Posts Tagged ‘hospital readmission’

Infographic: Overcoming Barriers To Improve Care Transitions

May 1st, 2017 by Melanie Matthews

Leveraging the right technology can improve post-acute patient outcomes, according to a new infographic by Ensocare.

The infographic looks at: the impact of streamlining multiple, disparate workflows; and how to strengthen post acute networks, simplify ongoing post-acute follow-up communications and improve patient engagement during care transitions.

The Science of Successful Care Transition Management: Leveraging Home Visits to Improve Readmissions and ROIA care transitions management program operated by Sun Health since 2011 has significantly reduced hospital readmissions for nearly 12,000 Medicare patients, resulting in $14.8 million in savings to the Medicare program.

Using home visits as a core strategy, the Sun Health Care Transitions program was a top performer in CMS’s recently concluded Community-Based Care Transitions (CBCT) demonstration project, which was launched in 2012 to explore new solutions for reducing hospital readmissions, improving quality and achieving measurable savings for Medicare.

The Science of Successful Care Transition Management: Leveraging Home Visits to Improve Readmissions and ROI explores the critical five pillars of the Arizona non-profit’s leading care transitions management initiative, adapted from the Coleman Care Transitions Intervention®.

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4 Population Health Management Tools to Identify At-Risk Patients

February 15th, 2013 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.