Related Experiment Video
Updated: Jun 30, 2025
07:13
Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
Published on: April 12, 2021
4.3K
Physicians' and Patients' Expectations From Digital Agents for Consultations: Interview Study Among Physicians and
Andri Färber1,2, Christiane Schwabe2, Philipp H Stalder1
1ZHAW School of Management and Law, Zurich University of Applied Sciences, Winterthur, Switzerland.
JMIR Human Factors
|March 18, 2024
Summary
Digital agents show potential to enhance patient consultations and health literacy, but require careful design. Patients and physicians are open to AI support, though reservations exist for all three tested agents.
Area of Science:
- Digital Health
- Artificial Intelligence in Healthcare
- Human-Computer Interaction
Background:
- Physicians face administrative burdens, limiting patient consultation time.
- Reduced consultation time negatively impacts health literacy, shared decision-making, and treatment adherence.
Purpose of the Study:
- To explore the potential of generative artificial intelligence (AI) digital agents to improve patient consultations, treatment adherence, and health literacy.
- To gather patient and physician opinions on three distinct AI digital agent prototypes: a silent digital expert, a communicative digital expert, and a digital companion (DC).
Main Methods:
- In-depth interviews were conducted with 25 patients and 22 physicians from diverse backgrounds.
- Interview transcripts were analyzed using deductive coding with MAXQDA software.
- Data was summarized, clustered, and interpreted to identify key themes and requirements.
Main Results:
- Patient and physician feedback was categorized across three consultation phases: during consultation (silent/communicative experts), handover to a DC, and post-consultation support (DCs).
- Both patient and physician groups expressed openness to digital support but also voiced reservations regarding all three tested AI agents.
- Key themes emerged regarding the integration and function of AI agents within the patient care journey.
Conclusions:
- Nine essential requirements for designing effective digital agents to support consultations, treatment adherence, and health literacy were identified.
- Findings highlight the need for user-centered design principles when developing AI tools for healthcare settings.
- The study underscores the potential of AI to address current limitations in patient-physician interactions and improve health outcomes.
Keywords:
adherence to treatmentdigital agentseHealthelectronic medical recordshealth literacymHealthmobile healthmobile phoneMore Related Videos
Related Concept Videos
01:31Confirmation Biases
5.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.5K
01:19Contact-dependent Signaling
44.6K
Contact-dependent signaling, as the name suggests, requires that communicating cells be in direct contact with each other. This is achieved either through receptor-ligand interactions or by specialized cytoplasmic channels that allow the flow of small molecules between cells. In animal cells, channels called gap junctions facilitate contact-dependent signaling in certain tissues, whereas, plasmodesmata perform a similar function in plants.
Gap Junctions
In animal cells, gap junctions are formed...
Gap Junctions
In animal cells, gap junctions are formed...
44.6K
01:18Guidelines and Strategies for Safe Computer Charting
803
The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
Maintain Confidentiality and Security:
803
01:15Expected Value
3.9K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.9K
01:14SBAR II: Application of SBAR
4.4K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
4.4K
01:30Current Trends in Nursing II
1.2K
Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
1.2K