Healthcare companies deal with an incredible amount of sensitive data day in, day out. Each electronic health record (EHR) contains a patient’s entire medical history. Every time a patient visits the doctor, they provide the patient with new information about their health. This is all stored online for doctors to interpret, and to inform diagnoses. As more information is added to the system, healthcare companies deal with increasingly large datasets and taxonomies. Additionally, EHR systems are continually improved, regulated, and developed. For example, the conversion from the EHR Incentive Programs to the Medicare Access and CHIP Reauthorization Act (MACRA) left many providers feeling overburdened by the plethora of information.
All of this makes it demanding for healthcare workers to extract useful information. This is where natural language processing (NLP) comes in. NLP helps healthcare works to make sense of the data, and to feed it into genuinely impactful ML models that can revolutionize the patient-provider dynamic.
What is NLP? In simple terms, NLP helps the computer understand the ‘natural language’ that humans use. It understands the basic techniques of word definition, phrases, sentences, and texts, as well as syntactic (knowledge of word meanings and vocabulary) and semantic processing (understanding the combination of phrases). The way humans interact with each other has a certain nuance behind it. NLP simply assists the computer to understand the meanings behind statements.
NLP is quickly becoming the norm in the medical community. It helps streamline processes and saves precious time for healthcare workers. Here are some of the ways NLP helps in creating a better environment in the medical community.
Since the switch from EHR Incentives to MACRA and its associated programs, the affinity to measure provider performance and find the breaches in inpatient care has become imperative. This is an essential step to receive value-based reimbursement from the Centers for Medicare & Medicaid Services (CMS). The implementation of NLP has proven to simplify the process to evaluate the skills of physicians by automating free text evaluation.
NLP systems are able to identify and evaluate language terms associated with the providers’ soft skills, which has opened the door for further evaluations. Paying attention to how the patient feels improves the overall experience, helps identify unhappy patients, and showcases steps to prevent poor experiences. Creating a better patient experience helps bolster retention. (See also how NLP has revolutionized the Customer Care industry.)
NLP uses speech recognition to improve doctor-patient relationships. Speech recognition allows physicians to dictate their notes out loud during appointments, which have multiple benefits:
Since the system is dictating notes, physicians can directly look at their patients instead of staring at a screen. This creates time for answering more questions and notice more nuanced physiological symptoms.
Patients listen to the dictation and make corrections if necessary, helping in storing accurate information which further helps in creating a valid diagnosis.
Dictating notes allows the physician to focus entirely on the patient and pick up on nuances to provide a perspicuous picture of the patient’s condition.
Patients who feel validated tend to rely on and trust their doctors. With this trust built, patients may be more inclined to give doctors more information: this can help in improving clinical decision-making.
Datasaur can help bolster patient care while maintaining privacy and trust. Here are a few of the ways that that's possible:
- Transcribing and classifying medical symptoms and diagnoses from audio recordings of physician encounters.
- Scanning scientific journals and academic papers for promising new medical treatments.
- Classifying and labeling medical claims and billing codes.
- Military-grade security for labeling. (SOC 2 and HIPAA compliant)
Datasaur leverages cutting-edge labeling tech to save as much time and effort as possible. With robust NLP labeling, you can identify all instances of a text span so annotators may easily pass through tedious, repetitive labeling. This lets you build high performance healthcare models on a foundation of quality, labeled data. Contact us to find out more about how Datasaur could transform your labeling workflows.