International Journal for Quality in Health Care 16:i27-i35 (2004)
International Journal for Quality in Health Care vol. 16 Supplement 1 © International Society for Quality in Health Care and Oxford University Press 2004; all rights reserved
Distinguishing hospital complications of care from pre-existing conditions
Divisions of Health Care Policy & Research and Biostatistics, Mayo Clinic, Rochester, MN, USA
Objective. To compare cases identified through the Complications Screening Program (CSP) as complications with cases using the same ICD-9 secondary diagnosis codes, where the identifying diagnosis is also indicated as not present at admission.
Design. Observational study comparing two sources of potential hospital complications: published computer algorithms applied to coded diagnosis data versus a secondary diagnosis indicator, which distinguishes pre-existing from hospital-developed conditions.
Setting. All patients discharged from Mayo Clinic Rochester hospitals during 1998 and 1999. The Mayo Clinic is a large integrated delivery system in southeastern Minnesota, USA, providing services ranging from local, primary care to tertiary care for referral patients. Approximately 35% of Mayo patients travel >200 km for medical care.
Study participants. Hospital patients (total = 84 436). The numbers of cases with complications ranged from 0 to 2444 per algorithm.
Main outcome measures. Percent of algorithm complication cases indicated as developing in the hospital, and percent of acquired conditions of that type detected by the computer algorithms. Incremental hospital charges, length of stay (LOS) and mortality associated with acquired complications.
Results. The percent of cases identified through the computer algorithm that were also coded as acquired varied from 8.8% to 100%. The ability of the computer algorithms to detect acquired conditions of that type also varied greatly, from 2% to 99%. Incremental charges and LOS were significant for patients with acquired complications except for hip fracture/falls. Many acquired complications also increased hospital mortality.
Conclusions. Complication rates based strictly on standard discharge abstracts have limited use for inter-hospital comparisons due to large variability in coding across hospitals and the insensitivity of existing computer algorithms to exclude conditions present on admission from true complications. However, complications do carry high costs, including extended stays and increased hospital mortality. Enhancing secondary diagnoses with a simple indicator identifying which diagnoses were present on admission greatly increases the accurate identification of complications for internal quality and patient safety improvements.
Keywords: hospital complications, positive predictive value, pre-existing conditions, quality indicators, sensitivity
Address reprint requests to J. M. Naessens, Division of Health Care Policy and Research, Mayo clinic, 200 First Street SW, Rochester, MN 55905, USA. E-mail: naessens.james{at}mayo.edu