Data provides a
life-giving pulse to
Collect, De-identify, and Annotate
large datasets by domain experts in Healthcare
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.
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.
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
AI-enabled systems are not going to completely replace human medical experts. But this technology will enhance their capabilities and effectiveness by automating the most repetitive activities prone to errors.
At Shaip, we believe data can positively impact the health of a global population. It’s evident in our cognitive data collection, de-identification, and annotation services. We help organizations to unlock new and critical information found deep within unstructured data i.e. physician notes, discharge summaries, and pathology reports. 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.
Data Cleansing & Enrichment
Data Licensing & Collection
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
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.
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.
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). Medical NER APIs can auto-identify and classify the named entities present in a text document, helping to convert unstructured data into structured data
- De-identify, tokenize, anonymize sensitive data for PHI, PII and PCI
- Conform with HIPAA and Safe Harbor guidelines
- Redact all 18 identifiers & scale data de-identification across multiple regulatory jurisdictions i.e. GDPR, HIPAA, & Safe Harbor.
- 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
- NER APIs leverage proprietary knowledge graph, with 20M+ relationships & 1.7M+ clinical concepts
Clinical Named Entity Recognition (NER) is a critical 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.
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..
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
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
Tell us how we can help with your next AI initiative.