Development of a Data Model to Predict Nursing Workload Using Routine Clinical Data.

The effective management of human resources in nursing is fundamental to ensuring high-quality care. The necessary staffing levels can be derived from the nursing-related health status. Our approach is based on the use of artificial intelligence (AI) and machine learning (ML) to recognize key workload-driving predictors from routine data in the first step and derive recommendations for staffing levels in the second step.

Staff Management with AI: Predicting the Nursing Workload.

The effective management of human resources in nursing fundamental to ensuring high-quality care. The necessary staffing levels can beis derived from the nursing-related health status. Our approach is based on the use of artificial intelligence (AI) and machine learning (ML) to recognize key workload-driving predictors from routine clinical data in the first step and derive recommendations for staffing levels in the second step.

Prediction of Nursing Workload in Hospital.

A dissertation project at the Witten/Herdecke University [1] is investigating which (nursing sensitive) patient characteristics are suitable for predicting a higher or lower degree of nursing workload. For this research project four predictive modelling methods were selected. In a first step, SUPPORT VECTOR MACHINE, RANDOM FOREST, and GRADIENT BOOSTING were used to identify potential predictors from the nursing sensitive patient characteristics.

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.

[Comment about the policy statement of the MDS. A position statement of the assessment group of the University Witten/Herdecke with regard to nursing documentation].

In 2005 the Medical Advisory Service of Social Health Insurance (MDS) in Germany published a policy statement with regard to the nursing process and documentation. According to the intention of the association, this statement should be considered as recommendations which are able to improve nursing practice and to contribute to streamlining of bureaucracy in nursing care. Recognising the broad impact of this publication on nursing institutions, a working group on nursing assessment of the University Witten/Herdecke conducted a critical review of the statement.

[Position statement of the National Nursing Assessment Group of Germany: regarding the basic "nutrition and fluid requirements" policy of the elderly by the MDS (Medical Service of the Central Associations of the Health Insurance Companies), July 2003].

Assessing the nutritional state of old and dependent people demands a distinctive consideration of all aspects which might influence the individual's nutritional habits. An isolated consideration of the body mass index (BMI) does not serve the needs of elderly, because the BMI seems not to be a valid indicator for their nutrtional state. In order to prove the risk of malnutrition, also the individual life conditions as well as social, religious, cultural and economic circumstances besides the amount of energy and nu triments are to be taken into account.

Time distribution of selected care activities in home care in Germany.

Aims And Objectives: To investigate the time required by family caregivers to carry out selected care activities. The project was initiated by the responsible ministry for German Long Term Care Insurance. The Long Term Care Insurance provides recommendations for specific time ranges for care activities as a basis for assessment.