Healthcare organizations have more access than ever to data-driven technology that can improve healthcare outcomes and drive business opportunities. It’s not easy for these enterprise systems to harness the trillions of gigabytes of health data and web content, but Natural Language Processing (NLP) in healthcare is a promising part of the solution.
As the digitization of healthcare continues, the industry is also looking to make better use of unstructured data. NLP describes the ways in which artificial intelligence systems gather and analyze unstructured data from human language to extract patterns, uncover meaning and formulate responses. In other words, NLP attempts to get to the heart of language formation and use that understanding to automate and improve human processes.
Leveraged properly, the technology enables providers to automate administrative workflows, invest more time in patient care and improve patient experience using real-time data.
In this article, we will cover the most beneficial uses of NLP for healthcare companies, including benchmarking patient experience, review management and sentiment analysis, dictation and EMR implications and predictive analytics.
Here are some of the top use cases for NLP technology in healthcare:
1. Patient Experience and Value-Based Care
The unstructured clinical record and the patient feedback that comes after a visit contain insights into the patient experience that aren’t available in the structured record. NLP technology can identify these gaps by pulling key words and phrases from free text that will inform care decisions and benchmark the patient experience across physicians and locations.
This type of data mining in healthcare, made possible by NLP, can help reduce subjectivity in decision-making and help organizations deliver better, more efficient care to patients.
Meanwhile, the shift to value-based reimbursement means healthcare organizations need to measure provider performance and identify gaps in care for reporting to payers and regulators.
The value-based care model incentivizes both providers and payers to demonstrate positive patient outcomes after leaving the clinical setting. Data-rich health systems are now using natural language processing to analyze post-care survey feedback, online reviews, social media posts, and many other sources of unstructured text. These insights are key to identifying positive and negative patient experience factors that, if optimized or improved, will lead to higher CAHPS scores and provider ratings.
A French research group developed an NLP-based algorithm that would help monitor, detect and prevent hospital acquired infections. It made sense of unstructured data from clinical notes and patient feedback, and used those insights to identify early signs of infections and notify clinicians.
2. Review Management and Sentiment Analysis
In addition to patient experience improvements, NLP can help healthcare organizations manage online reviews in a highly regulated industry.
Natural Language Processing technology can collect and analyze the thousands of healthcare reviews posted every day on third-party listings, finding protected health information (PHI), profanity or other content relevant to HIPAA compliance. It can also quickly analyze and evaluate human sentiment of unstructured comments, along with the context of how they are being used.
In this case study, learn how Temple University Health System leverages Binary Fountain’s NLP technology to analyze unstructured survey responses with an accuracy rate greater than 90%, turning qualitative data into quantitative business intelligence about patient experience.
Many healthcare systems also use text analytics to monitor the Voice of Consumer in reviews, so physicians understand how patients talk about their care and can better communicate using a shared vocabulary. Similarly, NLP systems can track consumer sentiment about your healthcare brand by pulling insights from positive and negative words or phrases within reviews or social media posts.
A Sant Baba Bhag Singh University study found that using sentiment analysis from social media data helped providers improve treatments by understanding how patients talk about their Type-1 and Type-2 Diabetes treatments, drugs and diet regiments.
3. Dictation and EMR Implications
An average EMR record runs between 50 and 150 MB per million records, and the average clinical note record is 150 times as large. To manage that administrative workflow, many physicians are replacing handwriting or typing with voice notes, which NLP tools can easily interpret and add to EMR systems.
This application of NLP allows physicians to automatically transcribe their conversation with patients, which means they can commit more time to improving the quality of care. But its implications go further.
Many of the clinical notes in EMRs are in unstructured form, but NLP offers a way to effectively, and automatically, interpret clinical notes. It can pull details from diagnostic reports and physicians’ letters, ensuring that all relevant information is uploaded to the patient health profile. For example, NLP systems could extract any notes in a patient’s electronic record that mention prescribed medications and if they were effective.
A 2018 study used NLP to process radiology reports looking for pulmonary embolism and postoperative venous thromboembolism, and found that unstructured data analysis identified 50% more cases than structured data alone.
4. Root Cause Analysis and Predictive Analytics
Another exciting, but more complex, benefit of NLP is how predictive analytics can solve population health problems.
Applying NLP to vast caches of electronic medical records can help identify subsets of geographic regions, ethnic groups or other population segments that face different types of health disparities. Existing administrative databases can’t analyze socio-cultural impacts on health at such a scale, but NLP could pave the way for further research.
An often-cited example of this NLP application for healthcare companies is its use for Kawasaki diseases, where delays in diagnosis can lead to critical complications. In a 2016 study, an NLP-based algorithm identified at-risk patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians.
At the same time, NLP can be used to analyze unstructured feedback and find the root cause of patients’ concerns or poor outcomes.
For example, healthcare-specific NLP can recognize phrases like “emailed us a video” as a positive sentiment concerning the topic of “Helping Patients Understand” within the “Provider” category. Monitoring long-term trends of sentiment surrounding topics in these categories, like “patient care,” “non-clinical staff” or “facilities,” can help organizations nail down the origins of negative patient experiences for providers or locations.
When patient experience personnel have a true understanding of a patient’s sentiment, they can conduct the appropriate outreach, perform service recovery and build a deeper relationship between the hospital and the patient.
A 2018 study used NLP to predict suicide attempts by monitoring social media, showing clear indicators of imminent suicide risk by Twitter users who changed their speech patterns on the platform in specific ways. The system’s prediction rate hit 70%, with just a 10% false positive rate.
NLP Made Specifically for Healthcare
Data-driven health systems looking to optimize patient experience, reduce costs and improve care outcomes should consider the myriad insights hidden in unstructured data. With a wealth of patient feedback available, it is imperative for healthcare providers to begin investing and implementing NLP-powered patient feedback management solutions to secure and ensure patient loyalty.
Natural Language Processing is not a one-size-fits-all solution, so NLP systems in the healthcare industry need to understand the sublanguage used by medical professionals and by patients. Binary Fountain’s NLP-driven technology platform was built specifically for the healthcare industry, and can help your organization get the most from both real-time and historical feedback data.
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