TY - JOUR
T1 - Development and evaluation of a categorization methodology for occupational back and shoulder injuries using claims data
AU - Heins, Sara E.
AU - Feldman, Dorianne R.
AU - Dugoff, Eva H.
AU - Wegener, Stephen T.
AU - Castillo, Renan C.
N1 - Funding Information:
Acknowledgments Data for this project was provided by a large, national insurance company. All authors received funding from the insurance company and Eva DuGoff received financial support from the Agency for Healthcare Research and Quality T32 National Research Service Award. The authors would like to thank Jonathan Weiner, Tom Richards, Chad Abrams, Huiyuan Zhang, and the Johns Hopkins ACG Team for their assistance in categorizing medical billing codes, Gerard Anderson for his insightful review of the manuscript, and our clinical panel (Dorianne Feldman, MD, MSPT, Stephen Wegener, PhD, APBB, Albert Wu, MD, MPH, John Carrino, MD, MPH, Lee Riley, MD, Edward McFarland, MD, and Terrence McGee, MSPT) for their input into the assignment methodology.
PY - 2013/12
Y1 - 2013/12
N2 - Administrative claims datasets have great potential for health services researchers who wish to evaluate patient care on a large scale across providers, but categorizing patients' primary health conditions from these data can be challenging. The goal of this work is to describe and evaluate a methodology to assign workers compensation claimants to meaningful groups within back and shoulder injuries using claims data. Claims data from a large multi-state workers compensation insurance dataset were used to assign eligible claimants to condition and subcondition groups using available ICD9 codes. Assignments were evaluated against body part indicators, severity indicators, resource utilization, and specific clinical interventions. Of the 575,967 claimants who met inclusion criteria, 54,066 claimants were designated as shoulder injuries and 118,772 were designated as back injuries. Within back and shoulder injuries, claimants were assigned to more specific groups known as subconditions. For both back and shoulder injuries, there were statistically significant differences between subconditions in several categories of resource utilization (p < 0.01 for all). For each of nine specific clinical interventions, the hypothesized corresponding subcondition had statistically significantly higher utilization than other subconditions (p < 0.01). This methodology could be an important tool to health services researchers who wish to target interventions or examine trends in cost and service utilization among meaningful groups of claimants.
AB - Administrative claims datasets have great potential for health services researchers who wish to evaluate patient care on a large scale across providers, but categorizing patients' primary health conditions from these data can be challenging. The goal of this work is to describe and evaluate a methodology to assign workers compensation claimants to meaningful groups within back and shoulder injuries using claims data. Claims data from a large multi-state workers compensation insurance dataset were used to assign eligible claimants to condition and subcondition groups using available ICD9 codes. Assignments were evaluated against body part indicators, severity indicators, resource utilization, and specific clinical interventions. Of the 575,967 claimants who met inclusion criteria, 54,066 claimants were designated as shoulder injuries and 118,772 were designated as back injuries. Within back and shoulder injuries, claimants were assigned to more specific groups known as subconditions. For both back and shoulder injuries, there were statistically significant differences between subconditions in several categories of resource utilization (p < 0.01 for all). For each of nine specific clinical interventions, the hypothesized corresponding subcondition had statistically significantly higher utilization than other subconditions (p < 0.01). This methodology could be an important tool to health services researchers who wish to target interventions or examine trends in cost and service utilization among meaningful groups of claimants.
KW - Administrative claims data
KW - Injury categorization methodology
KW - Occupational injury
KW - Workers compensation insurance
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U2 - 10.1007/s10742-013-0107-4
DO - 10.1007/s10742-013-0107-4
M3 - Article
AN - SCOPUS:84890120297
SN - 1387-3741
VL - 13
SP - 140
EP - 156
JO - Health Services and Outcomes Research Methodology
JF - Health Services and Outcomes Research Methodology
IS - 2-4
ER -