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I chose to research AI in the healthcare industry, as I am pursuing a healthcare social work career, and I’m interested to see how AI may affect my future career field. I’m mostly interested in how much I may interact with AI as a healthcare social worker, because while social work heavily relies on human interaction, I know AI is heavily relied on for data management and organization which is also an essential component to social work.

I will be researching and analyzing AI in healthcare by focusing specifically on Suki, a healthcare virtual assistant, Epic Systems, a clinical decision support system, and Quench SmartChat, a healthcare data management system. I’ll then be discussing some expected future trends in AI development generally and in healthcare, AI’s societal impact, as well as some ethical considerations. I'll finally reflect on my research and pair it with my personal insight of AI in healthcare. There will additionally be an overview video to reference all the content I'll be producing.

AI in healthcare is a significant topic for me to research to see how I can leverage AI in my future career, but also to be informed and aware of its ethical concerns to avoid disparities or conflicts in client care.

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Suki Assistant is an advanced and health system-approved AI healthcare virtual assistant that generates notes ambiently, performs dictation, recommends codes, and answers medical questions (Suki, 2017).

Suki and many healthcare virtual assistants use natural language processing, machine learning interfaces, AI algorithms, and image recognition to perform functions such as patient triage, conducting intakes, process documents, and even detect emotions (Glorikian, 2021; Teo, 2022).

Healthcare virtual assistants like Suki are more beneficial compared to human staff as they are available 24/7, and they can manage more data with better efficiency (Glorikian, 2021). These assistants also allow medical professionals to allocate their time and expertise for more complex tasks, rather than being occupied with simple tasks (Teo, 2022). These assistants also have improved active patient engagement, so AI responses are more human-like (Teo, 2022), and the patient is more engaged and motivated in managing their own health (Glorikian, 2021).

Healthcare virtual assistants are limited as they are not always reliable, and mistakes can pose significant health risks such as improper triage or treatment (Glorikian, 2021). There are additional ethical concerns with privacy and data security risks (Glorikian, 2021). AI robots also cannot replace the essential healthcare components of empathy, critical thinking, and human connection that is usually produced by human professionals (Glorikian, 2021).

I understand now that many healthcare professionals use virtual AI assistants like Suki as a bridge in communication between patients and themselves. To connect this back to my interest in being a healthcare social worker, I can see how these AI assistants can be helpful in performing initial patient intakes that I could leverage when facing new cases, considering AI assistants are capable of providing mental health services. Though I would not choose to fully rely on AI assistants for the actual care of my clients as a social worker, as it eliminates the essential component of human interaction in mental health services. But it could be a useful tool to collect initial information like demographics and symptoms.

Video about healthcare virtual assistants: https://www.youtube.com/watch?v=V4oztpQFabM

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Epic Systems is an AI clinical decision support system used widely in medical facilities to provide and manage care for patients, while also striving to improve future health rates (Epic, 1979).

Epic Systems and other clinical decision support systems use machine learning, statistical pattern knowledge, radio-frequency identification, and knowledge-based and non-knowledge-based imaging to perform functions including accurate diagnostics, medication administration, and taking and interpreting radiological images (Sutton et al., 2020).

AI Clinical decision support systems improve clinical documentation accuracy and interpretation while reducing clinician costs and patient stay times (Sutton et al., 2020). There have also been improvements in safety softwares including for dosing, duplication of therapies, and allergies to reduce medication errors (Sutton et al., 2020).

Though medication errors are still common with clinical decision support systems, including harmful combinations (Sutton et al., 2020). These systems also incorporate negative physician biases, poor accuracy, and poor system integration which can disrupt clinician workflow (Sutton et al., 2020).

To connect my research back to my interest in healthcare social work, I can see how social workers may use clinical decision support systems like Epic. I am pursuing a career as a Licensed Clinical Social Worker, which includes diagnosing clients and forming treatment plans. I see social workers using clinical decision support systems to suggest diagnostics and treatment as AI could assist me in interpreting pt data and info, but ultimately I would use my knowledge and expertise to decide patient care. I don’t think I could fully trust AI enough to make complete decisions for my patients without overseeing it’s work, which is counterproductive.

Video about clinical decision support systems: https://www.youtube.com/watch?v=2VvK03eKhL8

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Quench SmartChart is a physician engineered AI system that functions to organize and analyze medical documents at an efficient rate (Quench).

Quench SmartChart and other AI healthcare data management systems usesAI algorithms, natural language processing, and machine learning to perform functions including identifying and correcting manually-inputed data errors, handle data such as medical documents, and analyze historical data to detect and predict patterns in healthcare (Putty, 2025).

AI healthcare data management improves staff utilization and efficiency by performing simple organizational tasks, reduces the risk of manual data entry errors for healthcare and insurance information, and improves interoperability which reduces stress for patients and healthcare staff (Laserfiche, 2025).

AI managing mass volumes of personal and sensitive data poses concerns about personal data security including risks to informed consent, privacy, protection, ownership, objectivity, and transparency (Jiang et al., 2021). Also, as AI collects, analyzes, and interprets data from research articles, that can then be used by healthcare professionals, this data is susceptible to bias which makes healthcare professionals susceptible to implementing bias into their pt care (Jiang et al., 2021).

To connect this back to my personal interest in healthcare social work, I can see AI healthcare data management systems like QuenchSmart being a very useful tool. Out of all the examples of AI usage I researched, I would feel the most comfortable relying on Quench SmartChart. Data management seems to be more at risk of error when being handled by humans compared to AI technologies, and I can see it being useful in managing patients at a bigger and more efficient rate. I am also aware that social workers devote a lot of their time to paperwork managing pt info, therefore AI data management can assist me in allocating more of my time and cognitive energy to in-person patient interaction.

Video about data management systems: https://www.youtube.com/watch?v=LZb3RZJBmsM

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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As AI develops, it will grow to increasingly complete complex tasks such as recommending personalized prevention and treatment strategies, as well as provide healthcare professionals with increasingly helpful data they can implement into patient care(Glorikian, 2021; Putty, 2025). Teo (2022) predicts that the revenue for the AI medical market will increase at a compound annual growth rate of 26.29% between 2019-2029.

Keskinbora (2019) suggests utilizing ethicists, philosophers, scientists, and well rounded researchers to implement ethical safeguards into the development of AI. Making innovation policies is important to preserve human dignity, identity, safety, justice and equality in the development of AI (Keskinbora, 2019).

In order for AI to become increasingly functional in healthcare settings, it needs the increasing ability to collect mass amounts of data at an efficient rate while also prioritizing transparency, credibility, reliability, and recoverability (Keskinbora, 2019).

I believe stricter ethical laws and policies being put in place can combat the ethical risks of AI. With AI being relatively novel, there are various uncertainties being raised as it continues to fastly develop, and the lack of strict policies allows for the violation of ethical norms and values. I believe AI is being developed at a rate too fast, making it difficult for experts like ethicists, philosophers, psychologists, and scientists to do damage control. I overall think it is crucial for AI developers and deployers to prioritize ethical safeguards sooner than later.

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AI’s knowledge reflects inequalities and biases in society, therefore AI can reinforce stereotypes and discrimination. (Roy, 2024). It's important to implement technology that is trained to detect biases in the data that AI relies on to function, considering AI is being implemented into significant societal sectors such as healthcare, the workplace, and government (Roy, 2024).

AI has the ability to replace workers whose jobs or tasks are considered redundant or easily automated due to their repetitive nature (Roy, 2024). It's important for society to establish boundaries with AI so its incorporated as a collaborator and not a replacement, and also so the superiority of humanity isn't at risk (Roy, 2024).

After researching the societal impacts in AI some of my existing views on AI have been reinforced, while some of my other existing views have changed. I’m normally completely against using AI, but I’ve learned several ways AI can be an extremely helpful tool in healthcare and in society overall. Though I am still concerned about humans relying so much on AI that there may be power shifts in our society, with AI becoming more significant than us humans. I think AI can be helpful, but we shouldn’t use it to such extremes that we cannot function without it, and there needs to be a better balance between financial and ethical initiatives to create a healthy relationship with AI.

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Throughout this course, I have heard of AI technology tools such as machine learning and natural language processing, but I never knew what the function of those tools were. After researching specific technologies, I have a better understanding of the functioning and purpose of different AI technologies.

I struggled heavily with finding challenges and limitations of AI Healthcare Data Management like Quench SmartChart. With data management not only being a redundant task, but also very susceptible to human-error, there is not much literature on its challenges, as it is widely seen as a solution to a profound issue in healthcare. To combat my issue, I researched limitations of AI in healthcare in general and found ways these concerns could apply to healthcare data management.

After researching AI in the healthcare industry, I believe AI has an important place in healthcare to enhance healthcare systems’ efficiency, accuracy, and productivity. Though I believe there should be some boundaries to what healthcare functions AI can replace, as I think there are some functions and tasks that should be done with human leading or supervision to ensure patients receive proper and optimal care. Overall, I believe AI should be leveraged as a tool in healthcare, but it should not be blindly relied on to avoid health risks.

Before this research, I thought of AI as a dangerous tool for healthcare, as I did not see how artificial intelligence could possibly produce better health outcomes than humans with years of expertise. While I still think this for certain roles in healthcare systems, I now understand after research that healthcare uses AI as a tool for some redundant functions that actually mitigates human error as well as maximizes human attention and time. This research also showed me how AI is already being utilized in healthcare to much more of an extent than I was aware of. Overall, this research has shifted my perspective on AI’s place in healthcare, but I still believe there needs to be a balance between optimizing AI’s benefits in healthcare and being cautious about AI’s limits posing health risks.

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BMC. (2018). Using metadata to help translate clinical research into better healthcare. Youtube.

I chose this resource because it explains how metadata, an essential component of AI, can advance clinical research which can then advance healthcare

Epic. (1979). Epic Systems Corporation. https://www.epic.com/

I chose this resource because it is

Glorikian, H. (2021). Healthcare Virtual Assistants: A Prognosis for Improved Patient Outcomes. The Harry Glorikian Show.

I chose this resource because explains healthcare virtual assistants’ various applications, the benefits and limitations of its applications, as well as predictions for future trends

Jiang et al. (2021). Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. National Library of Medicine, 49(3).

I chose this resource because it explained general challenges and limitations of AI in the medical field, and I used it to connect to the risks of AI healthcare data management

Keskinbora, K. H. (2019). Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience, 64, pp. 277- 282.

I chose this resource because it suggested methods to consider ethics in the continuous development of AI, and I was able to connect this literature to considering ethics in the healthcare industry

La Plage Services. (2024). AI IN CLINICAL DECISION SUPPORT SYSTEMS. Youtube.

I chose this resource because it explains how AI can be used as a tool to support medical professionals in various ways

Laserfiche. (2025). How AI Is a Game Changer for Healthcare Data Management. Laserfiche.

I chose this resource because it explained AI use in healthcare data management as well as the many benefits it provides to healthcare data management

Prad, R. (2025). How AI is Enhancing Healthcare Data Management. Sayone.

I chose this resource because it provided an informational infographic about the important roles of AI in electronic health records, which I used to support my research on AI healthcare data management

Prajapati, J. B. & Prajapati, B. G. (2022). Clinical Decision Support System Braced with Artificial Intelligence: A Review. Springer Nature Link, pp. 531- 540.

I chose this resource because it provided a clear and simple description about how clinical decision support systems process data inputs

Putty, C. (2025). How AI is Enhancing Healthcare Data Management. thoughtful.ai.

I chose this resource because it thoroughly explained technologies like AI algorithms, natural language processing, and machine learning that are used in healthcare data management

Quench (n.d.). Quench SmartChart. https://www.projectquench.ai/?utm_term=ai%20medical%20records&hsa_acc=5851874646&hsa_cam=22123730367&hsa_grp=173723891419&hsa_ad=729276719177&hsa_src=g&hsa_tgt=kwd-2396204613341&hsa_kw=ai%20medical%20records&hsa_mt=p&hsa_net=adwords&hsa_ver=3&gad_source=1&gbraid=0AAAAAq1yWT0kS5BfScNa0-VlAFY2pZrD6

I chose this resource as it’s a direct source to learn about Quench SmartChat including its function, technology, and benefits

Roy, P. (2024). The Ethics of Intelligence Navigating AI’s Impact On Society. International Journal of Artificial Intelligence & Machine Learning, 3(1), pp. 155- 172.

I chose this resource because it dove into the benefits and threats of AI in the job market, as well as the challenges AI poses to our society in general

Staffingly. (2025). What are Healthcare Virtual Assistants and How Can They Save You Time. Youtube.

I chose this resource because it showed how AI can be helpful as a virtual assistant from a healthcare workers perspective

Suki AI (2017). Suki AI https://www.suki.ai/

I chose this resource as it was a direct source for my example to learn about Suki, it’s technologies, and it’s benefits

Sutton, T. R. et al. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. Npg digital medicine, 3(17).

I chose this resource because it was a well rounded article including a description of, the pros and cons, as well as the technology of clinical decision support systems

Teo, P. (2022). Healthcare Virtual Assistants: Use Cases, Examples & Benefits. KeyReply.

I chose this resource because it described healthcare virtual assistants, the technology they use, and its benefits