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Assessing Readmissions Risk

April 10, 2012
by Mark Hagland
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Leaders at Augusta Health in Virginia use analytics to look at patients’ individual risk for readmissions
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Augusta Health is a 255-bed community hospital in Fishersville, Virginia, located in the Blue Ridge Mountains about 35 miles west of Charlottesville, in the western north-central part of that state. The hospital’s service area encompasses a population of about 200,000, spread out across six predominantly rural counties. Clinical and administrative leaders at Augusta Health, anticipating changes in Medicare reimbursement based on the mandatory readmissions reduction program instituted under the Affordable Care Act (ACA), began a proactive effort in 2011 to quantify readmission risks for patients and to use extracted data to intervene clinically.

At Augusta Health, one key element in improving its readmissions profile, in anticipation of the coming reimbursement changes, has been to develop a tool to stratify the risk of individual patients for readmission, with the goal of most efficiently allocating hospital resources by capturing existing data from electronic documentation without manual review, and using that data to clinicians in a position to intervene on behalf of high-risk patients.

Among those leading the effort have been Roger Gildersleeve, M.D., a hospitalist and the organization’s chief medical information officer, and Penny Cooper, who is the integration services/decision support manager for Augusta Health. Gildersleeve and Cooper spoke recently with HCI Editor-in-Chief Mark Hagland regarding their current work in this area. Below are excerpts from that interview.

Can you describe the origins of your work in this area?

Roger Gildersleeve, M.D.: Like a lot of hospitals, there was a lot of interest in understanding readmissions, particularly because of interest on the part of CMS (the federal Centers for Medicare and Medicaid Reimbursement) in modifying reimbursement for readmissions. Fred Castello, MD, our Chief Medical Officer was early to catch on to the importance of this, and he moved us along to action.  A 2009 New England Journal of Medicine article helped shape our understanding and structure our thinking. 

Roger Gildersleeve, M.D.

What led you to apply IT to this?

Over the last 15 years, people have realized the pitfalls of making decisions based on gut impressions, or making ad hoc decisions. There’s been increasing interest in using data to drive decisions, and Penny has been the person whom everyone has turned to here, with regard to the data repository.

How long have you had a data repository?

Penny Cooper: We have had a data repository for about 10 years and so we’ve been using data to a greater degree than the typical community hospital.  We have a strong interest in using the data to its fullest and have the talent here to extract and analyze it. For this project we completed a two-year study, one year derivation and one year validation, built the model based on the study’s results and implemented it. We started out by reviewing recently published studies on readmissions and found that everyone was doing something different.  We extracted various patient demographic as well as clinical variables, compared them with our own data, and came up with the 12 variables to analyze, 3 of which after further analysis were not significant.

Penny Cooper

What are the nine variables you ended up with?

The variables that were significant for our patient population are:

>  acute admission
>  Charleston co-morbidity index, adjusted for age (a frequently used tool)
>  ER visits within the past 365 days
>  inpatient admissions within the past 365 days
>  current length of stay for the current analysis, and total length of stay for study period
>  male gender
>  inpatient medication count  two days prior to discharge
>  outpatient medication count prior to admissions
>  self-pay patient or not

Which were the very most significant of the nine?

Gildersleeve: An unplanned or Acute admission was the single most significant predictor.

Cooper: Depending on their frequency of occurrence though Inpatient admissions may carry more weight for a particular patient or the Charlson Comorbidity Index score may be higher for a very ill patient.

Gildersleeve:  The final score depends on multiple weighted variables more than any one alone.

What kinds of actions will be triggered?

Gildersleeve: So far, we have not yet released this tool for viewing by clinicians. I’ve informally polled clinicians about how they’d use this tool, and they’ve said, well, I’d keep the patient in the hospital longer. And in fact there might be an undesirable effect in keeping the patient in the hospital longer, since we found that longer length of stay is actually associated with a higher risk of readmission. On a higher level, we have a healthcare reform committee focused on reducing inappropriate readmissions, which is overseeing a new complex patient clinic.  Case managers will use this tool to detect currently admitted patients who are at a high risk of readmission.  Their attention may mean medication education, home health visits, and so on, in addition to referral to this clinic for select patients.

When might you actually release this for viewing by active clinicians?

Gildersleeve: That’s hard to say. We’ll start discussions in different department meetings over the next few months. As you know, clinicians already have so many things to be looking at.