Overview
Nursing Minimum Datasets (NMDSs) are defined as a minimal set of nursing information with uniform definitions and categories, designed to meet the information needs of multiple health data users. In long-term care, NMDSs can standardize documentation, support policy decisions, and enable interprofessional data use across diverse settings such as nursing homes, retirement communities, and rehabilitation facilities for older adults.
Why NMDSs Matter in Long-Term Care
The long-term care sector faces demographic shifts, higher complexity of resident needs, and increased digitalization. NMDSs offer structured data that can improve staffing decisions, resource allocation, and care quality. They also underpin research and policy analysis by ensuring data are valid, comparable, and retrievable from electronic health records. The COVID-19 pandemic highlighted gaps in long-term care data, reinforcing the urgency of robust NMDS development and implementation. Jurisdictions like Germany have advanced digital health laws to promote data use, interoperability, and research access, illustrating how NMDSs fit into broader health data ecosystems.
Key Initiatives and Global Landscape
Historically, the United States led early NMDS work, with subsequent translations and adaptations in Europe, Canada, the UK, and beyond. Notable strands include country-specific NMDSs, research-focused NMDSs (for nutrition, falls, diabetes, etc.), and intersectoral projects that blend nursing data with medical datasets. The UK’s Developing Resources and Minimum Data Set for Care Homes’ Adoption (DACHA) program and the European and North American translation efforts exemplify efforts to enhance data accessibility and interoperability in long-term care. While many datasets emphasize patient data, there is growing recognition of the need to better capture nursing-specific content and resident-centered outcomes.
What NMDSs Contain: A Structural View
Content across NMDSs is typically organized around three broad domains, following Werley et al.’s model:
- Patient data: demographics, physiological and psychosocial factors, diagnoses, and goals. Many datasets draw heavily from the U.S. NMDS, documenting sensory function, cognition, mood, behavior, nutrition, skin health, and functional status.
- Interpersonal data: nursing interventions, orders, and outcomes of nursing care. Some datasets specify outcome measures tailored to specific studies (e.g., falls and osteoporosis interventions).
- Institutional data: facility size, staffing, admissions/discharges, and other organizational factors that influence care delivery.
Despite diverse origins, most NMDSs emphasize patient data, with variable inclusion of the resident voice and nursing-specific indicators. The challenge remains to harmonize items and definitions to support cross-country comparisons and robust research.
Recommendations and Practical Implications
Scholarly work consistently advocates for:
- Uniform documentation and precise item definitions to enhance reliability and comparability.
- Clinician involvement and multidisciplinary input in NMDS development to ensure relevance and feasibility.
- Resident perspectives and goals to better reflect patient-centered care in the data model.
- Data privacy and governance considerations as data exchange expands across settings and systems.
- Bridging research and practice by integrating practical knowledge with theoretical models, thus improving knowledge transfer and data usability.
Managerial guidance stresses collaboration among medical directors, nursing home leaders, and staff to translate NMDS findings into care improvements, staffing decisions, and policy advocacy. The long-term goal is a shared core nursing dataset that supports both day-to-day care and large-scale health system learning.
Gaps, Limitations, and Future Directions
Current literature reveals uneven coverage of nursing-specific items and resident voices, with most contents documented for patient data rather than the nursing process. Future NMDS development should prioritize:
– Expanding nursing-focused indicators (interventions, nursing outcomes) across care settings.
– Ensuring international comparability through standardized definitions and cross-walking between datasets.
– Integrating medical and nursing data to render a comprehensive view of resident needs and service delivery.
Conclusion
NMDSs in long-term care are re-emerging as pivotal tools in the digital transformation of nursing. They hold promise for improving care quality, enabling research, and informing policy, provided that stakeholders design them with validity, comparability, and resident-centered content at the forefront. As digital health laws and intersystem data sharing mature, a well-implemented NMDS could become the backbone of nursing documentation and decision support in long-term care.
