Understanding Pediatric Surgery Cancellation: Geospatial Analysis.

Autor: Liu L; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.; Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, United States., Ni Y; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States., Beck AF; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.; Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.; Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States., Brokamp C; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States., Ramphul RC; Department of Government Relations and Community Benefits, Texas Children's Hospital, Houston, TX, United States., Highfield LD; Department of Management, Policy & Community Health, University of Texas Health Science Center School of Public Health, Houston, TX, United States.; Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Science Center School of Public Health, Houston, TX, United States., Kanjia MK; Department of Pediatric Anesthesiology and Pain Management, Texas Children's Hospital, Houston, TX, United States.; Department of Anesthesiology, Baylor College of Medicine, Houston, TX, United States., Pratap JN; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.; Department of Anesthesia, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Jazyk: angličtina
Zdroj: Journal of medical Internet research [J Med Internet Res] 2021 Sep 10; Vol. 23 (9), pp. e26231. Date of Electronic Publication: 2021 Sep 10.
DOI: 10.2196/26231
Abstrakt: Background: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood.
Objective: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods.
Methods: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set.
Results: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate.
Conclusions: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.
(©Lei Liu, Yizhao Ni, Andrew F Beck, Cole Brokamp, Ryan C Ramphul, Linda D Highfield, Megha Karkera Kanjia, J “Nick” Pratap. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.09.2021.)
Databáze: MEDLINE
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