Influence of Hospital Characteristics on Hospital Transfer Destinations for Patients With Stroke.
Autor: | Zachrison KS; Departments of Emergency Medicine (K.S.Z.), Massachusetts General Hospital, Boston.; Harvard Medical School (K.S.Z., L.H.S.), Boston, MA., Amati V; Social Networks Lab of the Department of Humanities, Social, and Political Sciences, ETH Zurich, Switzerland (V.A.)., Schwamm LH; Neurology (L.H.S., Z.Y.), Massachusetts General Hospital, Boston.; Harvard Medical School (K.S.Z., L.H.S.), Boston, MA., Yan Z; Neurology (L.H.S., Z.Y.), Massachusetts General Hospital, Boston., Nielsen V; Massachusetts Department of Public Health, Boston, MA (V.N., A.C.)., Christie A; Massachusetts Department of Public Health, Boston, MA (V.N., A.C.)., Reeves MJ; Department of Epidemiology and Biostatistics of Michigan State University, East Lansing (M.J.R.)., Sauser JP; Hankamer School of Business at Baylor University, Waco, TX (J.P.S.)., Lomi A; Faculty of Economics of the University of Italian Switzerland, Lugano, Switzerland (A.L.)., Onnela JP; Department of Biostatistics at the Harvard T.H. Chan School of Public Health, Boston, MA (J.P.O.). |
---|---|
Jazyk: | angličtina |
Zdroj: | Circulation. Cardiovascular quality and outcomes [Circ Cardiovasc Qual Outcomes] 2022 May; Vol. 15 (5), pp. e008269. Date of Electronic Publication: 2022 Apr 04. |
DOI: | 10.1161/CIRCOUTCOMES.121.008269 |
Abstrakt: | Background: Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke. Methods: Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework. Results: Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher). Conclusions: After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were important factor in determining transfer destination of patients with stroke. This study provides a starting point for future research exploring how relational coordination between hospitals may ensure optimized allocation of patients with stroke for maximal patient benefit. |
Databáze: | MEDLINE |
Externí odkaz: |