HealthCare and LifeSciences - Recosense Labs Inc

Recosense Labs Inc

HealthCare and LifeSciences

Healthcare and Lifesciences

Artificial Intelligence is revolutionizing Health and Life Sciences by enhancing diagnostics, drug discovery, personalized treatment, and patient care. From predictive analytics to image analysis, Recosense AI's potential to analyze large amounts of unstructured data (patient records, medical journals, and research articles) will transform the healthcare and life science sector.

RecoSense - Content Intelligence for Aviation

Invoice Processing

Invoice Processing

Swiftly process invoices with high accuracy. Hospitals and clinics can easily manage payments for services, medications, and equipment.

Patient Reports

Patient Reports

With RecoSense AI, doctors can efficiently generate and analyze patient reports, allowing them to make well-informed decisions about treatments and track the progress of their patients more effectively.

Patient Admissions

Patient Admissions

Streamline the patient admission process.
Allocate rooms, schedule procedures, and ensure prompt services to patients

Compliance Management

Compliance Management

Adhere to regulations and standards by
automatically monitoring and flagging any deviations or potential issues, ensuring a higher level of patient safety and data privacy.

MRI Acceleration

MRI Acceleration

Advanced computer vision and character identifications enhance the speed and the quality of information extracted from MRI scans leading to quicker diagnoses and improved patient experience.

Patient ID

Patient ID

Secure patient identification, ensuring accurate records and preventing confusion or mix-ups between patients. Provide the right care to the right person.

Medical Forms

Medical Forms

Automate the medical forms filling process based on the extracted data. The AI can even assist in automatically populating form fields across all departments and reduce human efforts.

Referral Management

Referral Management

Efficiently track and manage patient referrals to specialists. Ensure that patients receive timely consultations and collaborate better among different healthcare providers.

Medical Claim Automation

Medical Claim Automation

Simplify and accelerate the process of filing medical claims with insurance companies. Minimize the paperwork and administrative hassles for both healthcare providers and patients.

Health Records

Health Records

Patient health records are maintained digitally so that patient history and data are readily available for healthcare professionals and help them make informed decisions about treatments.

Use Cases

Aviation

Clinical Assertion Model

Helps doctors and medical professionals determine whether a statement in a patient's medical records is positive, negative, or uncertain. For instance, it can help identify if a symptom is present or not based on the notes in the records.

Clinical Deidentification Model

This model protects patient privacy by removing or masking personal information from medical records. It ensures that sensitive details like names and addresses are hidden while still allowing healthcare data to be used for analysis and research.

Clinical Entity Resolver

If there are different versions of a patient’s name or medical term found in various records, this model identifies the redundant data and marks them to one single patient’s file. This helps in keeping patient information accurate and coherent.

Clinical Named Entity Recognition General Model

This model identifies and extracts required key entities like names, medications, claim details, etc. from the records. This helps organize and understand medical records better.

Clinical Relation Extraction Model

This AI model looks at medical texts, figures out which terms are related, and helps identify the relationships between different pieces of medical information.

Financial Contract Named Entity Recognition

An NER model trained specifically for financial purposes. It can read and understand contracts related to medical services and insurance. It identifies important bits like payment terms, dates, and financial entities mentioned in the contracts.

Benefits

Auto Extraction with NLQ

Auto Extraction with NLQ

Data extraction becomes streamlined through Natural Language Queries (NLQ). Complex data sets are effortlessly analyzed and queried using plain language, yielding precise results for informed decision-making.

Easy Integration

Easy Integration

Seamlessly integrate into existing infrastructure, ensuring compatibility and synergy with established systems. This facilitates a smooth adoption process while enhancing the capabilities of current tools.

Reduced manual efforts

Reduced manual efforts

Automation capabilities alleviate the burden of repetitive tasks, optimizing resource allocation. This amplifies workforce efficiency, allowing professionals to focus on high-value responsibilities that require human expertise.

Improved Intelligence

Improved Intelligence

Data-driven insights empower strategic decision-making. Its advanced analytics discern patterns, forecast trends, and provide actionable recommendations, augmenting the intellectual capacity of healthcare and life science operations.

Unified Data Platform

Unified Data Platform

All the extracted and processed data are stored in a single unified data platform to ensure easy access to data throughout the healthcare organization.

Smart Search with Natural Language

Smart Search with Natural Language

Natural Language Processing empowers users to articulate queries in everyday language, obtaining accurate and relevant results swiftly, thereby expediting decision-making processes.

Blogs

Using AI and Automation in the Finance Back Office

With the advent of Artificial Intelligence (AI), Machine learning (ML), Natural Language Processing(NLP), and Robotic Process Automation (RPA), businesses can streamline workflows, cut costs, and foster sustainable growth amid market challenges. Despite advancements in enterprise resource planning (ERP) systems, a … Read More

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Top Trends in Investment Banking

The investment banking sector is currently grappling with several challenges. These include dealing with capital charges, digital adoption, rigid cost structure, intricate and layered technological stacks, and increased regulatory demands. As a result, various investment banks shifted their emphasis from … Read More

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