Capturing customer data from continuity of care documents (CCDs) is crucial for prior authorization approval, but is typically a time-consuming, manual process that distracts from patient care and inundates healthcare providers with ever more paperwork.

The current prior authorization process for CCDs is tedious, time-consuming, and inefficient. But automating this process – which involves several different steps – is complicated, with plenty of potential delays and errors that could crop up.

This white paper explores how various machine learning approaches – including intelligent extraction and the BERT Q&A model – can offer an orchestrated, coherent way to successfully automate prior authorization. We explore how these and other techniques can automate, configure, and optimize individual prior authorization tasks using advanced process automation.

CapeStart helped a major multinational digital health company develop a multi-omic, AI-powered classifier to predict patient responses to checkpoint inhibitors. Checkpoint inhibitors are a class of immuno-oncology agents often used to treat solid tumors.

The client needed a reliable, knowledgeable, experienced and affordable partner to analyze and label a large batch of de-identified multislice CT images. This data would then be used to train and validate the AI classifier, which would be used to drive better health outcomes for patients using checkpoint inhibitors.

Download the case study to learn how the CapeStart team’s extensive experience working in multislice PET scans – along with its innovative data labeling tool, ProNotate – helped them quickly identify and annotate images of cancerous lesions in the lungs.

A major healthcare provider needed an experienced, innovative partner to help the development of an AI-based natural language processing (NLP) classifier. The client’s goal was to use the classifier to automatically identify and categorize Breast Imaging Reporting and Data System (BI-RADS) scores, by extracting and understanding text from a dataset of breast radiology reports.

However, to be effective, the classifier needed to be trained. This required a large number of text-based mammography reports to be manually evaluated for one of the six BI-RADS categories, along with patient comorbidity indicators.

Download the case study to learn how CapeStart’s extensive experience working in medical NLP, mammography, and medical data of all types helped them quickly and efficiently label this large dataset of text-based mammogram reports with BI-RADS categories, allowing the client to train their NLP algorithm to yield very high accuracy.