Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Patients who accumulate multiple emergency department visits and hospital admissions, known as super-utilizers, have become the focus of policy initiatives aimed at preventing such costly use of the health care system through less expensive community- and primary care-based interventions. We conducted cross-sectional and longitudinal analyses of 4,774 publicly insured or uninsured super-utilizers in an urban safety-net integrated delivery system for the period May 1, 2011-April 30, 2013. Our analysis found that consistently 3 percent of adult patients met super-utilizer criteria and accounted for 30 percent of adult charges. Fewer than half of super-utilizers identified as such on May 1, 2011, remained in the category seven months later, and only 28 percent remained at the end of a year. This finding has important implications for program design and for policy makers because previous studies may have obscured this instability at the individual level. Our study also identified clinically relevant subgroups amenable to different interventions, along with their per capita utilization and costs before and after being identified as super-utilizers. Future solutions include improving predictive modeling to identify individuals likely to experience sustained levels of avoidable utilization, better classifying subgroups for whom interventions are needed, and implementing stronger program evaluation designs.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1377/hlthaff.2014.1186 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!