Posts Tagged ‘heart failure’

ROI and 12 More Rewards from Stratifying High-Risk, High-Cost Patients

May 21st, 2015 by Patricia Donovan

Health risk stratification—for example, grouping diabetics in a single physician practice or drilling down to an ACO’s subset of medication non-adherent diabetics with elevated HbA1cs—followed by risk-appropriate interventions can significantly enhance a healthcare organization’s clinical and financial outlook.

For 9.4 percent of respondents to HIN’s 2014 Health Risk Stratification Survey, risk stratification resulted in program ROI of between 3:1 and 4:1, while 6.3 said return on investment was greater than 5:1.

Stratification and targeted interventions also generated a healthy drop in healthcare cost, nursing home stays, ER utilization and time off work while boosting quality ratings, patient engagement levels and care plan adherence.

Survey respondents further quantified successes achieved from health risk stratification in their own words:

  • “„„Decreased readmissions and decreased skilled nursing facility (SNF) utilization.”
  • “Improved treat-to-target for diabetes, blood pressure, and depression care.”
  • “Reduction in readmissions by 20+ percent.”
  • “Reducing heart failure, pneumonia, acute myocardial infarction (AMI) and chronic obstructive pulmonary disorder (COPD) Medicare readmissions.”
  • “Patient compliance to care plan.”
  • “Patient health outcomes, quality of life, and satisfaction with services.”
  • “Member satisfaction.”
  • “More referrals to patient-centered medical homes and fair retention with limited resources.”
  • “Decreased primary care-sensitive ED visits and increased quality metrics.”
  • “One-on-one interaction w/members to promote behavior change.”
  • “A reduction of costs in the range of 6 to 8 percent of target spend.”
  • “Lower readmission rates for those patients on AIM 2.0 program with home health and more compliance with meds. We meet with FQHCs every other month and discuss issues and case management.”

Source: 2014 Healthcare Benchmarks: Stratifying High-Risk Patients

http://hin.3dcartstores.com/2014-Healthcare-Benchmarks-Stratifying-High-Risk-Patients_p_4963.html

2014 Healthcare Benchmarks: Stratifying High-Risk Patients captures the tools and practices employed by dozens of organizations in this prerequisite for care management and jumping-off point for population health improvement — data analytics that will ultimately enhance quality ratings and improve reimbursement in the industry’s value-focused climate.

Community Health Network Retools Readmissions Ruler for High-Risk Heart Failure Patients

September 9th, 2014 by Patricia Donovan

From the many evidence-based health risk stratification tools available, Community Health Network has adapted a popular hospital readmissions indicator for use with medically complex patients at high risk of readmissions. Deborah Lyons, MSN, RN,NE-BC, network disease management executive director for Community Health Network, describes the adaptation process.

HIN: Where do home visits for heart failure patients enter the picture?

Deborah Lyons: We do a high-risk home assessment while we have patients in the hospital. Fully 100 percent of our patients that are admitted to inpatient status are automatically screened and ranked in terms of readmission risk. That’s where we use the LACE/ACE tool. We embedded that tool in our software so it can predictively tell us which patients to focus on.

HIN: How did you decide on the LACE tool? Is the ACE tool different than the LACE tool?

Deborah Lyons: The LACE itself is evidence-based. We work with the advisory board. And they had just done an analysis of all the predictive models out there in terms of readmission risk when we started this work. There were only two tools that were moderately predictive for risk. LACE was one of them. LACE looks at length of stay (L), acute admission (A), (meaning they came in through the emergency room), their Charleston Comorbidity score (C) and the number of ED visits (E) they’ve had in the past six months.

All this information was easily available to us at the time that we did this because we were on a different computer system. But the concern was that the L factor (length of stay), might lead us to place the patient at high risk when they were leaving the hospital. Maybe they started at low risk and then on the fourth day of stay, because they had been there four days, now they moved to high risk but they’re being discharged. You really can’t do anything at day of discharge. We first set a threshold for LACE, which we tested and validated and then ran a correlation and asked ourselves, “If this threshold is a LACE high risk, what would a correlating threshold be if we dropped the length of stay?” That’s how we moved to an ACE score.

Source: Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data Analytics

Stratifying High-Risk Patients


Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data Analytics
Reviews a range of risk stratification practices to determine candidates for health coaching, case management, home visits, remote monitoring and other initiatives designed to engage individuals with chronic illness, improve health outcomes and reduce healthcare spend.

Replicating Home Care Telehealth for Non-Homebound Curbs Readmissions

July 15th, 2014 by Patricia Donovan

Community Health Network considers it a failure of the system if a patient with chronic illness has to go to the hospital. Deborah Lyons, Community Health Network’s disease management executive director, describes how the integration of telehealth into care of heart failure patients is helping to keep hospital readmission rates down for this population.

It’s all part of Community’s network strategy. That’s really a failure of the system if a patient has to go to the hospital—at least for those with chronic diseases. Our strategy has been to keep patients out of the hospital regardless of disease type. We want to keep patients out of the hospital.

As part of an integrated strategy, we’ve used our experts in home care to do high-risk home visits rather than creating a siloed entity to do this. Home care was doing telehealth for their homebound patients in home care, and we didn’t want to recreate this functionality. We chose to work with our home care telehealth experts and expand this to the non-homebound population.

This network strategy helps us to better manage the health of patients by looking at what expertise exists and then expanding it to meet the population needs.

When we first started developing the strategy, we also started with heart failure originally because we have a lot of heart failure patients and an issue with readmissions.

When we looked at the heart failure patients, we found that first, about 43 percent of our patients that were readmitted were patients that were discharged home to self-care, meaning they didn’t qualify for traditional home care. They weren’t going into a facility. These were people that were going home alone. And this group was driving 43 percent of our readmissions.

When we looked at what was occurring in our own network, we also found that our home care agency was doing telehealth with their home care patients and had a national best readmission rate. We asked ourselves how we could replicate this for our non-homebound patients. There are experts there that are getting great results. Now we want to apply this to our non-homebound population. And that’s where we decided to do this with IVR, the automated telephonic system that calls the patients at home.

Excerpted from: New Horizons in Healthcare Home Visits

3 Nurse Navigator Tools to Enhance Care Management

January 29th, 2014 by Jessica Fornarotto

Where does the nurse navigator spend their day? Certainly on transitions of care. Bon Secours Health System nurse navigators use a trio of tools to identify patients’ obstacles to care and connect them to needed resources, explains Robert Fortini, vice president and chief clinical officer of Bon Secours Health System.

One tool that our nurse navigators use that’s built into our EMR is the hospital discharge registry from Laburnum Medical Center, one of our largest family practice sites with about nine physicians. This tool is used to identify which patients the navigators need to work with, and it’s where the navigators begin and end their day. This registry provides a list of all the patients who have been discharged from one of our hospitals in the last 24 hours, and each patient is listed by the physician. The navigators have to reach out to each of these patients and make telephonic touch within 24 to 48 hours of discharge. Medication reconciliation is extremely important at this time and can be very challenging. When a patient goes into a hospital, often their medications get scrambled, and they come out confused and taking the wrong prescriptions. Nurse navigators spend a lot of time on medication reconciliation at this point.

The Navigators also conduct ‘red flag’ rehearsals with this tool, so that the patient knows the signs and symptoms of a worsening condition and what to do for it. We also schedule the patient with a follow-up appointment, either with a specialist who managed the individual in the hospital or with their primary care physician. We try to do it as close to the time of discharge as possible, within five to seven days, or more frequently if the risk of readmission is higher.

Second, nurse navigators also use a documentation tool to help manage the care of heart failure patients. This tool allows the navigator to stage the degree of heart failure using a hyperlink called the ‘Yale tool.’ The Yale tool allows us to establish what stage of heart failure the patient is in: class one, two, three, or four. Then, a set of algorithms is launched based on these stages’ failure; we manage the patient according to those algorithms. For example, if a patient falls into a class four category, we might bring them in that same day, or the next day, for an appointment rather than wait five or seven days because they’re at more risk. We might also make daily phone calls or network in-home health, as well as make sure that the patient has scales for weight management and an assessment of heart failure status. All of those interventions will be driven by the patient’s class of heart failure.

The last tool we use is a workflow for ejection fractions. The patient’s ejection fraction will define specific interventions that the navigator will follow.

Excerpted from: Profiting from Population Health Management: Applying Analytics in Accountable Care.

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.