Technology is transforming healthcare by systematically addressing and reducing health disparities across diverse population segments.
By David Martin, B.A.
Key Takeaways
Examining the different ways in which healthcare technology can address health disparities for underserved populations.
Assessing future potential considerations with AI advancements in reducing health disparities.
Considering potential risks associated with healthcare technology and how to navigate them successfully.
Exploring future areas for research and development in healthcare technology’s role in health equity promotion.
Introduction
Technological advancements have improved our lives in many ways. They have improved how we prepare food, drive, and watch movies. In healthcare, technology has aided in reducing health disparities across different segments of the population. A health disparity is a difference in health outcomes based on social, economic, and/or environmental disadvantage. It can arise from many factors, such as systemic discrimination, geographic barriers, and prejudice. Disparities can be based on class, race, culture, age, gender, and geographic location. However, technology has mobilized how we tackle health disparities and continues to do so. In this article, we will examine the reasons behind this phenomenon.
How Telemedicine Addresses Disparities
Virtual communication advancements via telemedicine have revolutionized how we deliver medicine. Patients now have the option of remotely communicating with their healthcare providers directly from the comfort of their homes so that they do not have to encounter potential traffic congestion or travel long distances for appointments. This not only streamlines the process but reduces travel time as well. Geographic barriers to healthcare access affect minorities, incarcerated individuals, and rural populations. Minorities, for example, tend to rely more on public transportation, and their professions can generally hinder their capacity to travel for specialized care. With telemedicine, these cumbersome transportation challenges can be successfully navigated to deliver equitable healthcare outcomes.
The Benefits of Text Messaging in Healthcare
In addition to telemedicine, HIPAA-compliant text message alerts have also demonstrated efficiency in improved patient responsiveness and reduced wait times. Now, patients can simply cancel appointments by text to expedite the process so that they do not have to place a call themselves or be on hold. They can also opt to receive reminder alerts to remind patients of appointments or prescription pickups, reducing missed appointments and uncollected prescriptions.
Furthermore, text messages not only alert patients of appointments and prescriptions but can also be a convenient tool to provide educational health material to patients who may not have traditional access to healthcare services, lack internet access, or simply lack time to search for that information. Moreover, non-native English speakers can significantly benefit from multiple-language text messages.
A study published by Software Advice shows that 20% of patients prefer to receive healthcare information by text rather than via patient portals.
Other IT-Based Solutions Using Data
Other methods can also demonstrate success in promoting health equity. Aggregate community-level data, for instance, can provide invaluable insights into a patient’s neighborhood, which is a key social determinant of health. This knowledge can then guide community-wide public health decision-making, such as ensuring that disenfranchised populations have direct access to nutritious foods, physical activity, and safe housing, among other considerations.
Harnessing electronic health records (EHRs) as a data source can also be helpful. EHR can provide many benefits for public health initiatives: they can help reveal uncommon disease patterns, provide clinical decision support (CDS), recommend treatment options, and shed insights on different demographic indicators, which can reveal population-based disparities.
Future Considerations With Artificial Intelligence (AI)
AI in particular, is a growing area of interest when it comes to advancing healthcare and effectively reducing disparities. AI, unlike humans, can easily store, process, and recall lots of data and provide objective extrapolations free of bias and error. Physicians can develop biases against patients based on their demographic characteristics, which might cloud their professional judgments, whereas machines rely more strictly on facts and objectivity. AI is also efficient in pattern recognition, making it adept at finding correlations between different factors, including social determinants, which might be more challenging for more human-centric approaches.
Potential Challenges and Risks Associated with Healthcare Technology
While technology comes with many benefits, we should also account for risks. Telemedicine can reduce health disparities, but it can also exacerbate them. The elderly population, in particular, may not be as digitally literate or have access to technology as the rest, rendering options like remote appointments and text messages out of their reach. In addition, ethnic minorities, rural populations, and those from a lower socioeconomic (SES) background are less likely to utilize home broadband service. Furthermore, many underserved populations and those with low health literacy are less likely to use or understand how to use online portals.
For older adults aged 65 or older, only 53% own a smartphone, 59% have broadband access, and 73% use the internet.
Privacy disclosures also pose a risk. Some text messaging platforms do not incorporate encryption, which eases the interception of in-transit messages to recipients. Additionally, the wrong recipients could receive messages, especially if there is no recipient verification, potentially leaking sensitive health information.
Missing or incomplete data can further exacerbate these issues. This can lead to many problems, such as misdiagnosis and delayed treatment. There can also be challenges with aggregating data from multiple health systems and ensuring their accuracy.
AI, while its trajectory is promising, presents its own set of challenges as well. For one, AI data is currently limited, and marginalized communities are not sufficiently represented in datasets. This can generate healthcare data bias and potentially lead to unequal treatment. Also, there is a lack of diversity among the various stakeholders in charge of AI algorithm development, and diverse representation in research teams is useful in identifying disparities and inequities.
Exploring Possible Solutions and Strategies
To mitigate some of these technological barriers, I will propose a list of possible ways to circumvent some of these challenges. With regard to telemedicine, healthcare providers can provide digital technology skills training and other educational resources to inform patients about free or low-cost internet access. They can also universalize the process of patient portals so that no patient is left out. The healthcare system can also provide visual representations, educate patients on health literacy, and make patient portals more user-friendly and easily accessible to diverse audiences, including non-native English speakers.
To prevent data breaches by relaying text messages, health systems should adhere to HIPAA laws regarding protecting sensitive information. They can do this by employing their own HIPAA-compliant text messaging platforms, which, unlike traditional SMS messaging, ensure that transmitted messages are fully encrypted and can only be viewed by their intended recipients. Additionally, it will require a login system so only authorized users can access their information, especially in the event of loss, hacking, or theft of their mobile devices.
Collaboration between various stakeholders is key for other IT data collection and analysis strategies. The more community and patient engagement there is, the more they can offer insights and perspectives to ensure the accuracy and reliability of the data. Engaging in more research about patient vs. community-level data and which data from each source is the most useful can help pinpoint social determinants and reduce health disparities. Researchers, providers, and patients can collaborate and increase open communication channels by implementing more user-friendly EHRs.
More research must be done to diversify the data sets and teams for AI. Data sets can include more diverse populations previously underrepresented, and diverse perspectives can be leveraged from various backgrounds on the implementation and integration of AI. Ethical standards should always be upheld.
Conclusion
Technology has proven to be an invaluable asset in addressing social determinants of health and health disparities. The proper planning, resources, and management can effectively reduce health inequity and service underserved populations. Patients, providers, and various other stakeholders should engage in a collaborative process to provide a diverse range of perspectives. Leveraging data and employing an evidence-based approach can also offer insights into disparities afflicting different communities. Promoting digital literacy, health literacy, and cultural understanding can go a long way to dismantling barriers in healthcare delivery. Future research could explore how different social determinants affect different populations and how the AI system could further support health equity.
Frequently Asked Questions
What is health literacy?
Health literacy is understanding and utilizing various resources to promote and maintain one's emotional and physical health and well-being. For example, a health-literate individual could have access to and understand how to utilize various resources, such as the Internet, to maintain healthier dieting.
How could AI be leveraged to reduce health disparities?
AI can collect, store, and analyze large swaths of data and recognize health trends, which could help our understanding of different social determinants of health. If implemented successfully, they can be unbiased and objective in their healthcare decision-making. They can also learn to speak many languages, which reduces translator dependence.
What constitutes underserved or marginalized populations?
These terms refer to communities that lack the same support or benefits as the rest of society. It could be based on race, class, sex, gender, sexual orientation, income level, age, religion, cultural background, or geographic location.
Are there any ethical dilemmas to consider when using healthcare technology?
Absolutely. Ensuring that data is private and confidential and reducing medical errors and bias are significant considerations.
Sources
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About David Martin, BA
I graduated from the University of Maryland, Baltimore County, with a Bachelor of Arts in Biological Sciences and a Minor in Sociology. I am pursuing my master's in public health with a concentration in epidemiology from Benedictine University. As an aspiring epidemiologist, health researcher, and advocate, I seek to utilize my skills in conducting analytical research to influence health policy decision-making and preventative healthcare to achieve equitable health outcomes for everyone.
