Semantic Interoperability of Nursing Data - Mapping an Interface Terminology to SNOMED CT.

Using nursing intervention data within the scope of eHealth from a primary system (hospital information system) in a secondary system (electronic patient dossier) requires a common terminology across systems. For this purpose, nursing interventions from the LEP nursing interface terminology widely used in Germany, Switzerland, and Austria were mapped to the international reference terminology SNOMED CT with the support of the National Release Center Switzerland. The completed mapping comprises 467 nursing interventions from LEP Nursing Version 3.5.0 mapped to pre-coordinated SNOMED CT version 2022-12-31 procedures. Moreover, 83 new submissions were included in the international SNOMED CT edition and seven in the Swiss SNOMED CT extension. As a next step, the mapping will be tested to examine the possibilities of semantic interoperability in nursing practice.

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.

Same Goals, Yet Different Outcomes: Analysing the Current State of eHealth Adoption and Policies in Austria, Germany, and Switzerland Using a Mixed Methods Approach.

Despite similar policy goals, the adoption of eHealth practices took different paths in Austria (AT), Switzerland (CH), and Germany (GER). We seek to provide a rigorous analysis of the current state of hospitals by focusing on three key eHealth areas: electronic patient records (EPR), health information exchange (HIE), electronic patient communication. For validation and in order to gain better contextual insight we applied a mixed method approach by combining survey results from clinical directors with qualitative interview data from eHealth experts of all three countries. Across countries, EPR adoption rates were reported highest (AT: 52%, CH: 78%, GER: 50%), HIE-rates were partly lower (AT: 52%, CH: 14%, GER: 17%), and electronic patient communication was reported lowest overall (AT: 17%, CH: 8%, GER: 19%). Amongst others, results indicate patient awareness about eHealth to be equally weak across countries, which thus may be an important focal point of future policy initiatives.

Identifying Outliers in Data from Patient Record.

It is important for health services to be able to identify potential outliers with minimal effort as part of their daily evaluation of care data from patient record. This study evaluates the suitability of three statistical methods for identifying nursing outliers. The results show that by using methods implemented in the nursing workload measurement system "LEP" with reference to real data, unusual LEP minute profiles (movement, nutrition and so on) can be identified with little effort and therefore seem promising for application to the health services' daily evaluation process. The lessons learned are used to create requirement criteria for the further development of software solutions. It is recommended that the methods for identifying outliers in the daily evaluation process should be standardized in order to increase the efficiency of secondary use of care data from patient record.

[Development of a set of nursing-sensitive indicators for Swiss practice]. / Die Entwicklung eines Sets von aufwandrelevanten Pflege-indikatoren für die Schweizer Pflegepraxis.

BACKGROUND: The SwissDRG prospective payment system is known to inadequately account for nursing intensity due to the DRG group criteria insufficiently describing the variability of nursing intensity within individual diagnosis-related groups. In order to allow for appropriate reimbursement and resource allocation, nursing intensity must be able to be explicitly quantified and accounted for. The aim of this project was to develop a set of nursing-sensitive indicators intended to reduce the variation within individual diagnosis-related groups, supplementary to existing SwissDRG group criteria. METHODS: The approach comprised a variety of methods. A systematic literature review, input from an advisory board and an expert panel, as well as three focus group interviews with nurses and nurse managers formed the basis for the synthesis of data and information gathered from these sources. RESULTS: A set of 14 nursing-sensitive indicators was developed. The indicators are intended to improve the homogeneity of nursing intensity within SwissDRG diagnosis-related groups. Before these nursing indicators can be adopted as group criteria, they must be formulated to conform with SwissDRG and tested empirically. CONCLUSION: This set of indicators can be seen at as a first step towards nursing intensity being adequately represented in SwissDRG diagnosis-related groups. The next challenge to be met is operationalising the indicators in codable form.

[Variability in nursing workload within Swiss Diagnosis Related Groups]. / Variabilität des Pflegeaufwands in SwissDRG-Fallgruppen.

Nursing care inputs represent one of the major cost components in the Swiss Diagnosis Related Group (DRG) structure. High and low nursing workloads in individual cases are supposed to balance out via the DRG group. Research results indicating possible problems in this area cannot be reliably extrapolated to SwissDRG. An analysis of nursing workload figures with DRG indicators was carried out in order to decide whether there is a need to develop SwissDRG classification criteria that are specific to nursing care. The case groups were determined with SwissDRG 0.1, and nursing workload with LEP Nursing 2. Robust statistical methods were used. The evaluation of classification accuracy was carried out with R2 as the measurement of variance reduction and the coefficient of homogeneity (CH). To ensure reliable conclusions, statistical tests with bootstrapping methods were performed. The sample included 213 groups with a total of 73930 cases from ten hospitals. The DRG classification was seen to have limited explanatory power for variability in nursing workload inputs, both for all cases (R2 = 0.16) and for inliers (R2 = 0.32). Nursing workload homogeneity was statistically significant unsatisfactory (CH < 0.67) in 123 groups, including 24 groups in which it was significant defective (CH < 0.60). Therefore, there is a high risk of high and low nursing workloads not balancing out in these groups, and, as a result, of financial resources being wrongly allocated. The development of nursing-care-specific SwissDRG classification criteria for improved homogeneity and variance reduction is therefore indicated.

The linkage of nursing assessment and nursing workload.

The automated linkage of nursing assessment, nursing interventions, nursing workload measurement, and outcomes supports the user in practice and increases the explanatory power of nursing data, e.g. in DRG systems. Practice relevant data should therefore be available for the different needs for information by policy, management, research and training. To this end, two projects have gradually linked the outcome-oriented nursing assessment instrument AcuteCare (ePA-AC) and the nursing intervention and workload measurement system LEP Nursing 3 with each other.