The demands of paperwork on doctors and nurses can be a tedious burden on medical care workflow. On top of one-on-one patient care, a doctor’s day can easily entail hours of charting, following up on paperwork, and tending to business needs that eat up a lot of time. Natural Language Processing (NLP) can help.
NLP is when machines read, listen, and process spoken words and written text. NLP can detect differences and similarities between words and phrases.
In medical practices, NLP is being used to prevent documentation burnout for physicians. This natural language processing software takes speech and transcribes it to where it’s relevant on medical records, doctors are able to spend more time with patients, ease workflow, and save lives.
A study published in the Annals of Internal Medicine found that physicians spend an average of 16 minutes and 14 seconds per encounter. The study has data from some 100 million patient encounters with about 155,000 physicians from 417 health systems. The most time-consuming tasks were: chart review (33%), documentation (24%), and ordering (17%). While tasks vary based on industry and specialities, these appeared as the most common.
A day in the life of physicians is a balancing act of conducting business and human healthcare. But NLP could help doctors ease the workflow by cutting the time spent on these tasks.
NLP to the Rescue: Saving Time, Saving Lives
Most people are familiar with speech-to-text capabilities via their smartphones that come with Siri, Alexa, or Google. The feature is meant to serve as a hands-free way of easing communication and as a timesaver.
When it’s used in a medical workflow, it can help increase the quality of healthcare to patients. A 2018 study published in the AMIA Joint Summits on Translational Science explored the time crunch of physicians burdened with documentation.
The study followed workflows of doctor-client sessions and the time doctors spent on post-session documentation. It found the majority of respondents noticed an improvement in documentation quality and completeness as well as a decrease in time spent on documenting a patient encounter after implementing a speech recognition solution.
NLP software eavesdrops on conversations between a physician and patient. The language processing system is programmed to look for keywords and patterns. The words are data that the machine categorizes and matches to other data within a doctor’s patient documentation. It looks for red flags.
For example, if there is a patient that has a pattern of symptoms, the machine is able to match them to potential diseases. It can also find if there are other patients who have expressed similar symptoms. This helps doctors to better care for patients. It gives them a more informed practice by helping them see patterns they may have missed on their own.
Microsoft recently partnered with clinical documentation company, Nuance Communications. The tech giant has developed a “software that understands patient-clinician conversations and automatically integrates that data into the patient's medical record,” according to health industry journal HealthDrive.
As speech-to-text becomes more common in the medical industry, this type of NLP technology will need a place to store information.
Amazon has tapped into this niche with Amazon HealthLake. This cloud-based service allows for information to be stored and shared between health systems and with third-party applications. In the patient care workflow, healthcare providers will be able to collaborate more effectively and ensure patients access to their personal medical information.
HealthLake, like any other technology, has to be HIPAA-approved before using. That helps protect client confidentiality with data.
Codazen Solutions: Speech-to-Text Capabilities
At Codazen, we know time is of the essence in organizational workflow and management. In an industry like healthcare, where the demands for human interaction are great, AI can really assuage the documentation-heavy workflow.
Our team of data scientists can help leverage NLP technologies for health-based organizations. We take audio transcriptions and turn them into text using open-source and in-house speech recognition packages in Python.
We also help figure out what speech is about by summarizing or classifying speech data. Our data science team can provide any sort of analysis that finds patterns in phrases or audio.
Whether your company needs assistance automatically transcribing thousands of pages of text, or analyzing the sentiment and finding patterns throughout the corpora, our team of dedicated and experienced data scientists can help you do just that.
We can help ease the demands on physicians and provide better healthcare to patients. Learn more about partnering with us.