Medizinische Qualitätsindikatoren in der Praxis: bessere Daten für die Qualitätsentwicklung

MQIs support quality development, not rankings

Medical quality indicators provide accountability, monitoring, and a basis for quality improvement, but they cover only part of nursing home care. They do not capture overall structure, process quality, or residents’ quality of life and are not suitable for ranking institutions.

Data quality determines indicator usefulness

Reliable MQI use requires clear measurement rules, standardized counting, staff awareness, and feedback loops. The transcript identifies variability in pain measurement, polypharmacy counting, interfaces, documentation, and professional role clarity as key barriers to valid public reporting and local quality cycles.

Three indicators emphasize interprofessional processes

Current development focuses on polypharmacy, medication review, and advance care planning. Polypharmacy counting is standardized and increasingly automated. Medication review is defined as an interprofessional process. Advance care planning documents treatment intensity preferences and requires coordination between nursing and medical professionals.

In the continuing education session “Medical Quality Indicators in Practice: Better Data for Quality Development,” organized by Klinik Barmelweid, Asst. Prof. Dr. Franziska Zúñiga presents the role of medical quality indicators in long-term care and explains how these data support quality development. She outlines the legal basis under Article 59a KVG, the current public reporting of six indicators, and the full data cycle from resident assessment to publication, emphasizing that these indicators are risk-adjusted and intended for quality improvement rather than for ranking institutions. A central focus is the problem of data quality, including inconsistent recording practices, unclear counting rules, software-related variability, limited feedback to staff, and challenges in interprofessional collaboration, particularly with physicians. Within the national implementation program “NIP-Q-UPGRADE,” she describes measures to improve data quality, strengthen data-based quality development using the PDCA cycle, and test new indicators. She discusses in detail the further development of the indicators on polypharmacy, interprofessional medication review, and advance care planning, including revised definitions, documentation requirements, exclusion criteria, and implementation challenges. For polypharmacy, she highlights the move toward standardized and automated counting of active substances; for medication review, she defines minimum interprofessional process criteria; and for advance care planning, she specifies the documentation of treatment intensity preferences regarding resuscitation, hospitalization, and life-prolonging treatment. In the discussion, she also addresses the unresolved question of financing medication reviews and notes that further evidence is needed to identify resident groups that benefit most from these interventions.