Comparison of Nurse Staffing Based on Changes in Unit-level Workload Associated with Patient Churn

Aim This analysis compares the staffing implications of three measures of nurse staffing requirements: midnight census, turnover adjustment based on length of stay, and volume of admissions, discharges and transfers. Background Midnight census is commonly used to determine registered nurse staffing. Unit-level workload increases with patient churn, the movement of patients in and out of the nursing unit. Failure to account for patient churn in staffing allocation impacts nurse workload and may result in adverse patient outcomes. Method(s) Secondary data analysis of unit-level data from 32 hospitals, where nursing units are grouped into three unit-type categories: intensive care, intermediate care, and medical surgical. Result Midnight census alone did not account adequately for registered nurse workload intensity associated with patient churn. On average, units were staffed with a mixture of registered nurses and other nursing staff not always to budgeted levels. Adjusting for patient churn increases nurse staffing across all units and shifts. Conclusion Use of the discharges and transfers adjustment to midnight census may be useful in adjusting RN staffing on a shift basis to account for patient churn. Implications for nursing management Nurse managers should understand the implications to nurse workload of various methods of calculating registered nurse staff requirements

Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-policy), whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We use a queueing theoretic and a discrete-event simulation approach to provide generic models that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Health care operations management

Health care operations management has become a major topic for health care service providers and society. Operations research already has and further will make considerable contributions for the effective and efficient delivery of health care services. This special issue collects seven carefully selected papers dealing with optimization and decision analysis problems in the field of health care operations management

An analytical comparison of the patient-to-doctor policy and the doctor-to-patient policy in the outpatient clinic

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room, while patients visit for consultation, we call this the Patient-to-Doctor policy. A different approach is the Doctor-to-Patient policy, whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We compare the two policies via a queueing theoretic and a discrete-event simulation approach. We analytically show that the Doctor-to-Patient policy is superior to the Patient-to-Doctor policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. Simulation results indicate that the same applies when the average travel time is lower than the average preparation time. In addition, to calculate the required number of consultation rooms in the Doctor-to-Patient policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation.We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Providing Language Services in Small Health Care Provider Settings: Examples From the Field

Assesses recent innovations in language service programs and activities at healthcare provider settings with ten or fewer clinicians. Includes an eight-step plan to help providers develop a strategy to meet the needs of their patients

Job Satisfaction Among Staff Nurses in Mental Health Units in a VA facility

Studies have indicated that work environment in mental health is stressful, however, few studies have focused on staff working in acute mental health settings (Jenkins & Elliott, 2004). The purpose of this study was to describe job satisfaction among a sample of mental health staff nurses who were caring for patients with acute psychiatric disorders in a federal hospital. The second purpose was to determine if there were relationships between global job satisfaction and ethnicity, years in the organization, current unit, field of nursing, working with patients with mental disorders and age of staff nurses. An anonymous survey was distributed to a convenience sample of 69 registered nurses who worked on the four mental health units using the McCloskey/Mueller Satisfaction Scale (MMSS). The scale is a 31-item questionnaire that identifies eight types of satisfaction. Thirty two responses were received out of 69 surveys distributed, a response rate of 46%. The findings revealed that mental health staff nurses were neither satisfied nor dissatisfied with the current jobs (mean score 3.4). Nurses were most happy about flexibility in work schedules and were most unhappy with balance and work. The demographic findings indicated that over 70% of the nurses were concerned about their personal safety while on duty. A Pearson correlations test revealed that there is no significant relationship between global job satisfaction and the seven variables mentioned. A chi-square test found no correlation between ethnicity and global job satisfaction. The study used a small, convenience non random sample, therefore findings cannot be generalized to all nurses at the VA or general nursing population. To determine the levels of nurses\u27 job satisfaction with a larger random sample, a repeat study is recommended to include mental health nurses in different facilities in California and other states. This research may guide future research in examining job satisfaction as a measure to the delivery of quality patient care and patient outcomes

Operating theatre modelling: integrating social measures

Hospital resource modelling literature is primarily focussed on productivity and efficiency measures. In this paper, our focus is on the alignment of the most valuable revenue factor, the operating room (OR) with the most valuable cost factor, the staff. When aligning these economic and social decisions, respectively, into one sustainable model, simulation results justify the integration of these factors. This research shows that integrating staff decisions and OR decisions results in better solutions for both entities. A discrete event simulation approach is used as a performance test to evaluate an integrated and an iterative model. Experimental analysis show how our integrated approach can benefit the alignment of the planning of the human resources as well as the planning of the capacity of the OR based on both economic related metrics (lead time, overtime, number of patients rejected) and social related metrics (personnel preferences, aversions, roster quality)