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Human interactions with technology
Trust has a pivotal role in shaping the dynamics of human interactions, encompassing not
only relationships between individuals but also their interactions with artificial intelligence
(AI) systems. Gaining insight into the trust dynamics that exist between artificial intelligence
(AI) systems and human beings is of paramount importance, particularly within the realm of
healthcare, where the preservation of human life is at stake (Asan et al., 2020).
AI research in healthcare has distinct obstacles compared to other technical fields.
Physical system models mathematically explain technical behavior in engineering. However,
the absence of quantitative models in healthcare applications like medical diagnosis raises
challenges in determining illness linkages and causation. The answers of professionals to the
same clinical cases differ dramatically. Training AI-based tools on subjective answers from
clinicians without ground truth knowledge is challenging. AI research must consider the
unique aspects of medical problems in various healthcare applications. Training a
mathematical model for an AI specialized in cancer applications may not be applicable to
cardiovascular applications. Additionally, failure to address disadvantaged populations like
minorities and people with impairments during AI system design may result in inadequate
data representation and unmet demands. A tailored AI method may be required for each
application, based on data type, patient demographic, data variability, and healthcare
decision-making (Asan et al., 2020).
Recommendations
Clinicians will be held accountable if they follow an AI recommendation that deviates
from the standard treatment process and has a negative impact on patient health. What would
this imply for an AI system and trust between AI and users? Clinicians who utilize AI
systems are expected to use them as a decision-making aid, not as a replacement for trained
clinical judgment. As humans remain the ultimate decision-makers, clinicians are still
responsible for any medical errors that may occur. Then, how can AI assist clinicians, and
will clinicians be able to use and evaluate an AI system’s reliability? The impact of these
factors on clinicians’ trust in AI applications requires additional research (Asan et al., 2020).
Quantifying the ideal level of confidence between clinicians and AI systems for accurate
and reliable healthcare choices is challenging due to the constraints of both human cognition
and AI technologies. The relationship between AI system design features and appropriate
trust levels is unclear. The analysis of this problem should consider the unique human aspects
of each user, as well as the significant variety and developing capabilities of AI technologies.
This analysis should inform regulatory policy (Asan et al., 2020).
Trust in AI is predicted to differ significantly between patients and clinicians. Patients
sometimes lack medical expertise compared to physicians. The AI system’s therapeutic
choices and suggestions will directly effect patients, regardless of their use of the system.
Increasing patient involvement in patient-centered treatment may lead educated patients to
question clinical judgments and seek information about AI advice. Further research is needed
to understand trust relationships between patients and AI systems, as they are crucial to
collaborative decision-making between clinicians and patients (Asan et al., 2020).
References
Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in
healthcare: focus on clinicians. Journal of Medical Internet Research, 22(6), e15154.
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The increased usage of electronic health records offers opportunities to enhance healthcare
delivery using big data. This makes data mining and predictive analytics crucial for
healthcare decision-making. Data mining in the healthcare field remains a burgeoning area of
research that holds much promise. Certain aspects of healthcare operations have garnered
more attention than others, such as the characterization of clinical pathways, quality of care,
and resource allocation. However, there exists an opportunity to utilize insights derived from
the utilization of big data to enhance the design of facility layouts and conduct process
analyses, thereby achieving operational excellence and enhancing patients’ satisfaction
(Malik et al., 2018).
Quality reporting is time-consuming and costly, leading to discontent among
practitioners. This study measures quality through standards-based data interchange,
requiring no additional time or effort from sampled institutions. Data transmitted through
clinical standards enables the calculation of various quality indicators. Additionally, data
incompleteness affects patient safety. Research indicates that sharing clinical data can reveal
high-risk drug use (cms156) that may not have been noticed otherwise. This score is based on
American Geriatrics Society consensus advice and studies linking certain medicines to
adverse events, worse health, and increased mortality risk. Data sharing impacts patient
safety and quality measurement at organizations with incomplete medication histories, as
indicated by the 68 measure changes from compliance to non-compliance (D’Amore et al.,
2021).
Value Based Health Care (VBHC) is a widely used healthcare method that focuses on
improving patient value, which is the ratio of patient-important outcomes to healthcare
delivery costs. Continuously improving patient value through quality, process, and structure
indicators is essential in VBHC. The Deming cycle (PDSA) can be used to update quality
research indicators continuously. Adding an outcome-based improvement cycle to VBHC has
shown significant utility. Integrating patient-relevant outcomes and costs into a PDSA cycle
across a multi-provider regional network creates a unique, reproducible, and structured tool to
enhance patient value throughout the care cycle (Van Veghel et al., 2022).
A proposed step-by-step technique for adopting VBHC principles in cardiac network
organizations is provided, including initial findings for atrial fibrillation patients. Successful
implementation of the methodology in a Dutch regional network led to increased patient-
relevant outcome registration, adherence to regional standards, and selection of first regional
projects to enhance outcomes and costs. Further research will determine how the
methodology affects patient value (Van Veghel et al., 2022).
References
D’Amore, J. D., McCrary, L. K., Denson, J., Li, C., Vitale, C. J., Tokachichu, P., Sittig, D. F.,
McCoy, A. B., & Wright, A. (2021). Clinical data sharing improves quality measurement and
patient safety. Journal of the American Medical Informatics Association, 28(7), 15341542.
Malik, M. M., Abdallah, S., & Ala’raj, M. (2018). Data mining and predictive analytics
applications for the delivery of healthcare services: a systematic literature review. Annals of
Operations Research, 270, 287-312.
Van Veghel, H., Dekker, L., Theunissen, L., Janssen, J., Burg, M. P., Huijbers, P., Voermans,
P., van der Wees, P. J., & Cremers, H. P. (2022). Introducing a method for implementing
value based health care principles in the full cycle of care: Using atrial fibrillation as a proof
of concept. International Journal of Healthcare Management, 15(1), 1-9.
AHMED 530
C OLLA PSE
Repetitive Activity done by healthcare professionals using upper body ( arms and hands )
Administering medical treatments: Healthcare professionals may perform procedures such as
wound care, dressing changes, or injections. These tasks often involve repetitive upper body
movements, such as cleaning and dressing wounds or manipulating syringes.
Ergonomics risk assessment by using assessment of repetitive task (ART)
The Assessment of Repetitive Tasks (ART) tool is a simple and effective way to assess the
ergonomic risk of repetitive tasks. It was developed by the Health and Safety Executive (HSE) in the
UK and is widely used around the world.
The ART tool assesses four key risk factors:
•
•
•
•
Repetition: How often is the task repeated?
Force: How much force is required to perform the task?
Posture: What are the postures of the neck, shoulders, back, arms, and wrists during the task?
Duration: How long is the task performed for?
For each risk factor, the ART tool assigns a score of 1 to 4, with 1 being the lowest risk and 4
being the highest risk. The total score for the task is then calculated by adding up the scores for each
risk factor.
score sheet 3.docx
Top three Ergonomics risk factors
When using the Assessment of Repetitive Tasks (ART) method to evaluate ergonomic risk
factors associated with administering medical treatments, several factors should be considered.
These factors can contribute to the development of musculoskeletal disorders (MSDs) among
healthcare professionals. Here are some ergonomic risk factors associated with administering
medical treatments:
1. Forceful Exertions: The force required to perform certain medical treatments, such as applying
pressure during wound care or administering injections, can put strain on the upper body. Forceful
exertions can increase the risk of MSDs, especially if performed frequently or for extended periods.
2. Awkward Postures: Administering medical treatments may require healthcare professionals to
assume awkward or uncomfortable postures. For example, reaching across a patient while
maintaining proper body mechanics or bending the wrist excessively during injections. Awkward
postures can strain muscles and joints, leading to discomfort or injury over time.
3. Repetition: The repetitive nature of administering medical treatments can contribute to the
development of MSDs. Tasks like cleaning and dressing wounds or performing injections often
involve performing the same motions repeatedly, which can lead to muscle fatigue, overuse injuries,
and cumulative trauma.
To minimize ergonomic risks associated with administering medical treatments, it is important to
implement ergonomic interventions and controls. These may include providing ergonomic equipment
and tools, promoting proper body mechanics and postures, implementing task rotation or job
enrichment, and offering training on ergonomic practices and risk awareness. Regular evaluations
and feedback from healthcare professionals can help identify and address ergonomic concerns in the
healthcare setting.
References
Assessment of repetitive tasks of the upper limbs (the ART
https://www.hse.gov.uk/pubns/indg438.htm,2023tool).
Assessment of repetitive tasks of upper limbs (the ART tool) | Safety and health at work EUOSHA. https://osha.europa.eu/en/themes/musculoskeletal-disorders/practical-tools-musculoskeletaldisorders/assessment-repetitive-tasks-upper-limbs-art-tool
ABDULLAH 530
Assessment of repetitive tasks (ART)
C OLLA PSE
work-related upper limb diseases (WRULD) are a well-known condition that causes job
impairment, productivity loss, and social expenditures globally. In Sweden in 2020, 58% of
people who claimed to have suffered from poor health owing to work circumstances other
than an accident reported symptoms in the neck and/or upper extremities. These illnesses are
linked to hand-intensive activity that requires severe effort, high repetition, extended
duration, awkward or immobile postures, and often a combination of these traits.(Eliasson et
al., 2022)
Physical factors in the workplace, including as intense effort, awkward postures, and
repetitive activity, as well as psychological and organizational factors, are linked to
WRMSDs in the neck, shoulder, and arms. As a result, risk evaluations of physical elements
are critical for identifying potentially hazardous work activities and prioritizing and
developing workplace interventions, both in terms of physical design of the workplace, work
technique, and work organization.(Nyman et al., 2023)
Carpal tunnel syndrome, non-specific arm discomfort, tenosynovitis (tendon
inflammation), and lateral epicondylitis (tennis elbow) are all common work-related upper
limb diseases. Upper limb problem discomfort accounts for about one out of every ten missed
working days, with the typical illness absence lasting 13 days. This comes at a high cost to
enterprises.(Bromhead, 2018)
Repetitive upper limb activities comprise a series of upper limb motions that are
performed every few minutes or more often. The duties are typically completed for one to
two hours every day or shift, with the dangers associated being repetition, force, posture, and
working environment. The HSE created the ART (Assessment of Repetitive tasks) tool to
assist with risk assessment activities. The instrument is designed to evaluate the frequency
with which light weights or other repeated jobs are handled, which might lead to upper limb
disorder.(Bromhead, 2018)
The ART tool is especially beneficial for repetitive strain concerns when non-neutral
postures and repetition are important risk factors. It allows for the detection of concerns that
would otherwise go unexplored during a routine manual handling risk assessment. After
completing a comprehensive evaluation using the ART tool, a mechanism for evaluating the
overall result is offered, along with suggestions on the need for any additional
action.(Bromhead, 2018)
The ART tool takes a step-by-step approach:
•
Stage A: Frequency and repetition of movements;
•
Stage B: Force;
•
Stage C: Awkward postures;
•
Stage D: Additional factors.
For each stage, follow the flow chart and/or assessment guide to determine the level
of risk for each risk factor. The levels of risk are classified in the table below.
•
G = GREEN Low level of risk
•
A = AMBER Medium level of risk – Examine task closely
•
R = RED High level of risk – Prompt action needed
Score sheet
Enter the colour band and numerical score for each risk factor in the table below. Follow the
instructions on page 15 to determine the task score and exposure score.
Left arm
Right arm
Risk factors
Colour
A1 Arm movements
A2 Repetition
B Force
C1 Head/neck posture
C2 Back posture
C3 Arm posture
C4 Wrist posture
C5 Hand/finger grip
D1 Breaks
D2 Work pace
D3 Other factors
Task score
Score
Colour
Score
D4 Duration multiplier
X
X
Exposure score
D5 Psychosocial factors
Reference:
Bromhead, A. (2018, January 12). A Guide to the HSE ART tool. Dr Alistair Bromhead
Ltd. https://www.abromhead.co.uk/a-guide-to-the-hse-art-tool/
Eliasson, K., Fjellman-Wiklund, A., Dahlgren, G., Hellman, T., Svartengren, M., Nyman, T.,
& Lewis, C. (2022). Ergonomists’ experiences of executing occupational health surveillance
for workers exposed to hand-intensive work: A qualitative exploration. BMC Health
Services Research, 22, 1223. https://doi.org/10.1186/s12913-022-08601-2
Nyman, T., Rhén, I.-M., Johansson, P. J., Eliasson, K., Kjellberg, K., Lindberg, P., Fan, X.,
& Forsman, M. (2023). Reliability and Validity of Six Selected Observational Methods for
Risk Assessment of Hand Intensive and Repetitive Work. International Journal of
Environmental Research and Public Health, 20(8), 5505.
https://doi.org/10.3390/ijerph20085505
525
The landscape of healthcare is undergoing a rapid transformation due to technological
advancements, with new inventions appearing all the time. Even if advances in technology
have the potential to make healthcare delivery and outcomes far better in many respects,
these advancements also bring new difficulties for human relationships (Greussing et al.,
2022).
Challenges of Human Interactions with Technology in Patient Care:
Wearable gadgets that track our health data are just one example of how technology is
playing an increasingly essential role in the healthcare industry. Artificial intelligence (AI)
systems that can diagnose ailments and offer treatments are another example. Nevertheless,
the incorporation of technology into medical practice brings with it a variety of difficulties
for interpersonal communication (Schueller, 2021).
One major obstacle is the potential for healthcare to become impersonal as a result of
technological advancements. It’s possible for patients to feel like their unique needs aren’t
being addressed when they communicate with a computer or machine. This might make it
harder for patients and doctors to connect on a personal level (Sittig et al., 2018).
The complexity and awkwardness of some technologies are additional hurdles.
Patients, especially the elderly and those with low levels of technology literacy, may find this
to be an obstacle. Medical professionals may struggle because they lack the resources (time
and expertise) to master these innovative tools (Schueller, 2021).
Problems with patient-provider communication are another potential outcome of
technological advancements in the healthcare industry. There are a number of reasons why
patients could be hesitant to provide personal information to a computer program, such as the
fact that they might not be familiar with the medical jargon that is frequently employed.
Providers of healthcare may also struggle to convey complicated medical topics to people
(Sittig et al., 2018).
Finally, technological advancements might cause ethical problems in medical
treatment. For instance, AI systems may be prejudiced, which might result in discriminatory
healthcare. Data breaches are another concern that might expose sensitive patient information
(Schueller, 2021).
Recommendations for Designing Computer or Mobile Application Interfaces for
Efficient and Effective Patient Care:
When creating the user interfaces for computer programs or mobile applications that
are used in patient care, it is essential to keep the following principles in mind (Greussing et
al., 2022):
•
Create a user-friendly interface with clear instructions and prompts for next steps.
•
Make it user-friendly for individuals of all abilities; the interface has to work with a wide
range of hardware and software.
•
The interface should employ robust security mechanisms to keep sensitive patient
information private and safe.
•
The data collection and usage processes should be made clear in the UI. Patients should have
an easy time getting in touch with support staff if they have any issues or inquiries.
•
The interface should be created in a way that makes it easy for patients and doctors to talk to
each other and share information.
The following are some instances of how these recommendations can be put into practice:
•
Avoid complicated terminology and stick to the basics.
•
Make use of big typography and contrasting hues.
•
Give concise guidance and constructive criticism.
•
Make the UI more intuitive by include graphics and icons.
•
Facilitate the search for specific data by users.
•
In order to keep sensitive information about patients safe, you should employ robust
encryption and other security measures.
•
Make it simple for patients to get in touch with customer service and provide a transparent
policy about their privacy.
•
Create a user experience that compels users to interact with their healthcare providers. Secure
communication and appointment setting are only two examples of what the interface may
support.
When designers adhere to these guiding principles and guidelines, they are able to
produce computer or mobile application interfaces that are effective, efficient, and simple for
patients to use. This has the potential to contribute to the improvement of patient care while
also making it more accessible to all individuals (Sittig et al., 2018).
References
Greussing, E., Gaiser, F., Klein, S. H., Straßmann, C., Ischen, C., Eimler, S., Frehmann, K.,
Gieselmann, M., Knorr, C., Lermann Henestrosa, A., Räder, A., & Utz, S. (2022).
Researching interactions between humans and machines: methodological
challenges. Publizistik, 67, 531–554. https://doi.org/10.1007/s11616-022-00759-3
Schueller, S. M. (2021). Grand Challenges in Human Factors and Digital Health. Frontiers
in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.635112
Sittig, D. F., Wright, A., Coiera, E., Magrabi, F., Ratwani, R., Bates, D. W., & Singh, H.
(2018). Current challenges in health information technology–related patient
safety. Health Informatics Journal, 26(1), 146045821881489.
https://doi.org/10.1177/1460458218814893
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Observations about the effectiveness of the strategy:
Data mining was determined to be a useful method for detecting and lowering patient
safety hazards by the study’s authors. Data mining was utilized to evaluate a massive dataset
of patient safety events, which revealed previously hidden patterns and trends. They
discovered, for instance, that some occurrences tend to happen more frequently at particular
times of day or in particular apartments (Leary et al., 2020).
After seeing these tendencies, the writers were able to put in place specific measures
to lessen the likelihood of future occurrences. For instance, in the units where specific
occurrences occurred more frequently, more training was provided to personnel. In addition,
they adjusted their scheduling policies to increase the presence of workers during peak
incident hours (Leary et al., 2020).
Methodologies used to define and diagnose the problem:
The authors mined the patient safety event database using a number of different methods
to extract useful information. Some of these methods were (Leary et al., 2020):
•
Association Rule Learning: This method was employed to establish connections between
various occurrence categories. Patients using particular drugs, the authors discovered, were at
a higher risk of falling.
•
Clustering: It was utilized to categorize patients into groups that were at high risk for
specific occurrences. For instance, the authors discovered that patients who were both elderly
and suffering from many chronic diseases had an increased chance of falling.
•
Classification: Using this method, models were created to foresee the likelihood of specific
incidences happening to specific patients. For instance, the authors built a model to foresee
the likelihood of falls among newly admitted hospital patients.
How they implemented the process improvement:
After analyzing patient safety event data, the authors took action to mitigate such
incidents in the future by putting into place many distinct interventions. The following
actions were taken (Leary et al., 2020):
•
Additional training for staff: The authors expanded training opportunities for workers at
hospitals and other facilities where certain occurrences occurred often. The employees in the
geriatric ward, for instance, received education on how to reduce the risk of falls.
•
Changes to scheduling practices: When the authors realized that certain times of day had a
higher risk of mishaps, they adjusted their staffing levels accordingly. For instance, during
the night shift, they boosted the number of emergency room doctors and nurses on duty.
•
Development of new policies and procedures: The authors established fresh guidelines and
processes to deal with certain threats to patient safety. For people who are at high risk of
falling, they implemented a new policy.
Metrics used to measure the improvement:
The authors employed a variety of indicators to gauge the rise in patient safety following
their treatments. Among these measurements were (Leary et al., 2020):
•
The number of patient safety incidents: After applying their treatments, the authors saw a 15
percent drop in patient safety occurrences.
•
The severity of patient safety incidents: After applying their treatments, the authors also saw
a reduction in the severity of occurrences related to patient safety.
•
The number of patient deaths: After adopting their changes, the authors discovered a 20%
reduction in patient mortality.
Was the improvement sustainable?
When the authors checked in with the healthcare system a year after implementing
their measures, they discovered that the gains in patient safety had persisted. Patient safety
incidents, patient safety incident severity, and patient mortality all stayed at historically low
levels (Leary et al., 2020).
Opportunities to improve the process that the authors did not include:
The authors left out the possibility of using data mining to detect near-misses, which
would be a significant improvement to the method. Events that almost resulted in patient
damage but luckily did not are called near misses. Identification of near misses can be as
useful in preventing future problems as identification of actual occurrences (Ekwonwune et
al., 2022).
Data mining, the systematic examination of large datasets, presents yet another
potential for process improvement. Patient safety incident reports were the only source of
data for this analysis. There are additional data sources that might be utilized to detect
hazards to patient safety and create interventions to address those risks, such as electronic
health records and patient questionnaires (Ekwonwune et al., 2022).
References
Ekwonwune, E. N., Ubochi, C. I., & Duroha, A. E. (2022). Data Mining as a Technique for
Healthcare Approach. International Journal of Communications, Network and
System Sciences, 15(09), 149–165. https://doi.org/10.4236/ijcns.2022.159011
Leary, A., Cook, R., Jones, S., Radford, M., Smith, J., Gough, M., & Punshon, G. (2020).
Using knowledge discovery through data mining to gain intelligence from routinely
collected incident reporting in an acute English hospital. International Journal of
Health Care Quality Assurance, 33(2), 221–234. https://doi.org/10.1108/ijhcqa-082018-0209
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