Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates

One Friday night I was working as a charge nurse in a large, inner-city emergency department when an ambulance arrived with a patient suffering from heart failure (HF). Events like this are typical, but the patient was not — she was my favorite aunt who had suffered an acute myocardial infarction (AMI) two weeks earlier. On that particular day, she started to develop shortness of breath, and by the time my uncle called 911, she was in serious respiratory distress from pulmonary edema, a condition caused by heart failure.

I have cared for hundreds of patients with heart failure over my career, but this was different — this was someone I knew and loved. The experience made me contemplate: what could been done to prevent heart patients from going through this kind of suffering?

Heart Failure Readmissions: One of the Most Common Sources of Readmissions

Heart failure, including what was formerly referred to as congestive heart failure,* is an extremely serious problem in the United States for many reasons. First, there are a high number of patients who are suffering and dying from this disease. Second, the financial burden to treat heart disease patients is becoming an alarming public health issue. The following facts highlight the costs associated with these patient populations:

  • Heart failure accounts for $38 billion of healthcare spending annually.
  • According to a 2008 Health Care Financing Review, heart failure 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. These facts show the startling readmission trends for heart failure patients:

  • 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
  • Appropriate medications prescribed
  • Medication reconciliation
  • Timely access to care after discharge
  • Hand-off communications between primary care providers and acute care facilities
  • Rapid distribution of hospital documentation to primary care providers
  • Home health interventions
  • Follow-up phone calls
heart failure readmissions

A heart failure readmission dashboard helps clinicians track the care they provide.

4 Ways to Leverage Healthcare Analytics to Reduce Heart Failure Readmission Rates

Now that hospitals have to comply with this new quality measure or face financial penalties, how should they address heart failure readmission rates? There is a solution: advanced analytics.

Sophisticated analytics are able to comb through terabytes of clinical data to reveal opportunities to improve quality and efficiency. What’s more: analytics provide a way for hospitals to leverage their data to analyze and better manage specific patient populations. Here are four ways hospitals can use their data to improve heart failure readmission goals:

1. Understand your current readmission rates for your heart patients. Why? Because you can’t improve what you don’t measure. It is important to establish readmission baselines, track performance metrics, and distribute information to everyone who is trying to reduce readmissions. This is the first step towards quality improvement. Health systems operating at level 5 or above on the Healthcare Analytics Adoption Model will be able to achieve this goal.

Analytics Adoption Model Comparison

Building the right analytics foundation provides a way for hospitals to wisely leverage their data, but most important helps them improve care.

2. Establish 30- and 90-day readmission baseline measures for heart failure patients. Realize, however, that if you’re looking old data, it’s difficult to engage clinicians in clinical improvement initiatives. Adopting an enterprise data warehouse (EDW) as described below could help ensure the data is current.

3. Be aware of balance measures (changes designed to improve one part of the system without causing new problems in another part of the system). For heart patient readmissions, there are three types of balance measures: patient satisfaction rates, emergency department (ED) visits, and observation stays. It is important to hold the gains with these measures while also improving readmission rates.

4. Use an enterprise healthcare data warehouse (EDW) to integrate clinical, financial, and patient satisfaction data. An enterprise healthcare data warehouse 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 HF 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 HF 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.

HF Readmission Dashboard

As shown in the Health Catalyst Heart Failure Readmission Dashboard, you can lower readmission rates by using bundles intervention tracking.

A recent client of ours implemented the Health Catalyst late-binding TM Data Warehouse and Heart Failure Advanced Application. The results to date have been positive. The client has experienced:

  • 14 percent reduction (seasonally adjusted) in 90-day heart-failure readmissions
  • 21 percent reduction (seasonally adjusted) in 30-day heart-failure readmissions
  • 2X increase in the number of phone calls made to patients within 48 hours of discharge
  • 63 percent increase in physician medication reconciliation post discharge

This client will continue to identify and implement additional interventions to further lower their heart failure readmission rates.

Gratitude for Readmissions Improvements

From a professional perspective, it’s important to understand the challenges health systems face as they work to reduce heart failure readmissions. But from the personal perspective watching a loved one suffer from a potentially preventable readmission, I’m grateful for the progress we’re making in healthcare. In specific, I am thankful for government scrutiny, progressive clinicians, and healthcare IT solutions that are beginning to contribute to the reduction in heart failure readmissions.

What solutions are you using to measure and implement quality improvement programs to reduce heart failure readmission rates? What wisdom can you share from your successes?

Are you satisfied with the new readmissions measures? If not, how could they be improved?

PowerPoint Slides

Would you like to use or share these concepts?  Download this heart failure readmission presentation highlighting the key main points

Click Here to Download the Slides


*The American College of Cardiology and the American Heart Association prefer the use of “heart failure” over “congestive heart failure.” http://www.onlinejacc.org/content/62/16/e147

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