Beamtree Holdings Limited (ASX:BMT) a leading provider of AI decision support and data insights solutions, announced today a key strategic partnership with Lean, Saudi Arabia’s leading provider of innovative health solutions. This strategic partnership follows the successful completion of Beamtree’s first major contract to support data transformation in public health services of the hospital network data analytics audit for Saudi Arabia.
Beamtree state it will extend their ability to provide further products and services into Saudi via this strategic partnership with Lean.
Beamtree has now received full payment for phase 1 of the data transformation project of US$1.45m (AU$2m). The next phase is the proposed implementation of Beamtree’s products across Ministry of Health hospitals and then more broadly across public and private hospital networks (over 450 hospitals in total).
The partnership with Lean is aimed at strengthening their offering in the Saudi market. The new partnership with Lean will promote Beamtree products and services in the Kingdom of Saudi Arabia and follows the successful completion of phase 1 of an audit of public hospitals in Saudi. The audit conclusions of phase 1 included strategic recommendations to improve analytics and use of important patient healthcare data for better patient outcomes, reduced wastage and operational efficiency.
Beamtree is a pioneer of AI data and decision support services and operates in 25 countries in the world – Saudi Arabia has put data and decision support at the heart of Vision 2030, its major national health reform program.
Lean is Saudi Arabia’s leading health technology enterprise, working as a key enabler of innovative health solutions in Saudi Arabia. Lean is contributing to digitalising the Saudi health ecosystem and boosting the health sector services by launching sustainable operational products and stimulating partnerships between the public and private sectors.
The partnership aims to support health services in Saudi Arabia improve the quality of hospital data and analytic insight through the audit and automation of clinical record classification.