For the past decade, hospital re-admissions has been a billion-dollar question that healthcare organizations and regulatory institutions have struggled to answer. When the Affordable Care Act established the Hospital Readmission Reduction Program (HRRP) in 2012 by most accounts it provided a practical framework and motivation for hospitals to target minimizing readmissions across the continuum of care. However, the addition of punitive measures for failing to meet certain re-admission thresholds has done relatively little to improve overall re-admission rates. What further steps can be taken to improve health outcomes quality of care delivery?
When the HRRP was initially introduced, many hospitals logically took the most direct path to reducing re-admissions by improving discharge metrics and providing for more thorough transitions of care across the entire patient mix. This dramatically increased the utilization, and cost, for post-discharge patient communication and care coordination. In principle, these post-discharge interventions should have impacted re-admission rates, but initial returns and data analysis were far less encouraging. Increasing clinical involvement would need to be targeted at specific at-risk groups to justify the ongoing costs. Thus, the increased focus on data analytics and re-admission algorithms to identify the correct patient population to drive these at times costly clinical interventions.
As more and more re-admission models and studies were funded and aggregated, it became clear that overall predictive models hardly reached the most basic standards effectiveness. Models based heavily on clinical data appealed to academia and seemed like they would yield straightforward results, but they eschewed more predictive and somewhat subjective measures such as social involvement, language, and access to health services by zip code. Applying models focused on specific populations and patient mixes also increased predictive capabilities and improved outcomes. As these algorithms become more and more intelligent, they will be able to justify their utilization far earlier in the clinical workflow, even potentially at pre-admission.
More accuracy in predicting eventual re-admissions will allow the majority of the interventions to take place over the course of the patient visit, as opposed to post-discharge. If clinicians have access to decision support tools that can aid in directing the care they provide with a specific focus dedicated to reducing chance of re-admission, both quality of patient care and re-admissions outcomes metrics should measurably improve. Ongoing care coordination pathways can be established from the moment a patient is admitted and further emphasis can be directed towards patient education and identifying support outside the care of the hospital.
In 2021 alone, CMS penalties for patient re-admission were more than $500 million. The cost of re-admissions to Medicare spending alone is estimated at more than $17 billion annually. These massive numbers demonstrate the purely financial implications that not tackling patient re-admissions presents. Overall impact to the well-being of patients represents an even greater opportunity. By optimizing post-discharge plans of care, improving current re-admission data models and their deployment, and providing crucial real-time clinical decision support to care providers, invaluable strides can be made towards besting hospital re-admissions.
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Softek’s mission is to help hospital systems get the most out of their investment in Cerner Millennium®. We do this by providing innovative software solutions and consulting services that can achieve more together than either can alone.
At Softek, our team of innovators and software developers brings expertise beyond the ordinary to every client. Our experts are involved with Cerner Millennium® hospitals throughout the country, consulting clients so they can optimize system performance and revenue integrity.
Softek delivers a full suite of consulting services and software solutions to assess and optimize EMR system performance, including revenue cycle integrity and patient accounting.
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