AI is having a moment. From ChatGPT to facial recognition, it seems as though intelligent technology is reaching parts of everyday life like it never has.
Healthcare is no exception to this expansion. Radiologists have started using intelligent imaging for quicker and more accurate diagnoses. Surgeons are leveraging AI to inform decisions on the best surgery techniques. And even payors have begun exploring this technology for supporting management of chronic conditions.
But can AI help health systems simplify how clinicians work? And, if so, how?
It’s critical to explore how natural language processing (NLP) — a form of conversational AI focused on understanding and generating spoken language — can support more frictionless experiences for care teams to begin answering these questions.
1. Help reduce in-basket message volume
Through digital channels, patients have more access to their doctors than ever before. And reviewing and responding to in-basket messages via care management systems is becoming routine for care teams everywhere. But a growing body of evidence points to links between higher-than-average volumes of these correspondences and clinician burnout — a key contributor to workers leaving healthcare.
One strategy to confront this issue could be to hire more staff. But onboarding new care team members not only takes time and resources, it also fails to address the root cause of the communication overload. While medical scribes might be able to temporarily free up physicians from data input, their effectiveness in reducing workload for doctors is questionable, and they might only act as band-aids to a continually growing influx of patient messages.
This symptom of healthcare’s growing digital front door needs treatment in the form of technological innovation. And NLP in healthcare could be that solution. In addition to using AI to organize information for clinician review, intelligent care enablement technologies — like Memora Health — incorporate NLP-backed SMS text messaging to proactively engage patients and answer routine questions about symptoms, appointment schedules, medications, and more.
As intelligent healthtech tools guide patients through their expected questions and symptoms, inboxes could feel lighter — meaning physicians could allocate more time and energy into performing at the top of their license.
2. Support better care coordination between providers
Some care episodes involve one provider. But more complex care journeys — like chronic care management — require coordination between multiple clinicians to support positive health outcomes for patients. In these cases, physicians need to be meticulous about sharing information and communicating next steps to ensure successful coordination of care.
Although care teams do their best to support successful care coordination, things don’t always go to plan. Patients with complex conditions may be managed by five or more clinical specialists who each view the patient through the lens of a particular specialty, leaving the patient to figure out how best to synthesize all of the recommendations and care plans they’re offered. Often these patients can be left with more questions than answers when shifting from one provider to another, and this lack of coordination leads to inefficiencies, driving up healthcare costs, subpar experiences for patients, and even harm.
As mentioned before, NLP could enable doctors to gain critical patient insights more quickly and accurately. But this function can go a step further. Forward-thinking digital health technologies intelligently record care histories and concerns doctors might need to properly treat their patients. And the best interfaces give clinicians the ability to chat provider-to-provider at the click of a button, right in the same care management application.
With NLP doing the heavy lifting of collecting data, organizing information, and supporting physicians with cross-functional tools, doctors could more readily and rapidly assess their patients’ needs and determine the best next steps for their treatment plans.
3. Extend care team capacity to help more patients
The current financial climate in healthcare simply isn’t favorable to health systems. Some hospitals continue to report downward revenue trends. Inflation has hit providers, with organizations reporting cost hikes in labor, supplies, and medications. And, even though patient volumes are starting to stabilize to pre-pandemic levels, the reversing trend isn’t enough to cover rising expenditures.
Unfortunately, costs of goods and services are largely out of the control of those who deliver care. And, as patient volumes recover, hiring new staff will demand a higher premium. That’s why leaders must look to technology to make operations more efficient, save precious productivity hours for care teams, and mitigate the need for additional full-time staff members.
That’s where healthtech care management tools that use NLP can play a critical role. With AI-supported digital assistants, components of care that used to require a clinical visit — like a prescription renewal or answering questions about unexpected symptoms — could be handled in an at-home or asynchronous care setting. And as fewer beds are occupied — and doctors are freed from overwhelming requests that could be fulfilled by intelligent care enablement technology — health systems’ patient capacities could significantly increase.
4. Streamline patient data collection and organization
Healthcare is adopting new technology at an unprecedented pace. Although some might argue the industry is long overdue for an update, there’s no question that advancing technologies — like care management systems — demand more and more data. And physicians often bear the burden of having to glean and piece together this information.
As a result clinicians are spending too much of their time on routine tasks and not enough time treating patients. Time studies have found that a whopping 25% of a physician’s day is spent on administrative tasks unrelated to direct patient care. This status quo is unsustainable as physicians are getting frustrated and burned out.
Luckily, the solution could very well lie in technology. Novel NLP-supported applications are trained on vast collections of linguistic references and programmed with algorithms that help them decipher language. Thus, they can gather and understand important information from patients with little input from physicians, and automatically assemble this data sensibly and cohesively for human review.
All of this means clinicians might be able to stress less about gathering information and focus more on delivering quality care.
One fertility program using Memora Health increased patient capacity by nearly 24% without hiring new staff
5. Assist physicians with remote patient monitoring
Glucose readers. Fitness wearables. Pulse oximeters. These are all monitoring devices that generate vital health metrics. And that information could give providers a better viewpoint of how their patients are doing outside of hospital walls.
But a barrier to accessing this information in an efficient and expedient manner reflects the first challenge outlined in this article: gleaning and organizing this data is overwhelming. Even if remote patient monitoring devices automatically feed into care management systems, doctors still need to remember to go into digital patient files and check for these insights.
Memora Health’s NLP-supported intelligent care enablement platform has started to address this point of friction in clinical workflows. It uses AI to identify concerning health indicators via monitoring devices, engages the user by checking in using SMS text messaging, and then leverages NLP to understand and automatically escalate concerning patient responses to their care providers.
With a streamlined way for collecting and assembling remote monitoring information into accessible and cohesive patient views for clinicians, care teams could address acute concerns more urgently and possibly help prevent more serious conditions later down the road.
Clinical workflows have become convoluted as legacy systems have become more complex. Indeed, one of the biggest challenges to improving the workforce experience, expanding patient capacity, and saving time and money lies in simplifying everyday care team operations. NLP could play a key role in mapping a clearer path forward for health systems everywhere.