Our goal is to ignite and accelerate the development of new or improved treatments and clinical solutions that benefit patients – and society at large.

This is an overview of our grant recipients. 

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

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. 

Novel Energy Harvester Technology (NEST) – Aalborg University Hospital

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.

AR-Assisted Surgery – Aarhus University Hospital

The Solnedgangkassen is designed to enable more patients to receive palliative care at home instead of in hospital.

Solnedgangkassen (Palliative Care Box) –Aalborg University Hospital

Pathology AI aims to address the future shortage of pathologists by simplifying and speeding up the diagnostic process using digital image analysis. 

For the past 50 years, the Danish healthcare system has collected detailed information on all deliveries. 

The fMRI fingerprint is an optical fiber technology that allows electrostimulation during fMRI scans.

DoseTracker is a real-time software-enabled tool that monitors the dose of radiation given to moving anatomy during cancer treatments.

DoseTracker – Aarhus University Hospital

One in four people has fatty liver disease without knowing it – and this condition can be asymptomatic for fifteen to thirty years.

Dermloop offers AI-augmented training, clinical feedback, and management of skin lesions suspicious for cancer.

Dermloop – Herlev and Gentofte University Hospital

An upwards of 15% of surgical or injured patients experience avoidable complications, such as infections, thrombosis events or organ failure.

Clinical deterioration is a frequent and unfortunate occurrence among hospital patients.

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.

Root of the Outbreak  - Rigshospitalet 

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. 

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.

Eventually, the team would like to see the Solnedgangkassen available for immediate delivery free of charge through emergency departments throughout Denmark. It's currently being piloted with 25 patients.

Anne Lund Krarup: Research Director, Attending Physician, Associate Professor; Department of Emergency and Trauma Centre, Aalborg University Hospital

Dorte Melgaard Kristiansen: Senior Researcher and Research Coordinator at Regionshospitalet Nordiyllad

Mike Sæderup Astorp: Clinician, Professor; Department of Emergency and Trauma Centre, Aalborg University Hospital

Solnedgangkassen (Pallative Care Box) –Aalborg University Hospital

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 goal is for pathology AI to succeed in quantifying the size and number of cells to support diagnostic safety and efficiency, which will positively affect waiting times and safety in tissue sample diagnostics for patients across the country.

Thomas Lindkær Jensen: Senior Consultant; Pathology Department, Danish National Hospital, Copenhagen. 

For the past 50 years, the Danish healthcare system has collected detailed information on all deliveries. 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 TOLAC App provides individualised risk assessments for women who have previously undergone caesarean sections. It aims to reduce complications and unnecessary operations while assisting clinicians in advising women on the right interventions at the right times during labour and delivery – empowering them to make the best decisions for themselves and their babies.

Lone Krebs: MD, Professor, DMSc; Hvidovre Hospital, Department of Obstetrics and Gynecology

Ida Näslund Thagaard: MD, PhD; Rigshospitalet, Department of Obstetrics and Gynecology

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 (e.g. long-term intensive rehabilitation, spine fusion surgery and implantation of neuromodulators for deep brain stimulation, vagus nerve stimulation and spinal cord stimulation). 

Louise Møller Jørgensen: MD, PhD, Consultant nNeurosurgeon, Associate Professor with fFnction as Innovation Ambassador; Center for Rheumatology and Spine Diseases, Rigshospitalet 

Anders Ohlhues Baandrup: R&D Engineer; University Hospital of Copenhagen - Roskilde 

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, giving the treatment team the support and confidence necessary to make informed treatment decisions and intervene in the event of a suboptimal dose delivery. 

The team’s ultimate goal is to replace the current practice of unmonitored radiotherapy with the use of real-time observations of anatomical changes and their dosimetric impact, instantly guiding adaptations to the planned treatment.

Thomas Ravkilde: Medical Physicist; Aarhus University Hospital

Per Rugaard Poulsen: Professor; Aarhus University Hospital

Bjarne Roed: Executive Innovation Consultant; Prev. Siemens Healthineers

While liver tests are some of the most frequently sampled blood tests, their accuracy remains a bit of a coin toss. The most common liver test, ‘ALAT’,  is only accurate 53% of the time. Evido’s algorithm-based solution aims to change this by detecting advanced fatty liver disease with an accuracy of 87% based on a series of standard blood samples that are already widely used in primary care today. 

Taus Holtug: MSc. Eng. in Design and Innovation; CEO and Co-Founder, Evido

Katrine Prier Lindvig: MD and PhD Student; Center for Liver Research, Odense University Hospital 


The clinical component of Dermloop enables interdisciplinary and cross-sectorial information sharing, enabling a safer and more efficient treatment-flow for skin diseases. This includes rapid virtual diagnostics and surgical planning (telemedicine), image-based follow-ups of borderline suspicious skin lesions, and continuous clinical feedback. 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.  

Niels Kvorning: MD, PhD; Herlev Gentofte Hospital 

Lisbet Rosenkrantz Hølmich: MD, DrMedSci, Professor, Plastikkirurgisk Afdeling; Herlev Gentofte Hospital 

Tine Vestergaard: MD, PhD, Associate Professor; Hudafdeling I og Allergicentret, Odense Universitets Hospital

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, resulting in fewer complications, less use of medication and fewer repeat surgeries.

Martin Hylletoft: Staff Physician, PhD, Associate Professor of Surgery; Copenhagen University Hospital, Rigshospitalet

External advisors in technology and business development from Aiomic ApS.

Clinical deterioration is a frequent and unfortunate occurrence among hospital patients. Clinical deterioration is a serious condition that can lead to respiratory failure, septic shock and death. Unfortunately, recognising the early signs leading up to clinical deterioriation often isn't enough to prevent it.

+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. It warns clinicians so that they can administer broad-spectrum antibiotics, which are proven to reduce the severity of infection while often preventing the dysregulated immune response that causes sepsis. This decreases the likelikood that a patient will need intensive care, thereby lowering treatment costs and reducing length-of-stay while reducing long-term sequelae (i.e. kidney failure, lung impairment, cognitive impairment, decreased quality of life) and death rates.

Ulrick Skipper Espelund: MD, PhD; Department of Anesthesiology and Intensive Care, Horsens Regional Hospital

Jeppe Lange: MD, PhD Orthopaedic Surgeon, Horsens Regional Hospital; Deputy Head of Department of Clinical Medicine, Aarhus University

Marianne Johansson Jørgensen: MPH, PhD; Director of Research,  Horsens Regional Hospital

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. However, hospitals do not have the possibility to track these today.  

This project aims to perform sequencing on a larger range of bacteria than is done today and to improve the epidemiological studies of identified outbreaks by applying Epilinx to identify the root of the outbreak and the patients involved. This can help to facilitate early detection of patient-to-patient spread, leading to the implementation of effective hygiene measures and the launch of intervention studies.

Frederik Boëtius Hertz: Senior Registrar at Department of Clinical Microbiology, MD, PhD; Rigshospitalet 

Karen Leth Nielsen: Senior Scientist, M.Sc. Eng. (BioTech), PhD;   
 Department of Clinical Microbiology, Rigshospitalet 

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. Acute myocardial infarction (AMI) is a condition that requires highly specialised care and subsequent follow-ups to avoid the development of conditions such as renewed symptoms of ischemic heart disease (IHD), left heart failure (HF) and arrhythmias. 

This project aims to improve intervention after AMI by introducing home-monitoring and other support for post-AMI patients who have undergone highly specialised cardiac treatment and who will continue rehabilitation. The project includes home-monitoring of vital parameters, guidance on critical topics (e.g. healthy lifestyle choices), pre-clinic questionnaires, and a communication platform to facilitate text messages between patient and clinic.

Christian Koerner: Director of Digitalisation; Rigshospitalet  

Peter Karl Jacobsen: Chief Physician; Department of Cardiology, Heart Centre, Rigshospitalet  

Ragnheidur Traustadottir: Digitalisation Consultant; Rigshospitalet 

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

Novel Energy Harvester Technology (NEST) – Aalborg University Hospital

AR-Assisted Surgery – Aarhus University Hospital

Solnedgangkassen (Palliative Care Box) –Aalborg University Hospital

Pathology AI - Rigshospitalet

TOLAC App – Hvidovre Hospital

fMRI Fingerprint - Rigshospitalet

DoseTracker – Aarhus University Hospital

Evido - Odense University Hospital

Dermloop – Herlev and Gentofte University Hospital

Real-Time Surgical Data - Rigshospitalet

+priokritisk – Region Hospital Horsens

Root of the Outbreak - Rigshospitalet

Mitigate - Rigshospitalet