Healthcare is changing like never before. An unprecedented global pandemic combined with advances in the adoption and capabilities of technology have connected patients even more to their care providers. But at the same time, care teams are stretched thin and burned out from industry-wide workforce challenges alongside a significant increase in patient needs and expectations.
As a result, health organizations seek digital tools to help ease the burden on care teams and provide faster and better care to patients. One such tool is conversational artificial intelligence (AI), which uses software to automatically engage a user (the patient, in this context) through web-, app-, or text-based chat interactions. But not all conversational AI functionality is the same, as advances in AI have enabled a new level of interactive technology called natural language processing (NLP).
To fundamentally understand this advancement and how it can be used to amplify care delivery, let’s delve into some NLP basics.
1. What is natural language processing (NLP)?
Natural language processing (NLP) is a subfield of AI that helps computers understand, interpret, and interact with human language. Essentially, it enables software to read and interact similarly to how we do in normal conversation.
At Memora, NLP is central to providing a personal experience and an unrivaled level of support to patients. We’ve heard that many patients feel more comfortable asking questions or sharing insights with our AI that they may not bring up to their clinicians otherwise. And with our NLP-powered conversational AI automating routine tasks, providers can spend more time delivering top-of-license care.
2. How is conversational AI with NLP different from rule-based chatbots?
A chatbot is a rule-based system that uses defined paths or decision trees to determine what to say back to a program user. Many chatbots struggle in conversations when user responses deviate even slightly from its predefined pathways, leading to very limited engagement and a lackluster user experience. Consequently, chatbots commonly leverage multiple-choice buttons to keep users within the limitations of pre-set decision trees.
Conversational AI, on the other hand, typically leverages NLP techniques to understand users’ questions and responses based on context, then assess them and respond in conversational ways. Combined with machine learning algorithms, conversational AI can learn from user interactions and improve future assessments and predictions.
That’s why conversational AI can empower users to interact with software systems using a more natural way of speaking.
3. What are the benefits of healthcare NLP?
NLP can power healthtech products to engage in natural, open-ended conversations with end users. With this technology, interactions that would otherwise feel sterile and transactional now can have a human touch.
NLP systems can interpret meaning out of unstructured data, enabling automation across more use cases and reducing tasks that would otherwise fall on human staff.
Healthcare organizations using Memora Health’s platform can offer patients a user-friendly way to engage with their care teams and receive friendly, evidence-based guidance through SMS. On the provider’s side, Memora’s platform cuts down on phone followup and the heavy influx of inbox messages associated with care management, helping them to spend more time delivering top-of-license care and supporting urgent and complex patient needs.
4. How can AI and NLP impact healthcare?
In healthcare, there simply isn’t enough time in the day to handle all the administrative work and provide effective, high-level care to patients inside and outside of hospital walls. Automated patient engagement software can have a major impact on clinical practice, as patient data can be collected without taking time from an already overloaded staff’s schedule.
But, depending on the solution, there are varying degrees of the effectiveness of that data. Chatbots with decision-tree-based approaches may help collect sufficient high-level information, but are limited in their depth as patient symptoms, outcomes, and concerns must fit into predefined buckets to progress through the algorithm. Yet patients and their health are complex, and forcing them into simple categories may omit crucial information.
NLP systems still do the heavy lifting to collect and filter data for clinicians, but allow patients much more flexibility in sharing information that could be relevant. This provides concise and intelligent clinical insights but allows much greater visibility to each patient’s unique experience. With Memora Health, for example, if a patient reports a question or concern that cannot be resolved by our conversational AI, an alert for followup is sent to the care team. And with our Live Chat feature, the provider can access the patient’s entire conversation history, enabling more holistic insight and even the ability to jump in and text directly with the patient so they can feel confident in making informed clinical decisions.
5. How do AI and NLP support more personalized care management?
By collecting better data, NLP systems can also provide patients with much more congenial, personalized experiences. With NLP-equipped platforms, people can navigate their care journeys while having friendly conversations and feel like they have continuous support from providers.
“I felt like I always had someone checking on me. I also had comfort knowing I could text any questions I have and get a health professional to quickly answer.” - A patient's experience with Memora Health
At the same time, care teams can trust that their automated digital health tools are effectively assisting their valued patients and extending their reach with a human touch — without adding extra work to their plates.
Ultimately, health organizations need a solution that will help care teams save time and become more efficient without compromising the quality of their patient care or clinical data. Memora Health offers clinical breadth and depth across a range of functional Care Programs, delivered to patients via NLP-powered, automated text messages. Clinical insights and action items are intelligently triaged and routed to the appropriate team member within existing workflows so care teams can focus where it matters most- patient care.