165 coordinated about discharge planning. By studying historical datasets, Signal 1 tailors their algorithm to each hospital’s local population to provide reliable predictions on which patients will be ready for discharge in the next 48 hours. Signal 1 integrates these notifications into a hospital’s existing workflows so that high priority patients are surfaced to multidisciplinary teams at the right time. This provides hospital frontline teams with better visibility on which patients they should prioritise for discharge activities and what is required to enable that. However, before you can determine if a patient is discharged, you have to properly diagnose them. Clinicians spend more than 35 per cent of their time capturing patient data, symptoms, family histories, lab results and other key information in the form of digitised patient records. Despite this, more than 60 per cent of patients with rare and hard-to-diagnose conditions are not diagnosed in a timely manner. This is due to a lack of scalable and speedy characterisation of patients, which is based on clinically actionable intelligence from such records, and helps clinicians map patient journeys and disease trajectories. And this is where another business shines. Founded in 2018, Pangaea Data is a life sciences technology firm that provides Pangaea’s Intelligence Extraction and Summarization (PIES). The solution is driven by novel, unsupervised AI to characterise patients across 4-5,000 hard-todiagnose conditions in a scalable and privacy-preserving manner, much like a clinician does manually using all relevant data from a patient’s record. Pangaea’s unsupervised AI reduces the bias observed in supervised natural language processing and text mining approaches since its does not require a preempted list of features to extract from a limited set of textual notes. It requires significantly less data to start with, since it already has a library to characterise patients across multiple conditions, combined with a medical knowledge base. PIES has a proven track record of success across different disease areas such as oncology, respiratory, cardiovascular, auto-immune, neurodegenerative and mental health. For example, in a dataset of 8,000 cancer patients, 51 had previously been identified as having a condition called cachexia, which causes unintentional weight loss, based on their patient records. PIES correctly identified the initial 51 patients and then found an additional 316 who also had the condition but were undiagnosed, misdiagnosed or miscoded. Following this study, PIES was deployed on a larger dataset of around 29,000 patients where it found 1052 per cent more cancer patients with cachexia, who were hidden in plain sight. These results were validated with help from clinicians. Both Signal 1 and Pangaea Data are helping improve patient outcomes by, using data and making it more readily available for analysis, generating analyses that result in more consistent care, and enabling clinicians to use data insights that drive effective care pathways. Sally Ann Frank is worldwide lead for health and life sciences at Microsoft for Startups Photo composite: Freepick/Microsoft and Pangaea HEALTHCARE
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