An 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. The collection of this information is what creates a health record. This can be as small as a recommended practice the doctor prescribed or an illness one has endured. This is all stored online for doctors to understand one’s issues better and helps in improved diagnosis.
As more information is added to the system, healthcare workers feel the pressure of the looming regulations that have come forth from the continued development of EHR systems. The recent conversion from the EHR Incentive Programs to the Medicare Access and CHIP Reauthorization Act (MACRA) has left many providers feeling overburdened by the plethora of information provided. It makes it demanding for health workers to extract useful information. This is where Natural Language Processing (NLP) comes to aid the health workers by assisting them to make sense of the data provided.
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.
How Natural Language Processing Is Being Used in the Medical Community
NLP is slowly becoming the norm in the medical community. It helps in efficient work and saves precious time for healthcare workers. These are some of the ways NLP helps in creating a better environment in the medical community:
Better Quality Care
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 Centres 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. It is important to pay attention to how the patient feels as it helps in providing top-tier patient care services. It also improves the overall experience of a patient. It helps identify unhappy patients and showcases steps to prevent poor experiences. If a patient is happy with their experience with a clinic or hospital, they will be sure to visit again whenever they need medical assistance: this helps in patient retention. The NLP algorithms help identify appellations that help in improving patient care:
-Respect among colleagues
Improved doctor-patient relationships
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.
Using Datasaur for Easy Medical Transcription
Datasaur believes in accelerating patient care while maintaining privacy and trust:
-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.
Datasaur leverages innovative labeling tech to ensure you save as much time and effort as possible. It is a powerful labeling tool that helps to identify all instances of a text span so annotators may easily pass through tedious, repetitive labeling.
NLP in healthcare is becoming popular due to its potential to search, analyze and interpret stupendous amounts of patient datasets. Using advanced medical algorithms, machine learning in healthcare and NLP technology services have the potential to utilize suitable insights and concepts from the data buried in text form. NLP in healthcare media can accurately grant a voice to unstructured data of the healthcare Industry, giving spectacular insight into understanding quality, improving methods, and better results for patients.