Healthcare AI
Data provides a life-giving pulse to Healthcare AI.
Collect, De-identify, & Annotate large datasets by domain experts in Healthcare
Featured Clients
Empowering teams to build world-leading AI products.
There’s an increasing demand for healthcare-based innovation, and AI plays a critical role by processing massive data sets that are far beyond the scope of human ability.
80% of all healthcare data is unstructured and inaccessible for further processing. This limits the quantity of usable data and also limits a healthcare organization’s decision-making capabilities. Unless you turn to Shaip.
We have a deep understanding of healthcare terminologies to unlock its potential as a result of years of experience in data transcription, de-identification, and annotation. Add to this we can also deliver the exact healthcare data you need to improve your AI engine.
Industry:
According to a study, 30% of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like pre-authorizing insurance, following-up on unpaid bills, & maintaining records, to ease the workload.
Industry:
As per recent research machine-learning algorithms can analyze 3D scans up to 1000 times faster than what is possible today. It can offer real-time assessment and critical inputs to a surgeon to make a more informed decision.
The global healthcare AI market size is expected to grow from USD 3.64 billion in 2019 to USD 33.42 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 46.21% during the forecast period.
A healthy amount of healthcare expertise
Then we give it structure, and purpose through natural language processing (NLP) that delivers domain-specific insights on symptoms, diseases, allergies, and medications. Now the healthcare community, through Shaip AI data, has the right insights to make better decisions that result in better patient outcomes.
Key Offerings
Data Cleansing & Enrichment
Data Licensing & Collection
Data De-Identification
Data Annotation & Labeling
Data Cleansing & Enrichment
- Converting handwritten data to structured digital format
- Converting unstructured digital data to a structured format
- Data cleaning of patient records, EHR data, etc.
Data Collection / Licensing
AI-enabled companies turn to us to create training data sets so that they can develop cutting-edge machine learning algorithms for the healthcare industry. View our full healthcare catalog.
From advancing care to providing healthcare organizations with a solution to control costs while improving patient outcomes, the right data can power AI and ML to achieve these goals through Shaip. After all, better data means better outcomes.
Readily Available Datasets: View Full Catalog
- 225k+ hours of physician dictation audio and corresponding transcribed records
- 31+ specialties Neurology, Radiology, Pathology, etc.
- 5M+ EHR datasets
Data De-identification
Our PHI/PII deidentification capabilities include removal of sensitive information such as names and social security numbers that may directly or indirectly connect an individual to their personal data. Its what patients deserve and HIPAA demands.
Our proprietary de-identification platform can anonymize sensitive data in text content with extremely high accuracy. APIs extract the PHI/PII entities present in text or image datasets and then mask, delete, or obscure those fields to provide de-identified data
Data Annotation & Labeling
Shaip annotation services can add the much-needed power to boost your AI engine. X-Ray, CT scans, MRI, and other image-based test reports can be easily screened to predict various ailments. We can help you annotate complex healthcare records i.e. text or images to develop your AI ML models.
We can scale to 1000s of people to manage any size project. The outcome? Faster healthcare image annotation to build your models within your timeframe and budget.
APIs
When you need data in real-time you should be able to access APIs just as quickly. This is why Shaip APIs provide real time, on-demand access to the records you need. With Shaip APIs your teams now have fast and scalable access to de-identified records and quality contextualized medical data to complete their AI projects right the first time.
De-Identification API
Patient data is essential in developing the best possible healthcare AI projects. But protecting their personal information is just as essential. Shaip is a known industry leader in data de-identification, data masking, and data anonymization to remove all PHI/PII (personal health/identifying information).
- De-identify, tokenize, and anonymize sensitive data for PHI, PII, and PCI
- Confirm with HIPAA and Safe Harbor guidelines
- Redact all 18 identifiers covered in HIPAA and Safe Harbor guidelines.
- Expert certification and auditing of de-identification quality
- Follow comprehensive PHI annotation guidelines to uniformly de-identify PHI data and adhere to the Safe Harbor guidelines
Comprehensive Compliance Coverage
Scale data de-identification across multiple regulatory jurisdictions including GDPR, HIPAA, and Safe Harbor.
Medical NER
Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. NER APIs empower developers to easily extract clinical entities such as diagnosis, procedure, medical device, labs, medication, and much more from Electronic Health Record (EHR) unstructured data. Developers can also use these APIs to codify extracted entities in SNOMED-CT and RxNorm.
Medical NER extracted by Shaip APIs:
- Entity recognition and extraction: Identify key concepts or phrases present in the source material
- Improve clinical data integrity by mapping data elements present in unstructured text to structured fields.
- Convert unstructured data into the machine-readable and machine-processable format.
- NER APIs leverage proprietary knowledge graph, with 20M+ relationships & 1.7M+ clinical concepts
Real World Solution
Data that powers brings Medical AI to life
Shaip provided high-quality data
for AI models in healthcare to improve
patient care. Delivered 30,000+
de-identified clinical documents adhering
to Safe Harbor Guidelines. These clinical
documents were annotated with 9 clinical
entity
Problem
De-identify and annotate clinical documents from domain experts
Solution
De-Identified & annotated 30,000+ documents per client guideline
Result
Gold Standard clinical data to develop client’s NLP and Healthcare
Comprehensive Compliance Coverage
Scale data de-identification across different regulatory jurisdictions including GDPR, HIPAA, and as per Safe Harbor, De-identification that reduces risks of compromise of PII/PHI
Recommended Resources
Blog
The role of AI in healthcare: benefits, challenges & everything in between
The market value of artificial intelligence in healthcare hit a new high in 2020 at $6.7bn. Experts in the field and tech veterans also reveal that the industry would be valued at around $8.6bn by the year 2025.
Blog
What is Healthcare Training Data and Why is it Important?
Data procurement has always been an organizational priority. More so when the concerned data sets are used to train autonomous, self-learning setups.
Data Catalog
License High-quality
Healthcare/Medical Data
for AI & ML Models
Our medical data catalog datasets are not only massive but have gold-standard quality data. Rest assured that the data you utilize is secure, de-identified.
Tell us how we can help with your next AI initiative.
Frequently Asked Questions (FAQ)
AI in healthcare involves using artificial intelligence technologies to assist in diagnosis, treatment, and patient management.
AI is utilized for disease diagnosis from medical images, personalized treatment recommendations, speeding up drug research, managing medical records, predictive analytics, assisting in surgeries, and offering virtual health assistance.
AI enhances accuracy in diagnosis, boosts efficiency, saves costs, enables personalized treatments, provides predictive insights, and increases healthcare accessibility.
Applications include medical imaging analysis, genomic research, drug discovery, optimizing treatments, remote health monitoring, chatbots for patient queries, and improving hospital operations.
AI manages vast medical data, facilitates early disease detection, optimizes resource allocation, reduces errors, accelerates research, and improves the patient experience.