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Imagine a world where an electronic health record (EHR) could tell a provider, in real time, that a patient is struggling. Or a system that would alert the administrator if something was happening that might jeopardize the center’s Five-Star Quality Rating. What if he or she knew the facility’s readmission rates and those of its competition?

 

What would this mean to patient care? Census? Reimbursement?

This function is slowly beginning to emerge. The data exist; it is just a matter of creating the algorithms that connect the dots. For the country’s health care system, leveraging a more predictive approach to care management should result in a dramatic decrease in the amount of money spent each year.

Now think of this from a hospital’s perspective. If a health system could access live, real-time data on all of its discharged patients now residing in skilled nursing centers, it could identify providers capable of driving down readmission rates, achieving benchmarks for functional improvement, and holding themselves accountable.

So what’s the holdup? The challenge lies in the silo nature of the U.S. health system’s EHRs.

Hospitals use a software platform specifically designed for their needs, while skilled nursing centers use a completely different product. Home health has yet another solution. And within each of these stops along the health care continuum there are multiple software companies with myriad applications. None of these solutions are integrated with one another. As a result, providers have all become dependent on outdated, inconsistent reporting from a national or state entity.

Visiting the Centers for Medicare & Medicaid Services (CMS) website and noticing that one’s quality measure scores have dropped does not afford a facility the opportunity to affect change. By then it’s too late. All the facility can do is implement a new process, hope for a positive impact, and wait another six months for the website to update.

The government understands this challenge and is taking steps to develop a new set of performance matrixes.

The Affordable Care Act (ACA) brought a transition to value-based purchasing initiatives that look at readmission rates, lengths of stay, outcomes, quality measures, and even customer satisfaction. In the not-so-distant future, post-acute care providers will be reimbursed on their ability to achieve set benchmarks in each of these performance areas.

Of the five performance measures associated with the ACA, three are uniquely suited for data aggregation and reporting: outcomes, readmissions, and quality measures. With the right data analytics partner, post-acute care facilities have the ability to report in real time, affecting change when patients need it most, at their point of care.

The objective, for all patients, should be for their health to improve in the right amount of time and in an optimal care setting. Simple enough, right? One would think.

The foundational challenge faced by providers is how to measure when a patient’s outcomes warrant being discharged to a skilled nursing care center and eventually to their home. The health care profession has struggled to certify when a patient is or is not ready for discharge.

For example: There are statistics to show average length of stay for a hip replacement. But can providers be confident that this is actually the optimal amount of time for a specific patient to heal? Until recently, the ability to unilaterally measure functional improvement, based on mobility and self-care across all settings of the health care continuum, has been elusive.

However, this year, in the notice of proposed rule change, CMS is finally taking steps to adopt a single matrix for measurement, dubbed Continuity Assessment Record and Evaluation (C.A.R.E.). Rolling out first in the skilled nursing care center environment, this uniform measurement tool will allow providers to track and measure a patient’s improvement levels over the course of their stay.

The hope is that in the near future, patients’ length of stay will be less relevant than their ability to meet certain benchmarks in functional improvement. Without these data, providers cannot affect change at the point of care.

Even if outcomes improve for a nursing center’s patients, lessening the likelihood of their being readmitted to the hospital, CMS reporting is still a lag measure of performance. The agency uses Minimum Data Set (MDS) submissions to calculate a readmission rate, but once data are captured, it may take up to six months before the results are published. It is therefore incumbent on providers to capture their own live data, understand their readmission rates, and take steps to reduce them.

Why is this essential? In a word, census. Referring hospitals need to know that when they discharge to a post-acute care facility that the providers will do everything possible to care for the patient and prevent an unneeded rehospitalization. It is paramount that post-acute care facilities understand and track their readmission rates and those of their competitors in an effort to find root causes of readmissions and eventually create preferred relationships with hospital partners.

The timeliness of these data is once again important, because without current data, the health care profession cannot identify the root cause of an increase (or decrease) in readmission rates in a manner that is quick enough to effect change.

For the past few decades, post-acute care providers were working in a fee-for-service world with little accountability for value. Reimbursement has been directly tied to the amount of care provided. Relationships with hospitals were based on doughnuts and handshakes, rather than value.

Nowadays, the perception of a facility’s value will be directly tied to reporting on key pay-for-performance measures. Anyone can develop a report. Being able to affect change at the point of care is the real magic.