Article
Quality indicators in neurosurgery – are administrative data suitable for adequate risk adjustment?
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Authors
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Stephanie Schipmann
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
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Nils Warneke
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
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Michael Schwake
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
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Dennis Keurhorst
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
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Sebastian Lohmann
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
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Julian Varghese
- Universitätsklinikum Münster, Medizinische Informatik, Münster, Deutschland
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Tobias Brix
- Universitätsklinikum Münster, Medizinische Informatik, Münster, Deutschland
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Walter Stummer
- Universitätsklinikum Münster, Klinik für Neurochirurgie, Münster, Deutschland
| Published: | June 18, 2018 |
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Outline
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Objective: The current version of the German Hospital Structure Law focusses on quality for reimbursement of performance. The Federal Joint Committee (G-BA) recently requested the Institute for Quality Assurance and Transparency in Health Care (IQTIG) to develop relevant quality indicators. A few quality indicators for neurosurgery are discussed in the current literature, and include the 30-day-readmission, reoperation or mortality rates, the length of stay and rates of nosocomial and surgical site infections. An often very heterogeneous patient spectrum complicates benchmarking processes between different neurosurgical departments due to the lack of risk adjustment. In this study, we performed an analysis on these previously reported quality indicators and determined whether administrative data are eligible for risk adjustment.
Methods: All adult inpatients treated for benign or malignant brain or spinal tumours at our department between January 2013 and June 2017 were retrospectively enrolled in the study and monitored for the above-mentioned quality indicators. DRG-related data such as the relative weight, the PCCL (Patient Clinical Complexity Level), ICD-10 major diagnosis category, secondary diagnoses, age and sex were obtained. The Age-Adjusted Charlson Comorbidity Index was calculated on the basis of the ICD-10 codes. Logistic regression analyses were performed in order to correlate quality indicators with administrative data.
Results: Overall, 2623 patients were enrolled into the study. Most patients were treated for glioma (n=1055, 40.2%). The Charlson score did not correlate with quality indicators, whereas PCCL showed a positive correlation with 30-day readmission and reoperation, SSI and nosocomial infection rates.
Conclusion: All reported quality indicators are easy to monitor based on administrative data. However, administrative data are not sufficient for adequate risk adjustment as they do not reflect the endogenous risk of the patient and are influenced by certain complications that affect the monitored quality indicators during the inpatient stay.
Appropriate concepts for risk adjustment should be compiled on the basis of prospectively designed registry studies.
