Predict 30% of ICU admissions 7 days in advance
An innovator and thought leader in digital health, Jose Almeida has enabled several globally recognized ‘Advanced Analytics & Big Data’ solutions. On HealthCode.io, he wants to help others build smart risk-prediction tools for hospitals.
Director of Analytics & Data Science, São João Hospital Center
Porto, Portugal
Impact Areas: Big Data & Analytics
Improved Clinical Procedures & Clinical Decision Making
Role(s): Co-creation, Mentoring
Stage: Phased out
My innovation story: I led the development of a solution called ‘HVITAL’ that transforms hospital big data into actionable insights at the São João Hospital Center, Portugal. Clinical teams are now empowered to act swiftly to save lives. HVITAL monitors and analyses in real-time clinical data scattered across different systems to automatically identify and promptly alert patients at risk of clinical deterioration and nosocomial infection. For example, our solution has demonstrated its ability to predict 30% of intensive care unit admissions at least 7 days in advance, making it one of the most advanced hospital big data platforms in the world. This clinical-decision support system has helped the 1200 bed hospital save €1.5 million in expenses.
In 2014, HVITAL won the Microsoft Health Innovation Award (USA) and the IT Europa ‘Big Data Business Intelligence & Analytics Solution of the Year’ Award (London). More recently, in November 2016, we have been the proud winners of the HIMSS-Elsevier Digital Healthcare Award.
My HealthCode.io journey: I would like to connect with my peers and other HealthCode.io members in the digital health sector to share the work we have done at the São João Hospital and help others implement similar solutions, while also learning from them in return. I am open for remote consultancy on using ‘Big Data’ and ‘Advanced Analytics’ in a hospital environment and developing novel tools that find patterns in medical records. These can assist in critical areas like hospital-acquired infections (MDR/XDR microorganisms), antibiotic stewardship (and its misuse) and risk prediction of clinical deterioration (‘failure to rescue’ scenarios).
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