Successfully deploying data analytics, as its use continues to evolve in the healthcare industry, is an area of critical concern. In our previous posts, ‘Can Analytics Save the NHS from Collapse?’ & ‘Towards a data driven healthcare system’ we discussed the necessity and the advantages of the NHS being a data-driven healthcare organisation. Being informed by descriptive, diagnostic, predictive and prescriptive analytics will enable NHS managers and employees to make better decisions. This can be in a variety of areas; from A&E performance and prediction, testing services (radiology & pathology) and bed management & reducing the delayed transfer of care (DTOC) to improved insights in specific areas of pharmacy, procurement and fraud detection.
The next step is to think about best practice for deployment. From our experience, we’ve seen that many CIOs and CFOs have questions about how to actually start a data analytics project. They know they need the analytics – but how do they go about it?
We have listed below the best practices to ensure success from your data analytics project in healthcare.
Ensure patient-centricity – not value for money
The NHS is under constant pressure to save money and to become a more efficient organisation overall. However, with analytics, it’s important not to cut corners. Although funding analytics is, of course, an important factor, it shouldn’t simply be about that. The main goal should be how efficient the data collection will be for the patients and what can be done for them, rather than how much money can be saved in the short-term. The goal should be, eventually, that the money invested in data analytics will improve patient outcomes – in turn, creating value for money in the long-term.
In a similar vein, although you may have a good idea of what sort of system you’d like, it’s worth thinking about a phased approach. You may end up spending hundreds of thousands of pounds for a huge programme, to then realise it’s not quite right for your organisation. Instead, think about it in small increments, for example: if I integrate this system into radiology, what kind of benefit will I expect to see? Eventually, you will have a mature programme, but you will have had clear visibility of where you’re heading all the way through. Your programme provider however, should consult and educate you about this. In addition, this phased approach will help with the cultural change for employees within the organisation.
Goal setting and sharing
Data analysts and employees should be completely aware of transparent goals. This should apply to both short and long-term goals and expected results. It could be getting patients seen by triage down from 30 minutes to 20 minutes (short-term), to improving the overall speed of the healthcare cycle for a patient by 50% year on year (long-term). This is extremely important to have in your mind before embarking on a data analytics programme – especially within healthcare.
Finding the right talent
The most vital element to the success of a data analysis project is the right people – you simply can’t do the best job without it. This could be an internal IT team or a collaboration of stakeholders (internal, external, partnership, consulting). The knowledge they possess collectively needs to match with the deliverables in terms of experience in data analysis, software, general healthcare operations, and so on.
Beyond the hospital
Internal datasets help you to improve efficiency within your own organisation. However, external datasets will also help you to benchmark and compare with other trusts – collaboration is key. A second reason to integrate and incorporate external data is to predict patient inflow by using data points such as weather, traffic and events. For example, if it is very cold, can you expect more respiratory issues, an influx of elderly patients? If there is a large football match nearby, could you expect assault injuries or alcohol-related issues? Nearing Bonfire Night, could you predict an increase in burns? The options are limitless.
These five best practices will lead your healthcare organisation to a cohesive data-driven organisation. The overall goal should be to leverage data to improve current AND future patient outcomes, in turn, creating a better healthcare industry.