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Cardiac arrhythmias (CA) frequently result in emergency room visits and hospital admissions, which is a significant economic burden on the healthcare budget. Telemedicine services may reduce costs and add patient value as these services are affordable and scalable. HartWacht is an eHealth infrastructure which monitors patients with cardiac arrhythmia. Patients use an ECG device which is connected to the patient’s smartphone. ECGs can be registered in case of CA and are automatically uploaded to the personal electronic patient file and interpreted by a dedicated team of healthcare professionals located in the eHealth control centre. HartWacht is in its prototype phase and is further developed and piloted by Heart for Health ICT and Cardiologie Centra Nederland. Preliminary analysis showed a 40% reduction in emergency visits and hospital admissions for patients with HartWacht after one year. Here, we aim to assess the patient value and cost-effectiveness of HartWacht in a randomised controlled trial, in which two homogenous groups of patients with CA are 1:1 randomised to standard regular care with- and without HartWacht. The initial experimental setting has been endorsed and evaluated by the Nederlandse Vereniging voor Cardiologie (NVVC), who recognised the potential for a national upscale within the reimbursed healthcare system, if the costs of HartWacht are significantly lower on an annual basis than with regular care, the quality of care is maintained, and the quality of life and patient satisfaction are improved. In this study, the patient value of HartWacht is assessed by including (new) arrhythmia detection, medication intervention based on ECG data, patient satisfaction and feeling of safety as endpoints. Cost-effectiveness is evaluated by the number of hospital admissions and the number of visits to emergency rooms and outpatient clinics for CA.
Society is changing into an urbanizing, growing and aging population causing increased pressure on sustainability of current healthcare organization. New digital technology enables patients to increase requests for healthcare delivery at home. This technology facilitates the shift of expert knowledge from ‘hospital-to-home’ by tele-diagnostic, tele-therapy, tele-prevention and tele-education. CARE-Vision aims to support homecare professionals, patients and their caregivers in their daily needs, using an intuitive headcam system to transfer expert contextual knowledge and skills. This way they are empowered with the right set of digital tools enabling them to instantly connect with a relevant expert. The expert remotely receives detailed contextual visuals and auditive information to assist with the diagnosis, treatment and prevention of problems encountered in homecare and family practices. As such quality care can be provided without consultation of general practitioners and paramedics. This will relieve the stress of the healthcare system and personnel, reducing costs, hospital admissions whilst enabling patients to stay longer at home, safely and healthy. The CARE-Vision headcam system enables users to safely and easily live-stream their view, with a zoom- and wide angle lens, simultaneously capturing detailed and situational awareness respectively. Communicating important contextual information regarding the situation and problems of the patient. CARE-Vision has several advantages above other innovative systems like smartphones and glasses. Namely, it does not interfere with the view of the beholder, enables handsfree usage thanks to its novel user interface, has beamed light for clear vision, allows two-way communication, will be ergonomically optimized and designed according to medical standards. IMDI’s support enables CARE-Vision to further develop the technology (show), exploring potential benefits in homecare (transfer1) with the aim to finally validate the technology in different situations and integrate in a virtual connected care system (transfer2).
Equinus or equinovarus foot after stroke and cerebral palsy is caused by a combination of plantar flexor spasticity and dorsiflexor paresis and affects safe mobility. Conventional ankle-foot orthoses (AFOs) support mobility by attaining foot clearance during swing, but at the cost of ankle motion and push off during gait and activities requiring a mobile ankle, e.g., dressing and car-driving. Within the Netherlands about 70.000 patients are registered AFO users. In the NWO-TTW NeuroCIMT-project ‘Stiffness as Needed’ we developed a novel AFO to compensate for the spastic ankle joint stiffness, appealing to the available dorsiflexor rest capacity of the patient. The AFO’s light-weight hinge at the ankle joint composes a CAM follower mechanism with a pre-loaded spring producing high torques with low hysteresis. “Show and Transfer I" will bring the design from a tuneable research prototype TRL4 to a demonstrator for home use TRL6. The current TRL4-prototype satisfies the needs of many patients in laboratory research setting at the expense of complexity and weight. For a home use demonstrator TRL6 the hinge should be redesigned for safety and durability (without exposed rotating parts) and should accommodate individual patients’ needs using personalized CAMs. With cross-sectional patient studies in lab setting currently underway, a “Validate and Transfer II” would enable longitudinal patient studies with home use. By appealing to the dorsiflexor rest function and improving ankle mobility 24/7 in the home situation, the project will reduce the complications of spastic paresis (e.g., contractures, high energy expenditure). Improved active mobility will secondarily enhance daily activities, physical and cognitive condition, and quality of life.
Patients with neuromuscular disorders experience high levels of fatigue in their daily lives, leading limitations in daily activity participation. Due to muscle weakness, the capacity of people with neuromuscular disorders is lower, which makes it difficult to balance between available energy and activity performance. Even when assistive technology is used, balancing of energy levels remains a challenge. Patients often notice too late that they have done too much, or they do to little out of fear for over burdening their muscles. To date, rehabilitation advice with regard to balancing daily activities only works for a short period, which results in recurring medical consults and expenses. Personalized advice, based on real-time biofeedback on a muscular level, offers a solution and assists patients with fatigue management at home. Through a lab-tested biosensor and smartphone application, users get biofeedback which directly notifies them about (the risk of) over- and under burdening themselves. During the IMDI project we will develop of a Minimal Viable Product (MVP), which consists of a biosensor (registration of movement and muscle signals) that can be used in daily life, a smartphone application for biofeedback, and a personalized algorithm that determines individual thresholds for optimal effort. The personalized advice will be based on patient experiences and physiological data. We expect that after implementation of real-time biofeedback the number of medical consults and the (treatment of) overburden will reduce significantly and that patients function better in daily life. The MVP will be developed in collaboration with multiple partners: rehabilitation center Klimmendaal (knowledge and inclusion of patients, rehabilitation process and physiology), Radboudumc (linking personalized biodata to daily life advice, rehabilitation process and physiology), HAN Seneca (facilities and knowledge about physiology), University of Twente (biomedical signals and systems, algorithm for biofeedback) and Yumen Bionics (sensor technology, wearable product design and business development).
Early diagnosis of chronic diseases in vulnerable patients is crucial to improve treatment and prognosis (e.g. chronic lung disease, cancer, chronic kidney or liver disease in children and elderly). Non-invasive analysis of exhaled breath in human has shown great potential for early diagnosis of various chronic diseases. In this project, we will use exhaled breath of vulnerable patients with chronic diseases and limited breathing capacity, to develop a one-stop diagnostic solution that characterize their complex breath profiles. The framework technology is formed by our TRL 4 lab prototype existing of carbon nanotube sensors, performing in a highly controlled environmental box and being processed by dedicated algorithms. In the demonstration phase, the TRL4 lab prototype will be developed into a TRL6, which will be tested by clinical consortium partners. To achieve this, we initially focus on the clinical case of children (1-4 years) suspected for asthma. The TRL6 prototype will have the required sensitivity by dedicated functionalization of the carbon nanotube sensors, ease of breathing by mimicking canine nasal airflow, and intelligent data processing by supervised machine learning to determine the complex breath profiles. Our approach will solve existing problems encountered with commercial eNoses and breath tests, such as unnatural prolonged breathing or incapacity to capture complex breath profiles. As the market for one-stop breath analyzers is expect to grow a staggering 30% in the coming decade, a competitive position is expected. This is further explored in the transfer phase that targets IP protection and market entrance strategy. We are seeking for the excellent mixture of clinicians experienced in the field of chronic diseases, scientists in the field of (bio)sensor engineering, R&D with the expertise to bring technology from lab prototype to clinical application. All with the mindset for market introduction of a medical device that aids thousands of people.
We research, develop and demonstrate a new biometrics and voice assistant system to enable an effective and cost-efficient personal elderly care data platform. This platform uses a new in-ear wearable (hearable) and collectively they are called “eCareBuddy”. The platform is used for monitoring of vital signs and intervention control in the cardiovascular domain. The hearable provides these vital signs and allows for instant audio communication between the elderly and the caregiver. The system will limit the deployment of care staff (in hospitals) and reduce the demand for care. Long term, seamless and affordable health monitoring, timely intervention, stimulating senior vitality and personal communication is the key to minimizing expensive hospitalisation or treatments. The eCareBuddy provides a holistic IoMT tool enabling these attributes and cost-savings, while reaching a much higher quality of personalized care both for the elderly as well as the caregiver. Through the eCareBuddy project, TU/e will research and demonstrate the concept of “Digital Twin”, a holistic version of the quantified self with the aim of significantly improving diagnostics and medical decision making. Furthermore— and this may be the most important feature for the future concept of medical care—it aims to generate better prognostics and preventive control based on continuous monitoring and feedback of the patient or elderly.
Around 40,000 major elective surgical procedures are performed annually in the Netherlands, almost 70 percent in elderly patients. Postoperative complications occur in 15-60 percent of patients and result in long term reduced functional status and life expectancy, loss of independence, and increased socioeconomic costs. Lifestyle and behavioral factors elevate the risk of complications including physical inactivity, smoking, hazardous drinking, overweight or malnutrition and adverse psychological factors. The surgical patient journey is considered a ‘window of opportunity’ for lifestyle change meaning that individuals are more receptive and motivated to change lifestyle and behavior. Current validated pre- and rehabilitation programs in surgery are facility-based and include laborious, inflexible and costly face–to-face interventions involving multiple mostly hospital caregivers. The projects initiative is a virtual connected home-based facilitated self-managed support to enhance functional status and well being and to change lifestyle behavior before and after major elective surgery. In this project the university hospital and a national digital health response center/provider will collaborate in order to provide home - and also on the go - based continuous monitoring services based on a intuitive and seamless device that measures besides vitals also sleep and stress. Cardiomio is a sensor patch that monitors heart health in real-time. To make the initiative outcome successful, there should be adjustments to the current product. Therefore, the focus of the first round is the MPV and preparing for the transfer to the first organization that will apply the technology. The investigation for the MPV includes: sensing of the vital functions, such as: lower HR during exercise, faster recovery, improved sleep quality, HRV stress reduction, etc. The importance of sensing the vital functions is to assess whether someone is making progress due to training in the validation phase. In addition, the investigation includes the personalisation of individual goals and thresholds, false alarm reduction (algorithm) and dashboard for self-management and insights. Which is important for the first transfer to understand the needs regarding the dashboard, such as: push notifications. The technology should be able to integrate with other systems and electronic files.
Annually ±175.000 new patients require extensive hand therapy after injury, surgery or degenerative diseases. Therapy consists of regular hand exercises to reduce pain and improve functional outcome, provided by physiotherapists specialized in hand kinematics, totaling between €350-€2000 for each patient. During these sessions the range of motion (ROM) of the finger joints are measured several times to determine progress. Furthermore, patients receive various training exercises to improve hand function to be repeated at home several times a day, for weeks on end. Frequent, adequate exercise improves outcome. Nowadays, 3D camera techniques have been developed by the gaming industry to digitize hand gestures for interaction with virtual objects. In our hospital we have conducted research utilizing such 3D gaming camera to automatically detect the hand and individual finger joints during exercise, enthusing patients and caregivers. We propose to develop a software-based platform together with and for patients and caregivers. In the form of a MVP, the ROM can be derived instantly from the 3D camera input, which saves approximately 10 minutes of caregiver’s time. Besides that, the patient can perform exercises in front of the camera which tracks the exercise and informs if it has been performed correctly. This happens in comfort of home, without the need to travel or take a day off work. Utilizing serious-gaming, the patient can actually have fun during exercising. Furthermore, the MVP will continuously collect data on the patient’s hand and fingers. In a later stage, big data analysis will determine the progress of the patient and compare this to other patients who had similar hand pathology and therapy. Caregivers can monitor and adjust treatment plans via the platform, and decide if a patient requires checkup. All in all, this innovation will reduce both healthcare and social costs while improving outcome in hand therapy.
MeaData stands for 'My Data'. MeaData focuses on the data generated by yourself using BIP technology, which stands for the Body Intelligence Processor. MeaBIP stands for My Body Intelligence Processor. The measured values are interesting, but what we do with these measured values is even more interesting. With current technologies, these values of wearables are often accommodated in an APP, database server or cloud. The danger in almost all existing methods is that if teh server, APP or cloud is hacked there is often access to all personal data of all those who are connected to this APP server or cloud. • MeaBIP sends generated data to MeaData, in an anonymous non-personalized data cloud or server. • Only the user has access to his/her MeaData with a DigID-like construction. • The user determines which data is shared, with whom and for how long. • If a hacker finds access, he comes into possession of non-personalized data from an ID. • Healthcare institution, care provider, sports institution etc can draw up management data from non-personalized data for many purposes. • Healthcare institution, caregiver, sports institution etc can receive alerts from non-personalized data on health, fall detection, injuries, activity over the year etc. • The treating (sports) doctor can directly view blood pressure, heart rate, etc. results over a longer period of time as long as the MeaBIP system is switched on. This is possible 24/7/365. • MeaData is set up in such a way that it can also connect with existing wearable devices in MeaData.
On a yearly basis 130.000 people get acquired brain damage (e.g. stroke). People with acquired brain damage often have problems in their activities and social participation because they cannot do what they were used to due to for example constraints in their mobility/cognition. Next to the personal problems it is also a societal problem. Next to the high medical costs of home- and psychiatric care, it can also result in the loss of jobs (productivity) in case of the subgroup of relatively young people with acquired brain damage. With a little support however, they would be able to control their physical and cognitive load better, resulting in a better quality of life, decreasing the need for care personnel and improve the accessibility of care in one’s own living environment. Our medical device, NAH Sherpa is a mobile application that improves the self-efficacy and lowers the burden of acquired brain damage by a combination of remote measurements (e.g. at home) on the domains of activity and social participation. Activity measured by use of IMUs in the mobile phone or Fitbit and social participation is measured by GPS -tracking in combination with questions as ecological momentary assessments. The mobile application measures and gives feedback and insights on these two domains to the client with acquired brain damage. With this feedback, based on solid guidelines, the person is able to have a better self-efficacy, i.e. regulating their physical and cognitive load better. Next to direct feedback, the acquired data will also be used in combination with the treatment at care institutions to personalize and optimize these treatments. Currently these treatments lack good outcome measures on an activity- and social participation level, to which NAH Sherpa is a perfect match.
In the Antoni van Leeuwenhoek - Netherlands Cancer Institute a fingerprick blood collection device is developed that enables patients to collect their own blood sample at home. The sample is then send to the diagnostic laboratory and the requested tests are performed under the responsibility of this health care organization. The laboratory reports the result to the treating physician. This procedures avoids the necessity of a venipuncture by a health care professional and reduces the patients’ time needed for venipuncture blood sampling. Furthermore this procedure can avoid unnecessary hospital or primary care visits since for these tests when they are OK and the patient has no further complains, often no clinical actions are needed. This project aims to develop and produce a CE-IVD compatible version of the collection system, to technically validate it for the HbA1C and PSA tests and to run a pilot study to demonstrate that patients can use it to collect an appropriate blood sample. This study will be run in The Netherlands Cancer Institute as well as in a large health care provider in the Netherlands supporting diagnostic services for primary care physicians as well as hospitals.
People with dementia or a severe mental disability are not always understood by caregivers. A key challenge caregivers face is how to identify and regulate stress in these people at an early stage. Stress development can lead to misunderstood behavior, which might result in escalations and high caregiver burden. Better understanding and early identification of stress via sensor-technology and data science can strengthen the relation between the care recipient and caregiver and may prevent misunderstood behavior from escalating. It will lead to better care and ultimately to increased quality of life and happiness of both care recipients and caregivers. The project targets the development of a prototype system that can determine the build-up of stress in a person by means of physiology measurements and behavioral models. The system makes it possible to identify the early signs of stress build-up in cognitive impaired people with limited verbal abilities and warns the caregiver before stress levels reach their critical point. The device thereby enables the caregiver to quicker and better respond to the needs of the care recipient preventing escalation of the situation and thus reducing the amount of care needed. The prototype system will consist of a garment-integrated sensor module that can capture the key physiological parameters related to stress. It offers the freedom to carry the device in any textile embodiment to make it easy to use, to increase the acceptability of the product and to limit the impact on the care recipient. The project will address the technical, behavioral, and ethical challenges related to developing and using a device that measures stress levels in cognitive impaired people and questions related to the implementation and acceptability of the device in the long-term care sector and the consequences for the quality of care.
Proctoring is the supervision of a less experienced surgeon by a very experienced surgeon in a specific field of expertise. Currently the (experienced) surgeons have to travel a lot to see and teach the less experienced surgeons in person which is time and cost intensive. In this project we will explore the possibilities regarding the use of virtual reality to improve the tele-reality for proctoring. There will be no need for the surgeons to meet in person all the time which contributes to more efficient education and training.
Vast.Rehab @Home- fysiotherapie thuis met behulp van virtual reality Voorbeeld: Patiënt heeft een beroerte gehad en komt na een ziekenhuisopname bij de fysiotherapeut voor herstel. Fysiotherapeut voert een nulmeting. Vanuit de nulmeting maakt de fysiotherapeut een oefenprogramma, gebaseerd op functioneel herstel en zet dit in het systeem van Vast.Rehab @home. Patiënt krijgt een set bestaande uit: computer/camera mee naar huis. Patiënt verbindt de set thuis met zijn of haar TV en werkende internetverbinding. Patiënt kan op ieder gewenst tijd de oefeningen uitvoeren. De Fysiotherapeut heeft minder tijd nodig voor het controleren van de oefeningen en kan bij sturen waar nodig is, waardoor hij in dezelfde tijd meerder patiënten kan behandelen. Vast.Rehab @home Is een hulpmiddel om patiënten gerichter te behandelen en therapietrouw te bevorderen. Kan de mobiliteit van gewrichten en bewegingsbereik precies vaststellen. Van een nulmeting (rombergtest, reachtest etc.) naar een oefenprogramma die aangestuurd wordt door middel van games. Draagt bij aan ondernemerschap, innovaties en creativiteit van de fysiotherapeut. Het levert de therapeut financiële voordelen, minder administratieve last en meer tijd voor de patiënt. Meerdere patiënten tegelijk laten oefenen. Op afstand meekijken/ bijsturen. Live chatten of berichten achterlaten. Metingen en resultaten live inzien en nakijken en importeren in administratie. Geeft de patiënt inzichten in de mate van beweging. Dit heeft als gevolg dat de behandeling online plaatsvindt, wat uiteindelijk zal leiden tot minder behandelingen. Minder behandelingen leidt tot een hogere waardering van de zorgverzekeraar. De continuïteit van de praktijk wordt versterkt door het handhaven van het contract met de zorgverzekeraar. Aldus Rabobank cijfers en trends, fysiotherapie 2019.
Uit onderzoek van het rehabilition in the Home programma van het Repatriation General hospital in Daw Park Australie blijkt dat:
Vast.rehab @home is beschikbaar en klaar om direct ingezet te worden als pilot.
Cardiac arrhythmias (CA) frequently result in emergency room visits and hospital admissions, which is a significant economic burden on the healthcare budget. Telemedicine services may reduce costs and add patient value as these services are affordable and scalable. HartWacht is an eHealth infrastructure which monitors patients with cardiac arrhythmia. Patients use an ECG device which is connected to the patient’s smartphone. ECGs can be registered in case of CA and are automatically uploaded to the personal electronic patient file and interpreted by a dedicated team of healthcare professionals located in the eHealth control centre.
HartWacht is in its prototype phase and is further developed and piloted by Heart for Health ICT and Cardiologie Centra Nederland. Preliminary analysis showed a 40% reduction in emergency visits and hospital admissions for patients with HartWacht after one year. Here, we aim to assess the patient value and cost-effectiveness of HartWacht in a randomised controlled trial, in which two homogenous groups of patients with CA are 1:1 randomised to standard regular care with- and without HartWacht. The initial experimental setting has been endorsed and evaluated by the Nederlandse Vereniging voor Cardiologie (NVVC), who recognised the potential for a national upscale within the reimbursed healthcare system, if the costs of HartWacht are significantly lower on an annual basis than with regular care, the quality of care is maintained, and the quality of life and patient satisfaction are improved. In this study, the patient value of HartWacht is assessed by including (new) arrhythmia detection, medication intervention based on ECG data, patient satisfaction and feeling of safety as endpoints. Cost-effectiveness is evaluated by the number of hospital admissions and the number of visits to emergency rooms and outpatient clinics for CA.
Shoulder movements are essential for activities of daily-life and industrial labor. A rotator-cuff tear is a lesion to a tendon of one of the muscles holding the arm in the shoulder socket, which can be painful and lead to shoulder dislocation. Lesions arise from overuse, chronic weakness, impingement and injuries from sporting and vehicular accidents. Rotator-cuff tears are the most prevalent shoulder injury with an incidence rate of 5-40% in the general public. Incidence rates are 25-50% for individuals over 60 limiting independent living in many elderly patients. The related treatment costs and loss of productivity are an enormous socio-economic burden. We propose to develop a disruptive and multi-disciplinary rehabilitation robotic interface to both protect the injured rotator-cuff muscles and improve circulation, range-of-motion and strength of the shoulder. The approach exploits recent advances in biomechanical modeling and human-robot interaction. The biomechanical model captures the musculoskeletal capabilities of the human shoulder and provides estimates of muscle forces and strains that cannot be measured directly. The model provides continuous feedback to the assistive robot on how to move safely with minimal strain on the affected rotator-cuff muscle(s). By personalizing the shoulder model from MRI and/or CT images, already part of the diagnosis and presurgical planning, a safe and interactive workspace can be monitored. For patients it means improved: blood circulation to promote healing; range-of-motion and strength during and after rehabilitation, and more complete restoration of function after injury. For society, shorter recovery and physical-therapist time means reduced health-care costs and productivity losses. For clinical research, the device provides repeatable, accurate and systematic therapy and measures (at high sampling rate) of various rehabilitation trials that are simply not possible with manual physical-therapy. The resulting data alone provides tremendous opportunities to discover better treatment paradigms and to personalize rehabilitation to individual patients.
Mr. de Windt has developed a custom- handmade silicone protector that has proven to protect diabetic neuropathic feet effectively for years. However, production is time consuming and needs up scaling to make it available worldwide. The aim is to provide for those with diabetic neuropathy the best custom made quality 3D printed silicone foot protector possible, based on a 3D scan of the foot. Unlike the usually used inserts and/or orthopedic shoes, that are made from materials that are less skin friendly and do not prevent rubbing from the foot against the shoe. Nor do they give exact pressure distribution or the protection needed. The silicon protector solves this with silicone specificity designed to the shape of the patient’s foot. With this design using pressure distribution from all sides of the foot unprecedented results of success can be achieved for the patient.
CloudCuddle Senior is a light weight inflatable bedtent that transforms every one-persons sized bed in a safe sleeping environment. It prevents people from falling out of bed or stepping out of bed unintentionally. CloudCuddle is much cheaper than bed boxes (we aim at € 3000 compared to €10.000,0 to € 15.000,- for boxes) and far more easy to store, transport and to clean. So it reduces care costs significantly. Compared to situations where people are not put in beds with rails, CloudCuddle reduces on the cost of personnel, because you need far less nurses to watch over patients to be sure they want fall out of bed at night or wander off.
More than 1900 patient suffer from an Out of Hospital Cardiac Arrest on a daily basis in the US and EU alone. The American and European Heart Associations state that the majority of cardiac arrest survivors have some degree of brain injury and impaired consciousness after hospital admission leading to death or lower quality of life. An important factor determining the survivor’s prognosis after cardiac arrest is whether and within which timeframe the patient's core body temperature core was brought down to 32-36˚C. ResQure will develop a targeted temperature control device, a disposable esophageal catheter and breathing mask allowing to start core body temperature control 4 hours earlier than current practice, immediately after or during CPR by ambulance personnel. ResQure's devices realize cooling to be continued all the way into the intensive care unit without interruption, it is minimally-invasive and can be used anywhere on this planet. When effective, this treatment will prevent brain damage and is anticipating to lead to less hospitalization, shorter and less intense rehabilitation requirements and lead to a higher quality of life for patient saving healthcare and society significant amount of costs.
The seasoned team of ResQure brings business and product development expertise and experience with bringing ideas from invention to market and develop that market. Arno Nierich (MD, PhD) M.D.,PhD, Thoracic-anesthesiologist-Intensivist. Founder of Medical2Market, inventor of 5 products, Mark v/d Camp CEO QRS Healthcare International & Medical2Market; Sales & Marketing medical devices and communication in Health care Wouter Jansen-Klomp MD PhD Cardiologist Isala Clinic, Principle Investigator Stroke2Prevent Jeanine Hendriks (PhD) Serial Entrepreneur, PhD regenerative medicine, cofounder of 4 startups, brought 5 products to the market in medical devices and regenerative medicine
Worldwide, chronic diseases are the leading causes of death and disability. In the upcoming decades, the number of people with one (or more) chronic disorders will rise as a result of aging population. Prevention as well as early diagnosis of chronic diseases is therefore crucial to minimize the demand for care. Living a healthy lifestyle, i.e. sufficient physical activity and healthy weight, is essential to prevent chronic disorders. To optimize treatment and training intensity, it is necessary to gain insight in the relation between exercise and fat metabolism, because this is known to be highly variable, between, and within individuals. A biosensor may provide real-time feedback of fat metabolism in relation to exercise intensity and nutrition. An acetone sensor, developed in a previous project, meets both goals of the call: it may support healthcare professionals, by getting more insight in energy expenditure. Furthermore, by stimulating people with chronic disorders to live healthy, the sensor may prevent them from getting worse and may reduce demand for care. The sensor is currently at TLR4. We already proved that the sensor detects various concentrations of acetone. The acetone that is released from the skin is a measure for fat-burning and offers possible individual real-time feedback. The technology will be further developed into a minimal viable product (MVP-TRL6), by improving the quality of the signal, working on an appropriate interface and minimizing the size of the sensor. The MVP will then be ready for implementation into the clinical working field. The aim of this study is to develop the acetone sensor from TRL4 to TRL6. Specifically: 1. To develop the sensor from alpha version to minimal viable product 2. To test effects of nutrition, physical activity on acetone secretion 3. To create an interface and study feasibility and usability with target group.
Stroke is the single largest cause of adult disability worldwide and the global burden is still increasing. In the Netherlands, the costs of stroke are estimated at approximately 2.3 billion Euros. In line with a recent task force to move forward, two key questions need to be answered in the next decades. First: “What is a patient’s potential for stroke recovery?” Second: “What is the best rehabilitation strategy for this person given her/his clinical profile?” Without answers to these two questions, clinicians struggle to make clinical decisions regarding the content and focus of therapy, and researchers design studies that inadvertently mix participants who have a high likelihood of responding with those who do not E-POS is a smart e-health solution for effective evidence-based early prediction of the potential of recovery and thereby functional outcome post-stroke. E-POS combines early prediction (< 72 hrs post-stroke) with long-term patient-specific functional recovery by using advanced statistical modelling and extensive existing data from cohort studies (EXPLICIT, EXPLORE, EPOS, PROFITS). E-POS is based on internationally recommended core sets for achieving patient-centred outcomes (ICHOM) (Salinas et al., 2016) and physical outcomes (Kwakkel et al., 2017).
E-POS will generate:
Deliverables of EPOS:
Px HealthCare, the company behind the OWise breast cancer app, has recently developed a first prototype of a prostate cancer app. This prototype was developed in close collaboration with patients (Maggie's Centre Glasgow and Prostate Scotland) and clinicians (NHS Lothian). Development of the prototype was funded by the Healthier Lives Data Fund of Nesta and the Scottish Government. February 2020, Px was awarded an additional grant by Nesta to develop a minimal viable product (MVP) of the app for Scottish prostate cancer patients. In this ZonMw proposal we want to apply for a second grant to simultaneously develop an MVP for Dutch prostate cancer patients. In the Netherlands, prostate cancer accounts for 22% of all cancers diagnosed in men and an estimated 1 in 10 men develop prostate cancer during their lifetime. Each year over 13,000 Dutch men are diagnosed with it. With a projected increase in prostate cancer cases of 40% between 2015 and 2040 there is a great need to improve clinical outcomes in cancer as well as patients’ experience and wellbeing. Particularly as around 27% of men with prostate cancer have reported to experience depression and anxiety. Consequently, this increase in patients’ numbers and needs will also lead to an increase in healthcare demands. Comparable to the OWise app for breast cancer patients, the prostate cancer app will enable patients to share their Patient Reported Outcome (PRO) data with clinicians. Additionally, the app will help patients to adhere to their treatment and inform them when treatment side effects increase or health problems develop. By contacting their general practitioner or hospital ‘just in time’, patients may prevent further health deterioration and extra medical costs related to emergency ward visits and hospital admissions. In this way the app will contribute to better quality of life and clinical outcomes.
Obesity and associated illnesses such as diabetes and heart disease are major global health concerns. Healthy lifestyle factors, including a healthy diet and eating behavior have been shown to play a key role in the prevention of obesity. However, it is extremely difficult to assess dietary intake and associated behaviours, which is needed to assess diet quality and the effectiveness of dietary intervention strategies. To assess food intake of an individual or on a population level health professionals and researchers are dependent of food intake reported in food diaries or 24hr recalls. These current methods lack accuracy and are highly susceptible to bias due to memory dependency and social desirable reporting and the effort of keeping food intake reports. Considering the fast development of technology in the past decade it is now urgent to develop new technology-based innovations to improve the accuracy of dietary and eating behaviour assessments to be able to guide individuals towards healthy and happy diets. The year 2020 is therefore the year to further develop our new way of measuring food intake that is not dependent on memory and requires very little effort from the individual. Our new dietary assessment technique, mEETr is a weighing scale placed within a regular tray and can measure food intake throughout the meal, an additional build in camera is used to determine food type. With mEETr we can determine the food type, amount eaten, number of bites and eating rate. Using MeetR health professionals can tailor dietary advice to the individual’s actual food intake to enhance better dietary and food intake behavior. This is important as both over- and under- consumption of food are common problems in clinical settings and elderly homes.
The MR-STAT technology, conceived in the University Medical Center Utrecht (UMCU), obviates the need to do multiple MRI scans (sequences) to obtain the requested clinical images. Instead, a single MR-STAT sequence allows to reconstruct quantitative maps of the fundamental MRI parameters. From these quantitative maps the MR images requested for the radiological inspection can be generated synthetically with the same image quality. Hence, with MR-STAT technology, MRI scan time can be strongly reduced, translating in a direct cost reduction, and efficiency & production increase for health care providers. MR-STAT can work on existing MR scanners. Currently, the proof-of-concept MR-STAT technology is being clinically validated in a study in the UMCU with a limited patient group. In this Demonstrator project we are aiming to validate and test an improved MR-STAT implementation in a multi-center setting including a non-academic hospital. This presents a more relevant test case. We are aiming specifically for Multiple Sclerosis (MS) patients, a large patient group (32.000 cases in The Netherlands) that receives MRI examinations on a regular basis due to the frequent relapsing character of MS. The MR-STAT technology can reduce the scant time from 20 to 5 minutes, a substantial gain in terms of patient comfort, healthcare costs. It also has the potential of increasing the capacity of MRI systems in The Netherlands. This demonstrator project will be executed in collaboration with Philips Healthcare, a major MRI vendor and a non-academic hospital. The project will show the impact of the MR-STAT technology for an important chronic disease in a realistic, non-academic setting. By executing this project together with Philips transfer of the technology to an industrial partner takes place within this project. We will involve other stakeholders such as health insurance companies who already made inquiries with us given the potential impact of the MR-STAT technology.
The detection of blood flow using non-invasive techniques is providing a solution to a need in modern societies of keeping the population healthy. With this project, we aim to develop a sensor based on laser speckle imaging together with Praxa Sense to overcome the effect of motion artefact when measuring blood flow pulsation on the skin. This sensor will be integrated into a device called Afi developed by Praxa Sense. Afi is a small diagnostic heart monitoring device.
Catheter-based RF-ablation is a curative minimally invasive intervention applied in patients with cardiac arrhythmias, mostly atrial fibrillation. In present-day practice, there is no clinically available technology that can provide the interventionist with direct feedback on the changes effected in the tissue. We previously demonstrated the potential of photoacoustic (PA) imaging to image and characterize RF-ablation lesions in atrial myocardium. The MORPHO technology presently exists as a lab demonstrator, built in-house at Erasmus MC. As an MVP, we aim to consolidate the design of the ablation catheter, based on an existing device (in collaboration with industry), and enable combined intracardiac PA/ultrasound imaging, based on the standard ICE probe and console with PA software. We will perform in vivo preclinical experiments and investigate the relation between the PA signal and a number of relevant procedural parameters, such as development of steam pops, contact force, and electrical functionality of the lesion. Results from these experiments will inform the preparation of clinical studies and the application for the second stage.
Current clinical guidelines recommend cystoscopy in all patients who present with microscopic hematuria to rule out the presence of a bladder tumor. However, the prevalence of bladder cancer is only 2-5% in patients presenting with microscopic hematuria, so many cystoscopies are unnecessarily performed. Although cystoscopy is considered the gold standard for bladder cancer diagnosis, it has important limitations. First, sensitivity of cystoscopy is 90% and secondly, it is an invasive and costly procedure. Therefore, an important challenge in daily urological practice is to safely reduce the number of negative cystoscopies in patients with microscopic hematuria. Previously, we have developed and validated a non-invasive urine test that accurately detected bladder cancer in two independent case-enriched retrospective patient cohorts. Subsequently, we validated this test in a large prospective cohort of patients presenting with microscopic hematuria (N=381) (van Kessel & de Jong et al. J.Urol 2020). The prevalence of bladder cancer in this prospective cohort was 4% (N=14) and the urine test showed a performance of 0.97 (AUC) with a specificity of 92%, a sensitivity of 93% and a negative predictive value (NPV) of 99.7%. Moreover, decision curve analyses showed this test is to be preferred as a triage tool, instead of current clinical practice in which all microscopic hematuria patients undergo cystoscopy. With the proposed randomized controlled trial, we aim to demonstrate the clinical utility of implementing a validated diagnostic urine test to triage microscopic hematuria patients for a diagnostic cystoscopy. Importantly, we strongly believe the proposed randomized controlled interventional study provides the last piece of evidence that is required for clinical implementation of this urine test. Furthermore, we aim to evaluate cost-effectiveness, as we expect at least an 85% reduction in cystoscopies for microscopic hematuria patients in the urine-first (intervention) arm, comparing this strategy to a ‘normal urology care’ study arm.
The number of older persons is increasing rapidly. This has a significant effect on our health care system. Therefore, governments have active policies to stimulate older people to live at their own home as long as possible to keep health care systems affordable. Fortunately, most older persons also want to stay at home, so there is a win-win situation. The FLOW project (referring to flow of water and life) wants to support this change. We will further investigate and develop an assisted living solution for independent living seniors that uses water to track home-based activity. Family, friends and/or professionals can remotely monitor this activity and check whether everything is OK. This provides peace of mind to all parties and thus supports older persons to live at home independently for a longer period of time. The technical solution consists of a water-flow sensor within the home of an independently living senior directly connected to a cloud environment via IoT radio protocols (LTE-M, NB-IoT). This cloud environment uses an algorithm to determine normal daily patterns and detect significant deviations. Significant/Important others will be able to see the activity of the senior by means of an app and can get alerts of longer periods of inactivity. Just as important is the social dimension of the solution. The number of available informal caregivers per independent living senior is diminishing and the caregivers themselves are getting older. There is also an increasing number of older people feeling lonely as well as older persons willing to make a meaningful contribution to society. We will therefore investigate how to setup and empower a social network where independent living seniors monitor each other and organizations like water companies and housing associations contribute to safe and independent living within their own home.
Prof. Dr Eveline Wouters is a lecturer in Health Innovations and Technology at Fontys Paramedische Hogeschool. She is trained as a doctor and epidemiologist. In addition to her work at Fontys, she works as an extraordinary professor 'Successful technological innovations in healthcare' at Tranzo, TSB, Tilburg University, academic workplace Technological and Social Innovations for Mental Health and as a senior research supervisor of the Master's degree in Physician Assistant to become a clinical obstetrician in Rotterdam. In addition, she is ao member of the Supervisory Board of Brabantzorg, member of the Program Renewal Psychogeriatrics Health Council, member of the Advisory Board of IGJ and member of the editorial board of ICT and Health. Eveline Wouters is supported in this initiative by Prof Dr Inge Bongers in Behavioural Sciences from the Tilburg University and Prof Dr Gerard Schouten in AI and Big Data from Fontys University. Tom Meijeraan is the founder of the SME SerEnergi. SerEnergi is an assisted living solution for independent living seniors which uses water to track activity in home and to notify (informal) caregivers when something is different than normal. It is a non-intrusive, easy to install and cost-effective solution to monitor if everything is OK in somebody’s home.
Fontys Paramedische Hogeschool (FPH) is unique because of its combination of paramedical studies (Physiotherapy, Medical imaging, Podotherapy, Orthopedic Technology and Speech Therapy). In addition to subject-specific education provided by training teams, profiling teams give substance to interprofessional collaboration, (care) technology and self-direction Tranzo is the scientific center for care and welfare of the Tilburg School of Social and Behavioral Sciences, Tilburg University. Tranzo connects science and practice in the field of care and welfare. In co-creation with practice we work on knowledge development and knowledge exchange. Through this way of working we promote evidence-based working and we develop products and services that can be used in practice.
The project initiative is to bring the Wavy Assistant solution from TRL4 to a practical solution. At the current moment, Wavy Assistant together with Fontys University have researched a solution to monitor smart wearable and smart home speaker data from cardiovascular disease (CVD) patients. This data can be analyzed with artificial intelligence to separate normal from abnormal behavior at the individual patient level. Based on these abnormal patterns, information can be shared instantly. By doing so, medical professionals can derive insights about the real-time well-being of their most vulnerable patients and even intervene remotely. This could save lives by means of earlier detection of symptom worsening, but also at critical events when there are no human caretakers around. Currently this technology only exists at TRL4, so during this project a consortium of research and care institutions work alongside a for-profit organization to create an MVP and gather real-world end-user feedback. Using this feedback we will be able to transform the technology into a practical solution. This project will also help us research on what the best way is to monetize the solution and scale out to help end-users in The Netherlands and beyond.
Orthostatic hypotension (OH), defined as a sustained systolic blood pressure drop > 20 mmHg or a diastolic blood pressure drop > 10 mmHg within three minutes after standing up is common in older adults with prevalence rates ranging from 6% in healthy older adults, 15% in geriatric outpatients and up to 41% in patients with dementia. Current management of OH in clinical settings is largely dependent on the presence of orthostatic symptoms such as dizziness, fatigue, headache and blurred vision, however a third of older adults with OH are asymptomatic. The diagnosis of OH is important due to its association with poor clinical outcomes in older adults, including mortality, impaired standing balance and increased falls. However, clinical orthostatic blood pressure measurements do not account for many of the symptoms and falls patients experience at home, due to the time varying and posture- and movement dependent nature of orthostatic blood pressure drops. Furthermore, cerebral autoregulation, i.e., regulation of cerebral blood flow during blood pressure changes may potentially attenuate the clinical consequences of OH and is therefore essential to understand the relationship between OH and falls. Novel means of continuous monitoring of cerebral hypoperfusion as a function of posture in daily life circumstances are required to reduce OH mediated falls in the elderly. The current project develops a novel unobtrusive sensor for 24-hour home based assessment of brain hypoperfusion based on Near Infrared Spectroscopy (NIRS). This application is foreseen to be used as an extended diagnostic tool in geriatric and general practices identifying individuals at risk of falls and may serve as a platform for real time warning systems.
In the short-to mid-term a shortage of highly qualified care personnel will be expected due to the aging society. In 2040, 50% of the Dutch population will be +65 years old. In other words, the available personnel will be expected to do more with less time on their hands. This means that trivial tasks, like hospital bed transportation (with or without patient) by highly qualified personnel are time consuming chores that should be supported or fully automated by novel technologies. This project entails the further development of TRL4 robotics technology aimed at autonomous hospital bed transportation in hospitals and care institutes. Developed in a European funded robotics research program, Eindhoven University of Technology created a versatile solution aimed at exploiting hospital logistics processes. Doing so, it created at prototype robot system that can be used for autonomous but also semi-autonomous transportation of beds, carts (laundry, waste, medicine, food) and/or medical devices. This project will focus on autonomous and semi-autonomous hospital bed transportation only. The project will have two focal points: (1) The creation and demonstration of a Minimal Viable Product (MVP) for autonomous and semi-autonomous hospital bed transportation. (2) Preparation for transfer of MVP product to the market, by involving end-users in the MVP creation, setting up and receive input from user-committee and setting up an innovation roadmap. Current example of the prototype technology can be viewed here: youtu.be/1OrZ0gQYg7A More information can also be found in the following article: https://mechatronicamachinebouw.nl/artikel/tu-eindhoven-bestrijdt-personeelstekort-in-de-zorg-met-robots/
People who are obliged to lie in bed or sit in a (wheel) chair and have difficulties to move, have the chance to develop pressure ulcers. Pressure ulcers are caused by prolonged pressure in the same place. Pressure ulcers is a complication that requires a lot of care money and the deployment of care staff. Especially in the home situation a lot of nursing and informal care is needed when treating pressure ulcers. It also negatively affects the self-reliance of the person with a chronic disorder. Many healthcare costs are involved in operations and long hospital stays, so preventing pressure ulcers has high priority in reducing healthcare costs and deploying staff. In 2009, Jasper Renalda published his PhD thesis on dynamic sitting: how to change pressure sores by regularly changing positions. Based on this knowledge, a dynasit chair was developed in which technology was integrated to enable dynamic sitting in different ways. In 2016, Lewis Seating Systems developed a sitting-measuring mat. This is used in healthcare institutions to identify complex seating problems. The information about pressure points thus obtained leads to a personalized approach to the sitting / lying problems. The Dynamic Sitting Project initiative aims at: 1) The further development of the measuring pad into application in the home environment: after extensive analysis with all stakeholders leading to a List of requirements, the mat is integrated into in a measurement method that makes these measurements possible in the home environment. This means that extensive analyzes do not have to take place in a Care center. 2) The further development of dynamic sitting in a personalized sitting unit (wheelchair or normal chair) for at home or nursing home.
Ageing population is a worldwide problem. Furthermore, decreasing service quality for the elderly due to government cuts, exacerbates the problem. From the society point of view, the family concerns increase and public opinion forces authorities to improve these services. Carers coping with lack of specialised staff and cost savings are forced to cut staff and/or reduce service quality, requiring higher efficiency to maintain care with less budget. Carers response to false positive alarms or remote distances results in extra cost, inefficiency, lower quality, stress and frustration and unsatisfied clients (elderly). With this innovative project, we embrace elderly care by providing sense of safety and security by 24/7 care and incident detection along with optimising care. Thus, greatly improving service quality for the elderly and meet their desire to live independently longer and enable their families’ to care for them. Our solution anticipates reducing budgets and insufficient specialised staff while improving care. Unlike existing systems our decision-making system and services and cutting-edge technology results in: • Creating/Providing the sense of safety, security and not being alone • Multi-dimensional cost saving in providing fast and efficient care • Improvements and efficiency in providing elderly care • More focus on providing care with less work force • Considerable shortening in carers response time Our technology improves daily lives as well as carers’ monitoring ability: Real-time alarm for life threatening situations or incidents, no (technical) learning curve for carers or clients, 24/7 data monitoring & data access everywhere, no interventions with carers daily work, zero-Administration Services (no waiting-list, no installation), real-time heartrate and body temperature measurement, remote detection on whether the bracelet is worn, incident based geolocation, wearing the bracelet feels more natural (not as a phone, neckless, etc.), secured patient privacy & data encryption, real-time voice communication with the carers after an incident, minimum administration requesting service subscriptions.
Approximately one‐third of older adults fall at least once a year and a median of 4.1% of falls result in fractures with subsequent high socio-economic burden. Falls are associated with physical disability, dependency in activities of daily living, institutionalization, morbidity and mortality. Low muscle mass and muscle strength (sarcopenia) is a major risk factor for falls. Sarcopenia can be counteracted by resistance exercise training which should be delivered progressively and over a time span exceeding six weeks. Resistance exercise training may reduce falls and consecutive fractures by 20-30 % (Ganz & Letham New Eng J Med 2020). Long lasting, personalized and progressive exercise training without excessive burden on health care professionals requires smart solutions. MuscleCoach is a low cost, easy applicable, modular motor driven device which integrates muscle strength measurement and -training. MuscleCoach:
In an e-health application, MuscleCoach allows for extension of institutional training programs at home with feedback of results and distant supervision by physiotherapists as an essential and evidence based part of falls and related outcome reduction in the elderly.
Machnet Medical Robotics BV is a University of Twente spin-off that develops and commercializes a medical robot for image-guided needle based interventions in MR. As a first application the robot will be used to improve the accuracy and reliability of the biopsy procedure used in breast cancer diagnostics, currently a cumbersome manual procedure performed by interventional radiologists. This robot is based on a patented 5th generation pneumatic steppermotor for MR-safe needle actuation, the Sunram 5. See youtube video www.youtube.com/watch Together with consortium partners this project will be used to re-engineer the Sunram 5 actuator in compliance with medical device regulations and to integrate it with needle steering software, an RF breast coil, and a controller to establish a demonstrator for image guided needle positioning. The commercial engineering partner will make sure that the re-engineered design allows for an optimal cost-effective production method. As an important step towards commercialization the demonstrator will be used to generate preclinical data for assessment of safety and efficacy. For further information: University of Twente TechMed magazine, page 10: https://www.pronuntio.utwente.nl/2-1gxo_sauRRfHMoFb9GaqxaZXBbNXkO2zkT-n8JFjsml-OUEH7u4WtXZDDtFhX9DErqfQh_hQuZyj2zaMKLorB1CKEvcLeWU9g8oDirysC8RtKfJVpQ8-aw
The vast majority of solutions proposed for monitoring health parameters of older people in their homes are based on wearable sensors or cameras. While effective, people may have concerns regarding comfort and compliance for the former, and regarding privacy and the “feeling of being watched” for the latter. To address the above, our project would consider the usage of contactless radar sensors. Their attractiveness is in their contactless capabilities (no wires or devices attached to the person’s body to carry around), in the possibility to embed the sensor in everyday objects (e.g. Google has recently put a radar in their new smartphones), and in the fact that no optical photos or videos of people and private environments are taken. For this project, we would specifically look at two applications. One is the analysis of activity patterns (where people move in their homes and how often, and what they do), with included the prompt detection of critical events such as a fall. For this application, we envisage a compact stand-alone box with the radar sensor, similar to Wi-Fi router, located in a corner of the room. Another application is analysis of respiration waveforms and possibly heartbeat. For this application, we envisage the radar sensor embedded into the back of a familiar armchair or below the mattress of the bed, leveraging on through-clothes propagation of the electromagnetic waves. Algorithms for both applications have been partially validated in controlled laboratory environment, generating encouraging results. This IMDI call appears particularly suitable to tackle some of the challenges to transform this research into a prototype to be validated in realistic living environments (e.g. real-time data management and processing, embedding of the sensor in nicely designed objects, validation of its effectiveness and usefulness of the information for medical and care professionals). This can be done for our sensing technology in isolation, or in combination with other technologies for enhanced multimodal information (e.g. radar plus smart watches or smart garments).
Respiratory disorders such as asthma and dysfunctional breathing (DB) are common in childhood. Exercise challenge tests can assess both the asthmatic and DB component of symptoms. However, exercise tests are hospital-based and expensive. Analysis of respiratory symptoms and assessment of efficacy of therapy in the home environment could provide the paediatrician and child an objective tool to acquire relevant data. We will study how a Wearable Breathing Trainer can signal respiratory parameters, detect and analyze respiratory disorders and provide real-time feedback to the child. The Wearable Breathing Trainer uses sensors and robotic textile and will be designed to support self-management. This project is a continuation of a Pioneers in Healthcare project in which a first functional prototype was developed. Focus of this next step will be on validation of the sensor technology integrated in the garment and evaluation of the developed prototype in clinical practice. Effects of using the Wearable Breathing Trainer on engagement with the therapy will be studied. This project moves care closer to home by connecting (wearable) electronics to healthcare professionals (e-health). This more general development is relevant for the target group: in the standard current care, children with dysfunctional breathing interact regularly with healthcare professionals in hospitals and labs. Moving care ( in this case monitoring and coaching of breathing) will benefit this target group and will invite them to regularly train with confidence while having fun. We expect that a successful project will contribute to a more efficient care process. It allows patient self-management, monitoring, requiring less interaction between patient and healthcare professional and furthermore can provide up to date information to health care professionals, optimizing care.
The number of patients with end-stage kidney disease is increasing. If a kidney transplantation is not possible, patients are treated with peritoneal dialysis (PD) or hemodialysis (HD). PD is a home-based therapy and HD can be done at home or in-center. Home-dialysis (PD or HD) is encouraged from a medical perspective, considering the increased flexibility and autonomy for patients, as compared to in-center hemodialysis. This often results in an improved quality of life and cost-effectiveness. However, patients and their family caregivers might experience a barrier to choose a home-based dialysis treatment, out of insecurity regarding their self-sustainability or fear of not being able to obtain medical attention as easily as during an in-hospital treatment. This can be an issue, particularly in the increasingly ageing dialysis population. The Happi-Homedialysis app could lower the barrier for patients to choose a home-dialysis treatment, as well as the care-burden for the treating physicians, nurses and family caregivers. This smartphone-app, linked to the patient’s electronic medical chart, would allow home-dialysis patients or their caregivers to bi-directionally share data related to the treatment with the in-hospital team. These data include: bloodpressure, ultrafiltration, weight, dialysis-solutions that have been used and need replacement, pictures of the catheter exit-site and questions regarding their treatment, laboratory results or medication. In this pilot-study, we aim to assess if the Happi-Homedialysis app, linked to the digital patient-charts of OLVG, Amsterdam UMC and Dianet Dialysis Centers, ensures a more reliable insight into the home-dialysis treatment, improves patient management and increases satisfaction and self-sustainability for patients and their caregivers. If proven successful, we aim to investigate if implementation of the Happi-Homedialysis app results in improved value-based healthcare and cost-effectiveness by: an increased number of patients choosing a home-dialysis treatment and reduction of the number of: (PD-related) infections or other complications, hospital-admissions and outpatient clinic consultations.
In many cases of larynx cancer, the larynx is completely removed. This is called a laryngectomy. With this operation not only the larynx is removed, but also the trachea. Following the procedure, the person breathes through an opening in the neck known as a stoma. These stomas are usually placed by adhesive plasters. The problem with these adhesive plasters is that the glue irritates the skin of the patient or that the plaster doesn’t stay put because of the irregular surface. Another solution is a cannula which is placed in the trachea on the throat of the patient. The problem with the current cannulas is that the sizes are fixed, causing a misfit on many patients. Some patients decide to not wear a stoma, causing serious health problems. The use of 3D printing can be a solution to this problem, because single-piece production is possible, enabling tailored design and production. Together with ENT-doctor A. van Bemmel, a concept of tracheal cannula is made, which can be designed using parametric modelling. In this way, the design of the cannula can easily be changed in order to achieve perfect fit for every single patient. This concept is tested with one patient as a proof of concept, but further development is necessary. Besides the further development of the parametric model, the material research needs to be extended for flexible material which can be used for medical purpose. This innovation will not only improve the fit of the cannula, but also the comfort for the patient in daily use. With this tailored cannula the patient is able to clean the cannula easier, resulting in a better enjoyment of life, less doctor-visits and thus lower costs for every single patient.
Osteoarthritis (OA) imposes a substantial burden on individuals and health care. Annually, 431.000 people are diagnosed with hip OA and 642.000 people with knee OA. These numbers are expected to increase by 37% in 2030 due to aging and an increase in obesity. Although joint replacement surgery is used to treat end-stage OA, a substantial number of patients is dissatisfied after joint replacement and requires additional care. Identification of unfavourable recovery in the early post-operative stage is key for timely treatment (adaptation) to minimize required care. Furthermore, physical functioning is often overestimated by patients, requiring objective assessment in addition to routine patient-reported outcomes. Objective assessment of daily-life gait behaviour using wearable technology can facilitate prognostic evaluation and rehabilitation of patients. In this project we will build a proof of concept (Minimal Viable Product) of a cloud-based platform that stores and transforms wearable sensor data from the individual orthopaedic patient, using a newly developed daily-life gait behaviour toolbox, into an application that visualizes essential feedback of clinical endpoints on gait behaviour to the aid of end-users (surgeons, therapists, and patients). We will develop a cloud-based platform to quantify daily-life gait behaviour (SHOW). In parallel, we will work on data merging and visualization of clinical endpoints, in co-creation with both patients and clinicians in two clinical settings (TRANSFER 1). End-stage OA patients who will undergo joint replacement surgery will be included in a multi-center prospective cohort study. Before and after surgery, patients’ gait behaviour will be objectively measured in their domestic environment using a wearable sensor, of which raw data will be converted to clinical endpoints in the platform. Finally, an outline will be made for validation of the product by (1) evaluation of the data platform for end-users, and (2) determination of added value of measuring gait behaviour in OA patients.
The project initiative ‘Mbrace the diabetic foot’ aims at demonstrating A) a new solution for improving the wound healing support of casting shoes used in the clinical setting by patients with diabetic wounds. For more severe ulcers these (temporary) solutions can offer better wound healing compared to orthopaedic shoes. This solution is based on current knowledge about the origin of these wounds and current Guidelines (IWGDF) for treatment and prevention. It improves the offload of the foot and lowers pressure around the wound. It also provides more insight into the patient walking specific movement characteristics. B) a new open measuring system, integrated in the casting shoe, for measuring several parameters relevant for wound healing, providing automated data retrieval for research purpose, with no effort needed to organize data retrieval. The data will be automatically available on a pre-set server for analysis, accessible by physician or research scientist. This extendable sensor platform uses low energy wireless sensors. This offers the opportunity to verify current expertise on diabetic wound healing, to validate state of the art solutions and to improve the level of knowledge to more understanding and to the design of new, improved, treatment.
Er zijn in Nederland 1,2 miljoen huidpatiënten. Onder andere veel patiënten met eczeem en Psoriasis. Europees onderzoek uit 2019 naar de kwaliteit van leven en emotionele consequenties van huidaandoeningen, wijst uit dat huidaandoeningen zorgen voor een hoge ziektelast in de vorm van een grote psychische belasting. Naast lichamelijke klachten zoals jeuk, pijn en vermoeidheid, hebben huidpatiënten ook dikwijls te maken met bijvoorbeeld depressie, angst, schaamte; of problemen op het werk, in persoonlijke relaties en sport. Dermatologen hebben maar maximaal tien minuten spreekuurtijd, terwijl er steeds meer complexiteit samenhangt met deze chronische huidziekten. Uit onderzoek blijkt dat 40% van de psychische klachten niet tijdens reguliere consulten wordt herkend. In samenwerking met dermatologen en patiënten is daarom de Happi Huid app ontwikkeld. In deze app houden patiënten met een huidaandoening hun ziekteactiviteit, gezondheidsbeleving, kwaliteit van leven en actuele huidbeeld bij. Dit levert veel voordelen op. Zo wordt het zorgpad door gebruik van de app individueler, meer persoonsgericht. De app registreert alle belangrijke momenten waardoor zowel patiënt als arts goed beeld krijgen van hoe het écht gaat. Beiden komen door deze inzichten beter voorbereid op het spreekuur. Daarnaast heeft de app een signaalfunctie: omdat huidveranderingen snel zichtbaar zijn, is eerder ingrijpen mogelijk. De juiste zorg op het juiste moment leveren, verbetert het welzijn van huidpatiënten aanzienlijk. Daarnaast zorgt gebruik van de app voor een enorme efficiëntieslag in het zorgproces. Als de patiënt zich verder goed voelt, kunnen de routine-controles namelijk vervangen worden door online-consulten. Dit betekent: meer gemak voor de patiënt, die het contact met de arts niet hoeft te verliezen. En meer tijd voor de arts, die te besteden is aan patiënten die wel zorg nodig hebben. Bovendien zijn er op deze manier uiteindelijk minder consulten nodig. Het bespaart dus zorgkosten.
Artificial Intelligence (AI) to Personalize Care and Aid Shared Decision Making for Patients with Wrist Fractures: Deep Learning Image Recognition to Predict Fracture Stability and Machine Learning Clinical Prediction Tools to Decide on Operative Treatment
There is great -undesired- variation in treatment for patients with wrist fractures. In the Netherlands, 20% of patients undergo surgery, while in Australia 80% of patients are treated operatively. There is no national- nor global consensus on the optimal treatment strategy for a specific wrist fracture in a unique patient. This leads to suboptimal workflow, physical impairment and unnecessary costs. The main aim of this study is to deploy Machine Learning (ML)-algorithms to personalize care for wrist fracture patients. Risk stratification has the potential to neutralize the influence of -biased- surgeons and successively overcome current treatment inconsistencies thereby improving patients’ functional outcome. Using a structured stepwise ABCD-approach, ML-decision support will be developed on high quality Traumaplatform AI-Data to provide tailored advice for every unique patient. This project focuses on the most common fracture in the Dutch population which accounts for 34.000 wrist fractures per year, an estimated 17.5% of all fractures. Due to the aging population, fracture incidence is increasing exponentially. Despite these large numbers, treatment algorithms in fracture care are historically based on small single-surgeon series of few dozens of patients treated by an expert in the field. Moreover, randomized controlled trials in trauma surgery are flawed by strong selection bias of including surgeons. The secondary aim is to validate the ML-algorithm prospectively in Dutch Trauma Registries. The overall aim is to implement ML-decision support tools in patients’ electronical medical records with active feedback-loop, to optimize workflow in daily practice and reduce healthcare costs associated with caring for wrist fracture patients with 50%.
Dr. J.N. Doornberg, orthopaedic trauma surgeon, associate professor and founder Traumaplatorm AI.
The project initiative ‘Mbrace your spine’ aims at demonstrating a new solution for improving the mobility of young adolescents with adolescent idiopathic scoliosis. Scoliosis is a curvature in the spine that occurs in approximately four percent of the population in the Netherlands. AIS leads to chronic back pain and deformation of the body posture and in more serious cases the correct function of the organs can be limited. If the curvature of the spine becomes exceeds 30-35 degrees, surgery is inevitable, however, for most cases treatment is aimed at preventing further tilting. In those cases, application of a brace is the choice of treatment. The current braces are custom-made corsets that fit tightly around the patient's body and can therefore stabilise the tilt of the body. In practice, the application of a brace is considered a relatively heavy therapy, with vast negative impact on quality of life. The new solution to be demonstrated is: (A) Application of brace with a high level of flexibility and therefore not limiting the range of motion (B) Monitoring of the patient and the effectiveness of the brace, by a MRI-based imaging solution specifically meant to follow the effect of the therapy without harm by X-ray radiation.
Hogeschool Utrecht, Dr. E.C.N. Puik, Lector MST
By 2040, the percentage of the population with cardiovascular disease, frequently related to advanced atherosclerosis, is expected rise to 65%, putting the sustainability of the present healthcare system at risk. Effective prevention and treatment of atherosclerosis can reduce disease burden and associated costs. Currently, the charting and influencing of risk factors within primary practices is performed by primary care practice assistants (PCPAs) in a manual fashion and not supported by digital systems or the use of mobile health technology (MHT). In the current project we assess how machine learning can be implemented as a tool for automated identification of patients with risk factors within information systems. In addition, we further develop a patient-operated mobile health platform for treatment and prevention of cardiovascular disease that combines comprehensive risk profile assessment and the use of MHT into personalized risk improvement strategies. Currently available software, developed within the research consortium, will be adjusted for the above-described purposes and combined with currently available MHT (Eg. blood pressure monitors, weight scales, activity and sleep tracking, glucose monitoring, etc) into a patient-operated mobile health platform. For this purpose, existing cardiovascular risk prediction algorithms of Pacmed B.V. will be refined. In addition, the Leiden University Medical Center mobile application that can be used to process MHT-generated data, will be adjusted for application within primary care practice, centralizing the role of patients and PCPAs. The prototype of the platform, will be tested in two family practices in the “Stevenshof” district in the city of Leiden. After a 2-month training period, in which PCPAs will be trained in using the system, the 1-year implementation period will start during which approximately 200 patients (100 patients per practice) will be included. Satisfaction, sustainability of life-style changes, medication adherence and persistent use of MHT will be measured.
Physical inactivity due to symptoms of muscle weakness, fatigue and pain is a common problem in individuals with neuromuscular diseases (NMD). Inactivity leads to deconditioning, further compromising daily life functioning. To prevent or reverse deconditioning in NMD, exercise is recommended. Currently, most exercise programs are executed in specialized NMD centers. This is a significant burden for the healthcare budget, as well as for the patients, for whom travelling to the center is difficult due to their limited mobility. Exercising at home may be an alternative, but people experience this as boring, leading to low adherence and ineffective care. Additionally, there is a higher risk of under- or overburdening due to the lack of supervision. Therefore, we developed ReVi, an app which supports individuals with NMD during their home-based exercise program. ReVi aims to improve adherence and reduce under- or overburdening by providing verbal encouragement and personalized instructions to maintain within the target intensity zones, based on real-time heart rate feedback. Besides, patients and care professionals can monitor training progress through an online dashboard. We have successfully built a prototype (RL4), containing one exercise program. However, as the response varies among individuals in this heterogeneous population, care professionals should have the possibility to prescribe other exercise programs as well. To make it ready for clinical use, the aim of the current “SHOW and TRANSFER I” project is to further develop ReVi to a Minimal Viable Product, containing four different exercise programs. In “VALIDATE and TRANSFER II”, ReVi will be applied in different care settings. We expect that the implementation of ReVi will reduce the number of medical consults and improves daily life functioning in NMD. Additionally, the collection of big data will allow us to eventually identify the most optimal exercise program for the individual patient, leading to more effective care.
The calf muscles are highly important for a stable and efficient gait pattern. Individuals with neuromuscular diseases often have weakness of the calf muscles, which will severely affect the gait pattern, leading to mobility problems like reduced balance, falls and increased walking energy cost. Treatment with stiffness-optimized ankle-foot-orthoses (AFOs) is an important therapy to support safe mobility and minimize walking energy cost. This can be achieved by individually tailoring the AFO’s stiffness mode-of-action to relevant patient characteristics, like degree of muscle weakness, as recently shown in our PROOF-AFO trial, for which we developed a modular AFO with replaceable dorsal leaf springs to vary stiffness within the same orthosis. The conclusion of this study was that to achieve the best walking performance, AFO optimization is required. We have coined this “precision orthotics”. While the current TRL4 prototype of our modular AFO has been shown valid in precisely defining the optimal stiffness, its design doesn’t allow to change stiffness directly. This makes selecting the optimal stiffness a labor-intensive, time-consuming process (8 hours per patient). In this project, we propose to design a novel wearable AFO of which the stiffness can be altered in real-time, without having to manually change the leaf springs. This allows for rapid optimization within one session, as the outcome, energy cost, can be continuously measured while changing the stiffness (human-in-the-loop-optimization). Currently, this is only possible with comprehensive high-cost equipment. As a MVP, we aim for a dedicated low-cost wearable model, enabling implementation of stiffness optimization in a large range of clinical practices. We expect that use of our stiffness adjustable AFO will dramatically shorten the optimization process (medical consult-time), thereby saving costs and it will considerably lower the burden for patients. Furthermore, selecting the individual optimal stiffness leads to a more effective treatment in terms of improving functioning.
Hypertension is the most common modifiable risk factor for premature death by any cause worldwide, especially cardiovascular diseases. In the Netherlands, 32% of the adult population is hypertensive and despite the availability of effective and affordable medication a large part remains untreated or uncontrolled. This leads to considerable morbidity and mortality and subsequent high healthcare costs. A personalized self-management tool could significantly reduce these numbers, without further use of already limited health care resources. The eHealth infrastructure in which such a tool can be integrated has already been developed (HartWacht). HartWacht patients with therapy resistant hypertension receive blood pressure monitors for home measurement. Measurements are directly integrated with their electronic patient files and are individually cross-checked by a dedicated team of healthcare professionals. The program is reimbursed by almost all insurance companies. The results are promising: in this complex patient population blood pressure was significantly reduced and over 60% of patients reached normotension during follow-up. However, the increasing amount of incoming data leads to higher workload for healthcare providers and therefore possibilities to scale up the current infrastructure to serve the high volume of hypertension patients in the Netherlands are limited. Therefore, in this project we will develop a smart algorithm using artificial intelligence, which can auto-interpret incoming blood pressure data and combine it with patients’ medical history, co-morbidities, risk profile, laboratory blood values, medicinal history, etc. It will thereby triage hypertensive patients, give personalized treatment advice and tailored messages about lifestyle and medication adherence. Only those patients with true therapy-resistant hypertension will be handled by healthcare professionals. The combination of this new algorithm with the existing eHealth infrastructure will provide personalized medicine for large volumes of patients without increasing the burden on healthcare professionals. As a result, we expect that significantly more people will have a well-regulated blood pressure without increasing healthcare costs.
Chest discomfort poses a large burden on the healthcare system as differentiation between cardiac and non-cardiac pain requires time-consuming and expensive imaging modalities. With an aging population, this problem will likely increase. Within the ongoing CVON ARGUS project, we have therefore developed a prediction algorithm that excludes coronary artery disease. Ultimately, the algorithm is meant to support shared decision making by the cardiologist and the patient, by providing integrated diagnostic support in safe exclusion of coronary disease. This will prevent unnecessary anxiety in patients and guide cardiologists in diagnostic decision making. In addition, it will lead to reduction of unnecessary use of expensive diagnostic imaging resources, leading to a higher sustainability of the healthcare system as a whole. However, to bring the ARGUS algorithm to clinical use, we need to develop it into a CE-marked digital health solution. Therefore, the objective of ARGONAUTS is to develop the ARGUS algorithm in an agile and multidisciplinary way and deliver an ethically sound, cost-effective diagnostic support system that is externally validated in a second line hospital and ready for use in a clinical study. This will allow CE-marking and facilitate swift implementation into the routine cardiology practice.