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The Integration of AI and Machine Learning in Allied Healthcare

In: Industry Insights

Updated: Monday, April 29, 2024 @ 10:07am

The Integration of AI and Machine Learning in Allied Healthcare

Every day, AI and machine learning is evolving to meet the specific needs of a wide range of industries. If you are a healthcare professional, machine learning may have already been touted to you as the magical tool to solve your allied healthcare quandaries.

But is AI or machine learning right for your healthcare institution? What are the benefits and fallbacks of this emerging technology? This blog offers a comprehensive look at the world of machine learning in allied healthcare.

What is AI or Machine Learning in Allied Healthcare?

Machine learning distinguishes itself from traditional rule-based systems by using advanced algorithms and statistical data to make accurate predictions.1 In allied healthcare, machine learning systems are used in a wide range of applications, including, but not limited to:

  • Inventory management
  • Billing
  • Appointment scheduling
  • Remote monitoring
  • Patient education
  • Personalized healthcare plans2

What Should Healthcare Professionals Know about Machine Learning?

In recent years, the allied healthcare industry has grown at a rapid pace, with further growth predicted. The Bureau of Labor Statistics projects 1.8 million job openings between 2022 and 2032.3 Therefore, to healthcare professionals, it may come as no surprise that new ways to process and utilize information is a growing need.

For many healthcare institutions, artificial intelligence, or machine learning, could provide the answer for these emerging needs. While many institutions have led the way to integrating machine learning in allied healthcare, others may have reservations.

The Importance of Machine Learning in a Remote Work Revolution

The onset of the decade has brought about a revolution in remote work for the healthcare industry. Both patients and healthcare professionals may benefit from remote healthcare.
In allied healthcare, benefits may include:

  • Increased support for healthcare providers and better support for trainees
  • Flexible health service delivery through virtual access to patients.
  • Reduced costs
  • Increased patient satisfaction by providing quick answers to medical questions and continuous support.4

Drawbacks of Machine Learning in Allied Healthcare

While remote work has provided these benefits and more, problems arise which AI may be equipped to address. Issues of remote work may include:

  • Difficulty adapting to technology.
  • Reduced interaction between healthcare professionals and patients, which can be crucial for care.
  • Potential decreased quality of healthcare due to collaborative difficulties.

For many healthcare professionals, AI may be the answer to mitigating these difficulties in facilitating remote work for healthcare. AI assists in communication and collaboration between healthcare workers. This added channel of communication may allow healthcare workers to process and share information seamlessly.

AI might analyze health records and patient data and may be able to detect patterns healthcare workers miss. Machine learning’s ability to predict certain health outcomes may help allied healthcare professionals monitor patients and prevent adverse outcomes.

Practical Applications of AI and Machine Learning in Allied Healthcare

Institutions are innovating to put AI to work for their specific allied healthcare needs. For instance, the University of Cincinnati recently developed a machine learning application to accurately determine the immunization status of students.5

A partner from UC’s College of Allied Health Sciences collaborated with their Digital Technology Solutions' Software Development Team alongside a partner from Integrated Data, Engineering & Application Services to create the application. Their goal was to integrate their rule-based COVID-19 immunization app with AI to take their knowledge to the next level.6

Meanwhile, AI is being used by recruitment professionals to staff healthcare institutions. Aya Healthcare, a digital staffing company, recently acquired Winnow AI. This machine learning tool aims to enhance physician recruitment through predictive matches. Aya Healthcare hopes this collaboration can address the nationwide physician shortage.7

The Future of AI in Healthcare

Healthcare professionals predict that by 2030, there may be a larger gap between the supply and demand for healthcare professionals. Worldwide, there is projected to be 18 million fewer healthcare professionals than there are now, with developing countries being the most affected.8 To meet growing demand, healthcare institutions may look to AI.

Potential Challenges

Looking ahead, there are still potential challenges that arise when considering the use of AI in healthcare. Top of funnel concerns include:

  • Data Security and Privacy: Because AI models rely on an extensive amount of sensitive patient data to make predictions, security and privacy is a major concern for healthcare professionals.
  • Accuracy: Many healthcare providers are hesitant to trust AI to perform healthcare tasks consistently and reliably. This may lead professionals wonder how to hold AI models accountable if they make a mistake.
  • Ethical Concerns: AI or machine learning models may be prone to biases regarding race, gender and other factors. Additionally, AI models may be unable to read nonverbal cues from patients, which is crucial for holistic health care.
  • Steep Implementation Curves: Implementing AI may be expensive, especially considering additional fees such as hardware, training, and expertise. Implementation may be time consuming for healthcare institutions. This can be an expensive burden for institutions who may not have the additional workforce to cover downtime.9

There are no easy solutions for the above concerns, however, the future of AI development may seek to address these potential setbacks.

Should Your Healthcare Organization Implement Machine Learning as an Allied Healthcare Solution?

Should your healthcare institution use AI or machine learning tools to assist with day-to-day allied healthcare operations? Machine learning present lucrative opportunities which may ease the emerging challenges of remote healthcare work. While challenges of adapting this modern technology to the healthcare industry arise, further innovation may see these issues mitigated in the future.

Healthcare professionals are encouraged to keep their nose to the ground concerning the evolution of AI technology. A diligent exploration of the benefits and setbacks of machine learning may help you make the best decision for your healthcare staff and your patients.

1 An Q, Rahman S, Zhou J, Kang JJ. A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors (Basel). 2023 Apr 22;23(9):4178. doi: 10.3390/s23094178. PMID: 37177382; PMCID: PMC10180678.

2 Nancarrow S. How AI is Revolutionising Allied Health Service Delivery. AHP Workforce. January 12, 2024. Accessed March 21, 2024.

3 Healthcare Occupations: Occupational Outlook Handbook. U.S. Bureau of Labor Statistics. September 6, 2023. Accessed March 22, 2024.

4 The case for remote work in health care – sponsor content from Siemens Healthineers. Harvard Business Review. September 9, 2020. Accessed April 17, 2024.

5 Garavand A, Jalali S, Hajipour Talebi A, Sabahi A. Advantages and disadvantages of teleworking in healthcare institutions during COVID-19: A systematic review. Inform Med Unlocked. 2022;34:101119. doi: 10.1016/j.imu.2022.101119. Epub 2022 Nov 6. PMID: 36373130; PMCID: PMC9637285.

6 Bresser E. Bearcats Health App Leverages AI to Automate Student Immunization Verifications. UC News. March 12, 2024. Accessed March 21, 2024.

7 Landi H. Digital Staffing Company Aya Healthcare Picks Up Winnow AI to Bolster its Physician Recruitment Tech. Fierce Healthcare. November 20, 2023. Accessed March 22, 2024.

8 Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095. PMID: 34286183; PMCID: PMC8285156.

9 Corn J. Council Post: Balancing the Pros and Cons of AI in Healthcare. Forbes. December 2, 2023. Accessed March 22, 2024.

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