AI and natural language processing (NLP) have the potential to transform care delivery to support care teams in unprecedented ways. From streamlining clinical workflows to helping clinicians better engage patients outside of hospitals, intelligent applications could play a major role in propelling healthcare into a new era of quality and sustainability. 

But inevitably, the question arises: “Will AI start to replace doctors?” With digital advancement accelerating in the industry, medical professionals are widely concerned about the impact of AI-supported technology on their fields. 

As leaders across healthcare grapple with how to generate buy-in for game-changing technologies with their employees, it’s critical to understand why people are apprehensive and how AI and NLP can empower — not eliminate — staff.

Why are some healthcare professionals concerned about NLP in healthcare? 

Nearly half of the healthcare workforce (roughly 9 million people) is worried AI could replace jobs. That’s a lot of anxiety for one sector. But on the flip side, 68% understand AI-supported technology could free them up to focus on important tasks. So how do we reconcile those two converging truths? Let’s start by diving into why healthcare professionals have reservations about intelligent advancements:

1. Job security

A significant cause for concern about AI within healthcare is job security. Naturally, hospital staff might notice trends in other industries where AI-supported technology is replacing some roles. One study predicts ​​robots could substitute 2 million more workers in manufacturing alone by 2025. However, the care experience involves unique levels of vulnerability, stress, and personal risk, and people are broadly cautious about leaving too much responsibility in the hands of digital assistants. For the foreseeable future, human input will be as important as it ever has been for delivering high-quality care and satisfying patients.

2. Unforeseen errors

Another root of physicians’ worries about adopting AI-supported technology is the question of accuracy. When treating patients, even one mishap could lead to unintentional patient harm. So introducing innovations that are still in development could cause panic. Additionally, no two patients are the same, so clinicians could be concerned about treating them in a one-size-fits-all manner rather than assessing individual needs. Luckily, many current AI and NLP platforms show high reliability, such as one interface that helps predict diabetes with an 80% accuracy rating, and this technology is increasingly developed with personalization in mind.

3. Built-in biases

One pervasive issue that challenges the credibility of AI in a healthcare setting is implicit  algorithmic bias. For instance, in genomics and genetics, data from white individuals comprise around 80% of collected information, meaning research and accepted results may be partial to this general group over underrepresented populations. When used to inform AI technology and NLP, the data could manifest as a built-in prejudice innate to the solution itself. More self-reflective AI developers take a plethora of social determinants of health (SDoH) into consideration when building out their platforms to reinforce health equity.

4. Data overload

With advanced technology comes more data. And the fear of having to manage the information firehose is perfectly valid. For instance, the EHR — once heralded as an all-solving digital health platform — has placed more pressure on care teams to collect and record notes. But many AI and NLP developers actually seek to relieve this pressure. For instance, Memora Health’s intelligent care enablement platform integrates with existing infrastructure to automatically catalog patient-reported concerns, remote monitoring intel, and other data to provide clinicians with an accessible, longitudinal view of each individual’s care journey. Ultimately, this technology helps care teams manage the data firehose by autonomously gleaning and processing information, only alerting providers when clinical judgment is necessary.‍

How can NLP in healthcare support care delivery?

The secret to building buy-in for AI- and NLP-supported technology among healthcare workers is to communicate real-world value. Here are some of the benefits of NLP in healthcare for care delivery:

1. Streamlining clinical workflows

Of all the benefits of NLP for care teams, simplifying burdensome clinical workflows tops the charts. NLP-supported technology can help improve clinical documentation by automatically extracting important information from unstructured data sources, such as clinical notes and patient messages. Some advanced platforms — like intelligent care enablement innovations — take things a step further by accurately answering basic patient questions, proactively checking in with people after visits, and escalating the most urgent requests to the right care team members. 

2. Supporting personalized treatment plans for patients

On the other side of the coin, AI- and NLP-supported technology can support more personalized treatment plans and care journeys. Using NLP, intelligent care enablement platforms engage patients before, after, and between visits with follow-up questions and information relevant to specific conditions and informed by evidence-based clinical input in development. By integrating health literacy and high-touch correspondence features, these advanced tools can act as virtual hand-holders for patients, and could give physicians more bandwidth to perform at the top of their license. 

3. Facilitating care coordination

Care teams do their best to educate patients and prepare them for transitions in care settings. But things don’t always go to plan. Ultimately, ineffective care coordination can cause confusion, administrative waste, and, in some cases, even cause patient harm. Advanced AI and NLP programs can empower clinicians with the best tools for successfully guiding patients along their care journeys. These include care coordination features that help providers communicate with and tag other specialists with the click of a button, condition-specific patient assistance messaging, and comprehensive information about reported patient data — all of which facilitate smoother experiences for patients and clinicians.

There’s no question AI and NLP will play key roles in advancing care delivery in the next several years. It’s important to remember this technology is relatively new within the industry, and it’s not surprising that employees might worry about the implications of adopting intelligent innovations on job security, processes, and data demands. With the right platform in place, you can show your workforce AI-supported programs can take care of the most demanding routine tasks to free up care teams to focus on the patients they care so deeply about.   

Want to learn the most important things to consider when selecting a digital healthcare platform for your organization? Check out our free 2023 digital health trends report!