What intelligence can hospitals gain from diagnostics?

Radiology could represent a far-more-important part of a hospital’s business intelligence. Sectra’s Chris Scarisbrick explores a goldmine that the NHS must look to unearth

It’s an age old story. The NHS has less while being asked to do more.

There must now be complete visibility of any lack of specialists, radiologists, equipment, training, or resource needed to ensure effective workflows and efficient running of the department. But, historically, IT has not been up to the job

Diagnostic departments are no exception with less cash, revenue and capital, and a requirement to meet increasing demand.

But could greater intelligence within our diagnostic departments, and greater use of a rich source of information held within the ‘ologies’, now be about to make a serious difference to the effectiveness and efficiency of the wider hospital enterprise?

Are hospitals taking full advantage of diagnostic information? And is enough being done to provide disciplines like radiology with the departmental intelligence needed?

Give diagnostics the intelligence needed so the whole enterprise can benefit.

Departmental managers today are having to be so much more accountable than ever before. They must prove their efficiencies, and it is now absolutely imperative that they have a full understanding of what is happening in their department from when the patient is first referred, right the way through diagnostic outcome and reporting. They need to see that end-to-end process.

There is a revolution on its way in the clinical and research side of big data. VNAs, which have served as image repositories, offer a goldmine of data to the NHS that is only set to grown

Our diagnostic teams need to be able to embrace business intelligence to identify departmental bottlenecks, manage workflow, and avoid delays in the working process that can impact on clinical processes and, ultimately, timely and cost-effective patient care.

Every potential delay to a report must be avoided. There must now be complete visibility of any lack of specialists, radiologists, equipment, training, or resource needed to ensure effective workflows and efficient running of the department. But, historically, IT has not been up to the job.

So far, it has been very difficult to access this data. Information hasn’t been stored in an intelligent way, or in a standard way and so gleaning any benefit from it becomes almost impossible.

Departmental managers and data analysts have had to build bespoke queries to analyse often-incomplete data – a cumbersome, inefficient, and sometimes-inaccurate way of measuring effectiveness.

Our children will look back in disbelief on ‘how they did medicine’ – a one-size-fits-all approach that doesn’t work

The way information has been presented too has often been nearly indecipherable without having data mining experts to interpret. Things must, and are, beginning to change.

Information must be structured and, through standards, made accessible in a consolidated view. In some instances a hospital’s electronic patient record (EPR) could be the means to access this information. But, for many people still today, there is a preference to utilise departmental systems in order to analyse, interact and report with information at the granularity required.

The modern departmental radiology information system (RIS), picture archiving and communication system (PACS) or vendor neutral archive (VNA), must therefore be able to support this, with business analytics at the core so that any bottlenecks can be identified and remedied at the earliest opportunity.

Real effective intelligence is about far more than only enabling departmental managers to drive their own efficiencies.

Ensuring it can be lifted out of the confines of a traditional radiology solution, and made useful at the trust or enterprise level, is key.

From an administrative point of view, this can mean understanding the time taken for patients to receive an appointment, a report, or a follow-up and actions that can be taken at the enterprise level to solve these challenges.

We have millions upon millions of CT scans, X-rays, ultrasound scans and a plethora of images that can be fed into machine learning algorithms, that can allow software to learn, and ultimately diagnose conditions we didn’t know were there or weren’t necessarily even looking for

But, at the same time, there is a revolution on its way in the clinical and research side of big data. VNAs, which have served as image repositories, offer a goldmine of data to the NHS that is only set to grown.

Genomic medicine means that one of the biggest datasets ever created could soon become a standard part of healthcare analytics.

By sequencing and analysing the human genome, we can understand the body better, and how illnesses like cancer work within an individual. It means the biggest revolution in healthcare – the reality of personalised medicine.

Every pill or prescription given in coming years could be tailored towards us as individuals, based on our individual genetic structure.

Our children will look back in disbelief on ‘how they did medicine’ – a one-size-fits-all approach that doesn’t work.

Techniques from analysing genetic sequences can be applied to other areas of diagnostics.

To maximise its overall intelligence, the hospital enterprise needs to think about the wealth of data it can harness from the diagnostic environment, to seize the opportunity now

We have millions upon millions of CT scans, X-rays, ultrasound scans and a plethora of images that can be fed into machine learning algorithms, that can allow software to learn, and ultimately diagnose conditions we didn’t know were there or weren’t necessarily even looking for.

This is not just about a financial goldmine, in the sense of less waste and more cost-effective targeted care, but represents the potential for pro-active diagnosis that would never be possible by human analysis of the vast data stored in our VNAs.

Utilising standards in the way information is stored is an absolutely-imperative part of making it useful.

We already have an opportunity to maximise intelligence from our RIS, PACS and VNA, so that workflow inefficiencies can be identified, but only if that information is created in a way that it can be used, and potentially harnessed through other systems, including enterprise wide EPRs or business analytics tools.

This is not just about a financial goldmine, in the sense of less waste and more cost-effective targeted care, but represents the potential for pro-active diagnosis that would never be possible by human analysis of the vast data stored in our VNAs

We are already having success in achieving this with our customers who can now use dashboards to visualise bottlenecks that jump out of the page.

But historic imaging held in our departmental systems and the onset of genomics mean that much-greater potential also exists.

To maximise its overall intelligence, the hospital enterprise needs to think about the wealth of data it can harness from the diagnostic environment, to seize the opportunity now.

Companies