The robot butler: How and why should we study predictive algorithms and artificial intelligence (AI) in healthcare?
Iben Mundbjerg Gjødsbøl1, Anna Kirstine Ringgaard2, Peter Christoffer Holm3
1Department of Public Health, Centre for Medical Science and Technology Studies, University of Copenhagen, Copenhagen, Denmark.
Digital Health
|March 26, 2024
Summary
Artificial intelligence (AI) in healthcare can aid aging populations, but understanding how clinicians use AI tools like the PMHnet algorithm is crucial for effective implementation and patient care.
Area of Science:
- Medical Informatics
- Health Services Research
- Clinical Decision Support
Background:
- Healthcare systems face increasing demands from aging and multi-morbid populations.
- Artificial intelligence (AI) and algorithms are proposed as solutions to resource scarcity in healthcare.
- Predictive algorithms offer potential to manage complex patient needs.
Purpose of the Study:
- To investigate the presentation and reception of the PMHnet algorithm, an AI prognostication tool for ischemic heart disease patients, among Danish cardiologists.
- To explore how clinicians respond to novel AI-driven decision-support tools in a hospital setting.
Main Methods:
- Ethnographic fieldwork was conducted within the Danish-led PM Heart research project.
- The study focused on the implementation of the PMHnet algorithm into electronic health records in Eastern Denmark.
- Qualitative data collection centered on cardiologists' experiences and perspectives.
Main Results:
- Identified three key themes in AI implementation: human-algorithm agency/autonomy, accountability in prognostication, and the association-causation link in predictive algorithms.
- Highlighted the need to understand cardiologists' hopes and concerns regarding AI adoption.
- Generated methodological questions for future research on AI in healthcare.
Conclusions:
- Local, qualitative investigations are essential to grasp the real-world implications of AI algorithms in clinical practice.
- Understanding how AI tools are used, embraced, or contested is vital for assessing their impact on clinicians, patients, and healthcare delivery.
- The study emphasizes the need for nuanced understanding beyond intended consequences of AI in healthcare.
Related Concept Videos
01:29Issues And Trends In Healthcare Delivery System
5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
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
01:30Health Information Technology and Healthcare Information System
837
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
837
01:18Steps in Outbreak Investigation
126
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
126
01:21Cancer Survival Analysis
345
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
345
01:19Overview of Biostatistics in Health Sciences
388
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
388