Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital

Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jonathan M Tan [verfasserIn] Vicky Tam [verfasserIn] Jorge A Galvez [verfasserIn] Grace Hsu [verfasserIn] William Quarshie [verfasserIn] Jack O Wasey [verfasserIn] Olivia Nelson [verfasserIn] Allan F Simpao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: International Journal of Population Data Science - Swansea University, 2018, 5(2020), 5
Übergeordnetes Werk:	volume:5 ; year:2020 ; number:5

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.23889/ijpds.v5i5.1644

Katalog-ID:	DOAJ085816906

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520			\|a Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care.
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allfields	10.23889/ijpds.v5i5.1644 doi (DE-627)DOAJ085816906 (DE-599)DOAJe061f5dff79949f297e41c950c519d29 DE-627 ger DE-627 rakwb eng HB848-3697 Jonathan M Tan verfasserin aut Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Demography. Population. Vital events Vicky Tam verfasserin aut Jorge A Galvez verfasserin aut Grace Hsu verfasserin aut William Quarshie verfasserin aut Jack O Wasey verfasserin aut Olivia Nelson verfasserin aut Allan F Simpao verfasserin aut In International Journal of Population Data Science Swansea University, 2018 5(2020), 5 (DE-627)885208897 (DE-600)2892786-2 23994908 nnns volume:5 year:2020 number:5 https://doi.org/10.23889/ijpds.v5i5.1644 kostenfrei https://doaj.org/article/e061f5dff79949f297e41c950c519d29 kostenfrei https://ijpds.org/article/view/1644 kostenfrei https://doaj.org/toc/2399-4908 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 5
spelling	10.23889/ijpds.v5i5.1644 doi (DE-627)DOAJ085816906 (DE-599)DOAJe061f5dff79949f297e41c950c519d29 DE-627 ger DE-627 rakwb eng HB848-3697 Jonathan M Tan verfasserin aut Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Demography. Population. Vital events Vicky Tam verfasserin aut Jorge A Galvez verfasserin aut Grace Hsu verfasserin aut William Quarshie verfasserin aut Jack O Wasey verfasserin aut Olivia Nelson verfasserin aut Allan F Simpao verfasserin aut In International Journal of Population Data Science Swansea University, 2018 5(2020), 5 (DE-627)885208897 (DE-600)2892786-2 23994908 nnns volume:5 year:2020 number:5 https://doi.org/10.23889/ijpds.v5i5.1644 kostenfrei https://doaj.org/article/e061f5dff79949f297e41c950c519d29 kostenfrei https://ijpds.org/article/view/1644 kostenfrei https://doaj.org/toc/2399-4908 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 5
allfields_unstemmed	10.23889/ijpds.v5i5.1644 doi (DE-627)DOAJ085816906 (DE-599)DOAJe061f5dff79949f297e41c950c519d29 DE-627 ger DE-627 rakwb eng HB848-3697 Jonathan M Tan verfasserin aut Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Demography. Population. Vital events Vicky Tam verfasserin aut Jorge A Galvez verfasserin aut Grace Hsu verfasserin aut William Quarshie verfasserin aut Jack O Wasey verfasserin aut Olivia Nelson verfasserin aut Allan F Simpao verfasserin aut In International Journal of Population Data Science Swansea University, 2018 5(2020), 5 (DE-627)885208897 (DE-600)2892786-2 23994908 nnns volume:5 year:2020 number:5 https://doi.org/10.23889/ijpds.v5i5.1644 kostenfrei https://doaj.org/article/e061f5dff79949f297e41c950c519d29 kostenfrei https://ijpds.org/article/view/1644 kostenfrei https://doaj.org/toc/2399-4908 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 5
allfieldsGer	10.23889/ijpds.v5i5.1644 doi (DE-627)DOAJ085816906 (DE-599)DOAJe061f5dff79949f297e41c950c519d29 DE-627 ger DE-627 rakwb eng HB848-3697 Jonathan M Tan verfasserin aut Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Demography. Population. Vital events Vicky Tam verfasserin aut Jorge A Galvez verfasserin aut Grace Hsu verfasserin aut William Quarshie verfasserin aut Jack O Wasey verfasserin aut Olivia Nelson verfasserin aut Allan F Simpao verfasserin aut In International Journal of Population Data Science Swansea University, 2018 5(2020), 5 (DE-627)885208897 (DE-600)2892786-2 23994908 nnns volume:5 year:2020 number:5 https://doi.org/10.23889/ijpds.v5i5.1644 kostenfrei https://doaj.org/article/e061f5dff79949f297e41c950c519d29 kostenfrei https://ijpds.org/article/view/1644 kostenfrei https://doaj.org/toc/2399-4908 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 5
allfieldsSound	10.23889/ijpds.v5i5.1644 doi (DE-627)DOAJ085816906 (DE-599)DOAJe061f5dff79949f297e41c950c519d29 DE-627 ger DE-627 rakwb eng HB848-3697 Jonathan M Tan verfasserin aut Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care. Demography. Population. Vital events Vicky Tam verfasserin aut Jorge A Galvez verfasserin aut Grace Hsu verfasserin aut William Quarshie verfasserin aut Jack O Wasey verfasserin aut Olivia Nelson verfasserin aut Allan F Simpao verfasserin aut In International Journal of Population Data Science Swansea University, 2018 5(2020), 5 (DE-627)885208897 (DE-600)2892786-2 23994908 nnns volume:5 year:2020 number:5 https://doi.org/10.23889/ijpds.v5i5.1644 kostenfrei https://doaj.org/article/e061f5dff79949f297e41c950c519d29 kostenfrei https://ijpds.org/article/view/1644 kostenfrei https://doaj.org/toc/2399-4908 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 5
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author-letter	Jonathan M Tan
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title_sort	spatial linkage of electronic health records and census data in a children’s hospital
callnumber	HB848-3697
title_auth	Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital
abstract	Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care.
abstractGer	Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care.
abstract_unstemmed	Introduction Social determinants of health (SDOH) has a significant impact on access to health. Risk stratification of patients who have difficulty accessing care could allow for triaging of peri-operative resources. Unfortunately, there is a limited amount of SES factors available to study in electronic health records (EHR). Objectives and Approach Our objective was to understand the SES and location risk factors that are associated with paediatric patients arriving late to the hospital for elective surgery. We conducted a retrospective study of paediatric patients requiring elective surgery from 2015-2019. Spatial linkage of EHR data with US Census–ACS 2017 data was conducted. Analysis was at patient and neighbourhood block group levels. Statistical analysis was conducted utilizing SAS, Python and ArcGIS. Results Our study had 40,943 patients with 7,453 patients (18.2%) who arrived ≥15 minutes late from their scheduled arrival. Patient level risk factors for arriving late included younger age, Black and Indian patients, non-English speaking, government insurance, increased co-morbidities and earlier appointments. The median time of arrival for patients arriving late was 23.0 minutes (18.0-33.0 minutes IQR), versus the on-time group of 7 min (4-22 minutes IQR) early. Median drive time and distance, using network analysis was not a significant factor. Statistically significant neighbourhood risk factors for arriving late included block groups with high unemployment, households receiving public assistance, low income households, higher number of high-school drop outs, female-headed households, more renter-occupied houses, and areas with high turnover. Logistic regression demonstrated neighbourhoods with the lowest quintile SES were 30% more likely to be late than the areas with the highest SES (p<0.001). Conclusion / Implications We successfully identified patient and neighbourhood socioeconomic risk factors for arriving late to the hospital leveraging geospatial methods and EHR data. Leveraging EHR data with geospatial analytics can augment our understanding of the SDOH that may impact the delivery of care.
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title_short	Spatial Linkage of Electronic Health Records and Census Data in A Children’s Hospital
url	https://doi.org/10.23889/ijpds.v5i5.1644 https://doaj.org/article/e061f5dff79949f297e41c950c519d29 https://ijpds.org/article/view/1644 https://doaj.org/toc/2399-4908
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author2	Vicky Tam Jorge A Galvez Grace Hsu William Quarshie Jack O Wasey Olivia Nelson Allan F Simpao
author2Str	Vicky Tam Jorge A Galvez Grace Hsu William Quarshie Jack O Wasey Olivia Nelson Allan F Simpao
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up_date	2024-07-03T17:01:48.339Z
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