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Distinguishing hospital complications of care from pre-existing conditions
James M Naessens1, Todd R Huschka
1Divisions of Health Care Policy & Research and Biostatistics, Mayo Clinic, Rochester, MN 55905, USA. naessens.james@mayo.edu
Insights
Hospital complication screening using computer algorithms shows significant variability in accuracy. Enhancing secondary diagnoses with an admission indicator improves identification for quality improvement and patient safety.
Area of Science:
- Health Services Research
- Medical Informatics
- Patient Safety
Background:
- Hospital-acquired complications are a significant concern for patient safety and healthcare costs.
- Accurate identification of complications is crucial for quality improvement initiatives.
- Existing methods for identifying complications, such as computer algorithms using ICD-9 codes, have limitations.
Purpose of the Study:
- To compare the accuracy of complication identification using the Complications Screening Program (CSP) against cases identified through ICD-9 secondary diagnosis codes.
- To evaluate the effectiveness of a secondary diagnosis indicator in distinguishing pre-existing conditions from hospital-developed complications.
- To assess the impact of acquired complications on hospital charges, length of stay, and mortality.
Main Methods:
- An observational study was conducted comparing two methods of identifying potential hospital complications.
- Data from 84,436 hospital patients discharged from Mayo Clinic Rochester between 1998 and 1999 were analyzed.
- The study compared published computer algorithms applied to coded diagnosis data with a secondary diagnosis indicator differentiating pre-existing from hospital-developed conditions.
Main Results:
- The percentage of algorithm-identified complications also coded as acquired varied widely (8.8% to 100%).
- Computer algorithms' ability to detect acquired conditions varied significantly (2% to 99%).
- Acquired complications, excluding hip fracture/falls, were associated with significant increases in hospital charges, length of stay, and mortality.
Conclusions:
- Standard discharge abstracts alone have limited utility for inter-hospital complication comparisons due to coding variability and algorithm insensitivity.
- Acquired complications represent a substantial cost to hospitals, increasing length of stay and mortality.
- Incorporating an indicator for conditions present at admission enhances the accurate identification of complications, supporting internal quality and patient safety improvements.
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.
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