BETA.HEALTH innovation grants aim to boost innovation projects at Danish hospitals.

Up to 30% of hospitalised patients experience severe complications that increase their risk of permanent injury and death. Manual NEWS-based monitoring processes place unnecessary demands on clinicians' time without improving outcomes. WARD-CSS saves time while providing 24/7 vital-sign monitoring.

Each year, 5,000 people in Denmark suffer out-of-hospital cardiac arrest (OHCA) – 4,400 of them do not survive. While the number of registered AEDs in Denmark has increased over the past 20 years, only 8.7% of patients are defibrillated before an ambulance arrives. That's because most incidents happen at home, where an AED isn't available. This project aims to produce an affordable and easy-to-use AED specifically for use in private homes.

Diabetes is on the rise, and with it, cardiac autonomic neuropathy (CAN) – a severe and possibly fatal manifestation of dysfunction of the autonomic nervous system. The ProCVT Smart-Sheet is a wireless ECG monitor intended for sleep recordings, which reduces the influence of external factors while helping clinicians to detect CAN and quantify the severity of dysautonomia.

Half of the 30,000 patients admitted to Danish ICUs each year require mechanical ventilation because they can’t breathe on their own. For patients who are expected to require mechanical ventilation for an extended period, a tracheostomy is often placed. trachAI uses performant explainable AI software to help physicians assess the likelihood that a patient will require mechanical ventilation for longer than a week.

80% of surgically treated middle-ear pathologies are caused by Eustachian Tube Dysfunction (ETD), which is often diagnosed only after complications have developed. This new medical device facilitates quicker and easier diagnosis of ETD by streamlining the testing process and using an algorithm to interpret and summarize the data.

Organ transplants save many lives but the body can attack the foreign organ, resulting rejection. This project uses organ-derived cfDNA to directly measure organ damage, which the team hopes will help to predict organ rejection much earlier and with higher precision than other known markers – enabling better optimisation of immunosuppressive therapy.

This automatic dose-planning solution consists of software-driven, site-specific ‘treatment templates’ that automate a large part of the standard radiotherapy planning process associated with cancer treatment. This frees up time and resources, enabling treatment planners to focus on particularly challenging cases.

Involuntary muscular activity causes repeated displacement of ultrasound probes during percutaneous heart valve procedures, causing stress and poor ergonomics for cardiologists and prolonged anesthetic exposure for patients. EchoVice essentially acts as a 'third hand' to stabilise the probe during these procedures.

Gait training is a fact of life for many patients with orthopedic or neurological conditions, but it's often tedious. HearWalk captures and synthesises motion-related data into real-time feedback that’s communicated via auditory stimuli, which has been shown to increase focus and improve patient compliance.

The Novel Energy Harvester System Technology for Self-Powering Intracardiac Pacemakers (NEST) creates a new generation of pacemakers that charge themselves through 'motion energy' every time the heart muscle moves.

New technology can enable surgeons to visualise complex anatomical structures while preparing for surgery. This project aims to bring 3D technology to clinicians who can benefit from a 3D representation of certain anatomy.

The Solnedgangkassen is designed to enable more patients to receive palliative care at home instead of in hospital. The box contains a standardized assortment of the necessary medicine, instructions and information to help caregivers provide a high standard of in-home care for patients in the late-terminal phase.

Pathology AI aims to address the future shortage of pathologists by simplifying and speeding up the diagnostic process using digital image analysis. In this project, machine vision observes the same area as the pathologist, accumulating 'experience' based on the pathologist’s input. 

The TOLAC App uses data-driven software to mine these extensive datasets and create personalised risk profiles for women in specific clinical situations to better predict the probability of a successful vaginal birth after a prior caesarean delivery.

The fMRI fingerprint is an optical fiber technology that allows electrostimulation during fMRI scans. This tool help will help clinicians predict the patient’s response to expensive therapies.

DoseTracker is a real-time software-enabled tool that monitors the dose of radiation given to moving anatomy during cancer treatments. It calculates and compares the administered radiation dose with the planned dose and provides a complete overview of clinically relevant metrics to help clinicians make informed decisions.

One in four people has fatty liver disease without knowing it – and this condition can be asymptomatic for fifteen to thirty years. Evido’s algorithm-based solution can detect advanced fatty liver disease with 87% accuracy based on a series of standard blood samples that are already widely used in primary care today. 

Dermloop offers AI-augmented training, clinical feedback, and management of skin lesions suspicious for cancer. With approximately 400,000 skin-related referrals in Denmark each year, this improved diagnostic flow stands to significantly reduce societal costs while ensuring that almost all skin cancers are diagnosed and treated with minimal delay.  

An upwards of 15% of surgical or injured patients experience avoidable complications, such as infections, thrombosis events or organ failure. This project uses AI-enabled analysis of real-time data (e.g. chart notes, lab results, vital-sign registrations) for individual patients to predict 18 different post-surgical complications.

Clinical deterioration is a frequent and unfortunate occurrence among hospital patients. +priokritisk is software that uses a model based on explainable (xAI) to analyse selected parameters from patients' electronic medical records (EMRs) to predict those who are at risk of developing sepsis.

Acute hospitalisation requires a special effort to involve patients in their own treatment and ensure that they're aware of their disease's progression – or the development of new symptoms requiring attention. This project aims to improve intervention after acute myocardial infarction by introducing home-monitoring and other support for patients who have undergone highly specialised cardiac treatment and who will continue rehabilitation.

Today, only a very limited subset of antibiotic-resistant bacteria is sequenced. Hence, we only see the tip of the iceberg when it comes to bacterial outbreaks as there is also definite transmission with fully antibiotic-susceptible bacteria. This project aims to change that by sequencing a larger range of bacteria than is done today.