Protecting NHS Patients with the Wolfram Language
December 16, 2016 — Robert Cook, European Sales
The UK’s National Health Service (NHS) is in crisis. With a current budget of just over £100 billion, the NHS predicts a £30 billion funding gap by 2020 or 2021 unless there is radical action. A key part of this is addressing how the NHS can predict and prevent harm well in advance and deliver a “digital healthcare transformation” to their frontline services, utilizing vast quantities of data to make informed and insightful decisions.
This is where Wolfram comes in. Our UK-based Technical Services Team worked with the British NHS to help solve a specific problem facing the NHS—one many organizations will recognize: data sitting in siloed databases, with limited analysis algorithms on offer. They wanted to see if it was possible to pull together multiple data sources, combining off-the-shelf clinical databases with the hospital trusts’ bespoke offerings and mine them for signals. We set out to help them answer questions like “Can the number of slips, trips and falls in hospitals be reduced?”
I was assigned by Wolfram to lead the analysis. The databases I was given consisted of about six years’ worth of anonymized data, just over 120 million patient records. It contained a mixture of aggregate averages and patient-level daily observations, drawn from four different databases. While Mathematica is not a database, it has the ability to interface with them easily. I was able to plug into the SQL databases and pull in data from Excel, CSV and text files as needed, allowing us to inspect and streamline the data.
Working closely with a steering committee comprising healthcare professionals, academics and patients, we identified a range of parameters to investigate, including the level of nurse staffing and training, average patient heart rate and the rate of patients suffering from slips and falls. Altogether, the team identified around 1,000 parameter pairings to investigate, far too many to work through by hand in the limited time available.
Some of the tools in the Wolfram Language that made this achievable include:
- DatabaseLink and Import
- Dataset framework to manipulate the multidimensional data quickly into a usable form
- Statistics framework for performing the regression work to identify the interesting trends, and hypothesis testing to ensure their significance
- Report generation framework and visualization tools for generating the output and final academic paper
These tools enabled us to rapidly scale up the analysis across this complex dataset, allowing more time to consider the validity of the relationships and signals that emerged. Some of these seemed obvious—wards where patients were more likely to be bed-bound for medical reasons had fewer falls. But not all the signals were this easy to explain. For example, an increase in the number of nurses appeared to be linked to an increase in falls.
This observation seemed surprising. Given that there is little variation in ward size, it seemed unlikely that more nurses would lead to a decrease in patient safety. But not all nurses are equivalent. When we considered the ratio of registered nurses to healthcare support workers, we saw a strong relationship between the increase in highly trained registered nurses and the increase in patient safety.
So we see an increase in falls in some wards that rely more heavily on healthcare support workers. Could these wards be forced to rely on these less qualified, lower-paid nurses when in truth fully licensed, registered nurses are needed? I can only speculate, and the data at this stage is insufficient to answer this question. But following this analysis, the hospital trust in question has changed its staffing policy to increase the level of registered-nurse employment. Whether it leads to an increase in patient safety or a new issue raises its head—we will have to wait and see.
For the full findings, see the paper published this week in BMJ Open.
This project has only started to scrape the surface of the complexities hidden inside this rich dataset. In a mere 10 days, relying on the flexibility designed into the Wolfram Language, we’re able to deliver some insight into this complex problem.
Contact the Wolfram Technical Services group to discuss your data science or coding projects.