Geospatial analysis of Hospital Consumer Assessment of Healthcare Providers and Systems pain management experience scores in U.S. hospitals

Abstract

Although prior work has investigated the interplay between demographic and intra-survey correlations of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores, these prior studies have not included geospatial analyses, or analyses which take into account location effects. Here, we report the results of a geospatial analysis (not equivalent to simple geographical analysis) of patient experience scores pertaining to pain. HCAHPS data collected in 2011 were examined to test the hypothesis that HCAHPS patient experience with pain management (PEPM) scores were geospatially distributed throughout the United States using Moran’s I, which measures the association between PEPM scores and hospital location. After limiting the dataset to hospitals in the continental U.S. with nonzero HCAHPS response rates, 3645 hospitals were included in the analyses. ‘Always’ responses were geospatially clustered amongst the analyzed hospitals. Clustering was significant in all distances tested from 10 to 5000 km (P < 0.0001). We identified six demarcated groups of hospitals. Taken together, these results strongly suggest a regional geographic effect on PEPM scores. These results may carry policy implications for U.S. hospitals with regard to acute pain outcomes. Further analyses will be necessary to evaluate policy explanations and implications of the regional geographic differences in PEPM results.

Perioperative Factors Associated with HCAHPS Responses of 2,758 Surgical Patients

“…Generalizations regarding anesthetic management are difficult to make from the available data. Previous studies have demonstrated that HCAHPS data are partially determined by non-treatment factors such as geospatial distributions, gender, preoperative expectations, and physician patient communications [4][5][6][7][8][9]. This study was unable to account for nonquantifiable factors, such as a patient's level of anxiety, education regarding their treatment or pain management expectations, and instead focuses on perioperative factors that can be retrospectively analyzed and were poten-tially modified in an effort to improve HCAHPS scores.…”

Benefits of a multiple‐solution approach in land change models

“…This test measures the global Moran’s index at a series of increasing distances, measuring the intensity of clustering and/or dispersion. The higher positive z ‐score values when statistically significant, the higher the degree of clustering for a given distance (Tighe, Fillingim, & Hurley, ). Figure depicts the incremental spatial autocorrelation peaking at a distance of about 12,000 m. Consequently, we set the dimensions of the moving window to 12,000 × 12,000 m (120 × 120 cell).…”

The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain

“…To identify the city of origin for each tweet, the function searched for tweets posted from within a 100-mile radius of the latitude and longitude specified for a set of 50 large, English-speaking cities from around the world. Although this approach provides a geolocation for each tweet, it is important to recognize that this approach did not capture nongeolocated tweets, potentially biasing the results toward those individuals with more sophisticated tweeting devices that were able to provide geolocation capabilities via Global Positioning System (GPS) and/or cellular location methods [ 47 - 50 ]. The United States was oversampled to provide a suitable basis for exploratory correlations between city demographic and climatic data with pain-related tweet sentiment.…”