Natural Language Processing (NLP) in Healthcare Tech

Ways that NLP Can Impact Healthcare

When it comes to healthcare, timing is everything—and unfortunately, it is also one of the most limited and crucial resources. No matter how advanced our understanding of medicine becomes, its potential is limited by our healthcare system’s capacity to serve people quickly.

For example, cancer treatment delay is a major problem in health systems worldwide. One 2020 study found that even a four-week delay of cancer treatment is associated with increased mortality across surgical, systemic treatment, and radiotherapy indications for seven cancers.

With people living longer, population growth, and a shortage of healthcare workers, time will only become more limited in the coming decades. According to the World Health Organization, 55 countries are experiencing healthcare worker shortages as of 2023.

The most significant way that generative AI benefits healthcare operations is to free up healthcare workers’ time. Here are some major ways that NLP is poised to accomplish this goal.

Optimizing Intake

We’ve all gone through the client intake process during initial appointments at new clinics, doctor’s offices, or visits to hospitals. It used to be done with paper forms to gather baseline data, such as your age, sex, medical conditions, history, etc.

In recent years, most providers have digitized this process. However, it’s still a time suck for clinic administrators, patients, and even doctors themselves, who often have to waste limited time at the beginning of appointments asking patients basic questions.

NLP’s ability to understand language and create responses can help here. Various software companies such as Kahun, Talkdesk, and Phreesia offer AI chatbots take note of patients’ symptoms, provide basic information, and suggest next steps, such as scheduling doctor’s appointments. They can also send reminders to patients, reducing the time spent by administrative staff.

The idea is not to replace physician-patient interactions with AI, but rather to complete the basic data collection process outside of the clinic, saving the scarce time that doctors have with their clients for more meaningful conversations.

Studies have shown that better physician-patient communication is linked empirically to care outcomes, including patients’ satisfaction, health status, recall of information, and adherence.

For years, chatbots have been unpopular due to their limitations. However, NLP-enabled chatbots have made improvements to previous technology.

“In the past, basic chatbots were powered by a variety of NLP or keyword-matching technologies. The ability of these chatbots to respond flexibly was limited,” says Hultman Kramer.

“The new technology that changes this is the large language model [LLM]. This is what powers ChatGPT. LLMs make it possible for chatbots to respond flexibly, to perform a variety of customer service tasks, and to have long-running conversations with users.”

NLP chatbots’ ability to learn from user interactions also means that the user experience will continuously improve.

Automating Notetaking

NLP software is already being used in contexts like sales to generate summaries of client calls and keep track of interactions. So, health-tech startups figured, why not apply the same concept to healthcare?

With the increasing popularity of telehealth, which was propelled by the pandemic, generative AI startups gleaned any opportunity to leverage NLP’s ability to understand dialogue and summarize large amounts of text rapidly.

With NLP-based telehealth software, healthcare organizations can get quick transcriptions of full patient conversations.

“All of the conversations between a patient and a provider are already being captured by microphone in a telehealth context,” Hultman Kramer explains.

Generative AI takes transcription a step further by formatting raw text. Widely used notetaking frameworks like SOAP and DAP help providers keep track of specific tasks and evaluate information for clinical reasoning.

Products like Daily’s, which is HIPAA-compliant, can summarize these transcriptions into neatly formatted reports, saving doctors, nurses, and administrators the time and trouble of doing this task manually.

“It has the potential to really save a huge amount of documentation workload,” Hultman Kramer says.

And according to Hultman Kramer, many companies are already prioritizing the development of a telehealth technology stack, creating an opportunity “to test and deploy these new technologies efficiently as part of ongoing work that’s already being prioritized.”

Electronic Health Records

An electronic health record (EHR) is a digital version of a patient’s paper chart, which stores individuals’ progress notes, problems, medications, vital signs, past medical history, immunizations, radiology reports, etc. EHRs are also often integrated with billing, as insurance companies must translate medical information into codes.

While EHRs are a huge upgrade on old-school paper-based systems, the tedious nature of EHR tasks accumulates into a significant chunk of time.

The average time physicians spend using an EHR is 16 minutes per patient, with 11 percent of that time being completed after hours. Daily estimates that physicians spend about 20 hours per week working in EHRs.

EHR tasks contribute to the mental load on healthcare workers who are already often overworked. A 2017 study that surveyed primary care residents and teaching physicians found that 85 percent of respondents said the time they spent working in an EHR affected their work-life balance. Respondents who spent more than six hours weekly after hours in EHR work were three times as likely to report burnout and four times as likely to attribute burnout to the EHR.

This is not only detrimental to healthcare workers themselves but also for patients. Studies have explored the potential of burnout to undermine the quality of care and contribute to medical error and found a concerning connection.

NLP software can improve this bottleneck in healthcare operations, too. Rather than having a human perform data entry and coding, generative AI can plug data from transcripts into EHR systems.

For instance, Daily partners with a company called ScienceIO, which builds customized large language models specifically for healthcare tasks like coding.

“It’s really amazing how they’re able to extract the medical terminology and the categorization from just a transcript of a doctor and a patient talking for half an hour,” Hultman Kramer says. “And those categorizations and those keywords are incredibly accurate.”

With this integration, providers only have to review the work done by NLP software, vastly reducing the time spent working in EHRs and opening up more time for providers to engage in their primary duties.