Probabilistic priority assessment of nurse calls

Current nurse call systems are very static. Call buttons are fixed to the wall, and systems do not account for various factors specific to a situation. We have developed a software platform, the ontology-based Nurse Call System (oNCS), which supports the transition to mobile and wireless nurse call buttons and uses an intelligent algorithm to address nurse calls. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff and patients into account by using an ontology. This article describes a probabilistic extension of the oNCS that supports a more sophisticated nurse call algorithm by dynamically assigning priorities to calls based on the risk factors of the patient and the kind of call. The probabilistic oNCS is evaluated through implementation of a prototype and simulations, based on a detailed dataset obtained from 3 nursing departments of Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls among nurses, and the assignment of priorities to calls are compared for the oNCS and the current nurse call system. Additionally, the performance of the system and the parameters of the priority assignment algorithm are explored. The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the probabilistic oNCS significantly improves the assignment of nurses to calls. Calls generally result in a nurse being present more quickly, the workload distribution among the nurses improves, and the priorities and kinds of calls are taken into account

Measuring the nursing workload per shift in the ICU

In the intensive care unit (ICU) different strategies and workload measurement tools exist to indicate the number of nurses needed. The gathered information is always focused on manpower needed per 24 h. However, a day consists of several shifts, which may be unequal in nursing workload. The aim of this study was to evaluate if differences in nursing workload between consecutive shifts can be identified by a nursing workload measurement tool. The nursing activities score (NAS) was registered per patient for every shift during a 4-week period in a prospective, observational research project in the surgical-pediatric ICU (SICU-PICU) and medical ICU (MICU) of an academic hospital. The NAS was influenced by the patient characteristics and the type of shift. Furthermore, the scores were lower during night shifts, in weekends and in MICU patients. Overall, the mean NAS per nurse per shift was 85.5 %, and the NAS per 24 h was 54.7 %. This study has shown that the nursing workload can be measured per working shift. In the ICU, the NAS differentiates the nursing workload between shifts, patients and units

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

<p>Abstract</p> <p>Background</p> <p>The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.</p> <p>The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient.</p> <p>Methods</p> <p>The <it>ontology-based Nurse Call System (oNCS) </it>was developed as an extension of a <it>Context-Aware Service Platform</it>. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient.</p> <p>Results</p> <p>The <it>oNCS </it>system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the <it>oNCS </it><it>system </it>and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed.</p> <p>Conclusions</p> <p>The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the <it>oNCS system </it>significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.</p

The development of a workload indicator for nursing based on the Belgian Nursing Minimum Dataset

This study describes the first step in transforming the Belgian Nursing Minimum Dataset (B-NMDS-2) into a workload measurement system. The research entailed two major phases: i) determining the coverage of the set of B-NMDS-2 against all nursing activities; ii) determining a standard time per nursing activity. Based on a work sampling by external observers, the average coverage was 47.6%. Time standards were determined by work sampling, direct time measurement by self-registration and subjective time estimation, resulting in a list with standard times per care item and per ward index