Feb 23, 2024 Assignment: Literature Review:Clinical-Systems
Assignment: Literature Review: Clinical-Systems
Assignment: Literature Review: Clinical-Systems
Introduction
This paper presents an annotated bibliography summarizing recent research on the application of clinical systems and their impact on healthcare outcomes and efficiencies. The purpose is to explore how various clinical systems have been used to improve patient outcomes and streamline healthcare delivery.
Annotated Bibliography
Research Article 1
Lu Wenjie, Zhang Jiaming, & Jiang Weiyu. (2023). The difference and clinical application of modified thoracolumbar fracture classification scoring system in guiding clinical treatment. Journal of Orthopaedic Surgery and Research, 18(1), 1–8. https://doi.org/10.1186/s13018-023-03958-4
In this article, Wenjie et al. discusses the clinical application of a modified thoracolumbar injury classification and severity score system (modified TLICS system) in guiding clinical treatment for patients with thoracolumbar fractures. The system was developed as an improvement to the existing TLICS system to address its limitations and enhance its effectiveness.
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The study found that the use of the modified TLICS system significantly improved patient outcomes. Over an average follow-up duration of 19.2 months, patients demonstrated significant improvement in various outcome measures, including visual analog scale (VAS) score, modified Japanese Orthopaedic Association (JOA) score, anterior vertebral height ratio, sagittal index, and Cobb angle. Additionally, neurological status also showed varying degrees of improvement. The systematic application of the modified TLICS system allowed clinicians to identify the severity of thoracolumbar fractures accurately and tailor treatment plans, leading to improved patient recovery and functional outcomes.
By implementing the modified TLICS system, the research showed that clinicians achieved more streamlined and efficient decision-making in clinical treatment. The modified TLICS system facilitated a comprehensive evaluation of various injury parameters, aiding in the accurate classification of thoracolumbar fractures. The system’s modifications addressed the limitations of the original TLICS system, enabling healthcare providers to make more informed decisions regarding the need for surgery and the appropriate treatment approach. As a result, the operation rate for the modified TLICS system was slightly lower than that of the traditional TLICS system. This suggests that the modified system contributed to the more efficient allocation of surgical resources while still achieving favorable patient outcomes.
The study provides valuable insights into the application of clinical systems in orthopedic settings. The development and implementation of the modified TLICS system offer a valuable lesson on how continuous improvement and refinement of existing clinical systems can enhance their practicality and effectiveness. By addressing the limitations of the original TLICS system, the modified version demonstrated its potential as a reliable tool for thoracolumbar fracture classification and assessment. The study emphasizes the importance of iterative research and continuous feedback from clinicians to optimize clinical systems for better patient care and healthcare efficiency.
Research Article 2
Parva Paydar, Shole Ebrahimpour, Hanieh Zehtab Hashemi, Mehdi Mohamadi, & Soha Namazi. (2023). Design, Development, and Evaluation of an Application based on Clinical Decision Support Systems (CDSS) for Over-The-Counter (OTC) Therapy: An Educational Interventions in Community Pharmacists. Journal of Advances in Medical Education and Professionalism, 11(2), 95–104. https://doi.org/10.30476/jamp.2022.95843.1661
Paydar et al, shows the implementation of a Clinical Decision Support System (CDSS) in the form of an over-the-counter (OTC) therapy application for community pharmacists resulted in several improvements in outcomes. Firstly, the application significantly enhanced the knowledge and pharmaceutical skills of pharmacists in managing OTC therapy. By providing decision support and relevant information, pharmacists were better equipped to take comprehensive patient histories, make appropriate pharmacological and non-pharmacological recommendations, and identify when to refer patients to physicians. This ultimately led to more effective patient counseling and improved patient outcomes. Moreover, the application also contributed to a reduction in unnecessary referrals to physicians. Before using the CDSS-based application, a considerable percentage of patients were wrongly referred to physicians.
While the application increased the time taken to manage scenarios, it had a positive impact on overall efficiencies in patient care. Pharmacists spent more time collecting complete patient histories, resulting in more comprehensive evaluations and appropriate recommendations. Although the initial increase in time may seem inefficient, the overall outcome of improved decision-making and patient care justified this trade-off. Additionally, the mobile-based nature of the application offered ease of access and use for pharmacists in busy pharmacy settings. It allowed them to promptly access OTC therapy information and decision support, thereby enhancing their ability to counsel patients effectively and manage OTC-related situations efficiently.
The study yielded valuable lessons for the future application of CDSS-based tools in pharmacy practice. It highlighted the significant impact of such tools on enhancing patient care and pharmacist performance. The application acted as a valuable clinical support system, guiding pharmacists through patient evaluations and treatment decisions. This underscored the importance of integrating CDSS-based applications to improve patient outcomes and streamline decision-making processes in pharmacy practice. Additionally, user feedback from the evaluation using the user version of the mobile application rating scale (uMARS) questionnaire was essential in understanding user experience and application quality. The feedback provided valuable insights into the importance of user-centric design and continuous improvement to enhance user satisfaction and application performance.
Research Article 3
Shujuan Cao, Rongpei Zhang, Aixin Jiang, Mayila Kuerban, Aizezi Wumaier, Jianhua Wu, Kaihua Xie, Mireayi Aizezi, Abudurexiti Tuersun, Xuanwei Liang, & Rongxin Chen. (2023). Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening. BioMedical Engineering OnLine, 22(1), 1–13. https://doi.org/10.1186/s12938-023-01097-9
Cao et al. explored the application of artificial intelligence (AI)-based fundus screening systems in the clinical environment has shown promising results in improving outcomes and efficiencies in the early detection and management of ocular fundus abnormalities. This study investigated the performance of an AI-based fundus screening system, focusing on diabetic retinopathy (DR), retinal vein occlusion (RVO), and pathological myopia (PM), in a real-world clinical setting. The study aimed to evaluate the system’s diagnostic effectiveness, and its application in population screening, and identify areas for further improvement and integration of systemic indicators.
Enhanced Diagnostic Accuracy: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO), and pathological myopia (PM), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) all exceeding 80%. This improved accuracy leads to more precise and reliable diagnoses, enabling early detection and timely treatment, ultimately improving patient outcomes and preventing irreversible vision loss.
Resource Saving and Efficiency: By automating the screening process, the AI-based system analyzes many fundus images quickly and accurately, reducing the burden on healthcare professionals. This increased efficiency translates to faster diagnoses, allowing for more patients to be screened and diagnosed promptly. The system’s efficiency enhances the overall workflow in clinical settings, leading to more effective patient management and treatment.
Scalability and Population Screening: The AI-based fundus screening system’s diagnostic capabilities in the clinical environment were comparable to those in population screening. This scalability allows the system to be applied in primary healthcare facilities for large-scale screenings.
Identification of Areas for Improvement: The study identified areas for improvement in the AI system’s performance, particularly in its sensitivity to age-related macular degeneration (ARMD) and referable glaucoma. Lessons learned from the study provide valuable insights for future developments and updates to the AI algorithm. Focusing on enhancing accuracy and precision for these conditions will further optimize the system’s diagnostic capabilities.
Integration of Systemic Indicators: The study highlighted the potential to integrate the AI algorithm with systemic indicators, such as HbA1c levels for diabetic retinopathy diagnosis. This integration could significantly improve the system’s diagnostic capabilities, providing more comprehensive assessments of patients’ overall health.
Research Article 4
Gholamzadeh, M., Abtahi, H., & Safdari, R. (2023). The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease. Journal of Healthcare Engineering, 2023, 8550905. https://doi.org/10.1155/2023/8550905
Gholamzadeh et al. discussed the application of clinical decision support systems (CDSSs) in chronic disease management has shown significant improvements in patient outcomes. By providing evidence-based recommendations and up-to-date information, CDSSs empower healthcare providers to make more accurate diagnoses and develop tailored treatment plans. This leads to better disease management, reduced complications, and improved patient health. CDSSs also help in identifying potential medical errors and providing timely alerts, contributing to enhanced patient safety and healthcare quality. With personalized patient care and targeted interventions, CDSSs play a vital role in improving clinical outcomes and patient well-being.
CDSSs have brought about substantial efficiencies in healthcare delivery. By automating the processing of patient data and presenting relevant information, CDSSs save clinicians valuable time and effort that would otherwise be spent searching for relevant medical literature and guidelines. This streamlining of the decision-making process allows healthcare providers to focus more on direct patient care and less on administrative tasks. As a result, CDSSs contribute to a more efficient and streamlined healthcare workflow, leading to enhanced productivity and resource utilization.
The implementation of CDSSs in clinical settings has provided valuable lessons for healthcare providers and developers. Challenges related to system integration, user acceptance, data quality, and clinician resistance to change have been encountered. To address these challenges, effective training, engagement with end-users, and continuous monitoring and evaluation of system performance have been essential. Additionally, adapting CDSSs to diverse clinical settings and patient populations has been critical for maximizing their impact.
In conclusion, the application of clinical decision support systems in chronic disease management has led to significant improvements in patient outcomes, enhanced efficiencies in healthcare delivery, and valuable lessons learned. By addressing challenges, embracing continuous learning, and upholding ethical considerations, CDSSs can continue to play a pivotal role in advancing patient care and healthcare quality.
Conclusion
The four peer-reviewed research articles presented in this annotated bibliography collectively provide valuable insights into the application of clinical systems and their impact on healthcare outcomes and efficiencies. These studies cover various domains within healthcare, including orthopedics, pharmacy practice, ophthalmology, and chronic disease management. A cohesive conclusion can be drawn from these findings to highlight the overall benefits and lessons learned from using clinical systems in diverse healthcare settings.
Firstly, the studies consistently demonstrate that the implementation of clinical systems leads to significant improvements in patient outcomes. In the orthopedic setting, the modified thoracolumbar injury classification and severity score system (modified TLICS system) improved patient recovery and functional outcomes for thoracolumbar fractures. In pharmacy practice, the Clinical Decision Support System (CDSS) for over-the-counter (OTC) therapy resulted in more effective patient counseling and reduced unnecessary referrals to physicians.
In ophthalmology, the AI-based fundus screening system showed enhanced diagnostic accuracy for various ocular abnormalities, leading to timely treatment and preventing irreversible vision loss. Moreover, the application of knowledge-based CDSSs in chronic disease management improved patient health, reduced complications, and enhanced adherence to evidence-based medicine.
Secondly, the research highlights the efficiencies gained by using clinical systems. In orthopedics, the modified TLICS system facilitated more streamlined and efficient decision-making, optimizing surgical resource allocation while achieving favorable patient outcomes. The CDSS-based application in pharmacy practice, despite increasing the time taken to manage scenarios, improved overall efficiencies in patient care by enabling more comprehensive evaluations and appropriate recommendations. The AI-based fundus screening system in ophthalmology automated the screening process, saving time for healthcare professionals and allowing for large-scale population screenings. In chronic disease management, CDSSs saved clinicians time and effort, leading to a more efficient healthcare workflow and enhanced productivity.
The lessons learned from these studies emphasize the importance of continuous improvement and refinement of clinical systems. The development of the modified TLICS system, the user-centric design of the CDSS-based application, and the identification of areas for improvement in the AI-based fundus screening system all highlight the value of iterative research and continuous feedback from healthcare professionals. Additionally, the studies underscore the significance of integrating clinical systems with systemic indicators and adapting them to diverse clinical settings and patient populations to maximize their impact.
In conclusion, the findings from these research articles collectively demonstrate that clinical systems play a crucial role in enhancing healthcare outcomes and efficiencies. By improving patient care, streamlining decision-making processes, and saving valuable time and resources, these systems contribute to overall healthcare quality and effectiveness. Moreover, the lessons learned from their implementation provide valuable guidance for future developments and improvements in clinical systems, ensuring continuous enhancement of patient care and healthcare delivery.
References
Gholamzadeh, M., Abtahi, H., & Safdari, R. (2023). The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease. Journal of Healthcare Engineering, 2023, 8550905. https://doi.org/10.1155/2023/8550905
Lu Wenjie, Zhang Jiaming, & Jiang Weiyu. (2023). The difference and clinical application of modified thoracolumbar fracture classification scoring system in guiding clinical treatment. Journal of Orthopaedic Surgery and Research, 18(1), 1–8. https://doi.org/10.1186/s13018-023-03958-4
Parva Paydar, Shole Ebrahimpour, Hanieh Zehtab Hashemi, Mehdi Mohamadi, & Soha Namazi. (2023). Design, Development, and Evaluation of an Application based on Clinical Decision Support Systems (CDSS) for Over-The-Counter (OTC) Therapy: An Educational Interventions in Community Pharmacists. Journal of Advances in Medical Education and Professionalism, 11(2), 95–104. https://doi.org/10.30476/jamp.2022.95843.1661
Shujuan Cao, Rongpei Zhang, Aixin Jiang, Mayila Kuerban, Aizezi Wumaier, Jianhua Wu, Kaihua Xie, Mireayi Aizezi, Abudurexiti Tuersun, Xuanwei Liang, & Rongxin Chen. (2023). Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening. BioMedical Engineering OnLine, 22(1), 1–13. https://doi.org/10.1186/s12938-023-01097-9
New technology—and the application of existing technology—only appears in healthcare settings after careful and significant research. The stakes are high, and new clinical systems need to offer evidence of positive impact on outcomes or efficiencies.
Nurse informaticists and healthcare leaders formulate clinical system strategies. As these strategies are often based on technology trends, informaticists and others have then benefited from consulting existing research to inform their thinking.
In this Assignment, you will review existing research focused on the application of clinical systems. After reviewing, you will summarize your findings.
To Prepare:
Review the Resources and reflect on the impact of clinical systems on outcomes and efficiencies within the context of nursing practice and healthcare delivery.
Conduct a search for recent (within the last 5 years) research focused on the application of clinical systems. The research should provide evidence to support the use of one type of clinical system to improve outcomes and/or efficiencies, such as “the use of personal health records or portals to support patients newly diagnosed with diabetes.”
Identify and select 5 peer-reviewed articles from your research.
The Assignment: (4-5 pages)
In a 4- to 5-page paper, synthesize the peer-reviewed research you reviewed. Be sure to address the following:
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Identify the 5 peer-reviewed articles you reviewed, citing each in APA format.
Summarize each study, explaining the improvement to outcomes, efficiencies, and lessons learned from the application of the clinical system each peer-reviewed article described. Be specific and provide examples.
References:
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Chapter 14, “The Electronic Health Record and Clinical Informatics” (pp. 267–287)
Chapter 15, “Informatics Tools to Promote Patient Safety and Quality Outcomes” (pp. 293–317)
Chapter 16, “Patient Engagement and Connected Health” (pp. 323–338)
Chapter 17, “Using Informatics to Promote Community/Population Health” (pp. 341–355)
Chapter 18, “Telenursing and Remote Access Telehealth” (pp. 359–388)
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–https://www.healthit.gov/faq/what-electronic-health-record-ehr
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https://class.waldenu.edu/bbcswebdav/institution/USW1/202030_27/MS_NURS/NURS_5051_WC/artifacts/USW1_NURS_5051_Dykes.pdf
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You must proofread your paper. But do not strictly rely on your computer’s spell-checker and grammar-checker; failure to do so indicates a lack of effort on your part and you can expect your grade to suffer accordingly. Papers with numerous misspelled words and grammatical mistakes will be penalized. Read over your paper – in silence and then aloud – before handing it in and make corrections as necessary. Often it is advantageous to have a friend proofread your paper for obvious errors. Handwritten corrections are preferable to uncorrected mistakes.
Health information technology is continuously adapting to attain the desires of health care providers. Today, the health informatics has contributed to drastic impacts on the health care delivery by efficient, high quality, and cost effective care, which leads to greater patient satisfaction (Scott et al., 2017). One of the risks associated with these technologies in the health care facility is potential errors and failures in handling this equipment.
The potential benefit associated with safety in healthcare information technology is the security and privacy features of the technology. However, the risk associated with technologies is the possibility of privacy breach by unauthorized users. Regarding the benefits on legislation, these technologies meet the FDA protocols on the electronic medical applications because it is anchored on patient safety. However, the evolving nature of health care implies that legislation on healthcare technologies can change anytime, which is a potential risk on legislation. Finally, the potential benefit of health information technologies on patient care include facilitation of effective communication through direct channeling of calls and alarms by patients to nurses and also secure texting and phone calls for health care providers among health care providers, which result in increase safety, more patient satisfaction, and high quality care (Kruse & Beane, 2018). However, the potential risk is possibility of insufficient and unreliable information that can misinform providers to deliver wrong care to patients.
The most promising health care technology is electronic health records (EHR). Since health care system is characterized by extensive data usage, EHR is valuable in sharing all the patient data to the right providers and help in fostering the process of health care provision by helping the clinicians to make informed decisions on patient care (Lim et al., 2018). Eventually, EHR is promising in ensuring safe, quality, and efficient health care.
References
Kruse, C. S., & Beane, A. (2018). Health information technology continues to show positive effect on medical outcomes: systematic review. Journal of medical Internet research, 20(2), e41.
Lim, M. C., Boland, M. V., McCannel, C. A., Saini, A., Chiang, M. F., Epley, K. D., & Lum, F. (2018). Adoption of electronic health records and perceptions of financial and clinical outcomes among ophthalmologists in the United States. JAMA ophthalmology, 136(2), 164-170.
Scott, P. J., Cornet, R., McCowan, C., Peek, N., Fraccaro, P., Geifman, N., … & Williams, R. (2017). Informatics for Health 2017: Advancing both science and practice. Journal of innovation in health informatics.
Use a standard 10 to 12 point (10 to 12 characters per inch) typeface. Smaller or compressed type and papers with small margins or single-spacing are hard to read. It is better to let your essay run over the recommended number of pages than to try to compress it into fewer pages.
Likewise, large type, large margins, large indentations, triple-spacing, increased leading (space between lines), increased kerning (space between letters), and any other such attempts at “padding” to increase the length of a paper are unacceptable, wasteful of trees, and will not fool your professor.
The paper must be neatly formatted, double-spaced with a one-inch margin on the top, bottom, and sides of each page. When submitting hard copy, be sure to use white paper and print out using dark ink. If it is hard to read your essay, it will also be hard to follow your argument.
ADDITIONAL INSTRUCTIONS FOR THE CLASS
Discussion Questions (DQ)
Initial responses to the DQ should address all components of the questions asked, include a minimum of one scholarly source, and be at least 250 words.
Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source.
One or two sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words.
I encourage you to incorporate the readings from the week (as applicable) into your responses.
Weekly Participation
Your initial responses to the mandatory DQ do not count toward participation and are graded separately.
In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies.
Participation posts do not require a scholarly source/citation (unless you cite someone else’s work).
Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week.
APA Format and Writing Quality
Familiarize yourself with APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required).
Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation.
I highly recommend using the APA Publication Manual, 6th edition.
Use of Direct Quotes
I discourage overutilization of direct quotes in DQs and assignments at the Masters’ level and deduct points accordingly.
As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content.
It is best to paraphrase content and cite your source.
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Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of s
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