Helping Leeds West CCG hit unplanned admissions target

Leeds West CCG is made up of 37 GP practices and covers a population of 350,000 in some of the most deprived and affluent parts of the city. Keeping people out of hospital was a priority before NHS England introduced the Enhanced Service for unplanned hospital admissions in 2014.

The CCG had used a risk stratification tool before it commissioned the EMIS Health risk stratification tool for every practice. Dr Paul Maddy, GP at the Hillfoot Surgery in Leeds, explains that its simplicity and the way it’s integrated into his practice clinical system are the best things about it.

He continues: “All the hard work has been done in developing the algorithm too”. The QAdmissions™ algorithm was created by clinicians and academics and has been clinically validated by peer review. It’s based on UK patient data and takes account of local deprivation and national priorities. It’s also reviewed annually to keep it up to date. Having been created by ClinRisk Ltd. using QResearch – EMIS Health’s not-for-profit research partnership with the University of Nottingham – also provides confidence in the clinical validity of the tool.

Fulfilling the Enhanced Service contract

The Enhanced Service was designed to help reduce avoidable unplanned hospital admissions by improving services for patients with complex health needs. To qualify for the Enhanced Service, practices must identify the top 2% of their patients at risk of unplanned hospital admission. This is important to ensure quality of care for patients but also for the practice to recoup income under the contract.

EMIS Health risk stratification has been designed for both clinical and administrative staff to use. At the Hillfoot Surgery, the practice manager reviews the QAdmissions™ searches and adds patients to their admission avoidance register. These patients can be added en masse which saves time and streamlines the process.

Dr Paul Maddy, GP, Hillfoot Surgery

“we can quickly identify the current top 2% at risk of hospital admission”

Dr Paul Maddy, GP at the Hillfoot Surgery

More complete patient information

It’s fully integrated within EMIS Web making it easier to use, no data is extracted from your clinical system. Keeping the practice’s admission avoidance register up to date is also much easier. Dr Maddy explains: “We can instantly run a new search if we need to update our register - so if patients move on to other practices or pass away, we can quickly identify the current top 2% at risk of hospital admission. It’s quick and easy. Giving patients a percentage score – not just an indication of high, medium and low risk like other tools - gives us a more accurate foundation for an admissions avoidance register.

“Other risk stratification models we’ve used have generated some unexpected outcomes – patients we’d never expect to be included. This seems to be due to the weighting given to patients with specific diseases, like cancers. This model is based on emergency admissions and not cost of treatment. If you use cost then patients with rare diseases may have expensive surgery which highlights them as high risk. We already know about these patients and have care plans in place to monitor and manage their conditions.”

Saving time and money

Dr Maddy also confirms that the tool has reduced the time the administrative team spends on defining and maintaining our admissions avoidance register. Without fulfilling the Enhanced Service, they would lose out on income they used to get automatically so, to remain viable, it’s really important that we can recover this income.

Ensuring continuity of patient care

The practice now uses it to identify patients who have recently been discharged from hospital, to ensure continuity of care, proactive case management and personalised care planning.

Ensuring patient confidentiality

Patients are increasingly concerned about secondary use and confidentiality of their data. EMIS Health risk stratification is based purely on practice data so there are no concerns about exporting or sharing patient information. Dr Maddy concludes: “We also know it’s based on the data within our system, so we’re more confident about the quality of the data we’re using”.