Document Type

Article

Publication Date

2-8-2021

Abstract

Objective:

Through geocoding the physical residential address included in the electronic medical record to the census tract level, we present a novel model for concomitant examination of individual patient-related and residential context-related factors that are associated with patient-reported experience scores.

Summary Background Data:

When assessing patient experience in the surgical setting, researchers need to examine the potential influence of neighborhood-level characteristics on patient experience-of-care ratings.

Methods:

We geocoded the residential address included in the electronic medical record (EMR) from a tertiary care facility to the census tract level of Orange County, CA. We then linked each individual record to the matching census tract and use hierarchical regression analyses to test the impact of distinct neighborhood conditions on patient experience. This approach allows us to estimate how each neighborhood characteristic uniquely influences Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores.

Results:

Individuals residing in communities characterized by high levels of socioeconomic disadvantage have the highest experience ratings. Accounting for individual patient’s characteristics such as age, gender, race/ethnicity, primary language spoken at home, length of stay, and average pain levels during their hospital stay, neighborhood-level characteristics such as proportions of people receiving public assistance influence the ratings of hospital experience (0.01, P < 0.05) independent of, and beyond, these individual-level factors.

Conclusions:

This manuscript is an example of how geocoding could be used to analyze surgical patient experience scores. In this analysis, we have shown that neighborhood-level characteristics influence the ratings of hospital experience independent of, and beyond, individual-level factors.

Comments

This article was originally published in Annals of Surgery, volume 2, issue 1, in 2021. https://doi.org/10.1097/AS9.0000000000000037

Peer Reviewed

1

Copyright

The authors

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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