Impact of diagnosis-timing indicators on measures of safety, comorbidity, and case mix groupings from administrative data sources.
BACKGROUND
Many attempts to identify hospital complications rely on secondary diagnoses from billing data. To be meaningful, diagnosis codes must distinguish between diagnoses after admission and those existing before admission.


OBJECTIVE
To assess the influence of diagnoses at admission on patient safety, comorbidity, severity measures, and case mix groupings for Medicare reimbursement.


METHODS
Cross-sectional association of various diagnosis-based clinical and performance measures with and without diagnosis present on admission.


METHODS
Hospital discharges from Mayo Clinic Rochester hospitals in 2005 (N = 60,599).


METHODS
All hospital inpatients including surgical, medical, pediatric, maternity, psychiatric, and rehabilitation patients. About 33% of patients traveled more than 120 miles for care.


METHODS
Hospital patient safety indicators, comorbidity, severity, and case mix measures with and without diagnoses present at admission.


RESULTS
Over 90% of all diagnoses were present at admission whereas 27.1% of all inpatients had a secondary diagnosis coded in-hospital. About one-third of discharges with a safety indicator were flagged because of a diagnosis already present at admission, more likely among referral patients. In contrast, 87% of postoperative hemorrhage, 22% of postoperative hip fractures, and 54% of foreign bodies left in wounds were coded as in-hospital conditions. Severity changes during hospitalization were observed in less than 8% of discharges. Slightly over 3% of discharges were assigned to higher weight diagnosis-related groups based on an in-hospital complication.


CONCLUSIONS
In general, many patient safety indicators do not reliably identify adverse hospital events, especially when applied to academic referral centers. Except as noted, conditions recorded after admission have minimal impact on comorbidity and severity measures or on Medicare reimbursement.
Naessens
James M
JM
Health Care Policy & Research, Mayo Clinic, Rochester, MN 55905, USA. naessens@mayo.edu
Campbell
Claudia R
CR
Berg
Bjorn
B
Williams
Arthur R
AR
Culbertson
Richard
R
D000368:Aged; D015897:Comorbidity; D003428:Cross Infection / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D005260:Female; D005547:Foreign Bodies / Q000453:epidemiology; D006620:Hip Fractures / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D011183:Postoperative Complications / Q000453:epidemiology; D019984:Quality Indicators, Health Care; D012449:Safety / Q000706:statistics & numerical data*; D017751:Safety Management; D012720:Severity of Illness Index