Risk Or Remedy: How Data Can Help Halt The Readmissions Crisis

By Mark Lockwood, Director of Product Marketing, Logi Analytics

Under pressure to do more with less, healthcare providers are pushing data analytics to new heights — looking to improve care, prevent readmissions, and reduce costs for stakeholders and patients.

One area where data is making a real difference is readmissions. Hospital readmissions are a critical problem for the nation’s healthcare system, estimated to cost Medicare almost $15 billion a year. Currently, 20 percent of Medicare patients are readmitted to a hospital within one month of discharge, a figure deemed excessive by the Centers for Medicare and Medicaid Services (CMS).

In an effort to improve the quality of patient care and lower Medicare spending, the Affordable Care Act of 2010 called for a Hospital Readmissions Reduction Program (HRRP) which started in fiscal year (FY) 2013. During that year, almost two-thirds of all U.S. hospitals were charged $280 million in penalties for higher-than-allowed readmits under HRRP.

So, given the status of these regulations, how can hospitals reliably reduce their readmission rates?

Data analytics can aid healthcare organizations in developing best practices for minimizing readmission rates, enhancing quality of service, and avoiding the costly federal penalties related to readmissions. Even better, by utilizing self-service analytics, any permissioned user in a hospital setting can view and understand the necessary information to determine who may be at a high risk for readmission, 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 minimize that risk. For example, administrators can dig into performance and compliance dashboards to ensure their hospital is on track. What are the readmission rates for all causes and patients group-wide? How is the hospital performing with respect to readmissions by those diagnoses that are on the CMS watch list?

Data also shows proper after-care is vitally important to averting hospital readmissions. Has the patient followed up with their general practitioner? Have they been taking prescribed prescriptions? Additional KPIs may include average length of stay, patient satisfaction, occupancy rate, mortality rates, average cost per discharge, and even demographics such as gender or race.

With this information readily available to a variety of hospital staff, the necessary steps can be taken to reduce readmissions. Data is no longer reserved for analysts alone; it is accessible to everyone, no matter how technical they may or may not be.

Getting The Right Information To The Right User At The Right Time

Just as important as determining the relevant KPIs to reduce readmissions is deciding who should be consuming and acting on the information. For example, an on-call nurse responsible for monitoring the ICU could use self-service analytics to refer to a patients vital dashboard, use a tablet to keep track of how many patients were readmitted to their floor, or how many beds are occupied at any given time.

The head of the clinical department, who wants to examine and compare the most common diagnoses among readmitted patients, can use self-service analytics in order to relay this information back to other teams and management on a weekly 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 individualized 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 organizations. 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.