continue to play a larger role in healthcare, the
role of health information technology in LMICs becomes critical. The potential for health
information technology to inadvertently create, maintain, or worsen disparities is
described in detail elsewhere in this issue ([Current Issue Reference Craig et. al.]).
Wealthy countries can more easily afford technology, and are more likely to live in areas
with a more developed infrastructure (e.g., a more stable electric grid).
Successful implementation of EHRs in LMICs requires a comprehensive understanding
of the local environment. This includes identifying and collaborating with local experts
who have the leadership skills, knowledge base, and established relationships to create
change. This increases the chance that any projects begun are designed to meet the
communities’ goals. The following paragraphs describe a successful framework for
implementing technology in LMICs: people, technology and process.42–45 This
framework views health information technology as a means to augment the people and
processes necessary to deliver high quality health care.
Figure 1. People, Process and Technology
People
Partnering with local entities is the cornerstone of any successful Global Health project.
A successful collaboration is one “that can reasonably be expected to have mutual
(though not necessarily identical) benefits, that will contribute to the development of
both institutional and individual capacities to advance child health at both institutions,
that respects the sovereignty and autonomy of both institutions, and that is itself
empowering.”46
This definition stands in stark contrast to the continuing prevalence of White Saviorism,
colonialism, and voluntourism (volunteer-tourism).47–50 The White Savior has long been
spoken of but gained renewed focus in 2012 with Teju Cole’s “The White-Savior
Industrial Complex”.48 It is the attitude that white people are needed to go to an
impoverished area and save people from themselves; it is deeply rooted in the idea of
colonialism and the need for white people to help “civilize” the world. Voluntourism is too
often undertaken by groups with too little knowledge, understanding, or dedication to
make a difference; something is NOT always better than nothing. It has been noted that
these “voluntourists” are often inexperienced and that they generally devote only a few
days to a few weeks to projects that would require decades to be successful.51 They
often disrupt the local economies and systems, and can redirect focus away from
important home-grown projects to those of interest to the voluntourists.52
This mentality extends to well-meaning mhealth research and digital health intervention
projects in LMICs. A search for “global mhealth pilot” yielded 350 PubMed results In
June 2021, up from 9 publications in 1998 and 63 in 2020 (in contrast, the same search
using “global health pilot” returned only 5 publications). Beginning in the early 2010s,
the term “pilotitis” began to emerge, used to describe stand-alone projects that fail to
scale and end after funding is depleted.53–56 The growing frustration expressed by LMIC
countries in which these pilots occur is the continued emphasis on small, narrow-
focused studies with very specific populations, instead of on more broadly applicable,
scalable and flexible interventions.53 Also of concern, these studies are often driven by
sponsors with specific interests and agendas, not by the interests of the local
communities.57–60 In 2012, the extent of the “pilotitis” phenomenon prompted the
government of Uganda to initiate a moratorium “demanding that future interventions
prioritized interoperability, sustainability and conformity to existing (Ministry of Health)
cyber laws and data requirements”53,61
Global Health Informatics may fall into these same patterns, especially given the
excitement surrounding technology. Voluntourists may believe their technical literacy is
superior, though many LMICs have a long history with mobile technology. The digital
maturity model emphasizes identifying technology that an environment is prepared to
implement and use effectively, thus teaching about one’s favorite technology is not as
effective if there is no understanding of the tools available in that environment.
Education on how to use a MacBook Pro will likely be less effective than how to use an
Android smartphone62 (most of the world has Android phones and not MacBook
Pros63,64). It may also be more appropriate to teach about using a spreadsheet, a
simpler, more widely used technology, than computerized physician order entry, a
complex technology that is often desired but not available in many locations.
The most meaningful collaborations are partnerships with mutual sharing and respect.
Local leadership and tailoring are known to generate success.42,46 Successful projects
are locally driven and require local leaders that push a project forward. With limited
resources, local leaders take on even greater importance.65 With generous funding,
more projects can be approved, and demonstrating immediate value is not as urgent.
When resources are limited, then having support from local leadership is an absolute
necessity.66
One successful example that demonstrated a clear understanding of the environment
and the necessary people involved came from Botswana in a trial for vision screening in
children. This trial was a collaboration between the Ministry of Health Ministry of Health
and Wellness (MoHW), the Ministry of Basic Education (MoBE), Peek Vision,
Botswana-UPenn Partnership (BUP), Botswana Optometrists Association (BOA), and
Seeing is Believing through Standard Chartered Bank. This program used mHealth via
a smartphone app, and engaged the population by training not just nurses and health
care assistants, but also school and department heads and counseling teachers.67
Barriers and facilitators to implementing health information technology in LMIC settings
are well known and best practices have been published.68–70 From a people and
process perspective, facilitators include engaging local leadership, clinicians, and
stakeholders in the implementation of the project. In-person oversight of the
implementation of a project is necessary at times, but not sufficient to ensure the
success of the project.71 Engaging local stakeholders and building relationships
between all stakeholders typically requires organizational change management skills
that are not often taught, especially in medicine. Effective engagement also requires an
understanding of and respect for local culture, norms and expectations. Successful
partnership also requires the belief that all parties involved are equal partners, and that
all are benefiting. When one group is seen as “giving” to the other, this creates an
unbalance in power dynamics. It often creates the illusion that the “receiving” partner
must be thankful and accept whatever is “provided” to them, without any ability to
demand a certain level of quality or responsibility. These ideas, while universal, are
especially important when implementing health information technology in LMICs, given
the history of LMIC exploitation described earlier in this paper.
A skilled health informatics workforce is a necessary part of ensuring quality for
programs, projects and healthcare systems. Whilte technical literacy is improving
around the world, comfort using digital tools, often in unfamiliar situations, continues to
be a barrier. The WHO has made the use of information and communication
technologies a core competency of the 21st century for the healthcare workforce72; this
should be a major focus of healthcare in the future. Most reviews that address training
in Informatics have been published in the United States, Europe and Australia. One of
the few reviews currently available for LMICs was published for Latin America.68 It
ranked the top subjects that experts in this area of the world believe need to be included
in informatics training, including: an introduction to biomedical informatics, data
representation, mobile health, issues on security, confidentiality and privacy, public and
clinical informatics and electronic health records.
In order to create such a skilled global health informatics workforce, Peru has created
an online certificate and master program of Informatics in Global Health.73 The program
was started in 2009 at the Universidad Peruana Cayetano Heredia (UPCH), through the
Faculty of Public Health and Administration. They received funding from the Fogarty
Center to develop the QUIPU: Andean Center for Research and Training in Informatics
for Global Health. This project was driven locally, and collaborated with multiple
countries (Argentina, Chile, Colombia, the United States, Mexico, England and Peru),
Peruvian entities (Universidad Peruana Cayetano Heredia, Universidad Nacional Mayor
de San Marcos, USAID, Instituto Nacional de Health, World Bank), groups in Latin
America (Universidad del Cauca, Hospital Italiano de Buenos Aires, CINVESTAT,
Institute of Clinical and Health Effectiveness), and international organizations (University
of Washington, University of Tulane, University of Pennsylvania, University of London
and University of Michigan). The first diplomas were offered in 2011, and it continues to
offer its certificate through today.74
Technology
Technology tends to be concentrated in the wealthiest countries. There is a belief that
there is no money to be made in the developing world and thus little attempt to create
technology within these countries.75 LMICs may be latecomers to technological
innovations, receive outdated and broken equipment, or find it necessary to adapt
software and hardware that was never designed for the developing world. To create
solutions that are appropriate in these settings, funding streams and awareness are
needed to focus various groups, companies, NGOs and governments on the technology
needs of LMICs.
Of course, technology is only a benefit when it works properly. While donating old
technical devices, is often applauded, they may not actually function (the estimation for
donated general medical equipment is that 40% is nonfunctional).76 This occurs when
medical equipment, such as a ventilator, is donated without considering that the
required voltage is incompatible with the electric supply, that there are few trained
health care workers to use the equipment, and that the electricity needed to keep it
operating is unreliable. Other examples include the inability to repair donated medical
equipment without adequate supply chains and local technical expertise.76
This is not only true for traditional medical equipment, but also for information
technology devices like computers and smartphones, and for disciplines that integrate
both, such as radiology.70,77–79 Many areas of the world don’t have workable land-lines,
so all electronic records must either be stored or must be transmitted wirelessly. Power
may be unreliable, and backup systems need to be in place. This may include battery
powered devices, backup generators, solar panels, and the use of devices that draw
less electricity, such as netbooks, tablets, fan and driverless computers and servers.
However, it is also necessary to protect the equipment from an unreliable power grid
and must include security such as good grounding and lightning protection.79 All of
these issues may affect clinical care. Consider the frustration of upgrading the software
for an electronic health record. During this time much of the software is unavailable and
contingency plans have to be made. In areas where electricity is unreliable, similar
plans for how to continue caring for patients when the power is out should be created
prior to any implementation of a digital record system.
Another concern that is always brought up in both high and low resource settings is data
handling, ownership, privacy, and security.65,69,79 Much of the concern surrounding
privacy and security is actually process-driven. For example, this would include
establishing a policy within the clinic or hospital of using passwords to access health
records, and securing laptops and tablets within the clinic. However, technical factors
must also be considered, including where and how data are stored, and who actually
owns, controls, and has access to the data. This is made dramatically more complicated
by mobile device use. When data are not stored in a central location but on many
different devices, each of those devices must be safeguarded. When a community
health worker is treating patients in a remote village, they must collect data without
internet access. This must then be stored in such a way that loss of the device does not
comprise access to health information, typically involving complete encryption of the
data on a device.
In recent years, cloud data centers have been suggested as a solution to these
concerns around data storage and maintenance.70,80,81 Given this potential solution,
additional work is needed to determine which regulations apply when data are stored in
a cloud that is not based in the same country from which the data originated. When
cross-country collaborations are developed, not only must each country’s laws
accommodate cloud storage, but also each collaborator must decide WHICH country’s
laws apply. If healthcare data from the US is stored in a server that is located in Europe,
those data have to abide by US laws or EU laws? This has not yet been resolved, but it
is an important question, especially for countries where their wealth could easily be
dwarfed by the company hosting their data.82
The digital maturity model again informs how to select technology. After an evaluation
using the maturity model, a clinic may decide to build capacity for technical literacy in its
workforce as well as implement health IT tools that are a good fit for the end users’
needs and technical proficiency. For example, if a health care clinic in a LMIC has only
paper data entry and regularly loses electricity, implementing a full EHR on desktop
computers is unlikely to be effective. Instead, a clinic may decide to either optimize
paper data entry forms for higher quality data capture, or introduce digital data entry via
tablet or mobile phone. This was demonstrated by a presentation at the 12th Health
Informatics in Africa Conference. Using REDCap (traditionally used as software only for
data collection), dermatology services in Botswana not only captured clinical data for
patients with Cutaneous Lymphomas, but quickly became the preferred tool amongst
both the patients and providers when gathering data needed for clinical care.83
Accompanying other challenges to health IT implementations in LMICs, poorly designed
electronic health systems may lead to dissatisfaction and departure of skilled healthcare
workers. As mentioned earlier, implementing a new electronic health record system has
been shown to increase clinician burnout and egress from the workforce.84 These
adverse consequences stem from poor design (EHRs have never been designed with
clinicians in mind85,86, they were originally designed for billing.87,88) Common problems
include interfaces and the manner by which the health care worker interacts with the
health IT tool. EHRs are often confusing for clinicians to use in their daily practice.
EHRs often add to the amount of work and documentation that clinicians are required to
do, while adding little to the clinicians’ ability to actually care for patients. In the US,
there is usually enough financial incentive to encourage physicians to use an electronic
health record system, even if it is not ideal (although most of us also had colleagues
that retired rather than go through the pain of learning a new system).89 The burnout
and loss of healthcare workers is critical to consider in settings where the healthcare
workforce is already limited.90–93
Process
When people and technology come together, they form processes. Much of the process
in informatics is designed to support the collection, transmission, evaluation and use of
data. A robust body of literature exists describing surgery in LMICs94, these include
country wide data collection reviews from Ghana, India, Kenya, Pakistan95, trauma data
from South Africa96, and ICU data in Brazil97. These successes in the secondary use of
data to study health problems highlights the opportunity to build toward more effectively
using clinical data to improve the quality of healthcare.
Figure 2. demonstrates the spectrum of digital health tools that are used during the
implementation of digital data collection systems. Most groups begin to collect data by
storing data in spreadsheets for easier access and creating summary statistics. As more
complex data collection is desired, specified forms are created that enable precisely
defined types of data to be collected (i.e., structured data capture) and analyzed. At this
stage data collection is still primarily for reporting. Beyond this step data begin to be
integrated into everyday clinical care. This is one of the largest steps with very broad
applications. This encompasses beginning to keep notes in digital forms, reviewing
records of labs and other test results on a computer, or entering orders from a mobile
device. Combined with other core functionalities beyond data capture, this comprises an
electronic health record.98 Monolithic EHRs are the systems most familiar to US
healthcare professionals. They are convenient by offering a single answer to the range
of healthcare delivery needs, but tend to lock systems into a single product, reducing
competition and stifling innovation, while also making information held within them
difficult to access ([Current Issue Reference Jenssen et. al.]). At a regional or national
level records may then be aggregated into a health information exchange (HIE). This is
a central data repository of information from different systems enabling access for
allowing patients and clinicians regardless of their local electronic health record.
Figure 2: Increasing Complexity of Digital Health Tools (abbreviations: EHR - electronic
health record, HIE - health information exchange)
In LMICs, data that are collected are generally expressed as large-scale public health
reports. However, when the focus is on data aggregation and reporting, less emphasis
is placed on using data to direct clinical care. Limited electronic tools may further
constrain healthcare teams’ ability to use clinical data to improve care. Figure 2
demonstrates how a health system typically begins with raw data capture (i.e. data
without specific context, and an inability to analyze and use it at the point of care), but
then must transition to a full-fledged electronic health record implementation (Figure 2).
This gap between basic data entry and full EHRs affects decisions made at both a micro
and a macro level. In systems that perform well, the clinician enters data and is able to
see the benefit of entering those data immediately when making decisions about a
patient. When clinicians perceive no immediate benefit, then data entry becomes one
more responsibility that does not improve their ability to care for patients.99 This can
result in poor data quality as clinicians create workarounds that facilitate the fastest
strategy to complete documentation.100
If implemented properly, electronic health records provide the clinician with an easy
approach to document, organize and find data they need to care for the patient, while
also collecting and reporting data at a regional or national level. In LMICs, few tools
exist that help span this gap between simple data collection and data use in clinical
settings. If an institution does not have the proper resources, then an EHR may simply
not be purchased or may be purchased and never implemented. This may happen if the
digital maturity model is not appropriately applied, and the institute purchases an
electronic health system they are not actually ready to implement, or for which they do
not truly have a need. In order to guide the transition from simple data collection to
collecting data in a manner that is meaningful to the clinician, creating tools to bridge
and understand this process are essential.
One proposal for such a solution recommended it would need to be locally-driven and
clinician-focused, while also being mobile-first, Fast Healthcare Interoperability
Resources (FHIR)®-based, modular and scalable.101 Locally driven and mobile-first has
already been explained above. Clinician-focused can be expanded to a focus on end-
users. Any software or tools created should be designed for those using the software,
not those who will be using the data secondarily (i.e. for research purposes or billing at
a later time).102 FHIR®-based can be expanded to using well-known data standards and
keeping the data freely available. Often healthcare data are unavailable, either because
it is stored in a format that is unusable outside of the EHR system, or because the EHR
system purposefully makes it difficult to access the data, even by the healthcare system
that owns it. As noted earlier, these systems are the most flexible when they are created
as independent modules that can be connected together. Such systems can be built up
over time, implementing only the functionality that is desired, when it is desired. This
approach enables the system to easily scale up to larger populations with more complex
functionality as required by the organization.
FHIR® is not the only healthcare data standard available, and as tools are implemented,
decisions regarding how to format data become particularly important. If a clinician
needs to open a document entitled “global_informatics.pdf,” he or she probably knows
what program is needed to open it. However, if the file was named
“global_informatics.ini.tar.gz”, the end-user would be much less likely to know how to
open the file. Similarly, to make the best use of data once they are collected, the data
must be in a format that can be shared and understood on other devices, in other
applications, and in other healthcare systems. Interoperability is the ability of different
information systems to access, exchange and cooperatively use data in a coordinated
manner.103 In the US faxing printed records is still a daily occurrence due to the lack of
interoperable electronic systems. LMICs also have major challenges with system
interoperability. Stakeholders in Botswana for example has invested heavily in its
healthcare information infrastructure. They have successfully implemented programs for
child health and nutrition, HIV care, and Tuberculosis. In 2019, they commissioned a
UNICEF Report104 to guide their continued development. The report noted that multiple
systems are currently in use, including a Patient Information Management System,
Integrated Procurement Management System, District Health Information Software,
Early Infant Diagnosis lab data system for HIV, OpenMRS for tuberculosis, Research
Electronic Data Capture (REDCap) for reporting, and a recently developed child welfare
application. Each of these systems works reasonably well within the single program for
which it was implemented, but the data produced from these disparate systems does
not easily integrate. This lack of integration results in a large volume of data that cannot
be easily shared among programs or levels in the healthcare system, leading to one of
the key recommendations in the aforementioned report, “...look for opportunities to
simplify, harmonize, reduce redundancy and roll-out the most reliable systems to all
districts and health facilities.” Of course, these needs are not unique to Botswana.
While some countries like Botswana have a fairly advanced information data system,
many are just beginning this process. In countries where little digital health data have
been collected, there is no set precedent or requirement on what systems must be
used. There is still time to learn from prior mistakes in health IT implementation,
especially when it comes to interoperability and data exchange. As stated above, if the
format of the data is aligned with already established international standards, it should
ease the sharing of data both within and among countries. Information collected is
easier to analyze if the format is one that is familiar to a larger number of people 71 and
as the COVID-19 pandemic has demonstrated, diseases do not respect national
borders. The ability to share data assists in understanding and controlling the spread of
many infections. While the world’s focus continues to be on COVID-19, there are other
illnesses of continued concern such as Ebola, SARS, Zika, or even influenzae. If data
are not interoperable, then it cannot be shared, and this diminishes the ability of public
health officials to work on a global scale.
Even when data are available and can be exchanged, effective use requires that
experts confirm the accuracy of the information. Most low-resource settings collect data
for reporting, although often this is done by hand, often in large ledgers, which are
copied from quickly written notes, and then copied again to a report. While many of
these locations do data audits, the reliability is still questionable.105–107 There is no gold
standard for the data, so it is challenging to ensure data quality. Often, even “Electronic
Data Capture” (EDC) is actually begun on paper and later transcribed into an electronic
record, introducing another opportunity for human error. The development of
methodologies to ensure that the data recorded are accurate, not just when compared
to the paper record, but also when compared to reality is urgently needed to ensure the
validity of EDC.
Final Thoughts
Global Health Informatics is a field that applies best practices learned from the field of
Global Health to support sustainable, capacity-building health information systems in
low-resource settings. To be successful, there must be a comprehensive understanding
of the local system. Projects must be driven by local stakeholders. Local leaders who
understand the environment must set the priorities to ensure that the goals of the local
community are met.66
LMICs have the potential to avoid the problems that plague many EHR systems, and
create modern, digital health systems that are designed with a focus on the patient’s
health. Systems within high-income areas have the opportunity to learn from and
partner with peers in LMICs to understand better how to implement technology that will
improve healthcare across the globe.
References
1. Classen DC, Holmgren AJ, Co Z, et al. National Trends in the Safety Performance of
Electronic Health Record Systems From 2009 to 2018. JAMA Netw Open.
2020;3(5):e205547. doi:10.1001/jamanetworkopen.2020.5547
2. Chaparro JD, Classen DC, Danforth M, Stockwell DC, Longhurst CA. National trends in
safety performance of electronic health record systems in children’s hospitals. J Am Med
Inform Assoc. 2017;24(2):268-274. doi:10.1093/jamia/ocw134
3. Bowman S. Impact of Electronic Health Record Systems on Information Integrity: Quality
and Safety Implications. Perspect Health Inf Manag. 2013;10(Fall). Accessed September
7, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797550/
4. Raposo VL. Electronic health records: Is it a risk worth taking in healthcare delivery? GMS
Health Technol Assess. 2015;11:Doc02. doi:10.3205/hta000123
5. Rudin RS, Friedberg MW, Shekelle P, Shah N, Bates DW. Getting Value From Electronic
Health Records: Research Needed to Improve Practice. Ann Intern Med. 2020;172(11
Suppl):S130-S136. doi:10.7326/M19-0878
6. Celi LAG, Fraser HSF, Nikore V, Osorio JS, Paik K, eds. Global Health Informatics:
Principles of EHealth and MHealth to Improve Quality of Care. MIT Press; 2017.
7. Yan Q, Jiang Z, Harbin Z, Tolbert PH, Davies MG. Exploring the relationship between
electronic health records and provider burnout: A systematic review. J Am Med Inform
Assoc JAMIA. 2021;28(5):1009-1021. doi:10.1093/jamia/ocab009
8. Nguyen OT, Jenkins NJ, Khanna N, et al. A systematic review of contributing factors of
and solutions to electronic health record-related impacts on physician well-being. J Am
Med Inform Assoc JAMIA. 2021;28(5):974-984. doi:10.1093/jamia/ocaa339
9. Why International Expansion Must Remain a Priority for Cerner, Epic, Allscripts,
MEDITECH. Accessed July 7, 2021. https://hitconsultant.net/2020/08/24/why-international-
expansion-must-remain-priority-ehr-vendors/
10. Zheng K, Ratwani RM, Adler-Milstein J. Studying Workflow and Workarounds in Electronic
Health Record-Supported Work to Improve Health System Performance. Ann Intern Med.
2020;172(11 Suppl):S116-S122. doi:10.7326/M19-0871
11. WHO | Basic documents. Accessed June 30, 2021. https://apps.who.int/gb/bd/
12. Koplan JP, Bond TC, Merson MH, et al. Towards a common definition of global health. The
Lancet. 2009;373(9679):1993-1995. doi:10.1016/S0140-6736(09)60332-9
13. Clinical Informatics | AMIA. Clinical Informatics. Accessed May 9, 2021.
https://www.amia.org/applications-informatics/clinical-informatics
14. Agarwal S, Perry HB, Long L-A, Labrique AB. Evidence on feasibility and effective use of
mHealth strategies by frontline health workers in developing countries: systematic review.
Trop Med Int Health. 2015;20(8):1003-1014. doi:10.1111/tmi.12525
15. WHO Global Observatory for eHealth, World Health Organization. MHealth: New Horizons
for Health through Mobile Technologies. World Health Organization; 2011. Accessed May
10, 2021. http://www.who.int/goe/publications/goe_mhealth_web.pdf
16. Turner A. How Many People Have Smartphones Worldwide (May 2021). BankMyCell.
Published July 10, 2018. Accessed May 10, 2021. https://www.bankmycell.com/blog/how-
many-phones-are-in-the-world
18. Wiggington, Craig, Curran, Michael, Brodeur, Christine. Global Mobile Consumer Trends.
Deloitte Accessed May 10, 2021.
https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media-
telecommunications/us-global-mobile-consumer-survey-second-edition.pdf
19. Wiu P. Emerging MHealth: Paths for Growth. pwc; :44. Accessed July 1, 2021.
www.pwc.com/mhealth
20. About OpenMRS | OpenMRS. OpenMRS. Accessed June 20, 2021.
https://openmrs.org/about/
21. Global Goods Guidebook. Digital Square. Accessed May 10, 2021.
https://digitalsquare.org/global-goods-guidebook
22. WHO | WHO Guideline: recommendations on digital interventions for health system
strengthening. WHO. Accessed July 7, 2021.
http://www.who.int/reproductivehealth/publications/digital-interventions-health-system-
strengthening/en/
23. Koppel R, Lehmann CU. Implications of an emerging EHR monoculture for hospitals and
healthcare systems. J Am Med Inform Assoc JAMIA. 2015;22(2):465-471.
doi:10.1136/amiajnl-2014-003023
24. Cresswell K, Sheikh A, Krasuska M, et al. Reconceptualising the digital maturity of health
systems. Lancet Digit Health. 2019;1(5):e200-e201. doi:10.1016/S2589-7500(19)30083-4
25. Johnston DS. Digital maturity: are we ready to use technology in the NHS? Future Healthc
J. 2017;4(3):189-192. doi:10.7861/futurehosp.4-3-189
26. Healthcare Provider Models. HIMSS Analytics - Europe. Accessed June 30, 2021.
https://www.himssanalytics.org/europe/healthcare-provider-models/all
27. Finkelman, Jacobo, D’Agostino M. Information Systems for Health (IS4H). Pan American
Health Organization (PAHO/WHO); :48. Accessed June 30, 2021.
https://www.paho.org/ish/
28. Ajami S, Ketabi S, Isfahani SS, Heidari A. Readiness Assessment of Electronic Health
Records Implementation. Acta Inform Medica. 2011;19(4):224-227.
doi:10.5455/aim.2011.19.224-227
29. Afrizal SH, Hidayanto AN, Handayani PW, Budiharsana M, Eryando T. Narrative Review
for Exploring Barriers to Readiness of Electronic Health Record Implementation in Primary
Health Care. Healthc Inform Res. 2019;25(3):141-152. doi:10.4258/hir.2019.25.3.141
30. Truglio J, Graziano M, Vedanthan R, et al. Global Health and Primary Care: Increasing
Burden of Chronic Diseases and Need for Integrated Training. Mt Sinai J Med N Y.
2012;79(4):464-474. doi:10.1002/msj.21327
31. Children. Accessed July 7, 2021.
https://www.mdpi.com/journal/children/sections/public_global_health
32. Beaglehole R, Bonita R. What is global health? Glob Health Action.
2010;3:10.3402/gha.v3i0.5142. doi:10.3402/gha.v3i0.5142
33. World Health Organization. Sustainable Development Goals. World Health Organization.
Accessed May 21, 2021. https://www.who.int/westernpacific/health-topics/sustainable-
development-goals
34. Roser M, Ritchie H. Malaria. Our World Data. Published online November 12, 2019.
Accessed June 4, 2021. https://ourworldindata.org/malaria
35. Cohn, David L, MD, Grzemska, Mlalgorzata, MD PhD MPH, MacNeil, Adam PhD MPH,
Salazar-Austin, Nicole MD, Starke, Jeffrey R. MD, Talwar, Amish MD MPH. Children with
TB: A global public health crisis. Healio. Accessed June 4, 2021.
https://www.healio.com/news/pediatrics/20190515/children-with-tb-a-global-public-health-
crisis
36. Pneumonia and Diarrhea Progress Report 2020. International Vaccine Access Center
(IVAC), Johns Hopkins Bloomberg School of Public Health; 2020. Accessed June 4, 2021.
https://www.jhsph.edu/ivac/wp-content/uploads/2020/11/IVAC_PDPR_2020.pdf
37. Early childbearing. UNICEF DATA. Accessed July 7, 2021.
https://data.unicef.org/topic/child-health/adolescent-health/
38. Kassa GM, Arowojolu AO, Odukogbe AA, Yalew AW. Prevalence and determinants of
adolescent pregnancy in Africa: a systematic review and Meta-analysis. Reprod Health.
2018;15(1):195. doi:10.1186/s12978-018-0640-2
39. Child poverty. Unicef. Accessed June 10, 2021. https://www.unicef.org/social-policy/child-
poverty
40. Nearly 385 million children living in extreme poverty, says joint World Bank Group –
UNICEF study. Accessed July 7, 2021. https://www.unicef.org/press-releases/nearly-385-
million-children-living-extreme-poverty-says-joint-world-bank-group
41. Indicators: Population ages 0-14 (% of total population). The World Bank. Accessed June
10, 2021. https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS
42. Lorenzi NM, Riley RT. Managing Change: An Overview. J Am Med Inform Assoc.
2000;7(2):116-124. doi:10.1136/jamia.2000.0070116
43. Kang’a SG, Muthee VM, Liku N, Too D, Puttkammer N. People, Process and Technology:
Strategies for Assuring Sustainable Implementation of EMRs at Public-Sector Health
Facilities in Kenya. AMIA Annu Symp Proc. 2017;2016:677-685.
44. Cresswell KM, Bates DW, Sheikh A. Ten key considerations for the successful
implementation and adoption of large-scale health information technology. J Am Med
Inform Assoc JAMIA. 2013;20(e1):e9-e13. doi:10.1136/amiajnl-2013-001684
45. Safi S, Thiessen T, Schmailzl KJ. Acceptance and Resistance of New Digital Technologies
in Medicine: Qualitative Study. JMIR Res Protoc. 2018;7(12):e11072. doi:10.2196/11072
46. Steenhoff AP, Crouse HL, Lukolyo H, et al. Partnerships for Global Child Health.
Pediatrics. Published online October 1, 2017. doi:10.1542/peds.2016-3823
47. Voluntourism: A Public Health Approach to International Volunteer Work. Accessed June
13, 2021. https://pha.berkeley.edu/2017/01/25/addressing-voluntourism/
48. Cole T. The White-Savior Industrial Complex. The Atlantic. Published March 21, 2012.
Accessed June 3, 2021. https://www.theatlantic.com/international/archive/2012/03/the-
white-savior-industrial-complex/254843/
49. Lough BJ, Tiessen R, Lasker JN. Effective practices of international volunteering for
health: perspectives from partner organizations. Glob Health. 2018;14(1):11.
doi:10.1186/s12992-018-0329-x
50. Chigudu S. An ironic guide to colonialism in global health. The Lancet.
2021;397(10288):1874-1875. doi:10.1016/S0140-6736(21)01102-8
51. Voluntourism: the good and the bad. World Vision. Accessed June 22, 2021.
https://www.worldvision.ca/stories/voluntourism-the-good-and-the-bad
52. Freidus A. Volunteer tourism: what’s wrong with it and how it can be changed. The
Conversation. Accessed June 22, 2021. http://theconversation.com/volunteer-tourism-
whats-wrong-with-it-and-how-it-can-be-changed-86701
53. Huang F, Blaschke S, Lucas H. Beyond pilotitis: taking digital health interventions to the
national level in China and Uganda. Glob Health. 2017;13(1):49. doi:10.1186/s12992-017-
0275-z
54. Leaving “pilotitis” behind—what it takes to become a full-scale population health manager.
Accessed July 8, 2021. https://www.advisory.com/Blog/2019/12/pilotitis
55. Bhatia A, Matthan R, Khanna T, Balsari S. Regulatory Sandboxes: A Cure for mHealth
Pilotitis? J Med Internet Res. 2020;22(9):e21276. doi:10.2196/21276
56. Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai AC. Scaling up mHealth: where is the
evidence? PLoS Med. 2013;10(2):e1001382. doi:10.1371/journal.pmed.1001382
57. Olusanya JO, Ubogu OI, Njokanma FO, Olusanya BO. Transforming global health through
equity-driven funding. Nat Med. Published online June 24, 2021:1-3. doi:10.1038/s41591-
021-01422-6
58. Khan MS, Meghani A, Liverani M, Roychowdhury I, Parkhurst J. How do external donors
influence national health policy processes? Experiences of domestic policy actors in
Cambodia and Pakistan. Health Policy Plan. 2018;33(2):215-223.
doi:10.1093/heapol/czx145
59. Garrett L. The Challenge of Global Health - New York Times. Accessed July 8, 2021.
https://archive.nytimes.com/www.nytimes.com/cfr/world/20070101faessay_v86n1_garrett.h
tml
60. Parkhurst J, Ghilardi L, Webster J, Snow RW, Lynch CA. Competing interests, clashing
ideas and institutionalizing influence: insights into the political economy of malaria control
from seven African countries. Health Policy Plan. 2021;36(1):35-44.
doi:10.1093/heapol/czaa166
61. A Ugandan mHealth Moratorium Is a Good Thing. ICTworks. Published February 22, 2012.
Accessed July 11, 2021. https://www.ictworks.org/ugandan-mhealth-moratorium-good-
thing/
62. Operating System Market Share Worldwide. StatCounter Global Stats. Accessed July 1,
2021. https://gs.statcounter.com/os-market-share
63. Desktop Operating System Market Share Worldwide. StatCounter Global Stats. Accessed
July 8, 2021. https://gs.statcounter.com/os-market-share/desktop/worldwide
64. Mobile Operating System Market Share Worldwide. StatCounter Global Stats. Accessed
July 8, 2021. https://gs.statcounter.com/os-market-share/mobile/worldwide
65. Fritz F, Tilahun B, Dugas M. Success criteria for electronic medical record implementations
in low-resource settings: a systematic review. J Am Med Inform Assoc. 2015;22(2):479-
488. doi:10.1093/jamia/ocu038
66. Vincent CA, Mboga M, Gathara D, Were F, Amalberti R, English M. How to do no harm:
empowering local leaders to make care safer in low-resource settings. Arch Dis Child.
2021;106(4):333-337. doi:10.1136/archdischild-2020-320631
67. Andersen T, Jeremiah M, Thamane K, et al. Implementing a School Vision Screening
Program in Botswana Using Smartphone Technology. Telemed E-Health. 2020;26(2):255-
258. doi:10.1089/tmj.2018.0213
68. Mirza M, Kratz M, Medeiros D, et al. Building the foundations of an informatics agenda for
global health - 2011 workshop report. Online J Public Health Inform. 2012;4(1).
doi:10.5210/ojphi.v4i1.4027
69. Akhlaq A, McKinstry B, Muhammad KB, Sheikh A. Barriers and facilitators to health
information exchange in low- and middle-income country settings: a systematic review.
Health Policy Plan. 2016;31(9):1310-1325. doi:10.1093/heapol/czw056
70. Odekunle FF, Odekunle RO, Shankar S. Why sub-Saharan Africa lags in electronic health
record adoption and possible strategies to increase its adoption in this region. Int J Health
Sci. 2017;11(4):59-64.
71. Craig S, Fraser H, Gong M, Huebner U, Orenstein E. Global Health Informatics
Collaborations: Practical Steps Towards Successful and Sustainable Partnerships.
Presented at the: November 16, 2019; Washington, DC. Accessed December 18, 2019.
https://symposium2019.zerista.com/event/member/602299
72. Hume AL. Preparing a Health Care Workforce for the 21st Century: The Challenge of
Chronic Conditions. J Pharm Technol. 2005;21(5):303-303.
doi:10.1177/875512250502100516
73. García PJ, Egoavil MS, Blas MM, et al. DEVELOPMENT AND INSTITUTIONALIZATION
OF THE FIRST ONLINE CERTIFICATE AND MASTER PROGRAM OF BIOMEDICAL
INFORMATICS IN GLOBAL HEALTH IN PERU. Rev Peru Med Exp Salud Publica.
2015;32(2):356-360.
74. Conducta Responsable en Investigación. Accessed August 1, 2021.
http://www.cri.andeanquipu.org/
75. Vasan A, Friend J. Medical Devices for Low- and Middle-Income Countries: A Review and
Directions for Development. J Med Devices. 2020;14(1):010803. doi:10.1115/1.4045910
76. Rage Against The Busted Medical Machines. NPR.org. Accessed July 1, 2021.
https://www.npr.org/sections/goatsandsoda/2016/09/08/492842274/rage-against-the-
busted-medical-machines
77. DeStigter K, Horton S, Atalabi OM, et al. Equipment in the Global Radiology Environment:
Why We Fail, How We Could Succeed. J Glob Radiol. 2019;5(1):e1079.
doi:10.7191/jgr.2019.1079
78. Zelmer J, Ronchi E, Hyppönen H, et al. International health IT benchmarking: learning from
cross-country comparisons. J Am Med Inform Assoc. 2017;24(2):371-379.
doi:10.1093/jamia/ocw111
79. Fraser HS, Blaya J. Implementing medical information systems in developing countries,
what works and what doesn’t. AMIA Annu Symp Proc AMIA Symp. 2010;2010:232-236.
80. King JD, Buolamwini J, Cromwell EA, et al. A Novel Electronic Data Collection System for
Large-Scale Surveys of Neglected Tropical Diseases. PLOS ONE. 2013;8(9):e74570.
doi:10.1371/journal.pone.0074570
81. Haskew J, Rø G, Saito K, et al. Implementation of a cloud-based electronic medical record
for maternal and child health in rural Kenya. Int J Med Inf. 2015;84(5):349-354.
doi:10.1016/j.ijmedinf.2015.01.005
82. Newman, Nathan. How Big Data Enables Economic Harm to Consumers, Especially to
Low-Income and Other Vulnerable Sectors of the Population.; 2014. Accessed July 11,
2021. https://www.ftc.gov/system/files/documents/public_comments/2014/08/00015-
92370.pdf
83. Kiragga A, Naggirinya A, Oseku E, Parkes-Ratanshi R. Evaluation of REDCap as an
alternative data management and quality improvement tool for cutaneous lymphoma care
in Botswana. In: From Evidence to Practice: Te Implementation of Digital Health
Interventions in Africa for Achievement of Universal Health Coverage. Vol 1. Koegni
eHealth, Innovation for Development e.V. Germany; 2020:57-63.
https://www.researchgate.net/profile/Rosalind-Parkes-
Ratanshi/publication/342658657_2019_Helina_Proceedings_Part1/links/5eff1575a6fdcc4c
a447714a/2019-Helina-Proceedings-Part1.pdf#page=57
84. Robertson SL, Robinson MD, Reid A. Electronic Health Record Effects on Work-Life
Balance and Burnout Within the I3 Population Collaborative. J Grad Med Educ.
2017;9(4):479-484. doi:10.4300/JGME-D-16-00123.1
85. Zahabi M, Kaber DB, Swangnetr M. Usability and Safety in Electronic Medical Records
Interface Design: A Review of Recent Literature and Guideline Formulation. Hum Factors.
2015;57(5):805-834. doi:10.1177/0018720815576827
86. Ellsworth MA, Dziadzko M, O’Horo JC, Farrell AM, Zhang J, Herasevich V. An appraisal of
published usability evaluations of electronic health records via systematic review. J Am
Med Inform Assoc. 2017;24(1):218-226. doi:10.1093/jamia/ocw046
87. Gur-Arie, Margalit. EMRs Were Designed for Billing and Not Optimized for Patient Care.
HIT Consultant. Accessed March 5, 2021. https://hitconsultant.net/2013/06/03/emrs-were-
designed-for-billing-and-not-optimized-for-patient-care/#.YEI1bDtKhhE
88. Glaser J. It’s Time for a New Kind of Electronic Health Record. Harv Bus Rev. Published
online June 12, 2020. Accessed June 15, 2021. https://hbr.org/2020/06/its-time-for-a-new-
kind-of-electronic-health-record
89. Diamond D. Why Doctors Really Quit. Forbes. Accessed July 15, 2021.
https://www.forbes.com/sites/dandiamond/2015/06/02/why-doctors-really-quit/
90. Saluja S, Rudolfson N, Massenburg BB, Meara JG, Shrime MG. The impact of physician
migration on mortality in low and middle-income countries: an economic modelling study.
BMJ Glob Health. 2020;5(1):e001535. doi:10.1136/bmjgh-2019-001535
91. Cometto G, Tulenko K, Muula AS, Krech R. Health Workforce Brain Drain: From
Denouncing the Challenge to Solving the Problem. PLOS Med. 2013;10(9):e1001514.
doi:10.1371/journal.pmed.1001514
92. Review D. Contextualization of the Global Medical Brain Drain in Sub-Saharan African
Countries. The Duke Student Global Health Review. Published March 20, 2019. Accessed
July 15, 2021. https://dsghreview.org/2019/03/20/contextualization-of-the-global-medical-
brain-drain-in-sub-saharan-african-countries/
93. Misau YA, Al-Sadat N, Gerei AB. Brain-drain and health care delivery in developing
countries. J Public Health Afr. 2010;1(1):e6. doi:10.4081/jphia.2010.e6
94. Knight SR, Ots R, Maimbo M, Drake TM, Fairfield CJ, Harrison EM. Systematic review of
the use of big data to improve surgery in low‐ and middle‐income countries. Br J Surg.
2019;106(2):e62-e72. doi:10.1002/bjs.11052
95. Dickinson FM, McCauley M, Madaj B, Broek N van den. Using electronic tablets for data
collection for healthcare service and maternal health assessments in low resource settings:
lessons learnt. BMC Health Serv Res. 2019;19(1):1-7. doi:10.1186/s12913-019-4161-7
96. Bommakanti K, Feldhaus I, Motwani G, Dicker RA, Juillard C. Trauma registry
implementation in low- and middle-income countries: challenges and opportunities. J Surg
Res. 2018;223:72-86. doi:10.1016/j.jss.2017.09.039
97. Colleti Junior J, de Andrade AB, de Carvalho WB. Evaluation of the use of electronic
medical record systems in Brazilian intensive care units. Rev Bras Ter Intensiva.
2018;30(3):338-346. doi:10.5935/0103-507X.20180057
98. Institute of Medicine (US). Key Capabilities of an Electronic Health Record System: Letter
Report. National Academies Press (US); 2003. Accessed June 30, 2021.
https://www.ncbi.nlm.nih.gov/books/NBK221800/
99. Collier R. Medical technology often a burden if designed without physician input. CMAJ
Can Med Assoc J. 2018;190(36):E1091-E1092. doi:10.1503/cmaj.109-5656
100. Howard J, Clark EC, Friedman A, et al. Electronic Health Record Impact on Work Burden
in Small, Unaffiliated, Community-Based Primary Care Practices. J Gen Intern Med.
2013;28(1):107-113. doi:10.1007/s11606-012-2192-4
101. Faulkenberry, Grey. Vigor: Using FHIR for Vaccine Tracking and Recommendations in
Low-Resource Settings. Presented at the: HL7® FHIR® DEVDAYS; June 7, 2021; Virtual.
Accessed August 1, 2021. https://www.devdays.com/june-2021/fhir-devdays-doctor-track/
102. Mirri S, Roccetti M, Salomoni P. Collaborative design of software applications: the role of
users. Hum-Centric Comput Inf Sci. 2018;8(1):6. doi:10.1186/s13673-018-0129-6
103. Interoperability in Healthcare | HIMSS. HIMSS. Published August 4, 2020. Accessed June
22, 2021. https://www.himss.org/resources/interoperability-healthcare
104. Dr Bigirimana, Francoiose, Dr Nuwagira, Innocent, Dr Shaffer, Nathan, et al. Technical
Support on Data Quality Improvement for the Botswana PMTCT Programme in Line with
Requirements of Validation of EMTCT for HIV and Syphilis. UNICEF; 2019:16.
105. Wagenaar BH, Hirschhorn LR, Henley C, Gremu A, Sindano N, Chilengi R. Data-driven
quality improvement in low-and middle-income country health systems: lessons from seven
years of implementation experience across Mozambique, Rwanda, and Zambia. BMC
Health Serv Res. 2017;17(Suppl 3):830. doi:10.1186/s12913-017-2661-x
106. Harrison K, Rahimi N, Danovaro-Holliday MC. Factors limiting data quality in the expanded
programme on immunization in low and middle-income countries: A scoping review.
Vaccine. 2020;38(30):4652-4663. doi:10.1016/j.vaccine.2020.02.091
107. Ndabarora E, Chipps JA, Uys L. Systematic review of health data quality management and
best practices at community and district levels in LMIC. Inf Dev. 2014;30(2):103-120.
doi:10.1177/0266666913477430
