LeanTaaS acquired healthcare automation company Hospital IQ. The two companies made the deal because their products complement each other — Hospital IQ provides staff optimization solutions, while LeanTaaS makes software to optimize capacity for operating rooms, infusion centers and inpatient beds.
Health systems are seeing increased operational efficiency after applying machine learning analytics to their operating room block scheduling. Given the multitude of challenges health systems face — from increased labor costs to material shortages to reimbursement woes — some health system digital executives say it’s critical that hospitals incorporate machine learning into their block schedules sooner rather than later.
One of the biggest sources of patient dissatisfaction is waiting. LeanTaaS founder and CEO Mohan Giridharadas says hospitals are much better off using SaaS products embedded with sophisticated artificial intelligence to optimize their capacity rather than relying on their EHR capabilities or in-house data analysts if they want to see measurable results.
After a challenging 2020, health systems are looking to deploy strategies for recovery, especially to bolster financial health. One strategy health executives are considering is investing in predictive modeling technology that can help them manage patient demand and outcomes in the coming year.
The health technology company raised a whopping $130 million in a Series D funding round. Its solutions aim to improve operational efficiency and better manage patient volume through predictive analytics, a growing need for providers as Covid-19 cases rise.
The California-based software company is focused on hiring staff, developing new products and convincing healthcare executives to take its products for a spin.
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The app is part of a larger effort in which LeanTaaS taps into non-sensitive electronic health records data, then builds predictive models to forecast usage patterns and allocate OR time efficiently.
The company's analytics tools calculates a wide range of factors to optimize patient appointments.