Transition Support Award CSF Chris Gale

1 in 11 UK babies are born prematurely; many of these need neonatal care that involves medical decisions about every part of a baby's care. Many babies who require neonatal care have medical and neurological problems that affect them throughout their lives; these may be influenced by decisions made during their neonatal stay.
The ideal way to work out which clinical decision is the best is through a randomised trial. Here each baby has an equal chance of being given each treatment option and this is chosen by chance, like tossing a coin. By including lots of babies we can work out which treatment option works best. Unfortunately, randomised trials are often very expensive and burdensome. As a result, only a small number of neonatal treatment options have been tested in randomised trials and so most decisions are only educated guesses.

I want to make randomised clinical trials cheaper and easier so all day-to-day neonatal decisions can be based on the best research - randomised clinical trials. I plan to do this is by getting rid of one very expensive part, data collection, by getting all the information straight from a baby's electronic health record (EHR), a computerised version of the medical notes. Information from these EHR systems is already used for lots of purposes, I want to use it for randomised trials to make them much cheaper and easier so more can be carried out, more simply and easily.

We have already completed the following work to show that large simple neonatal trials built into EHR systems are feasible:
1. We have worked with doctors, nurses, parents, patients and researchers to determine what the most important "outcomes" for large simple neonatal trials are. An "outcome" is a result of a trial, like whether a baby needs oxygen at home
2. We have shown it is possible to carry out a moderately large, novel and pioneering trial embedded within the neonatal EHR, and have measured how accurate, simple and inexpensive this is
3. We have involved parents, doctors and nurse to make taking consent for large simple neonatal trials easier and more straightforward, and have shown that this is acceptable
4. We have worked with parents to develop a system to give parents information from the EHR about their baby rapidly and easily through a mobile app (the BUDS app)

This completed work has shown that large simple neonatal trials are feasible, but also identified some problems - particularly around how accurate and complete some information is. This work has also shown that another way to make neonatal randomised trials easier and simpler is by using 'cluster trials' where instead of the treatment each baby receives being decided by chance, the treatment a whole neonatal unit uses is decided by chance.

We want to build on this work to see if we can make neonatal EHR data better by involving parents, and to see whether large simple 'cluster trials' are possible using the neonatal EHR system. We plan to do this by:
1. Testing to see whether giving parents information from the EHR about their baby rapidly and easily through a mobile app leads to more complete and accurate data in the neonatal EHR system
2. Looking at how accurate and complete neonatal EHR data need to be for different large simple neonatal EHR trials and cluster trials to work
3. Showing that we can measure the 'outcomes' that parents, patients, doctors, nurses and researchers identified as important from neonatal EHR data

Finally we want to make sure that knowledge from large simple neonatal trials can be quickly and effectively communicated across the NHS to improve the way babies are looked after, so we will learn from the successes and mistakes of other health systems in the USA that already do this using EHR systems. We will work with neonatal doctors, nurses and researchers and with EHR companies to find the best way of doing this in the NHS.

Aims of Transitional Support Award
1. Implement the BUDS app to allow parents near real-time access to their baby's data and quantify impact on routinely recorded EHR data quality
I. Continue app development
- Methods: Co-design, linkage with EHR data, implementation in London neonatal units
II. Evaluate impact on EHR data quality
- Methods: Pre/post evaluation of parent satisfaction and EHR data completeness/accuracy
III. Evaluate feasibility acceptability of BUDS app
- Methods: Structured questionnaires and qualitative interviews

2. Extract and analyse data to underpin a range of neonatal EHR-embedded trials
I. Calculate underpinning data e.g. intra-cluster coefficients/missing data proportions for core trial data items
- Methods: Analyses of EHR data in the National Neonatal Research Database (NNRD)
II. Model impact of degrees of missing/inaccurate data on individually/cluster randomised neonatal trials
- Methods: Simulate multiple individually/cluster randomised neonatal trials using NNRD data; trial effect sizes/outcomes based on published trials
- Outcomes: rates of missing/inaccurate data required to alter modelled trial findings

3. Validate the extraction of core neonatal outcomes in the NNRD
I. Identify core neonatal outcomes (prioritised earlier in the fellowship) in the NNRD; formalise extraction; validate against published and existing trial data
- Methods: Analyses NNRD data; systematic review of reported rates in similar high resource settings and large neonatal trials

4. Realise learning from research placement in Boston Veteran's Affairs and Children's Hospitals
I. Disseminate qualitative data from research placement
II. Consensus meetings to identify acceptable integration of "Learning Healthcare System" approach into NHS neonatal care