Morbidity and Mortality Prediction Model Improved by Incorporating Data from Electronic Records
Prof. Adam RoseCredt: Hebrew University[/caption] Prof. Adam J. Rose Shuli Brammli-Greenberg and Adam J. Rose share senior authorship. Faculty of Medicine, Hebrew University of Jerusalem Beit-Horon, Jerusalem, 9093500, Israel MedicalResearch.com: What is the background for this study? Would you briefly explain what is meant by the Elixhauser Comorbidity Model? Response: Patients admitted to the hospital can have very different levels of illness severity. In addition, different hospitals may admit different numbers of very sick patients. Therefore, comparing two hospitals regarding something like length of stay or in-hospital mortality is not valid unless one adjusts for the illness burden of the population of patients at each hospital. Risk adjustment is the name for the process of building a model to predict the risk of each patient for a particular outcome, such as mortality or readmission, based on what is known about them and their illness burden. By summing all the risks of patients at a hospital, one gets an aggregate sense of the illness burden at the hospital, and different hospitals can be compared. The Elixhauser Comorbidity Model is a widely-used risk adjustment model which performs well in the sense that it is very predictive of outcomes like mortality. It also has the advantage of being calculated from diagnosis codes, which are widely available data for hospitalized patients.
Dawn Wiest, PhD
Director, Action Research & Evaluation
Camden Coalition of Healthcare Providers
MedicalResearch.com: What is the background for this study?
Response: Understanding the role of care transitions after hospitalization in reducing avoidable readmissions, the Camden Coalition launched the 7-Day Pledge in 2014 in partnership with primary care practices in Camden, NJ to address patient and provider barriers to timely post-discharge primary care follow-up. To evaluate whether our program was associated with lower hospital readmissions, we used all-payer hospital claims data from five regional health systems. We compared readmissions for patients who had a primary care follow-up within seven days with similar patients who had a later or no follow-up using propensity score matching.











Dr. Javed Butler[/caption]
MedicalResearch.com Interview with:
Javed Butler MD MPH
Chief, Division of Cardiology
Stony Brook University
Health Sciences Center
SUNY at Stony Brook, NY
Medical Research: What is the background for this study? What are the main findings?
Dr. Butler: There is a lot of emphasis on reducing the risk of readmission after heart failure hospitalization. The main focus is on early readmissions as the risk for readmission is highest earlier post discharge. In this study, we described the fact that certainly there is some increased risk post discharge, the majority of the risk is actually dependent on the patient and disease characteristics at the time of discharge as opposed to true reduction in risk over time, which is partially related to differential attrition of high risk patients earlier post discharge.






