The Impact of Interhospital Transfers on Surgical Quality Metrics for Academic Medical Centers

Crippen, Cristina; Hughes, Steven J.; Chen, Sugong; Behrns, Kevin E.

doi:10.1177/000313481408000725

Cited by 17 publications

(19 citation statements)

References 16 publications

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“…A 2014 US study of surgical quality metrics for academic medical centres found that interhospital transfer was associated with higher mortality rate, longer length of stay and higher consumption of resources including intensive care (53 versus 32% for direct admissions), respiratory therapy (53 versus 42%), imaging (92 versus 82%) and rehabilitation services (63 versus 49%) . Mean total costs were over 50% higher for transferred patients compared with direct admissions.…”

Section: Discussionmentioning

confidence: 99%

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Interhospital transfer delays emergency abdominal surgery and prolongs stay

Limmer

Edye

2016

ANZ Journal of Surgery

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Section: Discussionmentioning

confidence: 99%

“…Mean total costs were over 50% higher for transferred patients compared with direct admissions. Mean intensive care unit (ICU) stay was 2.5 days longer and mean ICU costs were $17 823 and $11 887, respectively …”

Section: Discussionmentioning

confidence: 99%

Interhospital transfer delays emergency abdominal surgery and prolongs stay

Limmer

Edye

2016

ANZ Journal of Surgery

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“…Currently, national improvement measures do not account for this, and studies have shown that this penalizes hospitals accepting transfers. 7,9,12…”

Section: Discussionmentioning

confidence: 99%

The Association of External Transfer Status with Adverse Outcomes in Otolaryngology

Suresh

Gouveia

Kern

et al. 2018

Otolaryngol.--head neck surg.

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Objective To compare rates of morbidity and mortality in patients treated by otolaryngologists who undergo interhospital transfers vs those who do not and to quantify conditions requiring interhospital transfers in this population. Study Design Cohort study. Setting American College of Surgeons National Surgical Quality Improvement Program. Subjects and Methods We identified patients requiring surgery by otolaryngologists in the National Surgical Quality Improvement Program database from 2006 to 2013. We compared patients who were transferred from an outside institution to those admitted from home. Multivariate regression was used to adjust for patient characteristics, comorbidities, and case mix. The primary outcome was overall morbidity and mortality within 30 days of surgery. Results We identified 60,498 patients; 488 (0.8%) were transferred from another institution. Operations that were more common in the transferred group were incision and drainage (24.0% vs 1.2%), facial trauma repair (9.0% vs 3.1%), and oropharyngeal hemorrhage control (3.9% vs 0.4%). External transfer patients had significantly longer hospital stays (44.1% vs 4.4% >7 days, P < .05). On unadjusted analysis, transferred patients had a significantly higher rate of morbidity and mortality (odds ratio [OR], 11.3; 95% confidence interval [CI], 9.4-13.5). On multivariate analysis, transferred patients had a significantly greater rate of morbidity and mortality (OR, 3.1; 95% CI, 2.4-4.0). Conclusion Transfer from another institution is associated with worse outcomes independent of case mix, demographics, and preoperative comorbidities in acute otolaryngology conditions requiring surgery. Practitioners should be aware of this when caring for transfer patients, and transfer status should be considered when measuring hospital quality.

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“…However, securing these services often requires travelling burdensome distances away from patients’ homes and communities of support. Furthermore, these patients often arrive with poorly defined goals of care and experience unfavorable outcomes including higher rates of mortality [ 3 – 6 ]. The challenge is compounded by the fact that the transferred patients and families frequently do not understand the severity of illness, leading to unrealistic expectations and a potentially false sense of hope [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

Dunn¹,

Bellon²,

Bilderback³

et al. 2021

PLoS ONE

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Background Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. Methods and findings All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). Conclusions The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

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The Impact of Interhospital Transfers on Surgical Quality Metrics for Academic Medical Centers

Cited by 17 publications

References 16 publications

Interhospital transfer delays emergency abdominal surgery and prolongs stay

Interhospital transfer delays emergency abdominal surgery and prolongs stay

The Association of External Transfer Status with Adverse Outcomes in Otolaryngology

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

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