Staff planning for operating rooms with different surgical services lines.

We present a two-phase model for a staff planning problem in a surgical department. We consider the setting where staff, in particular nurse circulators and surgical scrub technicians, are assigned to one of different service lines, and while they can be 'pooled' and temporally assigned to other service line if needed, these re-assignments should belimited. In Phase I, we decide on the number of staff hours to budget for each service line, considering policies limiting staff pooling and overtime, and different demand scenarios. In Phase II, we determine how these budgeted staff hours should be allocated across potential work days and shifts, given estimated staff requirements and shift-related scheduling restrictions. We propose a heuristic to speed the model's Phase II solution time. We implement the model using a hospital's surgical data and compare the model's results with the hospital's current practices. Using a simulation model for the surgical operations, we find that our two-phase model reduces the delays caused by staff unavailability as well as staff pooling, without increasing the workforce size. Finally, we briefly describe a decision-support tool we developed with the objective of fine-tuning staff planning decisions.

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.

Determining diagnostic radiographer staffing requirements: A workload-based approach.

INTRODUCTION: The topic of healthcare human resource planning for diagnostic radiographers has received limited research attention to date. This research is concerned with developing a framework that can be used to determine diagnostic radiographer staffing requirements at a unit- or department level (i.e. at the micro-level). METHODS: An inductive approach is applied to formulate requirement specifications that inform the development of the framework. A number of verification and validation activities are performed, including theoretical verification and a case study application. RESULTS: The diagnostic radiographer staffing framework consists of seven steps that comprise a workload-based approach to determining the number of full time equivalent diagnostic radiographers that are required for each modality, or group of modalities. Both clinical and non-clinical activities are considered, and guidance is provided on calculating staffing requirements to cover leave allowances. A number of potential approaches to determining activity times are also discussed. CONCLUSION: The framework represents a holistic approach to determining the required number of diagnostic radiographers at a practice-level, that is designed to remain relevant as technological advances are made in the field of diagnostic radiography. IMPLICATIONS FOR PRACTICE: By providing a practical guideline, with accompanying examples, the framework is expected to hold value for individuals involved in the management of diagnostic radiography practices. The framework proposes an approach to a topic that affects every radiography practice in operation yet has received limited attention in literature to date.

Important factors for planning nurse staffing in the emergency department: A consensus study.

INTRODUCTION: Planning adequate nurse staffing in the emergency department (ED) is challenging. Although there are models to determine nurse staffing in EDs, these models do not consider all the factors. Inadequate nurse staffing causes overcrowding, poor quality of patient care, increased hospital costs, poor patient outcomes and high levels of burnout amongst nurses. In this paper, we report stakeholders' perceptions of important factors to be considered when planning ED nursing ratios. METHODS: We applied a consensus research design. The data was generated from modified nominal group techniques followed by an e-Delphi with two rounds. The factors were generated during two nominal groups by 19 stakeholders which included management and healthcare professionals working in EDs. The generated factors were then put on a survey format for use in an e-Delphi. Using purposive and snowball sampling the survey was distributed to 74 national and international experts for consensus. RESULTS: Ultimately, 43 experts agreed (a validity index of ≥ 80%) on four categories namely: hospital, staff, patient and additional categories which included 17 related factors. CONCLUSION: Ideal nurse staffing ratios are influenced by the complexity of the environment and interactions between multiple factors. The categories and factors identified emphasised the need for extensive further research to ensure a financially viable model that will be accepted by both staff and patient, and thus promote optimal outcomes.

Balancing nurses' workload in hospital wards: study protocol of developing a method to manage workload.

INTRODUCTION: Hospitals pursue different goals at the same time: excellent service to their patients, good quality care, operational excellence, retaining employees. This requires a good balance between patient needs and nursing staff. One way to ensure a proper fit between patient needs and nursing staff is to work with a workload management method. In our view, a nursing workload management method needs to have the following characteristics: easy to interpret; limited additional registration; applicable to different types of hospital wards; supported by nurses; covers all activities of nurses and suitable for prospective planning of nursing staff. At present, no such method is available. METHODS/ANALYSIS: The research follows several steps to come to a workload management method for staff nurses. First, a list of patient characteristics relevant to care time will be composed by performing a Delphi study among staff nurses. Next, a time study of nurses' activities will be carried out. The 2 can be combined to estimate care time per patient group and estimate the time nurses spend on non-patient-related activities. These 2 estimates can be combined and compared with available nursing resources: this gives an estimate of nurses' workload. The research will take place in an academic hospital in the Netherlands. 6 surgical wards will be included, capacity 15-30 beds. ETHICAL CONSIDERATIONS: The study protocol was submitted to the Medical Ethical Review Board of the University Medical Center (UMC) Utrecht and received a positive advice, protocol number 14-165/C. DISCUSSION: This method will be developed in close cooperation with staff nurses and ward management. The strong involvement of the end users will contribute to a broader support of the results. The method we will develop may also be useful for planning purposes; this is a strong advantage compared with existing methods, which tend to focus on retrospective analysis.