Periodic Reporting for period 2 - NIGHTINGALE (Connecting Patients and Carers using wearable sensor technology)
Período documentado: 2017-07-01 hasta 2021-09-30
In the Nightingale Precommercial Procurement (PCP) project, five European academic hospitals challenged industry to develop an advanced system for intelligent wireless wearable continuous patient monitoring in so-called ‘low tech’ care environments: regular hospital wards and the patient’s own home setting. Our primary goal was to help create this novel technology in order to improve patient safety and patient satisfaction.
After an intense Open Market Consultation with industry, we published the Nightingale Call for Tender. Nine companies/consortia were selected to develop a detailed technical and business plan (phase 1). Four companies were chosen to each develop a wearable wireless multi-parameter sensor prototype, which we tested in healthy human volunteers during phase 2. Obtaining input from patients and carers with ‘Value Sensitive Design’ methodology proved to be extremely valuable for developing the solution. Moreover, it was highly appreciated by industry. Two companies were selected to further develop their prototypes towards readiness for early clinical testing in high-risk hospitalized patients (phase 3). Due to Covid-19 only one company was able to progress towards readiness for clinical tests, starting December 2020. In phase 3 we performed clinical investigations with the Checkpoint Cardio Nightingale system in actual high-risk patients in each of the five participating academic hospitals. Interestingly, in 2020 that system was also CE marked and used clinically during the Covid-19 pandemic in several eastern European countries on hospital wards and for remote home observation. The most recent Nightingale project results were disseminated during a successful virtual Congress with 400 registered delegates on September 16th, 2021.
The Nightingale project has acted as catalyzer for the development of wearable wireless patient monitoring technology in Europe. The project is widely known throughout the global medical device industry. Our focus on sustainability proved to be very important, as finalists all developed reusable, rechargeable sensors. Our desire for a complete vital signs feature set has raised the bar for future wearable sensors; they should also be able to measure blood oxygen saturation and blood pressure in addition to easier-to-measure parameters as heart rate and respiratory rate.
The Covid-19 pandemic has clearly demonstrated the need for systems that allow patients to stay at home longer and postpone hospital admission, and/or to decrease the duration of hospital admission by allowing a safe early discharge home. The socio-economic impact of wide implementation of such technology is immense, as it will redefine the concept of hospital care and alter indications for admission. Nonetheless, there are remaining technical and scientific challenges; continuous measurement of blood oxygen saturation and blood pressure requires a valid ‘pulse plethysmogram’, an optical technique that is very sensitive to patient motion. Finally, the creation of our envisioned self-learning high-level clinical decision support system requires the generation of large data sets from thousands of patients, each wearing the wireless monitoring system during their period of illness, as well as prospective randomized trials to determine the effects on patient outcomes.
The Covid-19 pandemic has clearly demonstrated the need for systems that allow patients to stay at home longer and postpone hospital admission, and/or to decrease the duration of hospital admission by allowing a safe early discharge home. The socio-economic impact of wide implementation of such technology is immense, as it will redefine the concept of hospital care and alter indications for admission. Nonetheless, there are remaining technical and scientific challenges; continuous measurement of blood oxygen saturation and blood pressure requires a valid ‘pulse plethysmogram’, an optical technique that is very sensitive to patient motion. Finally, the creation of our envisioned self-learning high-level clinical decision support system requires the generation of large data sets from thousands of patients, each wearing the wireless monitoring system during their period of illness, as well as prospective randomized trials to determine the effects on patient outcomes.