Immediate Release of CapsicoHealth’s Autosoma™
Industry's First Cloud-Based No-Code AI workbench for the Healthcare and Lifesciences Markets

January 4th, 2022

After 2 years of validation with partners, we are very proud to announce during the 2022 JP Morgan week, health industry’s first cloud-based “no-code” AI workbench Autosoma™ to enable scientists and clinicians to rapidly develop, validate, and deploy predictive models, and generate healthcare insights from tens of billions of data points using robotic automation tools, all while gathering knowledge from published clinical and cost models for the health industry. Multiple challenges drove us to build this product during the pandemic.

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Workforce challenges and spiraling costs

Over the past few years our team found it hard to hire personnel who could combine multiple skills: data management and analytics with healthcare knowledge for value-based care and insights. We decided to pack in the smarts into a “Robo-scientist” that can assist human scientists and clinicians to sift through massively large information sets (such as EMR, claims, surveys, RPM and life-science data) by leveraging healthcare knowledge for insights and predictive model development in multiple disease areas.

Zero tolerance on poor data quality and errors

Data errors (often due to fatigue) during intake drives wrong decisions and sub-optimal care. Our workbench employs several clinical and business rules to ensure high quality data for AI/ML modeling and analyses. The workbench eliminates hundreds of hours of manual steps, data errors, and related costs, while empowering healthcare stakeholders to focus on reducing biases, selecting the right patient cohorts and building appropriate models based on factors such as population demographics, diagnoses, social and environmental.

Learning from peer-reviewed work, and driving collaboration

Often engineers and scientists do not have enough time to review the past clinical models or just ignore past results that have been peer-reviewed or published on specific diseases or outcomes. The workbench empowers analysts, clinicians and scientists to leverage a vast pool of predictive model registries and catalog-search prior to developing new models around hospitalization risk, resource utilization and cost forecasts, and allowing collaboration with clinicians on the appropriate risk models.

Robo-scientists to automate and reduce costs

Our team developed several hundred predictive models, tested and validated them over a short period of time using the AI workbench. A percentage of the prediction models were developed by Robo-scientists which performed nearly as well as a human data scientist in selecting the right data and models. We envision a future where Robo-scientists will be employed by humans to analyze complex data sets and assist decision-makers and clinicians.

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