Predictor Selection for Case Complexity Based on Routine Data for Needs-Based and Competence-Based Staff Planning.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

Classification of Healthcare Professionals.

To be able to compare job titles in healthcare, a proposal for a classification of healthcare professionals was developed. The proposed LEP classification for healthcare professionals is suitable for Switzerland, Germany and Austria and includes nurses, midwives, social workers and other professionals.

Providing evidence-based knowledge on nursing interventions at the point of care: findings from a mapping project.

BACKGROUND: In healthcare there is a call to provide cost-efficient and safe care. This can be achieved through evidence-based practice (EBP), defined as the use of evidence from research, context, patient preferences, and clinical expertise. However, the contemporary and process-integrated supply of evidence-based knowledge at the point of care is a major challenge. An integrative knowledge management system supporting practicing clinical nurses in their daily work providing evidence-based knowledge at the point of care is required. The aim of this study was (1) to map standardized and structured nursing interventions classification and evidence on a knowledge platform to support evidence-based knowledge at the point of care, and (2) to explore the challenge of achieving interoperability between the source terminology of the nursing interventions classification (LEP Nursing 3) and the target format of the evidence provided on the knowledge platform (FIT-Nursing Care). METHODS: In an iterative three-round mapping process, three raters, nurses with clinical and nursing informatics or EBP experience, matched nursing interventions from the LEP Nursing 3 classification and evidence provided from Cochrane Reviews summarized on FIT-Nursing Care as so-called study synopses. We used a logical mapping method. We analysed the feasibility using thematic analysis. RESULTS: In the third and final mapping round, a total of 47.01% (252 of 536) of nursing interventions from LEP Nursing 3 were mapped to 92.31% (300 of 325) of synopses from FIT-Nursing Care. The interrater reliability of 77.52% suggests good agreement. The experience from the whole mapping process provides important findings: (1) different content orientations-because both systems pursue different purposes (content validity), (2) content granularity-differences regarding the structure and the level of detail in both systems, and (3) operationalization of knowledge. CONCLUSION: Mapping of research evidence to nursing classification seems feasible; however, three specific challenges were identified: different content orientation; content granularity; and operationalization of knowledge. The next step for this integrative knowledge management system will now be testing at the point of care.

Automated Mapping of LEP Nursing Data to Nursing Minimum Data Sets.

Nursing Minimum Data Sets (NMDS) intend to systematically describe nursing care. Until now NMDS have been populated with nursing data by manual data ascertainment which is inefficient. The objective of this work was to evaluate an automated mapping pipeline for transforming nursing data into an NMDS. We used LEP Nursing 3 data as source data and the Austrian and German NMDS as target formats. Based on a human expert mapping between LEP and NMDS, an automated data mapping algorithm was developed and implemented in an automatic mapping pipeline. The results show that most LEP nursing interventions can be matched to the NMDS-AT and G-NMDS and that a fully automated mapping process from LEP Nursing 3 data to NMDS-AT performs effectively and very efficiently. The shown approach can also be used to map different nursing classifications and to automatically transform point-of-care nursing data into nursing minimum data sets.

Implementation of the Austrian Nursing Minimum Data Set (NMDS-AT): A Feasibility Study.

BACKGROUND: An Austrian Nursing Minimum Data Set (NMDS-AT) has been developed to describe the diversity of patient populations and variability of nursing care based on nursing diagnoses, nursing interventions, and nursing outcomes. The aim of this study is to test the feasibility of using this NMDS-AT by assessing the availability of data needed for the NMDS-AT in routine nursing documentation, and to assess its reliability and usefulness. METHODS: Data were collected in a general hospital from patient records of 20 patients representing 457 patient days. Availability of needed data was assessed by two raters in a chart review based on an NMDS-AT form. The interrater reliability (n=20) and intrarater reliability (n=5) was assessed using Cohen's kappa coefficient and intraclass correlation coefficient (ICC). Usefulness was assessed by verifying whether typical analysis questions can be answered by the documented NMDS-AT data. RESULTS: In the 20 patient records, thirteen nursing diagnoses, 50 nursing interventions, and five nursing outcomes occurred, representing 68 (58.6%) of the overall 116 data elements of the NMDS-AT. The data were found at different data sources (e.g., electronic nursing record or paper-based fever chart) and in various forms (e.g., standardized or free text). The interrater reliability of the thirteen nursing diagnoses showed kappa values (percentage of agreement) ranging from 0.35 (85%) to 1.00 (100%). The 50 nursing interventions showed ICCs ranging from 0.03 to 1.00. All nursing outcomes showed an ICC of 1.00. The intrarater reliability showed 100% agreement. Performing typical analysis questions showed that the extracted NMDS-AT data are able to answer questions of clinical management, of policy makers, and of nursing science. CONCLUSIONS: The NMDS-AT was found to be feasible: needed data was available in the analysed patient records, data extraction showed good reliability, and typical analysis could be performed and showed interesting results. Before the NMDS-AT can be introduced in healthcare institutions, the following challenges need to be addressed: 1. improve the quality of nursing documentation; 2. reduce fragmentation of documentation; 3. use a standardized nursing classification system; and 4. establish mappings between nursing classification systems and the NMDS-AT.

Development of the Austrian Nursing Minimum Data Set (NMDS-AT): the third Delphi Round, a quantitative online survey.

BACKGROUND: A Nursing Minimum Data Set (NMDS) aims at systematically describing nursing care in terms of patient problems, nursing activities, and patient outcomes. In an earlier Delphi study, 56 data elements were proposed to be included in an Austrian Nursing Minimum Data Set (NMDS-AT). OBJECTIVES: To identify the most important data elements of this list, and to identify appropriate coding systems. METHODS: Online Delphi-based survey with 88 experts. RESULTS: 43 data elements were rated as relevant for an NMDS-AT (strong agreement of more than half of the experts): nine data elements concerning the institution, patient demographics, and medical condition; 18 data elements concerning patient problems by using nursing diagnosis; seven data elements concerning nursing outcomes, and nine data elements concerning nursing interventions. As classification systems, national classification systems were proposed besides ICNP, NNN, and nursing-sensitive indicators. CONCLUSION: The resulting proposal for an NMDS-AT will now be tested with routine data.

[Nursing minimum data sets (NMDS) - a literature review relating to objective and data elements]. / Nursing Minimum Data Sets (NMDS) - eine Literaturübersicht bezüglich Zielsetzungen und Datenelementen.

BACKGROUND: In order to be able to follow the predicted changes in healthcare systems, there has long been a need for a unified database that could transparently compare nursing care data from different service providers. In Austria, a recommendation has been lacking thus far as to which nursing care data need to be documented as "basic data", and as a result of this, comparisons of nursing care data on a national basis have been hindered. The international development of Nursing Minimum Data Sets (NMDS) has demonstrated that nursing care data can be sufficiently compared. AIM: The aim of the systematic literature review is to raise the current level of knowledge regarding NMDS and to develop a structured description of NMDS which, above all else, can document the recorded data elements and the associated objectives with the use of NMDS. RESULTS: A total of seventy publications on the subject of NMDS were included in the literature overview. The analysis of the eight NMDS that were presented yielded six central objectives and six higher-level data elements. The identified objectives include: the description of the nursing care practice, distribution of the financial means, benchmarking, human resources planning, trend analyses, and quality assurance. The six data elements that were identified comprise operating data, demographic data, and the diagnoses, interventions, results (quality indicators), and intensity of nursing care (clinical data). DISCUSSION: What emerged was that no clear association is present between the objectives and the data elements of the minimum nursing care datasets that were studied.