Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives
Abstract
:1. Introduction
2. Related Works
3. Methods
3.1. Study Design
3.2. Participant Selection
Each participant self-reported having worked with, helped, or supported a patient with DCCs and is knowledgeable about and comfortable using emerging technologies. Very Knowledgeable participants include those who manage DCC cases daily and have over ten years of experience. Knowledgeable participants have experience with DCC patients, though it is not their primary role.
3.3. Interviews
- Perspectives on the role of AI in healthcare;
- Experiences with AI-generated recommendations;
- Assessment of AI accuracy, relevance, and usability;
- Concerns about AI integration into clinical workflows;
- AI’s potential to support clinical decision-making and patient care.
3.4. Data Analysis
4. Results
4.1. Empathy in AI-Patient Interactions
4.1.1. Positive Impact on Patient Outcomes and Experience
“A lot of people do not trust doctors who are very cut and dry, and have very poor bedside manner. Because it will make the patient think, ‘Oh, they don’t actually care about me. They’re very robotic, and they’re only doing their job. And they don’t really care about me as a patient, and the kind of care that I need.’’PP4
“It’s hard to overstate how crucial empathy is. Removing it could diminish patient health outcomes just as much as neglecting the medical treatment itself.”HP2
“With my chronic migraines, there was one nurse who sat me down and explained everything, making me feel truly comfortable.”PP3
4.1.2. Risks of Artificial Empathy
“Patients may actually emotionally develop a relationship with this AI…just actually never see anybody.”HP5
“…there’s a lot of give and take with body language, voice tone, interaction that you pick up. As you know, like you and I. Okay. And if I was just typing in the responses, you would have a different take. Now, if the computer, the AI is responding to input that has no personality or human characteristics, but it’s responding as if it’s human, It’s misleading.”HP5
4.1.3. AI’s Role in Sensitive Diagnoses
“We cannot deliver a diagnosis like HIV without a personal conversation. Some people have harmed themselves after receiving such news through a phone call.”HP9
4.2. AI-Assisted Administrative and Clinical Tasks
4.2.1. Augmenting the Healthcare Workforce
“Start with replacing things that doctors don’t want/like to do. Such as dictation or generation of notes. Will give doctors more time to spend with patients.”HP3
“Doctors should take advantage of AI. If you’re able to utilize AI to become a more efficient doctor and utilize it in your career, that will make you stand out. It’s actually just helping doctors be more efficient.”HP6
4.2.2. Improving Workflow Efficiency
“We use AI to look at interactions. We use AI if you have a patient that has 7, 8, 9 drugs. It’s so difficult to know each single interaction with each of them. So AI helps us crunch that out, you know, certain illness, certain conditions, you know, that we may not be aware.”HP9
“AI could pull a patient’s health record from their EHR to see the patient as a whole.”HP4
4.2.3. Enhancing Patient Education
“AI could enhance patient adherence by sending reminders for appointments and tests, along with the rationale for why these steps are crucial in their care.”HP4
“Patients might not have transportation. They might not have the funds. So having access to a tool online, like medical AI, to help them diagnose whatever condition they have that’d be very helpful. Also, because medical AI can be distributed very widely to people who don’t have funds to see a primary care doctor.”HP1
4.3. Challenges and Opportunities in AI-Driven Healthcare
4.3.1. Skepticism About AI’s Capabilities
“I think it can be extremely helpful. I don’t think it should drive decisions necessarily, but it could work with doctors to make their work more efficient.”PP1
“It’s a good start. Especially researching different things to bring up to your doctor. But, I will always stand by the fact of going about getting medication and discussing treatments and conditions should always lead back to a doctor.”PP3
4.3.2. Reducing Bias and Healthcare Disparities
“AI doesn’t have empathy, which allows it to assess the situation more objectively.”PP2
“AI’s ability to provide unbiased information could be a key factor in reducing care disparities.”HP9
“Make the response easy to read and understand, at a fifth-grade reading level.”HP1
5. Discussion
5.1. Integrating Emotional Responsiveness in AI-Driven Healthcare Decision-Making
5.2. AI for Administrative Efficiency and Its Integration into Providers’ Workflow
5.3. Using AI to Improve Patient Education and Address Healthcare Disparities
5.4. Emerging Trends and Implementation Strategies
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Participant | Medical Position | Medical Specialty | Knowledge About DCCs |
---|---|---|---|
HP1 | Director of integrated health | Family Medicine | Very knowledgeable |
HP2 | Medical doctor | Internal Medicine | Knowledgeable |
HP3 | Medical doctor | Family Medicine | Very knowledgeable |
HP4 | Medical doctor | Family Medicine | Very Knowledgeable |
HP5 | Medical doctor | Internal medicine | Knowledgeable |
HP6 | Medical doctor | Family Medicine | Very knowledgeable |
HP7 | Psychiatrist | Internal medicine | Knowledgeable |
HP8 | Physician assistant | Internal medicine | Knowledgeable |
HP9 | Physician assistant | Family Medicine | Very knowledgeable |
HP10 | Psychiatrist | Geriatric | Knowledgeable |
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Ongwere, T.; Nguyen, T.V.; Sadowski, Z. Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives. Information 2025, 16, 237. https://doi.org/10.3390/info16030237
Ongwere T, Nguyen TV, Sadowski Z. Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives. Information. 2025; 16(3):237. https://doi.org/10.3390/info16030237
Chicago/Turabian StyleOngwere, Tom, Tam V. Nguyen, and Zoe Sadowski. 2025. "Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives" Information 16, no. 3: 237. https://doi.org/10.3390/info16030237
APA StyleOngwere, T., Nguyen, T. V., & Sadowski, Z. (2025). Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives. Information, 16(3), 237. https://doi.org/10.3390/info16030237