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Predicting readmissions of elderly p...

Brandeis University, The Heller School for Social Policy and Management.

Predicting readmissions of elderly patients to the acute care hospital.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Predicting readmissions of elderly patients to the acute care hospital./
作者:	Collard, Ann Faas.
面頁冊數:	338 p.
附註:	Source: Dissertation Abstracts International, Volume: 49-08, Section: B, page: 3101.
Contained By:	Dissertation Abstracts International49-08B.
標題:	Gerontology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=8819283

Predicting readmissions of elderly patients to the acute care hospital.
Collard, Ann Faas.

Predicting readmissions of elderly patients to the acute care hospital. - 338 p.

Source: Dissertation Abstracts International, Volume: 49-08, Section: B, page: 3101.

Thesis (Ph.D.)--Brandeis University, The Heller School for Social Policy and Management, 1988.

Readmissions to acute care hospitals occur frequently, particularly in the case of elderly patients. Rehospitalizations are of considerable interest because we now know that patients experiencing multiple admissions account for a large portion of hospital expenditures.Subjects--Topical Terms:

168436
Gerontology.

Predicting readmissions of elderly patients to the acute care hospital.
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245 1 0 $a Predicting readmissions of elderly patients to the acute care hospital.
300 $a 338 p.
500 $a Source: Dissertation Abstracts International, Volume: 49-08, Section: B, page: 3101.
500 $a Chairman: Stanley Wallack.
502 $a Thesis (Ph.D.)--Brandeis University, The Heller School for Social Policy and Management, 1988.
520 $a Readmissions to acute care hospitals occur frequently, particularly in the case of elderly patients. Rehospitalizations are of considerable interest because we now know that patients experiencing multiple admissions account for a large portion of hospital expenditures.
520 $a This research evaluated the impact of a specialized geriatric unit on the readmission rate of elderly patients, identified the risk factors associated with readmission, and tested a model to predict hospital readmissions among elderly patients.
520 $a Utilizing data from a randomized trial of elderly patients, patient admissions to two community hospitals were studied. Patients who were readmitted to the hospital were compared to those who were not readmitted along various dimensions including: demographics, socialization, health and functional status, diagnosis, community resource use, and characteristics of the first hospitalization.
520 $a Utilizing multivariate logistic regression, the probability of rehospitalization was estimated for each patient taking into consideration several interactive effects among variables. Seven variables were found to significantly predict readmission: being cared for by a family member, being cared for by a friend or other person, being married, being married and receiving home care, requiring help with dressing, having a chronic illness as a primary diagnosis, and self reported use of hospitalization within the six months preceding admission. Overall, expected probability of readmission was 24.90 percent while actual probability of readmission was 24.86 percent. The model correctly predicted 75 percent of the placements for patients.
520 $a The impact of a specialized geriatric unit on the readmission rate of elderly patients at the two institutions was also evaluated. No differences were found between experimental cases and controls with respect to readmission rate. Further analysis revealed that length of stay and total charges were very consistent across admissions for each patient.
520 $a The model is useful in helping hospital nursing staff target patients who are at the greatest risk for readmission. It can assist these staff in prioritizing care, allocating resources, and improving discharge planning. The model also has application as a screening device for patients and as a tool to help monitor quality of care.
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