Weeks 3 and 4 Discussion: Peer Review of Draft Part 2
Perhaps the most helpful tool in developing a logical, readable draft of an essay is a peer review. A peer review is effective for both the reviewer and the writer being reviewed: The reviewer develops close-reading skills and learns to deliver feedback constructively, and the writer learns that effective writing can communicate its message to diverse readers.
For this discussion, you will provide and receive peer review comments on drafts of part 2.
Using the “C.A.R.E.S” table below to guide your review, please provide detailed and thoughtful peer review comments for your classmate.
Remember these general rules when you are the reviewer:
Attend to higher-order concerns first: thesis, audience, purpose, organization, and development (support).
Attend to lower-order concerns next: sentence structure, punctuation, word choice, and spelling.
Make comments in the spirit of helpfulness (and if you’re the writer, take comments in the spirit of helpfulness). The least helpful comment a peer reviewer can offer is, “It looks good to me.”
Use the following table to help you review your peers’ work. Please download the peer review table in MS Word format, complete it, and attach it to your post when you reply to a student whose paper you have reviewed.
Here are a few examples of suggestions to make as a peer reader (make sure you identify which paragraph you’re discussing):
The thesis needs to better identify the main point of the essay.
The topic sentence needs to identify the main point of this paragraph.
Each paragraph needs one overarching idea (or needs more specific evidence/needs more of the writer’s commentary).
Avoid repeating words, repeating ideas, or repeating sentence structures.
Avoid using vague language or using slang.
Clearly identify your sources (if used).
Trends in the Health Information management and technology Industry
An Important Emerging Issue
In the evolving landscape of health information management, one significant emerging issue is the integration of artificial intelligence (AI) in electronic health records (EHRs). This development is crucial for enhancing decision-making processes and improving patient outcomes. AI’s ability to process vast amounts of data quickly and accurately offers unprecedented opportunities for predictive analytics, which can inform treatment plans and predict patient outcomes with greater precision (Jiang et al., 2017). The integration of AI in EHRs is a transformative step that can lead to more personalized and efficient healthcare services.
Integrating AI
Integrating AI into EHRs is a significant step toward revolutionizing healthcare management and improving patient outcomes. While challenges such as costs and ensuring data privacy exist, the potential benefits make it a worthy investment. AI’s ability to process vast amounts of data quickly and accurately offers unprecedented opportunities for predictive analytics, which can inform treatment plans and predict patient outcomes with greater precision (Jiang et al., 2017). For instance, AI can identify patterns in patient data that may be overlooked by human practitioners, thereby facilitating early diagnosis and intervention. This aligns with the broader trend of digital transformation in healthcare, promising to improve clinical decision-making and patient care. Implementing AI-driven EHRs will require addressing technical challenges and ensuring data privacy and security, but the benefits far outweigh the potential drawbacks.
Other organizations and experts have recognized the potential of AI in healthcare, as evidenced by its growing adoption in various medical fields such as radiology and pathology. For example, AI has been used in radiology to detect abnormalities in medical images with high accuracy (Jiang et al., 2017). This not only improves diagnostic accuracy but also speeds up the diagnostic process, allowing for quicker treatment decisions. Similarly, in pathology, AI algorithms are being developed to analyze tissue samples and identify cancerous cells more accurately than traditional methods (McLeod & Dolezel, 2018). These examples illustrate the transformative potential of AI in healthcare and support the argument for its integration into EHRs. By leveraging AI technology, healthcare organizations can enhance their ability to provide personalized and efficient care, ultimately leading to better patient outcomes and a more effective healthcare system.
Counterarguments
Some might argue that the cost of implementing AI technologies in EHRs is prohibitive, especially for smaller healthcare providers. However, the long-term benefits, such as reduced healthcare costs through improved efficiency and better patient outcomes, justify the initial investment. Moreover, AI-driven EHRs can help mitigate the rising costs associated with chronic diseases by enabling more proactive and preventive care.
Conclusion
Integrating AI in EHRs is a significant step toward revolutionizing healthcare management and improving patient outcomes. While there are challenges to this integration, including costs and ensuring data privacy, the potential benefits make it a worthy investment. As healthcare continues to digitize, the role of AI in enhancing decision-making processes will become increasingly critical. Future research should focus on optimizing AI algorithms for healthcare applications and developing robust security measures to protect patient data.
References
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. doi:10.1136/svn-2017-000101
McLeod, A., & Dolezel, D. (2018). Cyber-analytics: Modeling factors associated with healthcare data breaches. Journal of Healthcare Information Management, 32(1), 123-134. doi:10.1097/HIM.0000000000000226
Wosik, J., Fudim, M., Cameron, B., Gellad, Z. F., Cho, A., Phinney, D., … & Katz, J. N. (2020). Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association, 27(6), 957-962. doi:10.1093/jamia/ocaa067
