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530:
Munirah:
Nurses frequently perform intravenous (IV) line insertion, which demands precise hand
movements and non-standard postures that can cause significant strain on the
musculoskeletal system. As the aging population continues to increase, the demand for
IV therapies rises, putting nursing staff at a higher risk of developing work-related
musculoskeletal disorders (WMSDs) if proper ergonomic measures are not taken
(Kamwendo & Medin, 2019).
This study aimed to identify ergonomic hazards associated with IV-line insertion by
utilizing the Assessment of Repetitive Tasks (ART) tool, which evaluates seven distinct
ergonomic risk factors related to upper limb tasks and assigns scores ranging from 0 to 3
for each factor, with higher scores indicating greater risk (Hignett & McAtamney, 2000).
In this study, a nurse was observed performing 20 IV insertions during a single hospital
shift, considering factors such as posture, force, repetition, duration, and other task
demands. The scores from these factors were then aggregated to identify the most critical
risk factors.
Table 1 provides a detailed breakdown of the ART scores. The total score of 13 signifies
a high ergonomic risk (Hignett & McAtamney, 2000). The highest scores were linked to
frequent deviations from neutral posture (3), sustained durations exceeding 2 hours daily
(3), and movements exceeding 2 per minute (3). Scores were moderate for force exertion
(2) and repetition (3). Other physical demands received lower scores.
Table 1
ART Assessment of IV Line Insertion
Abdulmajeed:
COLLAPSE
One repetitive activity in the healthcare setting done by healthcare professionals using their
upper body is patient handling, such as lifting and transferring patients. Using the
Assessment of Repetitive Tasks (ART) tool, the top three ergonomic risk factors for this
activity are force, repetition, and awkward postures.
Here is a sample score sheet for the ART tool:
Task
Patient
Force
lifting
and
Repetition
4
Posture
4
3
transferring
The score sheet rates each risk factor on a scale of 1 to 5, with 1 being low risk and 5 being
high risk. The scores for force, repetition, and posture are added up to give an overall score
for the task. In this case, the overall score is 11, which indicates a high risk for
musculoskeletal disorders (MSDs) associated with patient handling.It is important to note
that the ART tool is just one of many tools that can be used to assess ergonomic risk factors.
Other tools may provide different results depending on the specific task and workplace
conditions. It is also important to address ergonomic risk factors through a combination of
engineering controls, administrative controls, and personal protective equipment to reduce
the risk of MSDs.
520:
Maram Alangari
Data mining is the practice of looking through and analyzing a sizable collection of
unstructured data in order to find patterns and extract pertinent information. To find out more
about their clients, businesses utilize data mining tools. They can use it to create more profitable
marketing plans that will boost sales and cut expenses. Effective data collection, storage, and
processing are essential for data mining. Data mining is the process of examining and analyzing
huge chunks of data to discover significant patterns and trends. It is utilized in spam filtering,
fraud detection, and credit risk management. It can also be used as a tool for market research
to get a sense of how a certain set of individuals feel or think. Four steps make up the data
mining process: Data is gathered and loaded either locally or via a cloud service into data
warehouses. Management groups, information technology specialists, and business analysts
access the data and choose how to organize it. Data is sorted and organized using specialized
application software. Lastly, the end user displays the data in a manner that is simple to
distribute, such a graph or table. Algorithms and other methods are used in data mining to
transform massive data sets into useable output. The most often used kinds of data mining
methods are as follows: Market basket analysis and association rules both look for connections
between different variables. As it attempts to connect different bits of data, this relationship in
and of itself adds value to the data collection. For instance, association rules would examine a
business’s sales data to determine which products were most frequently bought in tandem; with
this knowledge, retailers might plan, advertise, and forecast. To assign classes to items,
classification is used. These categories express the traits of the items or the similarities between
the data points. The underlying data can be more precisely categorized and summed up across
related attributes or product lines thanks to this data mining technique. Clustering and
categorization go hand in hand. Clustering, on the other hand, finds similarities between objects
before classifying them according to how they differ from one another.
Here are a few examples of the different discrepancies brought on by using the incorrect data
mining techniques: 1) Making decisions that are not particularly accurate. 2) Lack of
knowledge. 3) Difficulties in upgrading knowledge.4) Time-sensitive performance
(expensive). Due to these issues, data mining must be used to give a framework for aid in the
diagnosing process. The obvious conclusion is that complicated data cannot be ad hoc analyzed
by humans or their statistical methods without making mistakes. If data mining techniques are
to be widely used in clinical practice, it is crucial in the fields of medicine and healthcare where
patient safety is paramount. The process’s objective is to take the medical information, which
contains a wide range of features, and identify those that are genuinely pertinent to the heart
disease’s diagnosis, symptoms, and outcome. We are looking at a very big amount of data that
can reach terabytes in size, making it very difficult to mine for them without automatic ways
for retrieving this information.
References:
Ekwonwune, E. , Ubochi, C. and Duroha, A. (2022) Data Mining as a Technique for Healthcare
Approach. International Journal of Communications, Network and System Sciences, 15, 149165.
Abdulmajeed:
After conducting a search, I was unable to find a specific academic article that demonstrates
how data mining supported process improvement in a healthcare organization. However, I
can provide some general information on how data mining can be used to support process
improvement in healthcare organizations.
Data mining is a process of discovering patterns in large data sets using statistical and
computational methods. In healthcare organizations, data mining can be used to identify
patterns in patient safety incidents, such as medication errors, falls, and hospital-acquired
infections. By analyzing these patterns, healthcare organizations can identify areas for
process improvement and develop strategies to prevent future incidents.
One study used the Consolidated Framework for Implementation Research (CFIR) to analyze
the implementation strategies for a patient safety reporting system in a healthcare
organization (Kim. 2022)
The study found that data mining was an effective strategy for identifying areas for
improvement and developing targeted interventions. The study also found that the use of
data mining was sustainable over time, as the organization continued to use the patient safety
reporting system to identify and address patient safety issues.
To diagnose patient safety problems, healthcare organizations can use a variety of
methodologies, such as root cause analysis, failure mode and effects analysis, and process
mapping. Once the problem has been identified, healthcare organizations can implement
process improvements, such as standardizing procedures, providing additional training to
staff, and implementing new technologies. Metrics can be used to measure the effectiveness
of these process improvements, such as the number of patient safety incidents before and
after the intervention, the severity of the incidents, and the cost savings associated with the
intervention.
In terms of opportunities for improvement, healthcare organizations can consider using
predictive analytics to identify patients who are at high risk for adverse events, such as
hospital-acquired infections or readmissions. By identifying these patients early, healthcare
organizations can develop targeted interventions to prevent adverse events from occurring.
References:
Kim, J. H., Kim, H. S., & Kim, J. H. (2022). Implementation strategies for the patient safety
reporting system using Consolidated Framework for Implementation Research: a
retrospective mixed-method analysis. BMC health services research, 22(1), 1-12.
Saad:
“The use of data mining by private health organizations for clinical decision support: A systematic
review” by Vincent et al. (2020).
The article demonstrated how data mining could offer significant advantages to healthcare
organizations, focusing on its application for clinical decision support.
The authors used a systematic review methodology to identify, analyze, and synthesize studies from
various databases like PubMed, ScienceDirect, and IEEE Xplore. They used PRISMA guidelines to
ensure a robust review process. The primary issues they were addressing were the challenges related
to clinical decision-making, including the increasing complexity of healthcare data and the necessity for
evidence-based practice.
The study found that data mining could significantly improve the process of clinical decision-making. It
highlighted the use of various data mining techniques, including regression analysis, decision trees,
and clustering, to extract useful knowledge from large datasets. This extracted information could then
assist clinicians in making more accurate diagnoses, predicting patient outcomes, and personalizing
treatment plans.
The effectiveness of the data mining strategies was measured using various metrics, like accuracy,
sensitivity, specificity, and area under the ROC curve (AUC). The authors found that these data mining
techniques generally provided high accuracy and were effective in supporting clinical decisions.
Regarding the sustainability of the improvement, the authors mentioned that as healthcare
organizations continue producing large amounts of data, the value of data mining is likely to increase.
However, they also noted that the sustainability of these improvements depends on factors like training
of healthcare staff, continuous updating of data mining models, and addressing privacy and ethical
concerns related to data use.
In terms of opportunities for further improvement, the authors did not discuss in detail how these data
mining strategies could be integrated into the existing workflow of healthcare professionals. Integrating
these strategies in a user-friendly way is crucial for their adoption. Therefore, future research could
focus on the development of intuitive interfaces that allow clinicians to easily utilize the insights provided
by data mining.
Moreover, the authors did not address the potential of real-time data mining. Many healthcare decisions
need to be made in a time-sensitive manner, and real-time data mining could provide immediate insights
that support these decisions. Therefore, developing and implementing real-time data mining strategies
could be another area for improvement.
References:
●
Vincent, M., Vincent, C., Ferreira, A. (2020). The use of data mining by private health
organizations for clinical decision support: A systematic review. Computers in Biology and
Medicine, 120, 103738.
Mezna:
Data mining is the process of sorting through large data sets to identify patterns and relationships that
can help solve problems through data analysis. In healthcare clinical data mining helps medical
scientists and experts reveal data patterns, trends, associations, and other fact correlations enabling
them to formulate important observations and conclusions (Wu, et al., 2021). Example of data mining
is the “Quick Sequential Organ Failure Assessment” (qSOFA). It is a diagnostic tools for predicting
hospital mortality among adults with suspected infection(Olivia, Nayak, Balachandra & John, 2020). It
is mainly based in patient clinical data that helps in early detection of infection such as sepsis and help
in start treatment as soon as possible to avoid any delay.
A study conducted by Asai et al. (2019) to measure the “Efficacy and accuracy of qSOFA and SOFA
scores as prognostic tools for community-acquired and healthcare-associated pneumonia”, in this study
the researcher assess the severity of pneumonia using qSOFA for 30 days, and assessed mortality rate
for 30 days. This study was conducted to evaluate the prognostic accuracy of the predictive values
which is the respiratory rate, altered mental status, or systolic blood pressure.
The researcher and his team have observed that the predictive ability of the SOFA score was superior
to other predictive assessments. In addition, they found that the 30-day and in-hospital mortality rates
for the patients in the current study were lower than those reported previously.
The researcher did a predictive analytics of patient condition using data mining techniques to analyze
historical data and identify patterns that could help predict future events or outcomes. Predictive
analytics can be employed to forecast patient prognosis, identify high-risk patients. This information
enables healthcare organizations to proactively intervene and allocate resources effectively, leading to
better patient outcomes and reduced costs.
In addition, qSOFA can help in clinical decision support systems (CDSS) that provide evidence-based
recommendations to healthcare practitioners(Olivia, Nayak, Balachandra & John, 2020). By analyzing
patient data such as respiratory rate, altered mental status, or systolic blood pressure can identify most
effective treatment options and identify patient outcomes.
The researcher have conducted this study as retrospective study, if it was done during patient admission
it could help in identify any barriers and clinical challenge to data analysis. However, the researcher
have concluded that the qSOFA scores were able to accurately evaluate the severity of communityacquired pneumonia (CAP) and healthcare-associated pneumonia (HCAP). And this tool could be
useful in the treatment of this condition.
In general, data mining provides healthcare organizations with the tools to extract valuable insights from
large volumes of data. Example of data mining is the qSOFA which prevent to support clinical decisionmaking, operational efficiency and quality of patient care. By leveraging data mining techniques,
healthcare organizations can drive positive change, leading to better patient outcomes, cost savings,
and enhanced overall performance.
Reference
Asai, N., Watanabe, H., Shiota, A., Kato, H., Sakanashi, D., Hagihara, M. & Mikamo, H. (2019). Efficacy
and accuracy of qSOFA and SOFA scores as prognostic tools for community-acquired and healthcareassociated pneumonia. International Journal of Infectious Diseases, 84, 89-96.
Olivia, D., Nayak, A., Balachandra, M., & John, J. (2020). A classification model for prediction of clinical
severity level using qSOFA medical score. Information Discovery and Delivery, 48(1), 41-77.
Wu, W. T., Li, Y. J., Feng, A. Z., Li, L., Huang, T., Xu, A. D., & Lyu, J. (2021). Data mining in clinical big
data: the frequently used databases, steps, and methodological models. Military Medical Research, 8,
1-12.
525:
Nouf:
blockchain can improve the interaction between humans and technological systems. It is
a valuable technology that helps improve data sharing and storage in the healthcare
setting. However, many healthcare organizations remain hesitant to adopt blockchain
technology due to threats such as security and authorization issues, interoperability
issues, and a lack of technical skills related to blockchain technology. (Abu-Elezz, Hassan,
Nazeemudeen, Househ, & Abd-Alrazaq, 2020).Blockchain is a relatively new technology,
and there is still a lack of understanding of how to secure it effectively which can make it
difficult to ensure the security and authorization of the data, also networks are often
incompatible with each other, making it hard to share data between different healthcare
organizations, and shortage of healthcare professionals with the technical skills is another
challenge that faced.
Despite the challenges, many advantages can be utilized by Technology. New technologies
have revolutionized nearly every aspect of human existence, including the ways that firms
market products and services to consumers. (Grewal, D., Hulland, J., Kopalle, P. K., &
Karahanna, E. (2020). Here is an interesting article that found digital platforms and
artificial intelligence (AI) have a good potential to improve prediction, identification,
coordination, and treatment by mental health care and suicide prevention services. AI is
driving web-based and smartphone apps; mostly it is used for self-help and guided
cognitive behavioral therapy (CBT) for anxiety and depression. (Balcombe, & De Leo,
2022).
In my opinion, the recommendations on how computer and mobile applications should
be designed for efficient and effective patient care are:
●
Designed in a way that serves patient needs and removes unnecessary features.
●
Alternative text is a brief description of an image that is displayed to users who
are blind or have low vision.
●
If the application is designed to be used on a mobile device, it is important to
ensure that the battery life is long enough to support the intended use.
●
In case the application is designed to be portable; it is important to make sure
that it is small and lightweight enough to be easily carried. That will make it
more convenient for users to use the application when they are away from their
homes.
●
The application must be secure to protect patient data, respect patient privacy,
and be able to interoperate with other healthcare systems.
In conclusion, technology plays an increasingly important role in our lives. We use
technology to communicate, to learn, to work, and to entertain ourselves. As technology
becomes more complex, it is important to design systems that are easy to use and that
meet the needs of all users.
Balcombe, L., & De Leo, D. (2022, February). Human-computer interaction in digital
mental health. In Informatics (Vol. 9, No. 1, p. 14). MDPI.
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology
and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing
Science, 48, 1-8.
Abu-Elezz, I., Hassan, A., Nazeemudeen, A., Househ, M., & Abd-Alrazaq, A. (2020). The
benefits and threats of blockchain technology in healthcare: A scoping review.
International Journal of Medical Informatics, 142, 104246.
Faten:
Technology has revolutionized the way we live, work, and interact with the world around
us. It has also had a profound impact on healthcare, enabling new forms of diagnosis,
treatment, and care delivery. However, there are a few of the challenges of human
interactions with technology and it is important to be aware of these challenges and to
take steps to mitigate them. Studies on the impact of poor health IT design (and
implementation) for both patients and clinicians are important specially when it comes
to patient safety.
Challenges of Human Interaction with Technology:
●
Usability: HIT systems are often complex and difficult to use, even for
experienced clinicians. This can lead to errors and frustration, and it can also
discourage clinicians from using HIT to its full potential. An analysis of
medication safety events reported by pediatric clinicians in three children
hospitals across the US showed that more than one-third (36%) were related to
EHR usability issues, in particular the lack of system feedback and poor visual
display of information (Carayon and Hoonakker, 2019).
●
Poor user interface design leads to errors in data input and comprehension
(Sittig et al., 2020). Poor user-interface design may also result in unintended
consequences that impair patient safety and outcomes because incorrect
information is used to guide future clinical decision making.
●
Increased cognitive workload for physicians
●
Communicating with patients in a changing digital landscape
Recommendations:
In my opinion, there are a few recommendations on how user interfaces should be
designed for efficient and effective patient care:
●
Consistency: Interfaces must have the same style and logic across the entire
system.
●
Minimalism: Eliminate unnecessary steps and distractions to enable users to
complete the required action with minimum clicks.
●
Display data in time-series: Implement graphs showing how a patient’s condition
has changed over time to enhance the readability of their health info.
Reference:
Carayon, P., & Hoonakker, P. (2019). Human Factors and Usability for Health
Information Technology: Old and New Challenges. Yearbook of medical informatics,
28(1), 71–77. https://doi.org/10.1055/s-0039-1677907
Sittig, D. F., Wright, A., Coiera, E., Magrabi, F., Ratwani, R., Bates, D. W., & Singh, H.
(2020). Current challenges in health information technology-related patient safety.
Health
informatics
journal,
https://doi.org/10.1177/1460458218814893
26(1),
181–189.
Fahad:
Human-technology interactions are critical for providing efficient and effective patient care.
Technology has the potential to improve patient safety, streamline workflows, and give patients
greater access to care. Telemedicine allows patients to consult with healthcare providers remotely;
EHRs allow healthcare providers to access patient medical records electronically; Patient portals
allow patients to access their medical records, schedule appointments, and communicate with their
healthcare providers online; and Smartwatches and fitness trackers, for example, can be used to
collect data on patients’ health and activity levels.
Human-technology interactions present a number of challenges, particularly in the
context of patient care. Among the most common difficulties are:
●
●
●
●
Inadequate user-centered design: Computer or mobile application interfaces should be
created with the user in mind. To use the technology effectively, the user should only need
a basic understanding of it.
Difficulty in developing an effective feedback system: It is critical to develop an effective
feedback system for users of health-related monitoring data. The health monitoring
system, with effective feedback, can promote better patient engagement while improving
the overall quality of the healthcare system.
• Implementation difficulties: Putting mobile applications into clinical practice can be
difficult. The team identified five major challenges associated with clinical mobile
application deployment and presented solutions to each of them.
• Specific and rigorous design requirements: Human-machine interface design is more
specific and rigorous in the medical and health fields.
Computer or mobile application interface design recommendations for efficient and
effective patient care:
●
●
●
●
Prioritize user-centered design: When designing computer or mobile application
interfaces, user experience should be prioritized. To use the technology effectively, the
user should only need a basic understanding of it.
Create an effective feedback system: It is critical to create an effective feedback system for
users of health-related monitoring data. The health monitoring system, with effective
feedback, can promote better patient engagement while improving the overall quality of
the healthcare system.
Provide error-resistant displays and alarms to ensure safety: Human Computer Interaction
design principles for smart healthcare mobile devices should ensure safety, provide errorresistant displays and alarms, and support patients’ and healthcare providers’ unique
relationship.
Differentiate end-user groups: Computer or mobile application interfaces should be
designed to differentiate end-user groups.
While technology can be an effective tool for improving patient care, it is critical to remember that
human interaction is still necessary. Healthcare providers must be able to establish rapport with
their patients and understand their specific needs. Human interaction can be facilitated by
technology, but it cannot be replaced.
References:
Ehrler, F., Wipfli, R., Teodoro, D., Sarrey, E., Walesa, M., & Lovis, C. (2013). Challenges
in the implementation of a mobile application in clinical practice: Case study in the
context of an application that manages the daily interventions of nurses. JMIR mHealth
and uHealth, 1(1), e7.
Guarascio-Howard L. Examination of wireless technology to improve nurse
communication, response time to bed alarms, and patient safety. HERD. 2011;4(2):109–
20
Tschopp M, Lovis C, Geissbuhler A. Understanding usage patterns of handheld
computers in clinical practice. Proc AMIA Symp. 2002:806–9.
ALAA:
Challenges of human interactions with technology for patient care, Technology can be
great for patient care, but it can also make it harder for humans to interact with each
other.
Some of the main challenges are:
●
Usability: Technology can be complex and hard to use, especially for older adults
or people with limited technical skills. This can lead to frustration and mistakes,
which can put patients at risk.
●
Trust: Patients need to trust that technology is being used safely and fairly. This
trust can be broken if people are worried about their privacy, security, or the
possibility of bias in AI-powered systems.
●
Communication: Technology can get in the way of human communication,
making it harder to build rapport and empathy with patients. This is especially
important for complex or sensitive conversations.
Recommendations for designing computer or mobile application interfaces for
efficient and effective patient care.
When designing computer or mobile application interfaces for patient care,
it’s important to keep the following in mind:
●
Focus on usability: Interfaces should be easy to understand and use, even for
people with limited technical skills. This can be done by using clear and concise
language, avoiding jargon, and providing clear instructions.
●
Build trust: Interfaces should be designed to build trust with patients. This can
be done by being open about how data is collected and used, putting in place
strong security measures, and giving users control over their data.
●
Support communication: Interfaces should support effective communication
between patients and healthcare providers. This can be done by providing
features for video conferencing, secure messaging, and real-time translation.
In addition to these general recommendations, there are also some specific design
considerations for different types of patient care applications. For example, applications
that are used by patients to manage their own health should be personalized and easy to
use. Applications that are used by healthcare providers to diagnose and treat patients
should be integrated with electronic health records and provide access to clinical decisionsupport tools. (Schueller, 2021)
Reference:
Schueller, S. M. (2021). Grand Challenges in Human Factors and Digital Health.
Frontiers in Digital Health, 3, 635112. https://doi.org/10.3389/fdgth.2021.635112
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