{"id":5045,"date":"2022-01-21T10:47:05","date_gmt":"2022-01-21T10:47:05","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/21\/using-artificial-intelligence-as-a-tool-for-health-disparities\/"},"modified":"2022-01-21T10:47:05","modified_gmt":"2022-01-21T10:47:05","slug":"using-artificial-intelligence-as-a-tool-for-health-disparities","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/21\/using-artificial-intelligence-as-a-tool-for-health-disparities\/","title":{"rendered":"Using Artificial Intelligence As A Tool For Health Disparities"},"content":{"rendered":"

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Medical practice has advanced at a breakneck pace over the previous two decades. The diagnosis and treatment of many medical diseases have progressed from relying solely on human involvement to being aided and remotely monitored by\u00a0artificial intelligence-<\/strong>enabled technologies and smart health devices.<\/p>\n

NLU and NLP<\/strong>\u00a0have the potential to make health literacy easier to understand. Patients and their carers should understand the crucial information in hospital discharge instructions. On the other hand, most health-related literature is frequently written at a level of readability that is too high for most intended users. The information above the conscious reading level often leaves people feeling baffled and powerless, and it can contribute to poor health outcomes, more readmissions, and higher healthcare expenses.<\/p>\n

While most current\u00a0Artificial Intelligence\u00a0<\/strong>solutions for the discharge process are focused on planning and care coordination, health organizations can help patients by improving the readability of discharge instructions and empowering them to take control of their health outcomes as they navigate the healthcare system. This is where NLU\/NLP can be of use.<\/p>\n

African-American Populations: Racial And Health Disparities<\/strong><\/h2>\n

Despite fast advances in technology and science, health inequities between races and ethnic groups exist in our healthcare system. Due to racial inequality in healthcare access and socioeconomic hurdles, black males are dying from diseases that may be prevented. Chronic health problems such as diabetes, hypertension, and asthma, all of which exacerbate Covid-19 outcomes in infected patients, continue to be disproportionately prevalent among African Americans. Despite this, they represent some of the country\u2019s most underserved groups.<\/p>\n

Hypertension, a risk factor for cardiovascular disease and stroke, is more common in African Americans. In 2018, black Americans were twice as likely as whites to die from hypertension, with a death rate of 206.6 per 100,000 for African American men.<\/p>\n

What Artificial Intelligence Can Do For You?<\/strong><\/h2>\n

Artificial intelligence<\/strong>\u00a0is a broad term that encompasses a variety of topics; nevertheless, at its core, it is the technology that allows machines to mimic human behavior. For example, natural language processing (NLP) will enable computers to understand human speech.\u00a0NLU and NLP\u00a0<\/strong>can improve clinical documentation and patient-provider communication (EHR) using patients\u2019 electronic health data. Machine learning (ML) is a subset of\u00a0artificial intelligence\u00a0<\/a><\/strong>that allows computers to learn and develop without continual programming. It can aid in detecting malignant tissues in pictures, predicting clinical outcomes, and modifying pharmacological treatment procedures.<\/p>\n

Wearable glucose monitors now use\u00a0AI solutions<\/strong>\u00a0to monitor diabetic patients\u2019 blood glucose levels and deliver data to smartphones or wearable devices. Physicians have secure access to their patient\u2019s electronic health records (EHR), which are kept in the cloud. Propeller Health\u2019s smart inhalers allow people with asthma and chronic obstructive pulmonary disease to track their symptoms, produce precise allergen forecasts, and communicate the results with their doctors (COPD). Other new applications include blood pressure diagnosis and monitoring.<\/p>\n

Limitations And Obstacles<\/strong><\/h2>\n

\u201cGarbage in, garbage out\u201d is a common phrase in the computing world. This means that\u00a0AI solutions\u00a0<\/strong>are only as good as the data used to train it. If the data only captures information from one race, such as diabetic foot ulcer symptoms in white people, it will struggle to identify the same symptoms in dark-skinned people. Similarly, if the\u00a0artificial intelligence\u00a0<\/strong>diagnostic for an eye issue relied solely on data from green and blue eyes, it could make mistakes when examining dark brown eyes.<\/p>\n

Bias can arise from various places, including how the data was collected, who the study group was, and even the personal preference of people involved in the data collection. The data must be all-inclusive to eliminate discrepancies that can impair the quality of service and overall patient satisfaction.<\/p>\n

Using AI To Address Inequities In Healthcare<\/strong><\/h2>\n

Artificial intelligence<\/strong>\u00a0has emerged as a powerful tool for encouraging equality in the patient-donor relationship and increasing healthcare access and quality for all people, regardless of race, gender, or socioeconomic position.<\/p>\n

The Black Barbershop Health Outreach Program (BBHOP) was founded in 2006 by Dr Bill Releford D.P.M, founder of the Releford Foot and Ankle Institute, and has screened over 30,000 African American men for diabetes and hypertension. BBHOP is dedicated to eliminating racial inequities that prevent black American males from receiving appropriate healthcare in the United States. Its video-based telehealth mobile app includes a symptom tracker and real-time remote vitals monitoring for easy, low-cost, and location-independent healthcare access between patients and doctors. It is now utilizing the enormous power of\u00a0\u00a0AI solutions\u00a0<\/strong>and technology to reach out to those who are underserved and under-resourced.<\/p>\n

Conclusion<\/strong><\/h2>\n

The quality of an algorithm is only as good as the data used to train it. The output will invariably suffer if a model is developed with incomplete data from homogeneous populations or humans with inherent biases. Without this cycle of ongoing human evaluation,\u00a0artificial intelligence\u00a0<\/strong>may come to conclusions that are incorrect and exacerbate existing health disparities.<\/p>\n

To tackle this issue, you must maintain constant awareness.\u00a0\u00a0AI solutions\u00a0<\/strong><\/a>aren\u2019t a one-size-fits-all solution that you can set up, turn on, and walk away from. On the other hand, algorithms necessitate substantial adjustment as they\u2019re being developed, as well as ongoing oversight once they\u2019ve been deployed, to evaluate how they affect all patients. We should use\u00a0\u00a0AI solutions\u00a0<\/strong>to aid clinical decision-making, but we must not become complacent as these robots become more intelligent. Humans may someday step aside and allow robots to handle some aspects of patient care once\u00a0artificial intelligence\u00a0<\/strong>reaches a certain level of maturity. Still, humans must stay a part of the equation very much for the time being.<\/p>\n

So if you wish to use artificial intelligence<\/strong> in your business, contact ONPASSIVE<\/strong><\/a> <\/a>to know how.<\/p>\n<\/p><\/div>\n