A digital and IT services organisation is breaking new ground by using predictive analytics to accelerate the understanding of mortality ratios at an NHS hospitals trust.
The pioneering work is being done by The Health Informatics Service (THIS) on behalf of its host trust, the Calderdale and Huddersfield NHS Foundation Trust (CHFT), which runs Huddersfield Royal Infirmary and Calderdale Royal Hospital in Halifax, West Yorkshire.
It means the hospitals have near ‘real time’ mortality ratios instead of waiting for the national publication of the Hospital Standardised Mortality Ratio (HSMR) data, which is delivered three months in arrears and is some of the most acutely monitored data available to health trust leaders and healthcare professionals all over UK.
THIS’ information services team is collating data from CHFT’s Cerner Millennium EPR and other far-reaching clinical systems to gather information that then populates predictive R analytics software, an open-source software used for all kinds of data science, statistics, and visualisation projects.
Initial findings show an accuracy level of 85 per cent when compared to the HSMR, thus providing invaluable insight for CHFT’s medical directors, mortality surveillance group, and medical review co-ordinators.
Oliver Hutchinson, THIS’ Performance Information Lead for Primary Care, who is leading the mortality ratio analysis, explains: “The benefits are twofold. The first is to be three months ahead of the national HSMR data, so that when the national publication does arrive, we’ve had a foresight that will help to dispel any undue concerns.
“The second is that it will help us pinpoint potential clinical areas to improve. For example, if we have a patient with a low likelihood of death and they died, we can investigate that case and hold a clinical review to see if there is anything we can learn to prevent a death in similar circumstances in the future.”
Predicting a patient’s chance of survival
Anyone arriving at hospital is assessed on several criteria – these include a patient’s age, gender, exposure to deprivation, how they were admitted, comorbidity, whether they have a palliative care code and the actual diagnosis.
Based on the information gathered, predictive analytics allocate an expected death calculation (EDC) ranging between 0 and 1 – a score approaching 1 indicating an increased likelihood of death, while a score towards 0 indicating almost certain survival.
Considering all known factors, the scores are worked out incrementally, so for instance, someone might be given an EDC of 0.376 – which can be expressed as a percentage, i.e., they have a 37.6 per cent chance of dying.
The head of THIS’ Information Structure team, Gawaine Carter believes the data will allow the Trust a clearer focus on which cases to review.
He says: “The trust has always reviewed a good proportion of deaths but there weren’t pointers to which ones they really should be focusing on to learn from. The difference now is that it helps to focus a limited resource. Prior to this, we didn’t know where to focus until way after the event.”
Another use for the system is to potentially shed light on errors in recording a patient’s medical history. For example, a clinical review into someone who died despite a low EDC could check to see if certain factors had been missing from their medical record, which would decrease their survival chances and therefore would have produced a different EDC.
Predictive analytics – an emerging asset
Creating the near real time HSMR is THIS’ latest predictive analytics project. Two previous projects have focused on ICU admissions during the pandemic and, more recently, A&E admissions, to predict the percentage of patients arriving at A&E being admitted to hospital or sent home – currently running at an average accuracy level of 85 per cent.
It has also published a white paper entitled Predictive Analytics – An Emerging Asset in the Healthcare Industry, exploring how this fast-growing area of informatics can be strategically applied to support the healthcare sector in the UK and globally.
You can download the White Paper here.