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H2020Individual fellowship2015–2017

IPSIBiM · Improved Patient Safety through Intensive Biosignal Monitoring

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2015-09-01 → 2017-08-31
EU contribution
€183,455
Participants
1
Scheme
MSCA-IF-EF-ST

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Results in brief

Improved Patient Safety through Intensive Biosignal Monitoring

In the IPSIBIM project, a system dedicated to the monitoring of post-operative orthopaedic patients was developed to detect early signs of patient deterioration. The deterioration is directly related to the concept of rescue failure, that is, the idea that although not all complications of medical care are avoidable, health systems must be able to identify and treat complications quickly when they occur. Physiological deterioration is difficult to detect: many studies have explored the difficulties that nurses face in recognising and responding to patient deterioration. The innovations in the project consisted in the measurement of vital signs in a wireless system and in the development of data analysis techniques for the interpretation of vital signs. The proposed concept was implemented in a pilot clinical study at The Royal Orthopaedic Hospital NHS Trust in collaboration with Sensium Healthcare Ltd, where the technical aims related to setting up the system were studied along with outcome measures for clinical evaluation. The other main objective of the project included multidisciplinary scientific training in vital sign monitoring, machine learning algorithms and clinical decision support to enable Dr. Vania Almeida to expand her expertise in the area of sensors, electronics and data acquisition to other relevant areas of Biomedical Engineering. In addition, the project included training in project management, financial, planning experience, communication skills and technical writing. The Fellow also collaborated actively during a clinical secondment with The Royal Orthopaedic Hospital, which allowed her to gain experience in communication and effective collaboration with users outside the academic domain. The successful implementation of this project helped Dr. Vania Almeida to secure a position as Lecturer in Electronics and Medical Instrumentation at Middlesex University.

Data: CORDIS, © European Union

Project objective

Hospitals can experience difficulty in detecting and responding to early signs of patient deterioration, leading to late intensive care referrals, excess mortality and morbidity, and increased costs. Joint replacement is a very common orthopaedic operation that raises additional clinical and safety questions. Caring for the orthopaedic patient requires a multidisciplinary team and treatment that includes: acute pain control, monitoring for post-operative complications venous thromboembolic prophylaxis, early ambulation, and rehabilitation. The major limitation of early warning systems is that they are based on manual checks performed by nursing staff, and that the observations, and therefore detection, only occur intermittently. Additionally, the false-alarm rate of such monitors is generally so high that the alarms are usually ignored. Despite recent developments in automated wireless systems that continuously record vital signals, a method for detecting patterns of deterioration that are specific to unique patient populations has not yet been studied. IPSIBiM targets the development of a system dedicated to post-operative orthopaedic patients monitoring based on wireless recording of real-time vital signs and analytical algorithms capable of providing guidance to clinicians of early signs of deterioration. The innovation in the project consists of four main elements: measurement of new vital signs in a wireless system; novel methods for deriving features and building models to measure recovery; automated pain measurement in post-operative patients; principled methods for novelty detection. The proposed concept will be implemented in a clinical feasibility study, where technical aims related to setting up the system, reliability and failures will be studied along with outcome measures for clinical evaluation. The research programme is highly multi-disciplinary as it will require expertise in sensor design, machine learning algorithms and medical applications.

Original text from CORDIS.

Participants

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Data: CORDIS, © European Union