OBJECTIVE: To estimate the potential mortality reduction if patients chose the safest hospitals for complex cancer surgery. BACKGROUND: Mortality after complex oncologic surgery is highly variable across hospitals, and directing patients away from unsafe hospitals could potentially improve survivorship. Hospital quality measures are becoming increasingly accessible at a time when patients are more engaged in choosing providers. It is currently unclear what information to share with patients to maximally capitalize on patient-centered realignment. METHODS: The National Cancer Database was queried for adults undergoing 5 complex cancer surgeries (pulmonary lobectomy, pneumonectomy, esophagectomy, gastrectomy, and colectomy) for a primary cancer between 2008 and 2012. Risk-standardized mortality rate (RSMR) methodology, currently used by Medicare-based hospital rating systems, was used to classify hospitals as "safest" and "least safe" by procedure. Patients were modeled moving from "least safe" to "safest" hospitals and the potential number of lives saved through patient realignment determined. As surgical volume has historically been used to distinguish safe hospitals, comparisons were made to models moving patients from low-volume to high-volume hospitals. RESULTS: A total of 292,040 patients were analyzed. In an optimally modeled scenario, realignment using RSMR would result in a greater number of lives saved (3592 vs 2161, P < 0.01) and require only 15 patients to change hospitals to save a life, compared to 78 patients using volume models (P < 0.01). CONCLUSIONS: Public reporting of hospital safety, specifically based on RSMR instead of volume, has the potential to lead to meaningful reductions in surgical mortality after complex cancer surgery, even in the setting of a modest patient realignment.
OBJECTIVE: To estimate the potential mortality reduction if patients chose the safest hospitals for complex cancer surgery. BACKGROUND:Mortality after complex oncologic surgery is highly variable across hospitals, and directing patients away from unsafe hospitals could potentially improve survivorship. Hospital quality measures are becoming increasingly accessible at a time when patients are more engaged in choosing providers. It is currently unclear what information to share with patients to maximally capitalize on patient-centered realignment. METHODS: The National Cancer Database was queried for adults undergoing 5 complex cancer surgeries (pulmonary lobectomy, pneumonectomy, esophagectomy, gastrectomy, and colectomy) for a primary cancer between 2008 and 2012. Risk-standardized mortality rate (RSMR) methodology, currently used by Medicare-based hospital rating systems, was used to classify hospitals as "safest" and "least safe" by procedure. Patients were modeled moving from "least safe" to "safest" hospitals and the potential number of lives saved through patient realignment determined. As surgical volume has historically been used to distinguish safe hospitals, comparisons were made to models moving patients from low-volume to high-volume hospitals. RESULTS: A total of 292,040 patients were analyzed. In an optimally modeled scenario, realignment using RSMR would result in a greater number of lives saved (3592 vs 2161, P < 0.01) and require only 15 patients to change hospitals to save a life, compared to 78 patients using volume models (P < 0.01). CONCLUSIONS: Public reporting of hospital safety, specifically based on RSMR instead of volume, has the potential to lead to meaningful reductions in surgical mortality after complex cancer surgery, even in the setting of a modest patient realignment.
Authors: Marianna V Papageorge; Benjamin J Resio; Andres F Monsalve; Maureen Canavan; Ranjan Pathak; Vincent J Mase; Andrew P Dhanasopon; Jessica R Hoag; Justin D Blasberg; Daniel J Boffa Journal: JNCI Cancer Spectr Date: 2020-07-07
Authors: Fabian Haak; Savas Soysal; Elisabeth Deutschmann; Giusi Moffa; Heiner C Bucher; Max Kaech; Christoph Kettelhack; Otto Kollmar; Marco von Strauss Und Torney Journal: World J Surg Date: 2022-03-16 Impact factor: 3.282
Authors: Jessica R Hoag; Benjamin J Resio; Andres F Monsalve; Alexander S Chiu; Lawrence B Brown; Jeph Herrin; Justin D Blasberg; Anthony W Kim; Daniel J Boffa Journal: JAMA Netw Open Date: 2019-04-05
Authors: Allison Lipitz-Snyderman; Jessica A Lavery; Peter B Bach; Diane G Li; Annie Yang; Vivian E Strong; Ashley Russo; Katherine S Panageas Journal: Cancer Med Date: 2020-01-09 Impact factor: 4.452
Authors: Benjamin J Resio; Alexander S Chiu; Jessica R Hoag; Lawrence B Brown; Marney White; Audry Omar; Andres Monsalve; Andrew P Dhanasopon; Justin D Blasberg; Daniel J Boffa Journal: JAMA Netw Open Date: 2018-11-02