Embracing AI in Healthcare: Overcoming Concerns Benefits All

Open dialogue and transparency will lead to acceptance and greater utilization

Over the past five years, machine-based learning tools, including artificial intelligence (AI) and predictive analytics and modeling, have begun transforming many industries, including healthcare. In 2021, Health IT Analytics reported that more than 90% of hospitals were using some form of AI. 

Over the past few years, the rapid advancement of AI has sparked many reactions, from enthusiasm and excitement to the inevitable unease new technology brings. Concerns over the use of advanced analytics and AI are relatively widespread among patients, healthcare providers, and stakeholders. It is crucial that we confront these concerns.

Now is the time to closely examine how these predictive models function in healthcare, clearly outlining their goals and benefits. These technologies are designed to support and augment—not replace—the invaluable insights of experienced medical professionals.

By ensuring that those who will use it have a better understanding of AI, how it is used, and its ultimate objectives, we can alleviate fears and ensure that the full promise of AI is realized. We’ll look at how the field of wound care in particular is using these tools to improve patient care.

How Do AI, ML, and Predictive Analytics Work in Wound Care?

Since there are a variety of AI and predictive modeling tools out there, let’s start by establishing basic definitions for AI technologies you may use or hear about specifically in your practice setting.

According to Healthcare Weekly, “[AI] in healthcare uses algorithms and data to perform tasks, identify patterns, and provide insights and solutions to medical problems. It can help with clinical decision support, drug discovery, care delivery, and information integration. It can also increase the healthcare system’s efficiency, innovation, and revenue potential.”

Machine learning (ML) is a subfield of AI that focuses on developing algorithms and statistical models that enable computers to learn from new data and past results. ML systems process large sets of data to identify patterns and relationships, training to improve their performance over time.

Predictive analytics, another subset of AI, uses ML and historical data to forecast future events. In wound care, this can mean predicting the likelihood of wound healing under different treatment regimens, identifying patients at high risk of complications, or suggesting personalized treatment plans based on similar cases. Predictive modeling refers specifically to the models built with predictive analytics to detail future outcomes.

AI in Healthcare: What’s in it for Wound Care?

AI’s capabilities are certainly great enough to inspire excitement about the technology. It can do everything from saving providers time to helping them keep up with current medical advances to handling tedious tasks like writing clinical notes or filling out forms.

AI-based technologies also aim to:

• Enhance diagnostic accuracy. AI can assist in diagnosing wound types and assessing their severity by analyzing images and clinical data, ensuring that patients receive accurate diagnoses and appropriate treatments promptly.
• Personalize treatment plans. By analyzing individual patient data and comparing it with large data sets of similar cases, AI can help tailor treatment plans to each patient’s unique needs, increasing the likelihood of successful outcomes.
• Predict outcomes and complications. Predictive analytics can forecast the likelihood of wound healing, the potential for complications, and the expected response to different treatments, enabling proactive and preventive care.
• Streamline workflow. AI can automate routine tasks such as data entry and documentation, freeing healthcare providers to focus more on patient care.
• Ensure data-driven decisions. AI provides healthcare professionals with data-driven insights, enabling them to make informed decisions based on comprehensive data analysis rather than solely on intuition or experience.
• Promote proactive care: Predictive analytics can identify patients at risk of complications, allowing for early interventions to prevent worsening conditions and reduce hospital readmissions.

Clinical and Operational Applications

AI in Clinical Settings

Today, there are a range of clinical and operational AI-powered solutions. On the clinical side, AI and machine learning are used to further understand and provide decision support for issues related to patient care, quality, and outcomes. When mobile wound imaging platforms are powered by AI and computer vision, they can autonomously segment, classify, and measure wounds consistently and accurately. The top platforms on the market today are over 90% more accurate than traditional wound measurement with rulers.

AI-powered predictive analytic solutions are transforming wound care by improving clinical decision-making and business operations, leading to better patient outcomes, reduced costs, and higher reimbursements.

Predictive analytics use machine learning algorithms to compare clinical or operational situations with past data uncover patterns that can help predict future outcomes. This enables providers to leverage vast amounts of data that would be extremely difficult for a human to analyze—and even find connections they didn’t know were there. The algorithms can analyze millions of data points in seconds, providing specific and targeted insights. Most importantly, it offers suggestions and guidance that lead to better results for clinicians and administrators.

Operational Insights from AI

Operational analytic solutions are designed to improve staffing, scheduling, workflow, marketing, and overall efficiency. Here are some examples.

• Demand forecasting: Want to predict patient numbers based on seasonality, or know how many sessions a patient needs to see results? AI can handle that data quickly and effectively. This data, often already collected by your EHR, helps with better scheduling, resource allocation, and cost savings.
• Reducing cancellations and no-shows: On average, 27% of healthcare appointments are missed in the U.S. yearly. What if you could predict which patients are most likely to cancel or miss appointments? AI can analyze socioeconomic data, visit histories, and weather patterns to identify these patients. With this information, your team can send extra reminders, check in with patients the day before, or double book slots likely to be no-shows.
• Improve employee scheduling quality: Predictive analytics also helps with staff scheduling. The technology can look through the types of patients you have scheduled and compare that data with your available team and their most vital skill sets. This can automatically schedule staff where their services will be the most effective. Your efforts will result in more efficiency, better patient outcomes, and more satisfied patients.

Emerging Concerns about AI

Of course, we can only discuss the benefits of AI by noting concerns and emerging issues. The Washington Post reports that in healthcare, “…the advances are triggering tension among front-line workers, many of whom fear the technology comes at a strong cost to humans.”

We can’t let that fear—often a fear of change or the unknown—hinder our adoption of promising tech. We do need to acknowledge and address those concerns. One of the chief worries for providers is the potential for diagnostic errors due to a need for more contextual understanding. AI, while able to use massive amounts of data, can’t actually think the way humans can. It doesn’t know how to make an inference based on external context or even when it’s made a mistake.

For instance, AI might misinterpret the severity of a wound based solely on basic images without considering the patient’s medical history or other crucial features.  Providers also fear that AI could reduce the human element essential for patient care, making patients feel like they interact more with machines than caring doctors and nurses. 

Patients are already aware of this issue. In a 2023 survey, The Pew Research Center found that “six in 10 U.S. adults say they would feel ‘uncomfortable’ if their own healthcare provider relied on artificial intelligence to do things like diagnose disease and recommend treatments.”

Data privacy is an area of concern for providers and patients alike. AI systems require vast amounts of patient data to function effectively, and providers and patients are rightfully anxious about the security of sensitive information and, increasingly, about data breaches.

One other crucial area to address is access. While AI is widespread in hospitals, adoption is lower in other settings. The Medical Group Management Association (MGMA) reports that as of a 2023 survey, 21% of medical groups that comprise the organization have added or expanded the use of AI tools but that the majority—74%—are “still on the sidelines.”

As the use of AI grows, the industry, payers, and government will need to ensure access to predictive analytics and other emerging technologies in rural areas so that they are available to all patients, not just those in large urban areas. How are we going to address concerns like this? To start, we will use communication, collaboration, and transparency.

For example, the access issue can be quickly addressed through mobile digital wound care platforms fully integrated with the EHR. Widely used smartphones, laptops, and other devices make advanced wound care tech and expertise easily available in even the most remote locations.

Concerns over patient privacy can be addressed by ensuring any platforms used comply with the strictest safety and privacy guidelines, including implementing strong encryption, access controls, regular security assessments, and staff training to protect sensitive health information.

AI Is a Supportive Tool, Not a Replacement

Those are good points for the wound care industry to bear in mind. However, there still needs to be additional discussion and debate to bring more wound care providers on board with AI and related tech.

It is crucial to understand that AI and predictive analytics are intended to supplement the insights and expertise of experienced wound care professionals. These technologies serve as decision support resources, providing additional information and data to help clinicians make more informed decisions. Predictive models may provide recommendations based on data analysis, but the final decision always rests with the healthcare provider.

The American Medical Association commonly refers to “augmented intelligence,” which stresses the importance of AI assisting, rather than replacing, healthcare professionals. The AMA goes on to emphasize that in the case of current AI applications and technology, healthcare professionals are still needed to provide:

• Clinical context for the algorithms that train AI.
• Accurate and relevant information for AI to analyze.
• Translation of AI findings to be meaningful for patients.

We should also highlight the many practical benefits of this tech. Busy wound care nurses in hospitals report that the ability to see a wound’s progress is of tremendous value in their busy workdays—this could save them hours a day. Plus, it provides a clear and objective measure of healing through enhanced identification, prevention, and care management.

Embracing AI in Wound Care: A Future of Better Outcomes and Enhanced Efficiency

What’s next on the horizon? Integrating genomic data, patient history, and real-time health metrics into AI might be the next big innovation in this tech. This could result in highly personalized treatment plans, optimizing wound care management, and improving patient outcomes. Future advancements may also better facilitate remote monitoring and telemedicine, allowing for continuous assessment and timely interventions, thus enhancing the quality and accessibility of wound care.

As technology advances, providers play a crucial role in ensuring promising innovations. Active participation in developing and implementing AI tools is essential to keep clinical judgment at the forefront. Advocating for transparency in algorithms and maintaining final authority on treatment decisions will ensure that AI supports, rather than replaces, the invaluable insights of wound care professionals.

By embracing AI and predictive models as collaborative tools, healthcare professionals can harness its power to deliver better patient outcomes and elevate the standard of care. With education, transparency, collaboration, and evidence-based outcomes, wound care providers can confidently integrate AI into their practice, enhancing their capabilities and supporting their mission to provide the best possible patient care.