ISO/TR 4419:2026

Technical
Report
ISO/TR 4419
First edition
Health informatics — Pathways
2026-01
for human-computer interaction
in electronic health information
record systems to reduce clinician
burden
Informatique de santé — Voies d'interaction homme-
machine dans les systèmes électroniques d'enregistrement des
informations de santé afin de réduire la charge de travail des
cliniciens
Reference number
© ISO 2026
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Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 4
5 Clinician burden . 5
5.1 Health information record systems’ unfulfilled promises .5
5.2 Health information record systems . .6
5.3 Health modules and decision support systems .6
5.4 Patient portals .8
5.5 Remote patient monitoring (RPM) and wearable devices . .8
5.6 Ontology-based systems .8
5.7 Knowledge repositories.9
6 Burden mitigation . 9
6.1 Approaches to reducing clinician burden .9
6.2 Human-computer interaction .10
6.3 HCI benefits . 12
7 Summary .13
Bibliography .15

iii
Foreword
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iv
Introduction
The implementation of electronic health information record systems (HIRS), defined in Clause 3, is one
of the primary factors contributing both positive and negative impacts on clinical practice and patient
experience. HIRS can be used to create complex hierarchical structures from basic primitive types. HIRS
has substantially altered the norms of clinician-patient interaction, often unintentionally diminishing the
[1],[2],[3],[4],[5]
most meaningful aspects of healthcare practice for the clinician and the patient. Among the
various segments of the healthcare industry, there is widespread agreement that the structure, capabilities
and operations within HIRS are not working for clinicians, patients, health organizations, health technology
vendors or the businesses who enable the provision of health insurance, equipment, biotechnology
services, research, and pharmaceuticals. The healthcare industry faces a diverse range of challenges
causing numerous serious issues for each of these groups. Challenges include rising numbers of preventable
[6]
mistakes, declining quality of care, sub-optimal health outcomes, out-of-control costs, financial pressure,
resource constraints, clinician burnout, and problems integrating technology with artificial intelligence
(AI) and telehealth. These ever-changing technical, regulatory, and environmental challenges, along with
cybersecurity threats to patient data requiring robust security measures and constant vigilance, demand
significant additional effort, planning and resources. These challenges impact operational efficiency and can
lead to workforce shortages. There is a growing gap in the availability of skilled healthcare professionals
and healthcare services relative to growing demand for healthcare services as populations age and the
prevalence of chronic diseases rises.
The root causes of many of these challenges relate to deeper issues with HIRS, including:
— inadequate standards for data quality and insufficient auditing and enforcement of existing data quality
standards;
— complex nonintuitive data structures and functionalities;
— lack of functionalities to mitigate cognitive overload;
[7]
— fragmented care, poor workflow integration, and lack of context and specialty specific support.
While the problems are daunting, they appear to have motivated many healthcare and non-healthcare actors
to start to identify solutions. This document reviews a large body of clinician opinion and research findings
suggesting that the HIRS status quo cannot continue and that comprehensive HIRS reform is necessary,
possibly leading to a completely new HIRS which would be termed the digital healthcare system (DHS).
During the transition to a new HIRS, clinicians need to continue to treat patients with the available HIRS;
the idea of moving to a more perfect DHS is an evolutionary process over a period of years. The goal now is
to use the knowledge, principles, standards and experience gained from the first 15 years of wide scale HIRS
implementation, and the advances made in several domains to support continuing efforts to improve HIRS
and move toward that more perfect DHS.
While the burden of disease is a known epidemiologic concept, clinician burden is a less known and more
[8]
recent term. Clinician burden occurs when the clinician’s environment and workload impose physical,
cognitive, psychological, and time burdens on clinicians without sufficiently improving quality of care and
[2]
clinician functioning. Multiple factors can contribute to clinician burden, including
— time and productivity pressures,
— excessive bureaucratic tasks of low clinical value,
— limited capacities of human cognition versus high demands of information-intensive clinical
[9],[10]
practice, , and
— limited clinician-centred design in health information record systems and clinical decision support tools,
which contributes to ineffective functionality and increased burden on clinical workflows.
While contributing to improved healthcare, the volume and velocity of new patient-related healthcare
information, patient-specific data, and underlying biomedical knowledge also unintentionally increase
[11]
clinician burden. A large and growing body of research suggests that poor HIRS usability and poor
integration within clinician workflows are important factors preventing electronic health information

v
record systems from achieving the clinician accuracy and productivity crucial for safer patient healthcare
[12]
outcomes. The human-factor design and clinician workflow burdens imposed on clinicians by current
HIRS have contributed to an epidemic of clinician burnout. This interplay of factors has contributed to
clinician shortages, patient dissatisfaction from rising wait times, preventable medical errors, declining
[3],[13],[14],[15],[16],[17]
health outcomes and rising healthcare costs.
A comprehensive international strategy to reduce clinician burden related to HIRS can focus on improving
human-computer interaction (HCI) as a fundamental principle. In 2022, the Health Level Seven International ®
(HL7 ) Reducing Clinician Burden (RCB) project completed an environmental scan of clinician burdens
[18] ®
associated with HIRS documentation and clinician workflows. The HL7 scan detected two underlying
categories of clinician burden:
— inefficient interactions with HIRS technology itself; and
— excessive additional requirements for administrative, regulatory, and organizational tasks imposed and
managed via the HIRS.
In this context, digital tools can be neutral, positive or negative. Digital tools are neutral when the tools are
effectively implementing an imposed task. The tools can be negative, adding to clinician burden, when digital
tools are poorly designed or poorly implemented. The tools can be positive when intelligent design enables
the tools to alleviate existing burden e.g. by reducing cognitive load or automating regulatory compliance.
[5] [19]
Prior studies of the length of progress notes and of electronic health record (EHR) usage patterns
around the world suggest that documentation and other EHR-related burdens can differ between the United
States (US) and other international healthcare systems, possibly due to unique healthcare regulatory
and payment models in the US. It also seems clear that the motivation and incentives for adoption and
implementation of EHRs in the international arena arise from different backgrounds and histories. Given
these differences, it is not clear whether the levels and causes of clinician burden and burnout around the
world would be similar to those seen in the US. However, conversations with informatics professionals
[20],[21],[22],[23],[24]
[informal communications, 2023-2025] as well as other studies in the literature suggest
there might be more similarities than differences. In order to define clinician burdens related to healthcare
information technology (health IT) and prepare for writing international standards that could reduce those
burdens, the ISO/TC 215/HL7 International Joint Reducing Clinician Burden Project (JRCB) developed and
distributed a survey to examine the level of clinician burnout in the international arena and determine
[25]
whether such burnout is related to the implementation and use of health IT.
The survey received 180 discrete responses from 21 countries (and one from the US), mainly from healthcare
clinicians, informaticians, and administrators. The overwhelming majority of respondents (92 %) rated the
level of clinician burnout in their countries as moderate or severe. Most respondents also reported that EHR-
related burden was substantial and that burden in their countries was not different from that in the US
(62 %). Eighty-nine percent of respondents felt that health-IT related burden was a significant contributor
to burnout. The majority of countries reported that primary care clinicians and specialists used different
EHRs (77 %), and 88 % of respondents felt that this contributed to burden in their countries. Although a
majority of respondents (56 %) indicated their countries had implemented some type of standards for EHR
development, most (>60 %) felt such standards were insufficient and frequently bypassed. A significant
majority (75 %) also reported that standards specifically related to clinician burden were not being
developed in their countries.
The results of the survey suggest that clinician burden and burnout are prevalent in multiple areas of the
world and represent an international concern regardless of local regulatory systems and payment models.
They also suggest that EHRs and other aspects of health IT are significant contributors to the problem. An
international standards-based approach can support the development and implementation of user-centred
HIRS, reducing unnecessary clinician burden, and it can stimulate the development of satisfying digital
[26]
tools.
vi
Technical Report ISO/TR 4419:2026(en)
Health informatics — Pathways for human-computer
interaction in electronic health information record systems to
reduce clinician burden
1 Scope
This document reviews the literature regarding human-computer interaction (HCI) theory and user-centred
design (UCD) principles in the design and development of electronic health information record systems
(HIRS). The focus is on some unintended consequences of HIRS design decisions over the last 15 years and
the way that application of HCI and UCD to HIRS data analytics, data representation, and clinical decision
support can help to alleviate HIRS-associated clinician burden.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
clinical burden
measure of extra effort imposed by the healthcare information environment when the clinician (3.3)
attempts to access, interpret, record or distribute information
Note 1 to entry: Adapted from Reference [27].
3.2
clinical workflow
clinician (3.3) physical and mental activities, technologies, tools, environments, teams and organizations
involved in patient care
Note 1 to entry: Clinical workflows are sequences of physical and cognitive actions, occurring over time and through
space, which are performed by clinicians, which recall, vet, consume, transform, or produce information, and which
are used to assess, maintain, or change the health of a patient.
Note 2 to entry: Adapted from reference [28].
3.3
clinician
healthcare professional involved in the delivery of healthcare services (3.12) to a patient or consumer
Note 1 to entry: Adapted from Reference [29].

3.4
clinician burden
effect of the clinician’s (3.3) environment and workload on patient treatment and outcome
Note 1 to entry: Adapted from Reference [30].
Note 2 to entry: ‘Clinical burden’ refers to workload from the health information environment. ‘Clinician burden’
refers to the effect of the health information workload on the clinician.
3.5
clinical decision support
CDS
system designed to assist clinicians (3.3) with clinical decision-making tasks for health improvement
Note 1 to entry: Adapted from Reference [31].
3.6
health decision support
HDS
system designed to empower individuals to make informed health decisions in their daily lives
Note 1 to entry: CDS (3.5) and HDS systems have complementary roles in improving overall health outcomes.
Note 2 to entry: Adapted from Reference [32].
3.7
digital healthcare system
DHS
system used to improve the efficiency of healthcare delivery, acting as repository of health-related
information about a subject of care, in a format that can be processed by computers
Note 1 to entry: Digital health encompasses a broad range of digital tools and capabilities. Health information systems
operationalize the digital health capabilities by structuring, managing, and facilitating the flow of health-related
information generated by non-clinicians, patients, devices, and digital platforms.
Note 2 to entry: Adapted from Reference [33].
3.8
electronic health record system
EHR
digital information system used by clinicians (3.3) to create, update, import, store and exchange clinical
information for patient care
Note 1 to entry: Adapted from the definition of “EHR system” in ISO/TR 20514:2005.
3.9
electronic health information record system
HIRS
body of computer-processable health information, stored securely, and accessible by authorized users
Note 1 to entry: Adapted from the definition of “electronic health record for integrated care” in ISO/TR 20514:2005.
3.10
healthcare data
items of data collected, used, produced in the course of healthcare activities
Note 1 to entry: Adapted from Reference [35].
3.11
health information record system
unifying term for the diverse digital systems managing health data
Note 1 to entry: The term HIRS (3.9) is a general expression covering different system names and implementations.

Note 2 to entry: HIRS encompasses the full scope of digital health and underpins semantic interoperability (3.17), data
portability, and cross-border and region information exchanges.
Note 3 to entry: Adapted from reference [32].
3.12
healthcare service
service provided with the intention of directly or indirectly improving the health of the person or populations
to whom care is provided
Note 1 to entry: Adapted from Reference [29].
3.13
human-computer interaction
HCI
discipline concerned with the design, evaluation and implementation of interactive computing systems
for human use and with the study of major phenomena surrounding the interaction between humans and
computers
Note 1 to entry: Adapted from Reference [36].
3.14
human factors
understanding of the interactions among humans and other elements of a system and of the theory,
principles, data and design methods used to optimize human well-being and overall system performance
Note 1 to entry: Adapted from Reference [37].
3.15
interface
process that permits the flow of data from one system to another in a structured manner
Note 1 to entry: Adapted from Reference [38].
3.16
information technology
IT
assembly of computer hardware, software, or both configured to collect, create, communicate, disseminate,
process, store or control data or information
Note 1 to entry: IT can be configured to perform any one or more of the named functionalities.
Note 2 to entry: Adapted from Reference [39].
3.17
interoperability
ability of a system to exchange electronic health information with and use electronic health information
from other systems without special effort on the part of the user
Note 1 to entry: Adapted from Reference [40].
3.18
natural language processing
NLP
subfield of artificial intelligence and linguistics which studies the problems inherent in the processing and
manipulation of natural languages, and natural language understanding devoted to making computers
understand statements written in human languages
Note 1 to entry: Adapted from Reference [41].

3.19
ontology
formal, explicit specification of concepts and their relationships within a specific knowledge domain
Note 1 to entry: Adapted from Reference [42].
3.20
remote patient monitoring
RPM
approach using technology to monitor patients outside of traditional clinical settings for clinicians (3.3) to
measure and track relevant health parameters
EXAMPLE 1 Setting: in the home.
EXAMPLE 2 Measurement devices:
— blood pressure monitors;
— pulse oximetry monitors;
— blood glucose monitors;
— cardiac rhythm monitors.
Note 1 to entry: The data collected from devices are electronically transferred to clinicians for care management.
Note 2 to entry: Adapted from Reference [43].
3.21
wearable device
portable medical or health electronic device worn directly on the body to observe, record, analyse, regulate
and intervene to maintain health or treat diseases with the support of technology
Note 1 to entry: The data collected from wearable devices may be electronically transferred to clinicians and others
on a patient’s health team for care management.
Note 2 to entry: Adapted from Reference [44].
3.22
workflow
sequence of actions carried out to achieve one or more complex objectives
Note 1 to entry: Adapted from Reference [45].
4 Abbreviated terms
AI artificial intelligence
DAI drug allergy interaction
DDI drug-drug interaction
DHS digital healthcare system
EHR electronic health record system
GDP gross domestic product
HCI human-computer interaction
HIRS electronic health information record system
®
HL7 Health Level Seven International SDO
ML machine learning
NLP natural language processing
POHR problem oriented health record
RPM remote patient monitoring
SDO standard development organization
UCD user-centred design
5 Clinician burden
5.1 Health information record systems’ unfulfilled promises
[9],[10]
Clinician burden existed prior to HIRS due to inherent human cognitive limitations; the adoption of HIRS
introduced additional burden. Human beings are incredibly complicated. In addition to complex patterns
of comorbidities, patients differ significantly in ethnic, genetic, physiological, molecular and socioeconomic
characteristics, in response to drugs and treatments, and in personal values and preferences. The biomedical
science knowledge base doubles 2 to 3 times each year with concern about the availability and accessibility
of important data affecting new lines of scientific reasoning. Eliciting complete patient data, synthesizing
the patient data into a coherent narrative, and coupling the narrative to an exploding knowledge base to
[9],[10]
accurately diagnose and treat disease is often a superhuman task. Clinicians routinely complete the
tasks of data location or recall, data vetting and synthesis, and clinical decision making, often within the
bounds of a time-limited encounter, and are increasingly aware important data could be unavailable.
HIRS were intended to assume part of this cognitive load, improve workflow efficiency, and reduce clinician
burden leading to more timely and accurate decision-making and improved care, health outcomes, and cost
outcomes. However, HIRS were designed based on the principles and paradigms of healthcare in the late
th
20 century, in the 1970s and 1980s, without sufficient attention to HCI design and workflow integration.
[27],[46]
This resulted in HIRS products that many clinicians find inefficient, confusing, and difficult to use.
st
The HIRS products of the 21 century have also been used as a mechanism to add many additional duties
of significantly lower clinical value to the average clinician’s workday. In sum, in the second decade of the
st
21 century, HIRS often add to rather than alleviate clinician burden. So, HIRS have failed to achieve an
st
appropriate balance between clinicians, patients, and technology. In sum, in the second decade of the 21
century, HIRS often contributed to, rather than alleviated, clinician burden, resulting in a net negative
impact on the clinical experience with heightened healthcare dissatisfaction, increased safety risks and
[3],[13],[14],[47]
failure to slow the rise in healthcare costs. The HIRS promises to enable better quality patient
care, better health outcomes, better patient experience, and reduced per capita costs remain unrealized.
According to a US National Health Data Brief, as of 2017, 94 % of hospitals used HIRS data to perform hospital
processes to inform clinical practice. In addition to patient care, data is commonly used to support quality
improvement (82 %), monitor patient safety (81 %), and measure organization performance (77 %). Hospital
characteristics significantly impact the use of HIRS data with small, rural, and non-teaching hospitals having
[48]
the lowest rates of using data. Although, as of 2021, electronic HIRS were used in in more than 95 % of
[49]
hospitals and nearly 80 % of physician offices, studies have shown:
[50],[51]
— no significant change in hospital length of stay or inpatient mortality;
[50],[51]
— no significant change in 30-day readmission rates or patient safety incidents;
[52],[53],[54]
— no improvement in life expectancy, infant mortality, or other population health metrics;
1)
— continued rapid rise in annual healthcare expenditures from USD 2 trillion in 2009 to over USD 4,5 trillion
[55]
in 2022 (nearly 18 % of GDP) with annual healthcare expenditures in the United States projected to
1) These expenditures are predominantly from the United States and might not reflect international context.

[56]
have risen 7,5 % in 2023 to USD 4,8 trillion, outpacing the projected annual gross domestic product
[57]
growth rate of 2,5 % ;
— decreased efficiency in healthcare delivery with information systems adding 1 to 2 hours to the average
[58],[59]
physician workday;
[13],[14]
— disruption of physician work-life balance associated with an epidemic of clinician burnout;
— modest improvement in care process metrics and guideline adherence only weakly correlated with
[60],[61],[62],[63]
system use.
5.2 Health information record systems
From another perspective, the wide-scale implementation of HIRS has imposed additional physical and
cognitive workload and additional time requirements (i.e. clinical burden) on professionals whose practice
is based on the direct provision of healthcare services to patients (i.e. clinicians).
The time consumed by the multiple technical inefficiencies related to ineffective HIRS design and
functionalities along with regulation-induced documentation burdens increases clinical workflow
complexity, disrupt work-life balance, and thus contribute significantly to the current epidemic of
[3],[5],[13],[64]
physician burnout seen in the US. By definition, clinician burnout is a syndrome emerging as a
prolonged response to chronic interpersonal and intrapersonal job stressors characterized by feelings of
emotional exhaustion, cynicism and detachment from work, and a sense of low personal accomplishment.
[65]
Approximately 50 % of US physicians report at least one symptom of burnout, twice the rate of the
general population; and 70 % of US physicians report at least one symptom of health IT-related stress.
[13],[66]
A recent systematic review suggests a significant association between both burnout and depression
[67]
and burnout and anxiety in physicians, and an important relationship between burnout and suicidality,
likely contributing to the increased rate of mental health issues and suicide seen in physicians compared
[68]
to the general population. A number of studies have also shown that the prevalence of clinician burnout
[3],[13],[16],[17]
correlates directly with usage of and frustration with electronic health records.
5.3 Health modules and decision support systems
Data organized by source rather than clinical or health problem, complex user interfaces, and confusing
navigation all render searching for, accessing, and organizing relevant information to locate complete
[69]
information for a given problem within HIRS difficult and unnecessarily time consuming. A lack of
consistency from one HIRS system to another often further impedes clinician access to content. Other
obstacles in the current generation of HIRS include mouse or keyboard interfaces, text-based documentation
models, and complex paper-derived data representations. These require clinicians to navigate deeply nested
[70]
menus and browse through long pull-down lists that are neither filtered nor contextualized. Data is
entered bit by bit requiring multiple keystrokes, points, clicks, and scrolls. This uses highly trained clinicians
as data entry clerks. One study measured an average of 216 mouse clicks or wheels and 729 keyboard clicks
[71]
per 20-minute patient visit. A number of studies also indicate that increased time interacting with the
[14],[71],[72]
computer is strongly associated with decreased patient satisfaction.
Information in HIRS is often not organized or aligned with the clinician’s mental model of care, making
important information difficult to locate. Even at its best, the unaided human mind has difficulty coping
with the massive volume and complexity of information needed to make optimal decisions, especially given
[73],[74]
that the total amount of biomedical knowledge doubles every 75 days. And human minds, affected
by time pressure, other stressors, and limiting heuristics are being asked to do something that is humanly
impossible. HIRS do not provide the tools needed to approach differential diagnosis in a complete and
organized manner and properly couple the patient’s symptoms and findings to the underlying biomedical
[9],[75],[76]
knowledge base.
In addition, it is possible that clinician’s mental model is not optimized for clinical decision making. In either
event, clicking, scrolling, switching between paths and screens, and counterintuitive data presentations
[69] [77] [78] [79] [80]
make HIRS challenging to access and important data difficult to process. , , , , Critical
[81]
information is often obscured in a plethora of less important text or values. Locating and importing data
from outside a clinician’s health system requires extensive effort and it is possible that such effort does not
[82]
succeed, raising concerns the available information represents only a narrow and incomplete view of the

patient. Nearly 60 % of ambulatory care providers report being dissatisfied with their own electronic health
[81]
record due to workflow and usability concerns, and approximately 70 % of primary care physicians
surveyed responded that HIRS contribute to physician burnout and HCI redesign is necessary to improve
[83]
inefficiencies and reduce screen time.
Effective HIRS, via CDS or HDS or both, provides the right information to the right person in the right clinical
[84]
intervention format through the right channel at the right point in workflow , with well-designed HCI
[78],[79]
methods. Using artificial intelligence tools to properly process data already in the HIRS at a more
sophisticated and granular level could be used to focus CDS outputs and refine CDS trigger levels to decrease
[85]
the number of low value alerts. Such an AI-enabled HIRS could also show the clinician the underlying
data and rationale leading to an alert or recommendation, at a clinician-selectable level of detail, and allow
the clinician to immediately act on the information. A properly interactive HIRS CDS system could also allow
clinicians to provide system feedback on the accuracy and clinical utility of alerts to be used in iterative
rounds of system optimization. In current CDS systems, modules and tools are typically created as a one-
off unique or customized configuration for every new HIRS implementation. The creation and sharing of
internet accessible data repositories of validated, curated biomedical knowledge modules and basic CDS
[86]
operational components (triggers, notifications, etc.) available as standard sets and templates could
accelerate the dissemination of CDS best practices and increase the availability of CDS systems that reduce
clinician burden.
A systematic review of twenty-eight randomized controlled trials of clinical decision support systems
[87]
integrated with EHRs showed no survival benefit and minimal impact on patient morbidity. As the
[73],[74]
medical and scientific knowledge base expands exponentially, physicians and care teams need
standardized tools to appropriately navigate, assimilate and apply information to complex healthcare
interrelationships found among the patient narrative, physical exam, laboratory data, radiographic
images and care delivery. Current HIRS decision support interventions often present in the form of pop-up
alerts notifying physicians and clinicians of warnings such as drug-drug interactions (DDI), drug allergy
interactions (DAI), dose ranges, etc. Other decision support formats include order sets with direct links to
medical literature or links to guidelines, calculators, or knowledge summaries. Unfortunately, many DDI and
DAI tools are interruptive and fail to integrate key pieces of data found throughout the HIRS, resulting in
large numbers of low value alerts, leading to “alert fatigue”. The proportion of overridden DDI alerts range
from 50 % to 90 % in various studies, with over 60 % of the overrides found to be clinically appropriate.
[88]
Linked literature and knowledge summaries frequently display far more information than needed at
the current point in workflow, requiring a long search to find the piece needed to complete the immediate
[89]
clinical task.
In various settings, nurses spend anywhere from 22 % to 33 % of patient care time in medication related
[90],[91]
activities. Researchers have found current barcode medication administration (BCMA) systems
can interfere with nurses’ problem-solving, ability to integrate medication administration with other
[92],[93]
care activities, and ability to collaborate and share workload. The lack of context specificity and
standardization in care tools and terminology challenges the nurse to efficiently spot trends in data over
time, effectively summarize data for communication across organizations or service lines, and accommodate
[94],[95]
variation in nursing knowledge and experience.
Nursing documentation is crucial in the nursing process, for communicating with other care team members,
for establishing comprehensive care plans, and for recognizing trends in patients’ needs and clinical
condition. A recent study found that nearly a fifth of patient files contained inaccurate medication dose
documentation, nearly a third showed one or more care orders was fulfilled late, and in nearly half, nursing
[96]
patient documentation was partially missing. Another study identified perioperative documentation,
[97]
including perioperative nursing notes, as a source of communication failures among providers.
[17]
A recent study administered the System Usability Scale (SUS), a validated metric of IT system
[98]
usability to over 8 600 US nurses who on average rated the usability of EHRs as ‘very poor’. The same
study also found poor SUS scores were significantly correlated with nurses’ levels of burnout as assessed
[99],[100]
by the Maslach Burnout Inventory. The volume of HIRS data has increased cognitive workload and
[47]
time requirements in practice. The National Academy of Medicine described system factors preventing
the optimization of direct patient care delivery as poor clinical data integration and poor data usability.
Poor nursing data leads to a nursing information burden and accelerates a need for a semantically and
[101]
syntactically aligned health ecosystem without requiring extra input or special effort. A partnership of

HIRS nursing practice documentation with structured, standardized, and coded HIRS nursing terminology
would advance the interoperability and usefulness of nursing data.
5.4 Patient portals
Patient portals provide patients with a communication platform and method of insight into personal health
conditions to help patients actively manage individual health, communicate with providers and carry out
a number of administrative tasks such as scheduling appointments, paying bills and other actions. A 2015
[102]
systematic review of patient portals showed significant improvements in patient self-management of
chronic disease and improvement in the quality of care provided by providers. The most prevalent positive
attribute was patient-provider communication, which appeared in 10 of 27 articles (37 %). The most
prevalent negative perceptions were security (concerns) and user-friendliness which occurred in 11 of 27
articles (41 %). These issues can be reduced by targeted training, enhancements in portal usability, and
clearer communication protocols.
In 2020 the acceleration of web-based patient-clinician interaction using patient portals was predicted to
achieve high-quality, patient-centred care. Studies to understand the healthcare professionals’ experiences
of web-based, patient-professional communication via patient portals find clinicians experience positive
[103]
as well as negative reactions to such communications. . Most commonly, the positive experiences seem
to be related to patient satisfaction and outcomes, such as having better patient engagement. Healthcare
professionals also report some negative experiences with portal-based communication, for example
operational deficiencies and a negative impact on clinician workload. The negative experiences might be
due to the poor functionality of patient portals and insufficient training or resources. The sheer volume of
patient messages received from patient portals requiring clinician responses are another factor creating
additional clinician burden that did not exist prior to HIRS and patient portals.
5.5 Remote patient monitoring (RPM) and wearable devices
In addition to transmitting high volumes of patient questions and communications, HIRS have also enabled
an explosion of inbound quantitative data via the ability to interface with RPM devices. The accuracy of
the data collected by RPM devices is a clinician concern. Inaccurate data can lead to incorrect diagnoses or
[104]
treatment plans. Patients can struggle with using the technology correctly or consistently, which can
[105]
affect the reliability of the monitoring. Integrating RPM data with existing HIRS and other healthcare
systems can be complex and time-consuming, and the cost of RPM devices and the infrastructure needed
[104],[105]
to support can be a barrier for some healthcare providers and patients. As RPM mediates direct
care delivery, clinicians face an increased workload due to the constant influx of data to be monitored and
[106]
analysed.
RPM requires robust data management practices and security protocols to protect sensitive health
[104]
information. Also required for its effective use is digital literacy for both patients and healthcare
[107]
providers. Some RPM devices provide continuous monitoring. This and other RPM activities outside of
scheduled clinical practice hours contribute to clinician burden in documentation. The volume of information
gathered and recorded by portals and RPM devices far exceeds the ability of clinicians to analyse and
respond to the data in real time. Such systems are required by certification standards to process, analyse
and sort incoming data, recognize urgent situations or trends, escalate to human clinicians for immediate
action with priority communications queued for human review.
5.6 Ontology-based systems
Ontology-based systems leverage the structured knowledge of ontologies to enable tasks such as knowledge
representation, semantic classification, and data integration. They are designed to facilitate reasoning,
inference, and decision-making. In healthcare, they are most commonly used as the basis for clinical decision
support (CDS) systems where they are often used to derive and support the rules and triggers underlying
CDS system operations. Many current CDS systems have challenges with rule management, reusability in
[108]
different contexts, interoperability and user experience.
Managing and maintaining ontologies can be complex due to the need for continuous updates and
[96]
alignment with evolving medical knowledge and standards. In order to
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