POPULATION HEALTH ANALYTICS

Case Study: Using Frailty Markers to Improve Readmission Rates

April 6th, 2023
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Care Management  |  
Population Health Management & Improvement
Case Study: Using Frailty Markers to Improve Readmission Rates

One of the many measurements used to identify overall patient health is frailty. Patients may be identified as frail from a prior visit, admitted as frail or they may become frail over the course of their inpatient stay. Frailty greatly impacts the ability of a patient to go home after their hospital stay and remain at home, and increases the likelihood of being readmitted to the hospital due to complications. Read more to learn how this ACG System feature can help your organization predict readmissions and guide long-term interventions.

Overview of the Study

A recent study discovered a strong correlation between frailty and the likelihood of a readmission (or death) for patients with a primary diagnosis of heart failure. The retrospective analysis looked at data collected from 2010 through 2018 and applied features from the ACG System to classify patients as “frail” or “non-frail.” The study revealed that frail patients who are diagnosed with heart failure typically had other comorbidities, which puts them in a higher category of both cost and need. Frailty was also identified as a key factor to include when analyzing comorbid or multimorbid patients, as its presence significantly improved predictability of readmission after discharge and in-hospital mortality.

Better Outcomes with the ACG System

This functionality is another example of how the Johns Hopkins ACG System is a leader in population health analytics. The ACG System has over 10 different frailty markers, including dementia, fall risk, malnutrition and social support. These features look at the whole health of the patient to give users a comprehensive and detailed view of the factors their patients face, some of which may not be evident at first glance.

By adding the ACG System frailty marker to other comorbidity-based risk-prediction models, health care systems and providers are better equipped to predict patient health events and outcomes, such as 30-day readmission and mortality.

Providers can also use this data to identify patients who are a good candidate for interventions, decreasing the likelihood of readmissions and mortality. In addition to lives saved, these features within the ACG System are also beneficial for health care systems participating in value-based reimbursement initiatives, such as the Hospital Value-Based Purchasing program and Hospital Readmissions Reduction program to reduce readmissions, in-hospital mortality and overall costs of care for frail patients.

For more information on frailty markers and how the ACG System can benefit your organization please email us at acginfo@jh.edu. If you are an existing ACG System customer, please contact your account manager.

 

 

 

 

 

 

 

 

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