4 Population Health Management Tools to Identify At-Risk Patients

Friday, February 15th, 2013
This post was written by Jessica Fornarotto

Our EPIC platform at Bon Secours Health System consists of different tools that our nurse navigators can use to identify at-risk patients, for instance the ability to create registries, states Robert Fortini, vice president and chief clinical officer at Bon Secours Health System. Bon Secours uses four main tools to help better manage the health of its population, including a tool that identifies barriers and non-adherence, as well as a risk calculator that measures frequent ER visits.

Inside of our EPIC platform, the documentation tool or encounter type that is created by using our discharge registry falls into one of four categories. It’s either a post-hospital admission, a post-emergency department visit, it could be for ongoing case management and the referral can come from any direction — the PCP, a managed care partner, or hospital case management. Then, if someone falls into a place where they’re at a gap in care, we use a number of different tools to identify those gaps in care.

To illustrate the documentation tool, take a patient who’s been admitted to the hospital, has spent some time there, and has been diagnosed with congestive heart failure (CHF). Everybody is focused on CHF these days because of value-based purchasing. And everyone is trying very hard to improve 30-day readmission rates now that there’s a penalty associated from that Medicare reimbursement.

We’re using a tool that allows our nurse navigators to stage the degree of heart failure. From within the documentation’s work space, we can launch the ‘Yale tool,’ which allows us to establish what stage of heart failure that patient is in; class one, class two, class three, class four. Then, a set of algorithms are launched based on these stages’ failure and we will then manage the patient according to those algorithms.

If a patient falls into a class four category, for example, we may bring them in the next day or that same day for an appointment, rather than wait five or seven days because they’re at more risk. We may also make daily phone calls or interventions; we may network in the home health and make sure that they have scales for weight management and assessment of heart failure status. All of those interventions will be driven by the class of heart failure that patient falls into.

The second tool that we use is a workflow around ejection fractions. Depending on the patient’s ejection fraction, we will define specific interventions that the nurse navigator will follow.

We have a third tool that’s part of the encounter type in the EPIC where we identify barriers and non-adherence. We look at several elements: Are there communication preferences that the patient requires in order to be clearly communicated with? Is there any cognitive impairment? Are financials a barrier? What are their utilities at home? What’s their learning style?

Each of these categories launches another subset or agenda that we can document in detail; specifically on what obstacles exist for that patient and then what goals we should be setting to breach those obstacles.

Finally, we have a risk calculator that’s specific to frequent ER visits. Using this risk calculator, we enter length of stay (LOS) in the hospital, acuity, comorbidities and the number of ED visits in the last six months. That will then generate a risk index. If that risk index is 11 or greater, that person is considered in a higher risk category and that will drive interventions that are more intensive; daily calls, being brought in sooner, maybe the implementation of a dosage titration, an algorithm around diuretic management for weight in a heart failure patient, etc.

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