Analyzing supply and demand on a general internal medicine ward: a cross-sectional study.

BACKGROUND: The capacity of general internal medicine (GIM) clinical teaching units has been strained by decreasing resident supply and increasing patient demand. The objective of our study was to compare the number of residents (supply) with the volume and duration of patient care activities (demand) to identify inefficiency. METHODS: Using the most recently available data from an academic teaching hospital in Toronto, Ontario, we identified each occurrence of a set of patient care activities that took place on the clinical teaching unit from 2015 to 2019. We completed a descriptive analysis of the frequencies of these activities and how the frequencies varied by hour, day, week, month and year. Patient care activities included admissions, rounds, responding to pages, meeting with patients and their families, patient transfers, discharges and responding to cardiac arrests. The estimated time to complete each task was based on the available data in our electronic medical record system and interviews with GIM physicians and trainees. To calculate resident utilization, the person-hours of patient care tasks was divided by the person-hours of resident supply. Resident utilization was computed for 3 scenarios corresponding to varying levels of resident absenteeism. RESULTS: During the study period, there were 14 581 consultations to GIM from the emergency department. Patient volumes tended to be highest during January and lowest during May and June, and highest on Monday morning and lowest on Friday night. Daily admissions to hospital from the emergency department were higher on weekdays than on weekends, and hourly admissions peaked at 8 am and between 3 pm and 1 am. Weekday resident utilization was generally highest between 8 am and 2 pm, and lowest between 1 am and 8 am. In a scenario in which all residents were present apart from those who were post-call, resident utilization generally never exceeded 100%; in scenarios in which at least 1 resident was absent owing to illness or vacation, it was common for resident utilization to approach or exceed 100%, particularly during daytime working hours. INTERPRETATION: Analyzing supply and demand on a GIM ward has allowed us to identify periods when supply and demand are not aligned and to demonstrate empirically the vulnerability of current staffing models. These data have the potential to inform and optimize scheduling on an internal medicine ward.

Nursing resource team capacity planning using forecasting and optimization methods: A case study.

AIM: To estimate the cost-minimizing size and skill mix of a nursing resource team (NRT). BACKGROUND: Nurse absences can be filled by an NRT at lower hourly cost than staffing agencies or nurses working overtime, but an NRT must be appropriately sized to minimize total cost. METHODS: Using all registered nurse (RN) absences at an academic teaching hospital from 1 October 2014 to 31 March 2018, we developed a generalized additive model (GAM) to forecast the weekly frequency of each of ten types of absence over 52 weeks. We used the forecasts in an optimization model to determine the cost-minimizing NRT composition. RESULTS: The median weekly frequencies for the ten absence types ranged between 12 and 65.5. The root mean squared errors of the GAMs ranged between 4.55 and 9.07 on test data. The NRT dimensioned by the optimization model yields an estimated annual cost reduction of $277,683 (Canadian dollars) (7%). CONCLUSIONS: The frequency of RN absences in a hospital can be forecasted with high accuracy, and the use of forecasting and optimization to dimension an NRT can substantially reduce the cost of filling RN absences. IMPLICATIONS FOR NURSING MANAGEMENT: This methodology can be adapted by any hospital to optimize nurse staffing.

Strategic analytics: towards fully embedding evidence in healthcare decision-making.

Cancer Care Ontario (CCO) has implemented multiple information technology solutions and collected health-system data to support its programs. There is now an opportunity to leverage these data and perform advanced end-to-end analytics that inform decisions around improving health-system performance. In 2014, CCO engaged in an extensive assessment of its current data capacity and capability, with the intent to drive increased use of data for evidence-based decision-making. The breadth and volume of data at CCO uniquely places the organization to contribute to not only system-wide operational reporting, but more advanced modelling of current and future state system management and planning. In 2012, CCO established a strategic analytics practice to assist the agency's programs contextualize and inform key business decisions and to provide support through innovative predictive analytics solutions. This paper describes the organizational structure, services and supporting operations that have enabled progress to date, and discusses the next steps towards the vision of embedding evidence fully into healthcare decision-making.