Banner Health Migrates Millions of Electronic Medical Records with Blue Prism
Logistics are often the largest challenge interfering with early patient referrals. With disparate systems and various interactions between healthcare departments, communication and patient experience tend to be the first fail points when processes break down. Staff must work hard to coordinate between these different departments, and that can leave patients waiting for critical care.
By deploying healthcare automation via intelligent automation (IA) to augment the way clinical staff manage referrals, digital workers can automatically validate non-clinical data and prioritize it based on relevance. This can cut referral time, which means the patient gets in to see a healthcare professional sooner.
Since IA digital workers are available 24/7, their automated bookings can drastically reduce backlogs. And with fully audited trails showing their performance, you get complete transparency of the automated referral pathway to ensure key treatment milestones are being continuously met – facilitating peer reviews and other compliance reports.
Effective referral management should have a centralized system, reliable auditability, checks for misplaced or missing referrals, and it should save time and resources.
Healthcare automation can help reduce wait times to get patients their care sooner, which can result in a better prognosis. And with staff time freed from repetitive, data-heavy tasks, they can focus less on admin and more on helping patients. Here are some areas where IA can decompress the administrative burden during the referral process:
“There are huge gains to be made in terms of incorporating intelligent automation into healthcare processes. For us, the imperative is patient safety and experience.”
Assistant director of informatics, Aneurin Bevan Health Board., LinkedIn
The NHS e-Referral Service (e-RS) already provides an easy way for patients to book specialist appointments online, on the telephone or through Referral Management Centers (RMCs) at the time of referral. This can accelerate their time to care. Yet there are still further opportunities to utilize IA in integrating, monitoring and managing appointments and moving patients through their pathway.
Here’s how two of our customers successfully automated their referral management processes.
The Ipswich Hospital Cancer Hub receives around 900 cancer and suspected cancer referrals each week. Each referral must be processed within 24 hours of receipt. Automating the process has therefore not only assisted in the return of staff time but has also reduced the risk of referrals taking longer than 24 hours to process, potentially delaying care.
Manually, the process was completed by one band four and one band two staff to exclusively process referrals, assisted by a further two band two staff and the service manager when available. These staff are now free to work on other tasks within the service.
For each referral, all referral documents are currently printed. These are then scanned back as one single file for uploading to Evolve. Each patient could have between 2 to 30 or more pages, therefore the total paper used per week can reach 2,000 sheets at the very minimum. The automation will replace the printing element, saving paper, removing waste and being in line with ESNEFT's ambition to be a paperless organization.
This process takes building blocks from other automated processes, meaning that the automation team could build, test and deploy the process at pace for the cancer team at Ipswich Hospital, but then duplicate it for the Cancer Services team at Colchester Hospital.
The Hutt Valley District Health Board (HVDHB) in New Zealand had a lot of manual work slowing down their e-referral processes. When a general practitioner (GP) refers a patient to a specialist, they create an e-referral in a patient management system. Then, clinical administrators have to collect the e-referrals and manually complete registration for each in two key systems. To complicate matters, the manual process required 37 separate steps, so administrators spent an average of 7.5 minutes to complete one e-referral registration. Then the administrator would access the system to complete the e-referral and generate and send a letter to the patient and specialist.
The team turned to an SS&C Blue Prism digital workforce to manage their e-referral registrations. The digital workers now collect the e-referrals from specialist service queues and complete each registration. At 6 p.m. every day, digital workers open the target systems, search for unregistered e-referrals, complete validation checks, update demographic data and create the e-referral record. If there is any incomplete or incorrect information, the digital worker passes the e-referral to a skilled staff member to investigate. Once an e-referral is registered, a digital worker closes the loop and generates a referral letter for the patient and specialist.
Read the full case study to find out how Hutt Valley delivers e-referrals with 100% accuracy, saving $527,000.
The healthcare industry has significant challenges in early patient referrals due to disparate systems and complex interactions between departments. These challenges often result in communication breakdowns and impact the overall patient experience as staff struggle to coordinate between different entities.
Digital workers powered by IA can automatically validate non-clinical data, prioritize referrals based on relevance and significantly reduce the time it takes for a patient to see a healthcare professional. They’re designed to reduce backlogs and streamline the referral process while maintaining compliance, security and accuracy.
Automating healthcare referrals will not only improve efficiency but also allow clinical staff to focus more on patient care by freeing them from repetitive, data-heavy tasks.
Get started on your automation journey with SS&C Blue Prism.
If your network blocks YouTube, you may not be able to view the video on this page. In this case, please use another device. Pressing play on the video will set third-party YouTube cookies. Please read our Cookies Policy for more information.