Artificial Intelligence in Healthcare
Streamline unstructured data to overcome everyday challenges. Simplify data analysis, derive greater insights, and deliver personalized care to patients with healthcare NLP.
Next-gen Healthcare AI
Next-gen Healthcare NLP leverages the power of sophisticated Natural Language Processing (NLP) to transform unstructured medical data into actionable insights. Built on a large language model (LLM) that’s been fine-tuned on an unprecedented scale of real patient charts, this innovative tech offers unprecedented precision and speed in processing and understanding complex healthcare data. From enhanced annotation services to custom model training, it delivers a comprehensive solution that drives improved outcomes, operational efficiency, and data security.
- Large Language Model in Healthcare: Harnessing a LLM fine-tuned on 30 mn real patient charts, HealthcareNLP provides unparalleled precision in processing unstructured medical data.
- Enhanced Annotation Services: Leveraging our advanced LLM, our refined annotation services extract critical medical information with speed and accuracy.
- Cloud Independence & On-Premise Hosting: We prioritize flexibility, offering cloud-independent solutions & on-prem hosting options for superior data control & security.
- Fixed Pricing, Unlimited Processing: Our straightforward, fixed-cost model ensures unlimited document processing with no hidden fees for scalable, predictable ops.
- Custom Model Training: Offering tailored model training on our LLM using real-world, deidentified patient data, we ensure robust and privacy-compliant healthcare apps.
The strongest clinical NLP APIs that deliver speed and simplicity
Extracting meaningful clinical entities from unstructured clinical data
API for De-identification of Protected Health Information (PHI), that strips all “direct identifiers” i.e all information that can be used to identify the patient.
SnoMed & RxNorm
Implement an API for medical billing and coding that utilizes Natural Language Processing (NLP) to scrutinize and derive Snomed CT and RxNorm identifiers.
Clinical API that inspects laboratory test orders and results. Unlock medical laboratory observations for identifiers, names and codes using our NLP.
Highly accurate API for medical coding that extracts billable ICD-10-CM and PCS codes from patient encounter documents at the click of a button.
Named Entity Recognition (NER)
Clinical NLP API that extracts medical entities, its context and relationship from large chunks of unstructured clinical data using Deep Learning NLP Models.
Tailor-made for personalized needs. Do you have a specific requirement? HealthcareNLP’s team of researchers and engineers will build it, especially for you.
Shaip’s Healthcare AI Benefits
Our NLP model has high accuracy in processing medical text.
No coding or NLP knowledge is needed. Get started in a matter of seconds.
Access simplified NLP implementation and usage.
Adapt and fine-tune to your organization's unique needs and requirements.
Integrate it with your existing healthcare systems and workflows seamlessly.
Highest Standards of Privacy & Security
Our Natural Language Processing (NLP) technology is designed and implemented with stringent measures to ensure complete safety and security.
- State-of-the-art encryption protocols
- Secured data storage
- Adherence to HIPAA and GDPR
Healthcare NLP is the application of Natural Language Processing technologies in the healthcare sector to extract, process, and understand complex medical data from various sources, including electronic health records, clinical notes, research papers, and patient feedback, among others.
NLP in healthcare can be used for disease prediction and diagnosis, treatment pathway recommendations, understanding patient sentiment, automating data entry, optimizing billing processes, health monitoring and alerting, and much more.
NLP can help healthcare providers to better understand a patient’s history, symptoms, and concerns, leading to more accurate diagnoses and personalized treatment plans. It also allows for the efficient processing of large amounts of data, facilitating research, predictive modeling, and proactive healthcare management.
Some challenges include dealing with unstructured and non-standardized medical data, ensuring data privacy and security, overcoming language and cultural barriers, and integrating NLP systems with existing healthcare IT infrastructure.
Healthcare NLP must comply with all relevant data privacy laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. This can involve anonymizing data, obtaining patient consent, and implementing strict data security measures.
Yes, Healthcare NLP can be a valuable tool in telemedicine by facilitating remote patient monitoring, interpreting patient’s spoken or written language in real-time, and helping physicians to diagnose and treat patients remotely.
NLP can assist in medical research by automating the process of literature review and data extraction, identifying patterns and trends in large datasets, and helping researchers to make sense of complex medical terminology.
Yes, by analyzing patterns in patient data and medical literature, NLP algorithms can predict the likelihood of diseases. These predictive models can assist physicians in early detection and preventive care.
NLP can extract and interpret important clinical information from EHRs, such as diagnoses, symptoms, and treatments. This can help healthcare providers to make better use of EHR data, leading to improved patient outcomes.
The future of Healthcare NLP may involve more sophisticated understanding of medical language, real-time processing of patient data, and seamless integration with other healthcare technologies. It holds the potential to revolutionize patient care, medical research, and healthcare administration.