The following resources will be used to complete your assignment:
- Taking screenshots
- Windows 10: Snipping Tool or Snip & Sketch
- MacOS: Grab tool
- Microsoft Word
This week you will finalize the draft of your proposal. You are expected to use a copy of your Week 3 file before you start finalizing the research proposal. Below are the areas and features you are expected to include as part of your document:
- The duplicated document should include at least one table that includes data with 3pt only for the border and a shadow. For example, you could provide a list of options with costs, or some high-level statistics about the topic you have chosen.
- Insert at least 2 images and apply a square type of wrap to them.
- Make sure your previous research and citations are added by using the citation and research tools covered during this week.
- Add a comment, using the comment feature, to the table section and explain why this table is important for your document. Enable track changes so you can review any changes made by the committee.
- Create a short letter to the committee explaining the benefits of this research. Download the attached file of(Links to an external site.) (Links to an external site.) to use for your mail merge. The following fields should be used as part of the letter. Save all the letters generated as a PDF document.
- One of the members of the committee does not speak English. Using the translation tools you have learned about, provide a document with a screenshot and instruction that shows how to translate a document.
Once you have completed all these tasks, you should have three different documents, that should be named as follows:
- The finalized research document in Word. Name file as “YourName_COMP150_W4_Assignment”
- The PDF document with all mail merge recipients. Name file as “YourName_COMP150_W4_Assignment_MailMerge”
- The document with screenshots showing how to translate it. Name file as “YourName_COMP150_W4_Assignment_translation”
Artificial Intelligence in Mental Healthcare
|We find ourselves in the age of the fourth industrial revolution. The new age is also called the digital revolution. Herein, humankind simply fuses various technology types. One of the contemporary forms of technology is artificial intelligence. Originally recognized in 1956, artificial intelligence has gradually evolved over the years (Graham, et al., 2019). This technology is widely adopted in numerous industries. However, the healthcare practice has been somewhat reluctant to embrace AI, especially in the field of mental health. Regardless of prevailing concerns, AI applications in healthcare are slowly increasing. Existing literature shows that there is great potential for adoption of intelligent machines and technologies in the healthcare practice.
John McCarthy coined the term artificial intelligence in the 1950s. He described AI as “the science and engineering of making intelligent machines” (Graham, et al., 2019). Current applications of AI in healthcare include discovery of novel treatments, optimization of treatment or medication dosages, assessment of disease progression, and early disease detection (Graham, et al., 2019). AI is embedded with capabilities to process and analyze a large dataset; a skill necessary in healthcare practice. This skill has been leveraged successfully in medical research, cancer detection, radiology, and ophthalmology (Graham, et al., 2019). Albeit it has been relatively easy to adopt AI in physical healthcare operations, it has been challenging to do the same in mental healthcare. Notably, mental healthcare requires a more hands-on approach and direct interactions between the care provider and patients (Graham, et al., 2019). In addition, mental health relies on softer skills. AI still bears numerous benefits for mental healthcare.
Benefits of AI in Mental Healthcare
Artificial intelligence can help improve access to mental care, reduce costs of care, enhance clinical outcomes, improve patient flow, enhance effectiveness of pre-diagnosis screening tools, and identify the most viable treatment options (Cecula, et al., 2021). With regard to patient flow, research shows that AI can help healthcare facilities to manage the flow of patients in an efficient and effective manner, thereby reducing the waiting time (Cecula, et al., 2021). Improve patient flow also helps to improve patient satisfaction levels. In addition, mental healthcare can benefit from the big-data capabilities of AI. Mental healthcare providers can leverage these capabilities to formulate risk models and increase effectiveness of pre-diagnosis screening tools in identifying a person’s predisposition to a given mental condition (Graham, et al., 2019).
Costs of AI
As with any other technology, the initial cost of implementing AI in healthcare is steep. This is because of additional costs incurred through training and acquisition of supporting infrastructure (Bohr & Memarzadeh, 2020). However, researchers argue that the potential costs savings of AI could help healthcare facilities recoup their initial investments. For instance, it is estimated that AI applications will help save the American healthcare system $150 billion every year, by 2026 (Bohr & Memarzadeh, 2020). The cost savings are mainly because AI helps shift healthcare from a reactive approach to a pro-active approach. Consequently, it would help reduce the number of hospital visits and need for treatment. Additionally, this technology can help develop more effective, tailored treatments and efficient follow-ups.
|Types of Cost Savings
|Administrative workflow assistance
|Dosage error reduction
Reasons to Implement AI in Mental Health
There are many reasons to implement AI in mental health. These include:
- It has potential to improve patient flow and enhance the process of patient care
- AI can help increase accessibility and affordability of mental healthcare across the country.
- Research shows that AI can help reduce healthcare costs through development of better treatment plans and enhancements in pre-diagnosis
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