What is data science in healthcare?
Data, defined as ‘a collection of items of information’ by the Oxford English Dictionary, is a small word with an immeasurable impact, particularly in a healthcare setting.
Data science in healthcare is devising and delivering cutting-edge systems, where the data being collected is benefiting all concerned - patients, who possibly won’t always notice the difference it makes, and healthcare professionals of all standing, who most certainly do.
The headline benefits of healthcare data management are threefold:
- Improved patient care and safety by creating more time for clinicians to actually do their jobs.
- Improved efficiency across an organisation, from the simplest task like finding an appointment for a patient to dashboards providing a ‘ward to board’ understanding of metrics that impact on the patient experience and outcomes.
- Improved cost-savings, such as avoiding duplicated appointments and the costs they incur.
The Health Informatics Service (THIS) has worked closely with its host, Calderdale and Huddersfield NHS Foundation Trust (CHFT), for the past 10 years to evolve and embed data-driven healthcare within its culture, using it to make operational decisions, reshape patient pathways and reconfigure service delivery to benefits patients.
Together, THIS and CHFT are using data science to eliminate health inequalities, shorten waiting lists for elective treatment, reduce the number of appointment DNAs (did-not-attends), increase endoscopy efficiency, lessen the pressures on A&E, even ‘taking the robot out of the human’ by using bots to carry out mundane, repetitive tasks to alleviate the workloads and pressures on time-poor, administration-heavy teams.
Key components of data science in healthcare and how it works
Such is the breadth, depth and complexity of NHS, data it comes under the umbrella term of big data – huge and diverse collections of structured, unstructured, and semi-structured data that grows exponentially over time.
To channel this, CHFT and THIS have developed an in-house knowledge portal which links to the Trust’s data warehouse and electronic healthcare records (HER), whereby data becomes an operational tool fed by countless integrations.
Examples of digital healthcare innovation include machine learning and automation, asset scanning and tracking, voice activations, electronic drug monitoring and ordering, handheld tablets and predictive analytics, which first proved invaluable during the Covid-19 pandemic and its aftermath.
For example, voice recognition technology reduces repetitive tasks surrounding the recording, checking and despatch of patients’ clinical notes and letters. This enables clinical and secretarial staff to spend more time on patient care, minimises unwarranted variances in patients’ records, and reduces staff frustration and burn out blamed on heavy paperwork duties.
Predictive analytics in healthcare assess tens of thousands of data points ranging from a patient’s condition on arrival at hospital, including whether they arrived in an ambulance, car or on foot, to their individual medical records and broader socio-economic or demographic information, such as their home postcode and ethnicity.
With this data, predictive analytics can identify the best course of treatment for that patient. What’s more, it also helps to predict patient outcomes such as short-term risks, like heart failure; and the likelihood of any longer-term obstacles, such as a patient being re-admitted to hospital.
You can read more about both examples, and many more, at: https://www.this.nhs.uk/insights
The importance of data science in healthcare
CHFT became the first and only organisation in Europe to achieve a HIMSS stage six validation for its use of data and its approach to data science when it was assessed in the summer of 2024.
HIMSS (Healthcare Information and Management Systems Society) is a not-for-profit, global advisor, thought leader and member-based society committed to reforming the global health ecosystem through the power of data and technology.
Its validations range from zero to seven and certifications are arrived at via a series of pre-validation exercises and an on-site visit. The validation focuses on four key areas:
- Infrastructure
- Governance
- Capability
- Data content
In a letter confirming the stage six validation, HIMSS’ EMEA Senior Director, John Rayner, said: “On being validated at stage six, you are clearly demonstrating your commitment to improving patient safety and the overall quality of clinical care through the effective use of advanced analytics, appropriate governance and robust infrastructure.”
Sign up for the data science white paper
THIS and CHFT have collaborated to produce a white paper that includes chapters on how the trust has used data science and analytics to reduce appointment backlogs, tackle health inequalities, create departmental efficiencies and improve outcomes to a range of issues, such as mortality ratios, with predictive modelling.
Writing in the white paper’s conclusion, Rob Birkett, Chief Digital and Information Officer for CHFT and THIS, says: “Predictive analytics, intelligent modelling and the use of AI will go some way to enable the change required to meet the new 10-year plan for the NHS, including the continued shift from analogue to digital to support operational and clinical transformation.”
To get a copy of the white paper, visit out Data Science Whitepaper page.
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