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Here are the top three reasons for medication adherence failure and how to overcome them.
With the expansion of accountable care organizations, bundle payments and other value-based payment models, the economic impact of readmissions has intensified. Hospitals have made significant investments in a wide array of intervention programs to reduce avoidable readmissions, such as enhanced discharge planning, nurse navigators, post-discharge follow-up calls and health coaches. These investments are beginning to show results. For example, the readmission rate for Medicare fee-for-service patients with heart failure has declined by 10% from 2008 to 2014, according to the Kaiser Family Foundation. However, to avoid readmission penalties, hospitals must stay ahead of risk-adjusted national benchmarks.
Poor medication adherence following hospitalization costs the U.S. healthcare system roughly $100 billion annually, according to a New England Journal of Medicinestudy, and is the most significant cause of readmissions. The job of improving medication adherence can be daunting because there are so many potential points of failure. The top three reasons for medication adherence failure:
SmileyPatients are particularly vulnerable to medication adherence problems after hospitalization. During the hospital stay, standard medication routines are interrupted and upon discharge, existing medications may be discontinued and new prescriptions written. In the transition from hospital to home, scripts for new prescriptions may never make it to the pharmacy or if they are transmitted electronically, patients fail to pick them up. In some cases, transportation issues or economic barriers keep patients from getting their medications.
Hospitals often overlook a very simple and profoundly impactful opportunity to improve medication adherence: deliver medications to high-risk patients at bedside prior to discharge, sometimes called “meds to beds.” Proactive delivery of meds to high-risk patients provides an opportunity to eliminate potential transportation issues, proactively identify economic barriers and address questions regarding new medications.
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%). 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.
Some hospitals attempt to delivery medications to all patients prior to discharge. However, due to cost and staffing limitations, a one-size-fits-all meds to beds program can be unprofitable for the hospital pharmacy and still miss patients that are most at risk of medication adherence failure. But who is high risk? Most hospitals employ some kind of readmission risk score, such as LACE (L=Length of stay, A=Acuity, C=Comorbidities, E=Emergency department visit history). 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 use analytics to target those patients that have a high readmission risk who are also most likely to be helped by getting their meds prior to discharge. 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% 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 rate of more than 20%, if they are not getting their medications prior to discharge.
While most hospital readmission reduction programs represent a significant expense to the organization, 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.
After leaving the hospital with their medications in hand, patients may still have difficulties in taking their medications correctly. Data analytics can provide insight into which patients are most likely to need a little extra help. For example, patients that have had gaps in fill patterns prior to hospitalization or that have been prescribed complex or difficult to manage medication regimens may be particularly vulnerable. Using automation, pharmacists can match call intervals to risk levels, configure call questions to the patient’s specific conditions, reinforce medication instructions and address side effects that can cause patients to abandon their treatment plan.
For those patients on chronic medications, timely refills are critical to avoid re-hospitalization. Many community pharmacies have automated reminder systems to help encourage patients to get their refills on time. However, following a hospitalization, hospitals and community pharmacies need to work together to ensure a smooth hand-off of discharge medications. Health systems and accountable care organizations can utilize automation to periodically survey medication fill patterns for panels of high-risk patients (sometimes called a “panel pull”) to ensure that critical maintenance medications are getting refilled on a consistent basis. When there are gaps between prescribed medications and pharmacy medication fill data, case managers can be alerted to follow-up.
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 focus delivery of medications at bedside for high-risk patients. Automation can enable efficient use of post-discharge pharmacist calls to patients that are particularly vulnerable to medication adherence problems. Finally, systematic surveillance of gaps between prescribed medications and refill patterns can enable proactive engagement of patients that have abandoned chronic medications that could lead to re-hospitalization. A comprehensive data-driven medication management program to address the three primary points of medication adherence failure can provide a cost-effective and tangible strategy for health systems to reduce readmissions through improved medication adherence.
Neil Smiley is CEO of Loopback Analytics.