With the market for Natural Language Processing (NLP) in healthcare expected to reach $4.3 billion by 2024, we’re seeing an explosion in NLP and other artificial intelligence (AI) technologies that aim to help transform raw healthcare data into meaningful intelligence to improve healthcare outcomes.

While the technologists are in a frenzy to launch new solutions, most healthcare providers aren’t utilizing these tools to their full potential. For some, cost and time for implementation is an obstacle, but for others, there’s an innate fear that advanced automation in health care will de-humanize the experience, further reducing already short patient-provider interactions.
However, the reality is that AI technologies can actually create more opportunity for meaningful, productive patient-provider interactions, primarily by reducing administrative burden. Here are some examples:

1. Automating Repetitive Tasks

By incorporating non-human actors like chatbots to triage and assess symptoms for minor complaints, this frees up physician time and facility space for patients with more serious or acute needs. AI-based solutions can help to prioritize patients and ensure that the most urgent cases get access to immediate care and prevent minor ailments from clogging ERs and doctors’ offices.

Automated medication reminders, appointment scheduling solutions, documentation requests, and diagnostic procedures can also reduce staff burden, as well as provide greater convenience for patients. Machine learning makes these tools smarter and more effective over time, while alleviating these tasks from health care providers and staff provides more time for face-to-face interaction.

2. Reducing Non-care Burden

Physician job satisfaction is declining at an alarming rate, with nearly half feeling burned out. This stems largely from the overwhelming burden of documentation, billing challenges, and increased regulatory requirements, all of which take away from patient care. AI-based solutions that leverage NLP and machine learning can alleviate that burden by automating back-end tasks like risk stratification, charting, coding, and billing. This allows physicians to work smarter, become more involved in direct care and spend more time with patients. Here, AI-based solutions can become a true assistant to physicians, reducing the non-care burden, while also improving accuracy and insight to help doctors better understand context and correlation of conditions and symptoms.

3. Unlocking Useful Data

In many cases, some of the most insightful data for diagnosis or identifying confounding issues is locked away in unstructured data— narrative physician notes and other formats that make it impossible to efficiently analyze and ensure no critical clinical insight is overlooked. NLP-based solutions that can capture and analyze this data from across multiple sources can give physicians greater insight into patient needs, as well as improve accuracy in coding and billing, and reveal previously undetected insights in patient subsets.

This added data and intelligence allows doctors to make more accurate diagnoses and devise effective treatment plans without spending hours poring over patient records. This gives them time to glean insight from patients and provide more holistic care advice.

AI technology applied to image recognition has already improved diagnosis time and accuracy, and NLP/AI-based documentation assistance is also helping physicians to improve charting efficiency. By introducing these tools in other areas, specifically in the patient-physician relationship, hospitals and practices can further streamline administrative tasks and reduce non-care burden.

For physicians and their staff, this saves time, reduces costs, lowers administrative burden, and puts providers where they need and want to be: in front of patients, instead of in front of a computer. For patients, that means more in-depth, meaningful interaction with physicians, and ultimately better and more timely care.

Published by insidebigdata.com on September 28, 2018