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Background <p>Advanced surgical training is essential for maintaining the quality of care; however, Germany still lacks validated quality indicators despite the legal requirements for quality-linked funding under §17b of the Hospital Funding Act.</p> Objective <p>The aim of this work is to develop practical and measurable quality indicators that enable an objective assessment of the structural, procedural and outcome quality of advanced surgical training.</p> Material and methods <p>To develop these indicators, legal frameworks were analyzed, a&#xa0;systematic literature search on quality assessment surgical training was conducted and international training models were analyzed as a comparison. Key quality domains were then identified and consolidated into a structured indicator set through expert consensus.</p> Results <p>A&#xa0;three-stage set of indicators is proposed: 1)&#xa0;Structural indicators cover aspects such as curriculum quality, supervisor-trainee ratio, network-based training and teaching qualifications. 2)&#xa0;Process indicators include structured training discussions, operational supervision including surgical and procedural keys (OPS) coding for training interventions and continuing education activities. 3)&#xa0;Outcome indicators relating to the duration of training, the success rate of the specialist medical examination and standardized satisfaction. Digital tools, such as e‑logbooks and training registers enable valid and comparable data to be collated for the first time.</p> Conclusion <p>Standardized quality indicators achieve transparency, enable benchmarking and foster a&#xa0;learning data-driven advanced training culture. Annual digital reporting and centralized anonymized assessments are essential for quality assurance and to support future quality-linked funding models.</p>

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Qualitätsindikatoren für die chirurgische Weiterbildung

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Frederik Schlottmann,
  • Joscha Mulorz,
  • Benedikt Braun,
  • Tobias Huber,
  • Louisa Schuffert,
  • Juliane Kröplin,
  • Sabine Drossard,
  • Sarah Lif Böhm,
  • Maria E. Dey Hazra,
  • Marit Herbolzheimer,
  • Romina M. Rösch,
  • Frederic Bouffleur,
  • Stefanie Brunner,
  • Josefine Schardey,
  • Arash Motekallemi,
  • Gerrit Freund,
  • Manuela Oberlechner,
  • Hruy Menghesha,
  • Anna Lawson McLean,
  • Thomas Schmitz-Rixen,
  • Sebastian Schaaf

摘要

Background

Advanced surgical training is essential for maintaining the quality of care; however, Germany still lacks validated quality indicators despite the legal requirements for quality-linked funding under §17b of the Hospital Funding Act.

Objective

The aim of this work is to develop practical and measurable quality indicators that enable an objective assessment of the structural, procedural and outcome quality of advanced surgical training.

Material and methods

To develop these indicators, legal frameworks were analyzed, a systematic literature search on quality assessment surgical training was conducted and international training models were analyzed as a comparison. Key quality domains were then identified and consolidated into a structured indicator set through expert consensus.

Results

A three-stage set of indicators is proposed: 1) Structural indicators cover aspects such as curriculum quality, supervisor-trainee ratio, network-based training and teaching qualifications. 2) Process indicators include structured training discussions, operational supervision including surgical and procedural keys (OPS) coding for training interventions and continuing education activities. 3) Outcome indicators relating to the duration of training, the success rate of the specialist medical examination and standardized satisfaction. Digital tools, such as e‑logbooks and training registers enable valid and comparable data to be collated for the first time.

Conclusion

Standardized quality indicators achieve transparency, enable benchmarking and foster a learning data-driven advanced training culture. Annual digital reporting and centralized anonymized assessments are essential for quality assurance and to support future quality-linked funding models.