Healthcare workers want to spend more time caring directly for patients, and less on paperwork. Natural language processing (NLP) is a game changer. It can take over a lot of this work, freeing healthcare personnel to spend more time on direct care.
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written — referred to as natural language. It is a component of artificial intelligence (AI).
NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.
An overworked workforce has been a known problem in the healthcare industry long before the COVID-19 pandemic. The ever-increasing use of tech adds more layers of documentation forcing healthcare providers to spend their valuable time on reporting and documentation, which is otherwise reserved for patients. This is where CloudMedx’s NLP platform can step in to provide some much-needed respite using natural language processing to automate many of these processes. Highlighted below are some areas where we are using NLP for improved productivity for IT, clinical and back-office staff.
Automates Data Ingestion Processes – fast, scalable, and versatile
Making sense of medical records aggregated from diverse sources is laborious and time-consuming. CloudMedx’s NLP pipeline can efficiently ingest medical records from one or multiple healthcare facilities. Our versatile platform parses structured and unstructured data stored in diverse formats (CSV, XML, JSON, HTML, RTF, pdfs, and more), collected by various means (OCRs, APIs, scanned), from various sources (EMRs, third-party solutions, patient-reported outcomes, etc.). The NLP understands the context of ingested files, flags them accordingly and makes them ready for downstream use.
Our NLP Parser can parse a medical record in less than a second and generate relevant information such as ICD-10, CPT, and procedure codes. It identifies medications, hospitalizations, sensitive terms, body parts, pain scores, length-of-stays, and much more. With our NLP parser, you can quickly convert unstructured text to a standardized structured format.
Automated Data Normalization for Improved Interoperability
If your data comes from multiple entities, or you have been in healthcare for a while, you have faced problems arising from differences in how the data is coded or presented. CloudMedx NLP extracts key clinical and non-clinical terms from these data sets and provides results with standardized vocabularies and terminologies regardless of the data source – such as conversions from PDF, CCDA etc. to FHIR.
In addition to normalizing data with standardized vocabularies and terminologies, CloudMedx NLP also normalizes healthcare providers’ diverse documentation and reporting habits. Our transformer was trained on millions of medical records from thousands of distinct medical practitioners. It was designed to understand different abbreviations of the same term, such as ‘l shoulder’ or ‘l shldr,’ even if they are misspelled. For example, the transformer will understand the misspelled words ‘pruritus,’ ‘sagittal,’ and ‘humerus’ in sentences such as:
Predictive Insights using ALL Relevant Data Points (including unstructured Doctor Notes)
Medical records contain a host of diverse content with 80% of the data being in unstructured format. The information within that unstructured content (such as SOAP notes, discharge summaries, etc.) is not used in analytics or downstream applications in an automated manner in most healthcare organizations.
At the heart of our platform is a transformer, state-of-the-art NLP module trained on millions of diverse medical records. It can ingest complete patient history (including unstructured text) and use all the information to predict disease progression, chances of hospitalization, and the corresponding length of stays with industry-leading precision and accuracy.
Improving Risk Adjustment Accuracy
Successful risk score calculation depends on successful data collection, which comes from disparate sources such as enrollment files, medical claims, pharmacy claims, and supplemental files containing diagnoses discovered after claim adjudication. CloudMedx NLP pipeline can automate this process, extract RA-eligible records, validate scores and prevent costly audits.
Please click here to learn more about our Risk Adjustment Solution – RA Analyzer.
Automated Registry Building
We also provide complete end-to-end automated workflows from data ingestion and extraction to saving them in registries such as the Chest Pain MI Registry, the AFib Ablation Registry, the CathPCI Registry, the EP Device Implant Registry, the Impact Registry, Kidney Registry, and more.
Please click here for a case study for automated registry building.