Special Report: Business intelligence: using PACS and RIS data
Feature
Using PACS and RIS data: see what can be done
Imaging systems hold a wealth of data, and suppliers are increasingly working with trusts to make sure they can use it to improve efficiency and patient pathways. Kim Thomas reports.
Along with the picture archiving and communications systems used for storing images, radiology information systems are part of the standard toolkit of hospital radiology departments.
The RIS plays a critical role: handling referrals, making appointments, managing workflow and keeping a record of radiologists’ reports on imaging procedures.
As a result, it holds a rich repository of operational data that can show how efficiently the department is performing – how many films are unreported, for example, or which patients are regularly not attending appointments.
Most PACS and RIS vendors offer business intelligence systems to collate and analyse that operational data, so that trusts can use it to improve performance.
Help!
Clemens Janus, general manager, analytics, cardiology and high acuity care at GE Healthcare, says that demand is very high.
“Every single customer is asking for help. The reason is that everyone understands that there are process inefficiencies, a shortage of radiologists, increasing patient numbers, all happening at the same time.”
This is confirmed by one PACS manager who did not want to be named, but whose department is using the HSS CRIS Insight tool: “Currently the NHS is under pressure to deliver diagnostics in a defined time frame, whilst constantly trying to meet an ever increasing demand for these services.
“There is therefore a pressing need for readily available data to ensure that services are monitored closely in terms of both performance, and also planning of the outstanding workload ahead.”
Improving operational efficiency
Janus says there are two main ways in which RIS data is used to improve operational efficiency: waiting time optimisation – looking at how many patients are waiting too long for a CT scan, for example – and productivity.
It’s possible to benchmark within the same hospital, comparing waiting times or output between different teams in the same department, and analysing why some are more efficient than others. As Anjum Ahmed, global marketing manager, enterprise imaging at Agfa, points out, business intelligence tools offer a hard return on investment.
This type of data analysis is usually known as descriptive analytics, but vendors are now seeing demand for tools that can deliver predictive analytics, says Thierry Verstraete, product manager for the business analytics at Carestream.
“Our customers are asking us about making predictions with a certain level of confidence for the number of CT exams or the number of MRI exams in the next three or six months or year.
“Essentially, customers are looking to leverage predictive analytics to do proper capacity planning, to understand when they need to add a modality to the department or at least budget for it, or when to start looking at adding another radiologist to their reading teams.”
Predictive analytics are not an exact science, says Verstraete – they give predictions within a certain range – and have to be used with care, because a significant change, such as a modality breaking down for an extended period of time, will affect the data used as the basis of the prediction. Seasonal fluctuations similarly need to be taken into account.
Customers are also beginning to use radiology data to inform an enterprise-wide view, says Ahmed: “The intelligence you’re driving out of the system should be based on following the patient journey.”
Getting a better view of the enterprise
Jane Rendall, managing director of Sectra, agrees: “On an enterprise level, you need to be able to push the information out of your RIS, out of your PACS, out of whatever other applications you’re using, into a separate database which is accessible remotely from the department.
Then, the chief operating officer or chief information officer can drag data and understand how it’s impacting the whole service.
“One of the things they want to do is to get patients out of bed quicker, so where are the hold-ups? Why has this patient not been transferred out of hospital? Is it because they're waiting for an ultrasound in radiology?
“When I look at radiology and see that this patient has been waiting for x number of days, why is that? So you begin to get a more holistic view of how the enterprise works.”
Trusts can also work together to use business intelligence for comparison purposes, says Janus: “We can compare the CT team in hospital A, with the CT team in hospital B, and identify what's going on, and also drill down to identify the bottleneck in the hospital that has longer waiting times.”
Starting to think about demand
At the same time, Ahmed points out, business intelligence provides the opportunity for trusts within a region to manage the care pathway more effectively across their organisations.
An example of what might be possible in future comes from Ireland, where McKesson has rolled out RIS to every hospital in the country, connected by a single central database.
This means that approximately a million HL7 messages come into the database every day, says Ray Cahill, vice-president sales at McKesson.
“Each one of those messages tells a story about a patient journey. If you apply proper BI techniques and methodology on top of that, that supplies fantastic insight.”
That could include, for example, information about how many patients have to wait more than six months for an urgent MRI, making it possible to shift patient care to a different, less busy centre to reduce waiting lists.
This national analysis of operational data is something that McKesson is now working on with Ireland’s Health Service Executive, along with more advanced analytics, linking the reason for referral to the patient outcome.
Getting into the predictions business
There are two other major trends on the horizon. One, says Verstraete, is towards prescriptive analytics, where data is used to provide guidance on what steps should be taken to achieve particular business goals.
The other is unlocking the valuable information held in trusts’ PACS stores that now date back ten years or more. Roy Kinnear, UK sales director of Intelerad, says a new generation of tools can analyse those images to inform radiologists’ decision-making.
The radiologist would go through the normal reporting workflow, but at the same time algorithms would be applied to thousands of current and prior studies to identify patterns and alert the radiologist to areas of potential interest.
“The algorithm is picking up from the current and historical studies and highlighting where they may be issues,” says Kinnear.
It is still early days for the technology, though Intelerad is in discussions with an NHS trust. But at a time of increased pressure on the NHS, a tool that saves time for the reporting radiologist is likely to have a strong appeal.
Kinnear adds: “It may save 12 or 24 months of treatment or endless callbacks by picking up issues that can be treated earlier in the patient’s pathway.”
Really, really big data sets
In the longer term, Rendall thinks that imaging data could aggregated and analysed across trusts (at, for example, a regional level), providing greater potential for computer-aided diagnosis.
“If you are looking at a chest X-ray and at a specific disease, say pneumonia, you have a huge number of pneumonia patients with a clear diagnosis, clear treatment path and clear imaging.
Now if you got a computer to look at millions of images and say, ‘This is what pneumonia looks like on a chest X-ray,’ maybe in the future, a computer could say, ‘Have you considered pneumonia in this situation?”
The aggregated data could also be analysed to see, for example, “how many people came for a chest X-ray, what sex they were, what age they were, what the outcomes were.”
This, says Rendall, will require vendors to collaborate so that their applications will be able to drill down into relevant imaging data where necessary. The move to local devolution in Greater Manchester, she believes, could provide the ideal vehicle for mining large-scale datasets to better inform clinical pathways.
“The NHS has a golden opportunity to own that data,” Rendall adds. “They need an efficiency-based approach to measure the patient's entire journey and to understand where the key issues and bottlenecks are and the opportunities to improve.”