Modifying ICD-9-CM Coding of Secondary Diagnoses to Improve Risk-Adjustment of Inpatient Mortality Rates

Michael Pine, Harmon S. Jordan, Anne Elixhauser, Donald E. Fry, David C. Hoaglin, Barbara Jones, Roger Meimban, David Warner and Junius Gonzales
Additional contact information
Michael Pine: Michael Pine and Associates, Inc., Chicago, IL, The University of Chicago; Chicago, IL, michaelorjoan@yahoo.com, mpine@aol.com
Harmon S. Jordan: Abt Associates Inc., Cambridge, MA, Tufts University School of Medicine, Boston, MA
Anne Elixhauser: The Agency for Healthcare Research and Quality, Rockville, MD
Donald E. Fry: Michael Pine and Associates, Inc., Chicago, IL
David C. Hoaglin: Abt Associates Inc., Cambridge, MA
Barbara Jones: Michael Pine and Associates, Inc., Chicago, IL
Roger Meimban: Michael Pine and Associates, Inc., Chicago, IL
David Warner: Abt Associates Inc., Cambridge, MA
Junius Gonzales: Abt Associates Inc., Cambridge, MA

Medical Decision Making, 2009, vol. 29, issue 1, 69-81

Abstract: Objective . To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources . Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. Methods . Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. Results . More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. Conclusions . Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Keywords: Key words: risk adjustment; hospital mortality; quality of health care; data collection; data sources; International Classification of Diseases; medical records. (Med Decis Making 2009; 29:69—81) (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:29:y:2009:i:1:p:69-81

DOI: 10.1177/0272989X08323297

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