One percent of the population - mostly elderly patients with multiple chronic conditions - consume 53 percent of all hospital bed capacity for unplanned care. Their conditions progress and ultimately, if nothing is done, they have little alternative to acute care. HN Predict is designed to particularly benefit this cohort of patients, and the healthcare providers who care for them.
Our data driven approach is designed for equitable health and can overcome recognised biases and inequalities built into traditional case finding and patient referral models. Having timely access to precise information about individual patients enables healthcare providers to better guide clinical decision-making to optimise secondary prevention. This generates better patient outcomes and releases pressure on frontline clinical staff and system.
As we developed HN Predict, more risks and future patient outcomes have been incorporated into the platform. Today, HN Predict can provide highly meaningful predictive information, on individual patient level, about a number of events that guide clinical decision making and preventative actions at point of care.
HN Predict collects and processes unstructured and structured healthcare data through an IG-compliant, secure cloud-based AI engine.
The AI analyses each individual patient’s data by using validated algorithms and provides a list of highlighted patients with elevated risks for certain events such as unplanned hospitalisation, rapid frailty progression or falls. This is information that can be used to better prioritise or to actively reach out to those at high risk for targeted prevention.
HN Predict can run on different primary, secondary or integrated datasets and feedback securely to be accessed at point of care through integration with existing EPRs or/and separate dashboards.
It is fully configurable enabling clinicians to define the most important events, outcomes, and patient target groups to predict.
HN’s research shows that many unplanned clinical events – including deaths in patients with chronic conditions – are to a large part predictable and preventable events. This means they can be anticipated well in advance and prevented.
In clinical trials, HN Predict demonstrated that it can reduce unplanned admissions and deaths while increasing Patient Reported Outcomes Metrics (PROMs). The positive impact was most pronounced in socioeconomic vulnerable patient groups.
Eight out of ten patients who will experience an unplanned hospitalisation can be identified while they are still at home, and while there is time to provide preventive support.
HN Predict meets the NHS DTAC standard and the Cyber Essentials Plus requirements. Elements of the HN Predict technology is undergoing PCT patenting.