Imagine two patients, both discharged to their home after being treated at a hospital.
Mary is an elderly woman with congestive heart failure and mild cognitive impairment, whose strong support network is available to help cook, clean, and drive her to follow-up appointments with doctors. However, her network is only available to help a couple hours each day.
The other patient, Tom, was discharged home with his wife after a colostomy without supplies, and he was unsure how to empty or change his bag.
If you had to guess, which one would you say was readmitted to the hospital?
Believe it or not, the answer is both. While the reasons were different, both cases were more or less preventable.
These cases have something else in common: Early intervention and the use of evidence-based readmission risk and needs assessment tools could have changed their outcomes.
Historically, as many as one in five patients returns to the hospital within 30 days of discharge. By last estimate, preventable readmissions cost the Centers for Medicare & Medicaid at least $26 billion per year. Payers have fought hard in recent years to bring the number of preventable readmissions down, yet more than half of all hospitals incur financial penalties for failing to reduce readmissions.
At the same time, payers have begun shifting away from fee-for-service payment models and toward value-based care systems under which a hospital or provider is given a set payment amount for each patient admitted, but receives nothing for readmissions.
Under some new payment structures, a hospital may find that sending a high-risk patient to a skilled nursing facility at the hospital’s expense is better for the patient, and financially safer than sending a patient home and risking a costly readmission.
Value-based care emphasizes the need for research-based decision support tools that help providers determine where to send patients after discharge and which facility will yield the best outcomes for the patient.
Finding the right tool
There is an abundance of risk and needs assessment tools on the market today. The question is how clinicians, and the executives making the purchasing decisions, should evaluate these tools and then operationalize their use in a way that enables them to measure outcomes.
What they tend to have in common is the ability to assign a numerical risk score based on dozens and perhaps hundreds of risk factors, like comorbidities, length of stay, current diagnosis, previous diagnoses, and the acuity of the current illness.
Their differences can be quite large.
Some tools focus on treatment options, risks, and care plans for specific conditions like COPD or congestive heart failure. Others take a broader approach, looking at the entire population of patients and their communities.
The tools that focus on a specific diagnosis sometimes struggle when it comes to patients with multiple comorbidities.
Planning tools should be dynamic, contextual, and applicable to all patients. That is, they need to take into account your patient population, and they must consider the community a patient lives in.
For example, we know that poverty is a risk factor in readmissions. But a low-income patient discharged to his home in a rural area will face different risks than a low-income patient discharged in an urban setting near an academic hospital. Readmission risk and needs assessment tools should factor this in and continually “learn” from regional data. They shouldn’t ignore levels of poverty, education, access to transportation, and psychological factors like depression or dementia that raise readmission risks.
Tools that store and analyze large numbers of variables are important, but the analysis must also be relevant. Look for a product that has the most statistically significant results, one that drives the broadest impact across the largest segment of the patient population. These tools tend to be research-based and derived from very large data sets.
Start planning early and reassess often
Let’s return to the examples from the beginning of the article.
While the reasons for Tom’s return to the hospital were foreseeable, we can still learn from his case. Without a doubt, he should have been given supplies and he and his primary caregiver should have been trained on proper care. The question isn’t what should have been done, but why it wasn’t. If these needs had been anticipated and proactively managed by way of a phone call, video chat, or home visit, a readmission could have been avoided.
A system’s failure to timely identify at-risk patients and begin planning for their discharge is one of the most common factors in readmission.
In this respect, Tom’s case had something in common with Mary’s. Mary was readmitted to the hospital because she did not understand that the medicine prescribed at the hospital was a generic version of what her primary care physician had prescribed. She took both pills, doubling her dose. In addition, Mary’s family was not made aware at discharge how much supervision she would require, and couldn’t put in the time required.
The most important thing that hospitals can do to take advantage of discharge planning tools is to use them early in the patient’s care. That should have happened with Mary and Tom. Evidence-based tools utilize data to identify at-risk patients that may otherwise slip past an initial assessment, creating opportunities to proactively intervene to avoid poor outcomes and readmissions.
Bake assessments and discharge planning right into the care process. One model that generates a lot of success is including the assessment with another specified task, one that is done 100 percent of the time. Nurses perform the initial admission assessments – usually on a laptop or tablet – typically within 24 hours of admission. Data collected at that time could be combined with other factors in the record to provide a readmission risk score to the social worker or case manager, drawing attention to the patient’s post-discharge needs and predicted level of care. Alerting staff early offers more time for interventions and to proactively plan the transition. This avoids last-minute planning that results in readmissions.
Measure and manage
After using a readmission risk and needs assessment tool for a few months you’ll begin to see some patterns. Some skilled nursing facilities, or other post-acute providers, will have better readmission rates than others. You might see that patients with certain conditions, or those who have been treated by certain doctors, have high readmission rates. Finally, you may see a combination of these factors arise – some skilled nursing facilities do better with certain patients than others.
Dynamic systems help managers and executives continuously gather and evaluate data about providers, helping them make decisions about which facilities are best for various patients’ needs.
When implementing a readmission risk and needs assessment tool, success doesn’t just come in the form of declining readmission rates. Evidence-based planning tools give you the power to drive further gains by promoting informed decisions about patient care early in the patient experience.
In a value-based care world, hospitals can uncover significant cost savings by improving patient outcomes. Evidence-based readmission risk and needs assessment tools used at the time of intake, and regular reassessments throughout the patient’s stay, can prepare practitioners, patients, and caregivers for discharge and ensure appropriate levels of post-acute care can be met. This reduces the chance of unnecessary readmissions and the costs or penalties associated with them. And most importantly, it ensures patients receive the appropriate level of care so they can eventually be sent home successfully.
Cheri Bankston is the director of clinical advisory services for naviHealth. She has more than 30 years of nursing experience, both inside and outside hospitals.