Hospital at Home for Elderly Patients With Acute Decompensation of Chronic Heart Failure

Tibaldi, Vittoria; Isaia, Gianluca; Scarafiotti, C.; Gariglio, Federico; Zanocchi, Mauro; Bo, Mario; Bergerone, Serena; Ricauda, Nicoletta Aimonino

doi:10.1001/archinternmed.2009.267

Cited by 102 publications

(141 citation statements)

References 37 publications

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“…While many studies focus on only "high risk" patients, [25][26][27][28] we show that readmission is very common and unpredictable among all medicine patients. While better prediction tools would allow interventions to be directed only at the highest risk patients, the most recent such tools have had only modest predictive value, reiterating the complex nature of general medicine readmissions.…”

Section: Discussionmentioning

confidence: 89%

Inability of Providers to Predict Unplanned Readmissions

et al. 2011

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BACKGROUND: Readmissions cause significant distress to patients and considerable financial costs. Identifying hospitalized patients at high risk for readmission is an important strategy in reducing readmissions. We aimed to evaluate how well physicians, case managers, and nurses can predict whether their older patients will be readmitted and to compare their predictions to a standardized risk tool (Probability of Repeat Admission, or P ra ). METHODS: Patients aged ≥65 discharged from the general medical service at University of California, San Francisco Medical Center, a 550-bed tertiary care academic medical center, were eligible for enrollment over a 5-week period. At the time of discharge, the inpatient team members caring for each patient estimated the chance of unscheduled readmission within 30 days and predicted the reason for potential readmission. We also calculated the P ra for each patient. We identified readmissions through electronic medical record (EMR) review and phone calls with patients/caregivers. Discrimination was determined by creating ROC curves for each provider group and the P ra . RESULTS: One hundred sixty-four patients were eligible for enrollment. Of these patients, five died during the 30-day period post-discharge. Of the remaining 159 patients, 52 patients (32.7%) were readmitted. Mean readmission predictions for the physician providers were closest to the actual readmission rate, while case managers, nurses, and the P ra all overestimated readmissions. The ability to discriminate between readmissions and non-readmissions was poor for all provider groups and the P ra (AUC from 0.50 for case managers to 0.59 for interns, 0.56 for P ra ). None of the provider groups predicted the reason for readmission with accuracy. CONCLUSIONS: This study found (1) overall readmission rates were higher than previously reported, possibly because we employed a more thorough follow-up methodology, and (2) neither providers nor a published algorithm were able to accurately predict which patients were at highest risk of readmission. Amid increasing pressure to reduce readmission rates, hospitals do not have accurate predictive tools to guide their efforts.

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Section: Discussionmentioning

confidence: 89%

Inability of Providers to Predict Unplanned Readmissions

et al. 2011

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“…These reasons could be evaluated and lessons learned [86] • Early discharge planning [40] • Taking care of aftercare: calling all patients a day after they have been discharged or close nursing follow-up [87][88][89] • Accelerating discharge [47]. The transfer to health care facilities receiving the patient can often be carried out more swiftly [5] and some hospital-related care can be given at home [90][91][92] or with more support in the community [93] Gap 1: Evidence-based interventions found in literature, but not proposed by the professionals:…”

Section: Matches Between Interventions Proposed By the Professionals mentioning

confidence: 99%

Fifty ways to reduce length of stay: An inventory of how hospital staff would reduce the length of stay in their hospital

Borghans

Kool

Lagoe³

et al. 2012

Health Policy

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“…The Hospital at Home Service (HHS) is operating in Torino at S. Giovanni Battista Hospital, a large urban University teaching and tertiary-care hospital (Aimonino Ricauda et al, 2004Ricauda et al, , 2005Tibaldi et al, 2004Tibaldi et al, , 2009). …”

Section: The Hospital At Home Service Of Torinomentioning

confidence: 99%