In this article, Tom Cahill, vice president for EMEA at Logi Analytics, looks at how data analytics can aid healthcare organisations in developing best practices for minimising re-admission rates, enhancing quality of service, and avoiding the costly federal penalties related to readmissions
Under policies that penalise hospitals if a person is readmitted within 30 days for something considered avoidable, English hospitals can lose up to £1billion a year in funding
NHS healthcare providers are facing increasing government pressure to meet manadate targets or face significant financial penalties.
Under policies that penalise hospitals if a person is re-admitted within 30 days for something considered avoidable, English hospitals can lose up to £1billion a year in funding.
Not wanting to face these steep penalities, NHS providers are examining every inch of their organisations to address complex challenges of re-admissions and improving overall patient care.
In order to tackle the re-admission crisis, hospitals need to track a range of data, including the re-admission rates of high-risk patients. These might be patients that have multiple chronic diseases who are taking several medications, or complex-care patients that require varying levels of treatment within, and outside of, the clinical setting. Inefficient management of these patients as they transition between healthcare teams and get discharged can cause an increased risk that a patient will need to be re-admitted within a short time of leaving.
To get a clear view of patient activity and improve care, hospital administrators should adopt analytic processes and solutions to help them minimise re-admission rates, enhance quality of service, and meet the Government’s new policy regulations
So, given the status of these regulations, how can hospitals reliably reduce their re-admission rates?
To get a clear view of patient activity and improve care, hospital administrators should adopt analytic processes and solutions to help them minimise re-admission rates, enhance quality of service, and meet the Government’s new policy regulations.
Even better, by using self-service analytics, any user with the appropriate permissions within the hospital setting can view and understand necessary patient information to determine who may be at a high risk for re-admission, or where there are compliance issues.
By looking at relevant data points and key performance indicators (KPIs), users across the hospital can implement follow-up procedures to minimise that risk. For example, administrators can dig into performance and compliance dashboards to ensure their hospital is on track. Data also shows that proper aftercare is vitally important to averting hospital re-admissions. Users can track whether the patient has followed up with their general practitioner and if they have been taking prescribed prescriptions.
With this information readily available to a variety of hospital staff, the necessary steps can be taken to reduce re-admissions.
Just as important as determining the relevant KPIs to reduce re-admissions, is deciding who should be consuming and acting on the information.
For example, an on-call nurse could use self service to refer to a patient vitals dashboard, keep track of how many patients were re-admitted to their floor, and monitor how many beds are occupied at any given time.
As quality of care becomes more important in both the public and private sectors, measuring that quality through data must become a compulsory process for healthcare organisations
The head of the clinical department who wants to examine the most-common diagnoses among re-admitted patients, can use self-service in order to relay this information back to other teams and management on a regular basis to keep everyone updated and informed.
Or a financial analyst within the hospital, who frequently needs to perform cost studies and allocations based on settlement claims, can use analytics in order to provide recommendations for improvement in the future.
By catering to these users with individualised needs and levels of expertise, it is significantly easier to provide all users with the ability to understand data and derive insights to make their own conclusions and decisions, no matter their skill level. Access to such data can allow hospitals to make real-time data more visual and actionable. The result of this is overall performance improvements, and the ability to reallocate resources to use for cure, rather than care.
As quality of care becomes more important in both the public and private sectors, measuring that quality through data must become a compulsory process for healthcare organisations. It’s clear that hospitals will need to turn to data analytics to meet the evolving challenges ahead, improve quality of service and curtail reimbursement penalties.
