Hospital Readmission: Predicting the Risk

Abstract

This approach focused on identifying specific variables that predict the likelihood of readmission. It involved clinical, utilization, and demographic variables that are generally available on hospital computer abstract databases. The approach included a process for identifying and comparing individual variables with the highest risk of readmission. It also contained a procedure for assembling risk populations including combinations of variables. The approach demonstrated the potential for using risk analysis to maximize the focus of clinical management on patient outcomes while reducing the amount of resources required for this process.

Contributing Factors to Hospital Readmissions

“…The highest number of readmissions was among patients with heart failure and one or more of the following co-morbidities: diabetes, renal failure and cardiomyopathy. Notably it was found that patients in the age groups 60 to 69 years and 70 to 79 years had readmission rates 35.4 percent higher than those of the total study population (Lagoe, Noetscher & Murphy, 2001). …”

“…A study by Lagoe, Noetscher, and Murphy (2001) identified such risk factors. The study included hospitalized inpatients in four acute care facilities.…”

Effectiveness of a discharge-planning program and home visits for meeting the physical care needs of children with cancer

“…When discharge planning does not occur or is inadequate, patients return to the hospital with serious complications. For this reason, comprehensive discharge planning needs to be programmed to prevent patient readmissions [20]. Studies examining discharge teaching, planned/unplanned hospital visits, readmissions, and length of stay have been conducted more in elderly patients with diagnoses such as congestive heart failure, chronic obstructive pulmonary disease, and cancer [31].…”

“…An unplanned clinic visit/readmission was defined as unplanned hospitalization for fever, problems with nutrition, mouth ulcers, catheter problems, or any other reason. Clinic visits and hospital readmissions were recorded for experimental and control groups that occurred during the five monitoring times [20,27,30,31].…”

Variation in Hospital Length of Stay: Do Physicians Adapt Their Length of Stay Decisions to What Is Usual in the Hospital Where They Work?

“…SPARCS is a major management tool assisting hospitals, agencies, and health care organizations with decision making in relation to financial planning and monitoring of inpatient and ambulatory surgery services and costs in New York state. Several research articles have been published that are based on SPARCS data (Pasley, Lagoe, and Marshall 1995;Westert and Lagoe 1995;Hainsworth et al 1997;Lagoe 1998;Murphy, Noetscher, and Lagoe 1999;Noetscher et al 1999;Lagoe, Noetscher, and Murphy 2001;Noetscher, Morreale, and Lagoe 2001;de Jong et al 2004). We used 1999We used , 2000We used , and 2001 SPARCS data to study seven diagnosisrelated groups (DRGs): two medical (DRGs 88 and 127), one surgical (DRG 209), and four obstetrical (DRGs 358,359,370,and 371).…”