Why Our Data Should Take On a Greater Voice in Patient Care?

Why Our Data Should Take On a Greater Voice in Patient Care?

Martin Howard, CIO, Amedisys

Martin Howard, CIO, Amedisys

Providing healthcare creates an enormous amount of data. Think of all the details on all the forms mandated by regulation and company policy and all the available comparative data from diagnosis to medication to demographics, all the specifics about patient history, and so on.

“IT should be more widely recognized as a potential cornerstone of patient care, particularly for care in the home”

Yet with such abundant data, we have yet to realize its potential. Rather, the data we’ve amassed so aggressively amounts to the IT equivalent of a tower of Babel. So many different languages are spoken at the same time that virtually everything is lost in translation. As a result, we’re nowhere near answering the fundamental question: What care will result in the best outcomes for the lowest cost?

So now what?

For IT departments, delivering value depends on delivering actionable information. If we stick to touting our performance on system availability, abandoned call rates, and self-service password reset success, we are a utility limited to tactical support.

Traditional IT metrics have almost no meaning. Our IT spend, for instance, may be 2 percent of revenue, or 4 percent, or 6 percent. And even though we need those numbers, what do they tell us about quality?

How about measuring our value by how well we support decision-making? We should be asking “How good is IT at delivering the information we need to improve our care and our operations and achieve our strategic objectives?” We should score how well IT helps adapt to population health management, assuming risk, reducing operating expenses and, of course, how IT contributes to improving patient outcomes.

IT should be more widely recognized as a potential cornerstone of patient care, particularly for care in the home. And, this should be initiated by, and demanded of, IT departments.

In general, leading health systems and payors have become more sophisticated than most providers of post-acute care, and are more practiced in leveraging data to understand and improve performance. But home health is increasingly standardizing protocols and operations, even as we customize and personalize care for each patient.

The job of the healthcare CIO is to articulate the case for transforming IT. Right now IT focuses on legacy regulatory and contractual claims processing and internal operations. Instead, we should function as a source of information essential to enhancing ongoing operations and care.

To be blunt: all IT’s customers, internal and external – patients, payors and regulators – demand and deserve proof, both qualitative and quantitative, that the home health sector delivers better outcomes for lower cost. And we in IT should provide it.

How to do so?

For our organization, the question is how to best aggregate and understand the data we gather from the eight million home health and hospice visits we make every year. How can we learn to listen to what the data is trying to tell us? The key is toorganize our applications and databases and data to provide predictive and prescriptive analytics to provide better information—to drive improvements across clinical and operational outcomes.

Advances in analytics and in analytical tools can enable us to understand the inter-disciplinary complexities of caring for people with multiple chronic conditions over long periods of time. We need to identify, as a case in point, the best care plan for an octogenarian with congestive heart failure, COPD, diabetes and hypertension who has gone to the ER four times in the last year for dehydration and medication errors.

Multiple systems are now available to perform such functions. We’re currently building a system we call the Amedisys Care Engine, ACE for short. It will combine, standardize, integrate and present healthcare information to our clinical and operations teams with a scientific rigor that emphasizes the practical.

We’re upgrading our IT infrastructure on other fronts, too. We’re about to implement an enterprise-level CRM. We’re also rolling out Homecare Homebase, the leading EHR for home health and hospice care. Now in 225 of our 421 care centers, HCHB will be companywide by November.

All of these initiatives are ultimately intended to improve our ability to listen to what the data sets can tell us. The insights we gain—about patient visits, caregivers, payors, demographics, outcomes and other metrics—inform the clinical decisions that lead to better care for our patients.

How does that look in action?

Let’s say a new patient is discharged from a small community hospital. Our data tell us that this facility has both a high readmission rate and a high hospital-acquired infection rate. Knowing this means we can adjust our treatment plan to mitigate risks. We also know, from the initial referral, that this patient’s insurance company often delays or denies claims for certain errors in documentation. With this information, we’ll take early and extra precautions to avoid such problems. And finally, our new patient unfortunately lacks a social support system of family and friends. As a result, we’ll take action to establish a substitute support system, courtesy of help from nurses, social workers, home aides and perhaps clergy.

Just as we know that the tools IT professionals have at hand is less important than how expertly those tools are used, the potential value of any robust data set is truly useful only when all the professionals involved—clinical, operational and analytical—come together as one for the common good.

We have to bear in mind that healthcare is literally relocating. Once headquartered in the hospital, post-acute care is gradually moving into the home. The baby boomer generation is expressing an overwhelming preference to “age in place,” to stay healthier longer. All the more reason, then, to make sure we’re logging the right metrics – and above all, hearing the most telling clues that such data yields.

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