Recap - Innovatieve oplossingen voor chronisch zieken
BLOG: Recap teleheath research project reaches important milestone with heart failure patients
Written by: Nicholas Offer and Julie Price
Partner: Health Enterprise East and NELFT
The Recap partners in Hub 3 are North East London NHS Foundation Trust (NELFT) and Leuven University (KU Leuven) in Belgium and led by Health Enterprise East (HEE) in the UK. Our hub has a couple of closely related aims: As well as being Hub Leader, HEE has produced a market research report on the management of heart failure (HF), including telehealth practices. We are also carrying out a telehealth research project specifically working with HF patients - NELFT recruits HF patients on to the research programme and asks them to use telehealth to monitor both their physiological and psychological indicators; the anonymised data is sent to KU Leuven in Belgium, where it is independently analysed to investigate the possibility of developing a decision making software for HF nurses. Patient data collection is undertaken for a 6-week period with each patient and it is envisaged that a minimum of 350 datasets will be analysed by KU Leuven.
Since obtaining ethics approval in the autumn of 2012, the NELFT HF team has embedded the approved processes within the team. This includes selecting suitable patients for participating in the project, liaising with the acute Trust to ensure a flow of newly diagnosed HF patients, issuing consent and instruction to patients, installing telehealth devices and monitoring the patient data daily. In addition, the performance team has set up routine ‘extracts’ to ensure the data can be anonymously collated and sent to KU Leuven to analyse. By the end of October 2013, 200 complete data sets had been extracted and sent to KU Leuven, and approximately 50 patients are linked to telehealth at any one time.
Initially, the project was using only 19 devices manufactured by a UK company, Docobo. In order to expedite further data set collection, further investment was made by NELFT in 2013 with Philips telehealth equipment. These devices are quite different to Docobo: Philips use the patient’s own television in order to submit daily measures and stream educational videos; Docobo are ‘stand-alone’ units in which patients enter data, and are not able to receive video streaming.
After analysing the initial data sets, it became apparent that a model ‘decision support software tool’ for nurses would be unfeasible. This is because not enough ‘significant events’ are apparent in the data sets i.e. admission to hospital or significant medication change. On reflection, this is not altogether surprising as the purpose of the NELFT HF team is to work with patients to manage their condition and avoid ‘significant events’. Whilst initially disappointing, the project plan had highlighted in the timeline that there was a cut-off point for this decision to be made i.e. it was recognised from the outset of the project that a decision support tool may not be able to be developed.
However, it is possible to still achieve significant outcomes from this project:
1) KU Leuven are able to detail the patterns shown in the datasets and describe their findings;
2) KU Leuven will be able to detail what further indications are needed in order to model a decision support tool (e.g. GP and acute data sets to compliment the community data and a control group) so that this Recap research further informs any following research project;
3) NELFT are now using 2 distinct telehealth equipment types – one ‘stand-alone’ device with no educational video streaming; the other an integrated device with the patient television, enabling video streaming of educational information. An a recognised HF management evaluation questionnaire has now been introduced to all patients taking part in the trial to assess whether the video education function has benefited the patient in terms of both better understanding their condition, and in better coping with their symptoms.
This project has had to overcome some substantial obstacles to produce what is likely to be a meaningful analysis of a significant dataset.