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Updated: Jun 6, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

[Quality assurance using routine data. Is outcome quality now measurable?].

T Kostuj¹, R Smektala

¹Abteilung für Unfallchirurgie und Orthopädie, Knappschaftskrankenhaus Bochum-Langendreer, Klinikum der Ruhr-Universität Bochum.

Der Unfallchirurg

|November 16, 2010

Summary

Quality assurance for hip fracture surgery in Germany is limited. Routine administrative data offers long-term outcomes but suffers from coding biases, making register-based conclusions more valid for assessing health service quality.

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Last Updated: Jun 6, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Area of Science:

Health Services Research
Medical Informatics
Quality Improvement

Background:

External quality assurance programs (BQS) in Germany provide in-hospital data but lack post-discharge outcome information.
Secondary routine administrative data can offer insights into long-term outcomes like mortality and revision after hip fracture surgery.

Purpose of the Study:

To evaluate the utility and limitations of secondary routine administrative data for assessing health service quality in hip fracture treatment.
To compare the validity of quality assurance conclusions drawn from routine data versus register-based data.

Main Methods:

Analysis of secondary routine administrative data from health insurance providers (Knappschaft Bahn-See, AOK) in a cooperative study with the WidO.
Leveraging existing departmental experience with the BQS data for hip fracture treatment.

Main Results:

Routine administrative data revealed significant biases due to poor coding quality and ambiguous ICD-10 code interpretations.
Quality assurance derived from routine data was found to be less valid compared to register-based assessments.

Conclusions:

Routine administrative data, while containing long-term outcome information, is prone to biases that limit its validity for quality assurance.
Medical expertise is crucial for accurate interpretation of routine administrative data to mitigate misinterpretations and ensure valid quality assessments.