Implementing best practices to reduce readmissions is an important step, but the ability to measure whether and how much such interventions really affect readmissions is essential. That's where healthcare analytics comes into play.
Heart failure is a serious problem in the United States. Consider these statistics:
- 5.1 million Americans suffer from heart failure.
- One in nine deaths in 2009 included heart failure as a contributing cause.
- Approximately half of those who develop heart failure die within five years of diagnosis.
In addition to the toll heart failure takes on individuals' lives, the financial burden to treat heart failure patients is becoming a serious public health issue. Heart failure costs the nation $32 billion annually. Additionally, heart failure is among the most expensive conditions billed to Medicare. It accounts for 43 percent of Medicare spending even though this patient population only makes up 14 percent of Medicare beneficiaries.
The situation becomes even more costly because many of these patients will be readmitted to the hospital soon after being seen for their heart condition. In fact, the readmission trends for heart failure patients are startling:
- More than 25 percent of patients hospitalized for heart failure will be readmitted to the hospital within 30 days of discharge.
- According to 2012 research, the top reason for readmission with the Medicare fee-for-service patient population is for patients suffering from heart failure.
Because excessive readmissions tend to indicate suboptimal care, government and commercial payers are focusing on 30-day readmission rates as a new quality measure for hospitals. The intent behind the measures is for the hospitals to provide better care by following evidence-based practice guidelines, which in turn, will reduce heart failure readmission rates. Some proposed methods include patient education at discharge, medication reconciliation, follow-up appointment within 48-72 hours after hospital discharge and follow-up phone calls.
Implementing these best practices to reduce readmissions is an important step, but the ability to measure whether and how much such interventions really affect readmissions is essential. That's where healthcare analytics comes into play.
Using healthcare analytics to reduce readmissions
Sophisticated healthcare analytics systems are able to comb through terabytes of data to reveal opportunities to improve quality and efficiency. What's more, analytics provides a way for hospitals to leverage their data to analyze and better manage specific patient populations. Here are three ways hospitals can use their data to achieve heart failure readmission goals:
1. Understand your current readmission rates for your heart failure patients. Why? Because you can't improve what you don't measure. This is the first step towards quality improvement. Specifically, you must establish 30- and 90-day readmission baseline measures for your heart failure population. Then you can use your analytics system to track performance metrics and distribute information to everyone who is trying to reduce readmissions. You will, of course, use historical data to establish your baselines. But realize that, as you move forward, it will be difficult to engage clinicians in clinical improvement initiatives if they only have old data to look at. Adopting an enterprise data warehouse as described below could help ensure the data is current.
2. Be aware of balance measures. Make sure that changes designed to improve one part of the system do not cause new problems in another part of the system. For heart failure readmissions, three types of balance metrics should be tracked: patient satisfaction rates, emergency department visits and observation stays. If a healthcare system is reducing heart failure hospital readmission rates but that same patient population is now over utilizing the emergency department, no benefits are being observed. It is important to hold the gains with these measures while also improving readmission rates.
3. Use a healthcare enterprise data warehouse to integrate clinical, financial and patient satisfaction data. An EDW identifies all patients with a primary diagnosis of heart failure and then stratifies the populations as either high or low risk for readmission. Using this data, multi-disciplinary teams examine the root cause of readmissions to implement evidence-based, best-practice intervention plans for heart failure patients. The teams implement these interventions and track their impact on readmission rates and the balance measures. The goal of these efforts is to provide patients with the care and services they need to optimize and maintain their health and prevent readmission. In addition to data integration, the EDW offers real-time data about readmissions rates. Without an EDW, the numbers for overall readmissions rates could lag by as many as 180 days.
Today's health systems face real challenges as they work to reduce heart failure readmissions. But advances in healthcare technology — particularly EDWs and sophisticated analytics solutions — can empower providers to significantly reduce preventable readmissions and help resolve an important public health problem.
Kathleen Merkley, APRN-DNP, is an engagement executive for Health Catalyst, a Utah-based data warehousing and analytics company. She previously was the corporate clinical IT implementation manager for Intermountain Healthcare and practices clinically as a family nurse practitioner.