Generally speaking, hospital readmission is a situation in which a patient who has been discharged from a hospital is admitted again to that hospital or another hospital within a certain time frame. This is usually pegged at 30 days.
A higher readmission rate indicates that previous hospitalization treatments were ineffective. However, health services have recently begin to examine hospital readmission because of the way healthcare costs have begin to increase, especially among certain groups of patients. These patients usually had multiple chronic conditions and were hospitalized on multiple occasions to manage them.
Hospitalizations can be stressful for patients, especially if they result in later readmissions. While many readmissions are unavoidable, many others can be prevented. To effectively reduce readmissions and meet value-driven care goals, health systems must apply analytics to their actual systemwide procedures.
So, in this post, we will be taking a closer look at hospital readmission and how implementing analytics can help reduce it. Just keep reading!
What Usually Causes Hospital Readmissions And How to Prevent Them?
Hospital readmissions are expensive and potentially dangerous for patients. In fact, according to the Centers for Medicare and Medicaid Services (CMS), about 2 million patients are estimated to be readmitted each year, totaling a whopping cost of about $26 billion for Medicare every year.
In 2012, the CMS came up with the Hospital Readmission Reduction Program (HRRP) in order to address this issue of surging readmissions in hospitals. The program imposes financial penalties on hospitals that have a high rate of Medicare readmissions.
But we may not be able to figure out the way out of this problem if we have no idea of what’s causing it in the first place. So, let’s quickly take a look at some of the causes of hospital readmission.
Patients not following release instructions
Patients who go through surgeries like hip replacements or heart surgery are commonly given a list of discharge instructions that they must follow. These instructions might involve caring for the medical place, the beginning of physical therapy, and what medications should be taken.
Unfortunately, many patients do not follow these instructions properly or do not follow them at all, which can lead to complications and ultimately readmission to the hospital.
Medication problems
Medication errors are the second most common reason for hospital readmissions. Among these are individuals who do not take their medications as prescribed, medication-related side effects, and the interactions between different medications. All these can contribute to a patient’s readmission into the hospital.
Accidental injuries
Patients 65 and older are more vulnerable to falls due to weakness, chronic illnesses, and multi-drug treatments. Among hospital patients, falls are the most common cause of injury bringing people to the hospital for emergency admission. While many hospitals have some preventive measures in place to prevent falls, they still happen anyway.
Post-surgical complications
These involve wound infections, pneumonia, sepsis, and urinary tract infections. Any of these might result in the patient being readmitted to the hospital. The first step in preventing post-surgical complications is proper wound care. This means keeping the injury dry and clean. The patient should also follow up with their doctor as directed.
Another common complication that can occur after surgery is pneumonia. This is why patients must practice deep breathing exercises and cough frequently.
Why Do Hospitals Want to Reduce Readmissions
There are three main reasons why hospitals should make every effort to keep patients from returning for additional treatments:
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Full insurance isn’t covered by Medicare
This is the reason CMS imposes a payment reduction fee of about 3% on healthcare centers that goes beyond a certain limit for readmission rates. That means that CMS only pays 97% of covered healthcare costs instead of the entire 100%. Imposing a penalty is one way to discourage hospitals from profiteering.
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Readmissions are both financially and medically risky for patients
The cost of healthcare in America is among the highest in the world. Medical costs are a contributing factor, even though it is debatable to what extent they influence people’s decisions to file for bankruptcy. Many Americans find it agonizing to need multiple treatments, especially those who are living on a tight budget.
Additionally, frequent visits to the hospital and longer stays in a healthcare facility increase the risk of developing a hospital-acquired infection. Who would ever want to be caught up in the expensive downward spiral that results from this?
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A high readmission rate can hurt the reputation of the hospital
People would naturally want to avoid hospitals that have a high readmission rate because they are perceived to be providing poor-quality care.
Top 7 Big Data Analytics Strategies to Reduce Hospital Readmissions
Health organizations now have more materials than ever before to generate clinical insights, ranging from electronic health records to newer data assets such as genomic data. Let’s explore some of the methods hospitals can apply using big data analytics to lower costs, decrease hospital readmissions, and enhance patient results.
#1: Predictive Analytics
Healthcare organizations should use predictive analytics to identify patients who are at high risk of readmission. There are predictive analytics tools developed to reliably identify the cause of 30-day pediatric readmission risk before the hospital discharges the patient.
By analyzing large databases that include patient demographics, medical histories, clinical records, and social factors of health, predictive models can help identify patients who are likely to be readmitted. This allows healthcare providers to take action with targeted measures and proactive care management to prevent readmissions.
#2: Real-Time Monitoring
This refers to the use of real-time monitoring systems to collect and analyze patient data during hospital stays. Healthcare providers can detect early warning signs of potential complications and take timely action to prevent readmissions by using streaming data analytics. This could include tracking vital signs, medication abidance, or changes in clinical signs to trigger interventions.
#3: Care Coordination and Communication
Hospitals should improve care coordination and communication between healthcare service providers and caregivers involved in a patient’s care journey.
By integrating data from various sources, such as electronic health records (EHRs), care plans, and patient-reported outcomes, big data analytics can facilitate effortless information exchange and collaboration. This ensures that all participants are well informed, enabling better post-discharge care and reducing the likelihood of readmissions.
#4: Social Determinants of Health (SDOH) Analysis
Medical organizations should analyze the social determinants of health to gain insights into the factors that impact readmissions. By integrating data on socioeconomic status, housing conditions, access to transportation, and social support systems, healthcare providers can identify patients who may face challenges adhering to post-discharge instructions or accessing follow-up care. Targeted interventions addressing these social determinants can then help reduce readmissions.
#5: Fall Risk Assessment Data
As we mentioned before, another reason patients return to hospitals is their vulnerability to falls. By integrating fall risk assessment data into the analytics system, healthcare providers can gain valuable insights into patients’ exposure to falls and potential readmissions related to fall-related injuries.
When fall risk assessment data is integrated, healthcare providers can modify treatments and care plans to address fall prevention strategies, thus further reducing the likelihood of readmissions associated with falls.
#6: Text Mining and Natural Language Processing (NLP)
Text mining and natural language processing (NLP) techniques can be used to extract valuable insights from unstructured data sources such as physician notes, discharge summaries, and patient feedback.
These techniques can help uncover hidden patterns, conduct an analysis of emotions, and identify specific risk factors associated with readmissions. Through the use of this data, healthcare providers can personalize interventions and discharge plans to address individual patient needs.
#7: Machine Learning for Prediction of Readmission
Develop strong readmission prediction models by using machine learning algorithms. Healthcare providers can accurately identify patients at high risk of readmission by training these models on historical data and continuously updating them with new patient information.
Machine learning algorithms can also uncover complex relationships in data that traditional statistical approaches may miss.
Taking a Broad Perspective
Even as hospitals begin to use predictive analytics to reduce readmission rates or improve existing programs, healthcare executives are discovering that these analytics can be used as part of a larger program of care improvement.
Predictive analytics can be used to prioritize workflow in any area of operations and for any outcome. For example, the dialogue between hospital administration and physicians about quality improvement is altered because of the accessibility of this kind of data.
These data-driven decisions improve hospital care quality by potentially shortening stays and lowering readmission rates. When patients receive the best possible treatment for their illness, stay in the hospital until they are recovered enough to return home, and are treated by medical professionals who understand the effect of quality care on the overall well-being of patients, lower readmission rates will be the natural result.