ARTIFICIAL INTELLIGENCE (AI) is making significant inroads into the medical field, and oncology is no exception. Today, I will discuss how I think AI will impact rural healthcare.
As a radiation oncologist, I’m witnessing firsthand how AI transforms various aspects of cancer care, from diagnosis and treatment planning to patient monitoring and outcome prediction.
AI enhances my ability to deliver precise and personalized radiation therapy, streamlines my workflow, and ultimately improves patient outcomes.
Today, I want to discuss how artificial intelligence may broadly impact healthcare for our rural friends.
A New Era of Healthcare
All right, perhaps not such a new era.
Stanford University (USA) researchers introduced the concept of artificial intelligence in the 1950s.
MYCIN was a computer-based machine-learning consultation system that assisted clinicians in diagnosing and treating patients with bacterial infections.
Learn about the history of artificial intelligence at Stanford here:
60 Years of Artificial Intelligence at Stanford
Entering its seventh decade of innovation in all things artificial intelligence, Stanford reflects on the people who…engineering.stanford.edu
AI and Mental Health
I think that artificial intelligence may accelerate diagnoses in mental health.
A prime example of an unmet need is for diagnosing type 1 bipolar disorder.
Listen to Dr. Kay Redfield Jamison, a professor of psychiatry at the Johns Hopkins School of Medicine (USA) and author of “Touched with Fire: Manic-Depressive Illness and the Artistic Temperament”:
“The average length of time between a person’s first episode and getting the correct diagnosis is eight years.”
While depression is a well-known condition, mania, a key aspect of bipolar disorder, often goes unnoticed. Bipolar disorder can significantly disrupt a person’s life, yet its symptoms may not be apparent to those around them.
I can only imagine the degree of distress and symptom misinterpretation.
AI and Bipolar Disorder
The use of machine learning techniques for the automatic diagnosis of psychiatric disorders, particularly bipolar disorder (BD), has garnered significant interest in both psychiatric and artificial intelligence fields.
These methods typically leverage biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data.
A review of 26 studies using traditional machine learning methods and deep learning algorithms to detect bipolar disorder automatically showed this:
- EEG studies were 90 percent accurate in diagnosing bipolar disorder.
- MRI imaging of the brain had an 80 percent accuracy.
- Deep learning techniques generally achieved accuracies higher than 95 percent.
Research applying machine learning to EEG signals and brain images has demonstrated the potential of this cutting-edge technology to assist psychiatrists in differentiating between individuals with bipolar disorder and healthy individuals.
Limited Healthcare Access in Rural Areas
Unfortunately, in America, individuals living in rural areas may not have access to doctors within hours of travel.
Recruiting (and retaining) sufficient clinicians in rural areas remains a tremendous demographic problem.
The number of medical school entrants from rural areas dropped by over one-quarter (28 percent) from 2002 to 2017.
This decrease signals that the rural provider shortages are likely to worsen in the future.
Limited Access Implications
Healthcare provider scarcity in rural areas harms these communities’ health and economic well-being.
The lack of available physicians has substantially increased appointment wait times, further exacerbating healthcare disparities.
Additionally, the closure of rural hospitals is forcing patients to travel significantly longer distances to receive basic medical care, with even greater distances required for specialized services such as addiction treatment.
Here are the numbers:
- Fewer physicians led to a 24 percent growth in wait times between 2004 and 2017.
- Hospital closures can force rural individuals to travel an average of 20 additional miles to receive common healthcare services and nearly 40 miles further to receive alcohol or drug treatment services.
This lack of access to timely and appropriate care can lead to delayed diagnoses, worsened health outcomes, and increased healthcare costs for rural residents.
Moreover, productivity takes a hit, with folks missing work or struggling to get child or other family care.
Add in the higher costs of travel, food, and lodging, and … well, you get the picture.
Especially for Women
The healthcare disparities for rural dwellers are particularly striking in women’s health, as only a small percentage of OBGYNs and neonatal care physicians practice in rural areas, despite these areas being home to a significant proportion of women of childbearing age.
Only 4.3 percent of obstetricians and gynecologists and 1.4 percent of neonatal care physicians reside in more rural areas, despite those areas being home to 10.6 percent of all women ages 15 to 49.
This mismatch between the need for and availability of specialized care significantly increases pregnancy risks and contributes to higher rates of maternal mortality in rural communities.
AI and Limited Healthcare Access in Rural Areas
I don’t think there is an easy fix for the healthcare provider shortages in rural areas.
Artificial intelligence may offer these folks a more convenient (and less expensive) option to access medical knowledge and advice.
Here are some examples:
- Virtual care: AI enables remote connection between patients and healthcare providers, a valuable resource for rural patients facing travel challenges.
- Remote patient monitoring: AI-powered devices alert patients and providers to changes in vital signs, enabling early intervention.
- Health education: AI tools provide rural patients with accessible health information through online platforms and mobile apps.
- Automated administrative tasks: AI streamlines administrative processes, allowing healthcare providers to focus more on patient care.
- Improved clinical decision-making: AI analyzes vast amounts of data to support healthcare providers in making informed decisions, leading to better patient outcomes.
- Personalized care: AI tailors care to individual patient needs, ensuring timely and effective treatment.
- Robust chat tools: AI enhances online chat experiences in healthcare, assisting with inquiries, treatment recommendations, prescription refills, and appointment scheduling.
I know that artificial intelligence (AI) will revolutionize my healthcare field.
Let’s hope AI makes medical care more accessible, affordable, and effective for all, including our rural counterparts.
Get an email whenever Dr. Michael Hunter publishes.
drmichaelhunter.medium.com.
Thank you for reading “How AI Will Impact Rural Healthcare.”
