Strategies to reduce at-risk patient readmissions
Written by Neil Smiley, CEO of Loopback Analytics | March 22, 2017

Over the last several years, hospitals have been hard at work to develop strategies to avoid penalties and denial of payments associated with high readmission rates.

More recently, the expansion of bundle payments and other value-based payment models have further increased the importance of this effort. To reduce readmissions, most hospitals have made significant investments in a wide array of intervention programs, such as enhanced discharge planning, nurse navigators, post-discharge follow-up calls and health coaches. The cumulative effect of these investments is beginning to show results. For example, the readmission rate for Medicare fee-for-service patients with heart failure has declined by 10%1 from 2008 to 2014. Nevertheless, making further reductions in readmissions remains a difficult and expensive proposition.

While there are many potential causes of readmissions, lack of medication adherence following hospitalization continues to top the list. Poor medication adherence costs the U.S. healthcare system roughly $100 billion annually2. Improving medication adherence can be difficult because there are so many potential points of failure, including medication fill errors, patients taking the wrong medications, duplicative therapy and patients not following medication instructions. However, hospitals often overlook a very simple and profoundly impactful opportunity to improve medication adherence that is firmly within their control: delivering medications to high risk patients at bedside prior to discharge, sometimes called “meds to beds.”

In multivariate analyses published in the Journal of General Internal Medicine, 28% of new prescriptions go unfilled. Non-adherence was highest for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%)3. Other patients are late in starting therapy due to delays in getting their medications from a retail pharmacy in their community after hospitalization. When that patient is at high risk, the result is often an avoidable readmission. A recent case study by University of Tennessee Medical Center showed that by using a data-driven approach to deliver medications to high risk patients prior to discharge, they were able to reduce 30-day readmissions by more than 20% compared to patients of similar risk that did not get their medications delivered.

But who is high risk? Almost all hospitals employ some kind of risk score to identify patients that may be at risk for readmission. However, for an effective data-driven meds to beds program, hospitals need to identify patients that are vulnerable to readmission because of their medication risk. Most risk prediction models in use by hospitals do not include risk factors that are specific to medication adherence, such as gaps in medication fill patterns prior to admission, the numbers of concurrent medications, social determinates and flagging of medications that are difficult for patients to manage, such as certain blood thinners.

By combining medication adherence risk factors with other clinical encounter data, hospitals can ensure that the resources devoted to their meds to beds program reach the patients that most need it. With a data-driven approach that targets high risk patients, a meds to beds program that is staffed to engage only 30% of the inpatient population can impact more than 60% of the hospital’s total readmission risk. The typical all-cause readmission rate for a hospital across all inpatient stays may be around 9%, whereas the top 30% of patients at highest risk for medication adherence failure can have readmission rates of more than 20%, if they are not getting their medications prior to discharge.

Most hospital readmission reduction programs represent a significant expense to the organization. However, a data-driven meds to beds program can pay for itself. The incremental labor cost of pharmacy techs needed to round at bedside to deliver medications to the top 30% of high risk patients can be more than offset by higher pharmacy gross margins.

Reducing preventable readmissions will continue to be an important strategy of health systems as healthcare shifts to value based care. Hospitals with an onsite outpatient pharmacy can leverage predictive analytics to strategically target delivery of medications at bedside for high risk patients. A data-driven meds to beds program is a simple, cost-effective and tangible strategy to reduce readmissions through improved medication adherence.

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