Implementing an Integrated Generalist-Led Inpatient Care Model: Results of a Mixed-Method Evaluation.

Introduction: Healthcare integration has become prevalent as health systems manage a growing population of older adults with multi-morbid conditions. The integrated general hospital (IGH) is the latest example of how services can be remodelled to achieve greater care integration. Methods: We conducted a mixed-method evaluation to identify factors impacting the implementation and effectiveness of the IGH model. Data were collected through in-depth interviews (n = 15) and focus group discussions (n = 8 groups) with hospital staff, and a staff survey (n = 226). Results: Staff perceived improvements in clinical practice and better clinical outcomes for patients. The care model empowered nursing and allied health staff through a more collegial team structure. However, staff reported an unequal workload distribution; a third reported burnout; and some observed inconsistencies between leaders' aspirations for IGH and what was happening on the ground. For IGH to sustain, staff's education on the IGH model needs to be improved. Further examination of work processes is recommended to boost staff morale and prevent burnout. Conclusion: Overall, IGH provided better integrated, team-based care. The model challenged traditional team structures and empowered staff to expand their roles and responsibilities. Policymakers could consider the IGH model a successful approach for integrating services across the care continuum.

Modeling Emergency Department crowding: Restoring the balance between demand for and supply of emergency medicine.

Emergency Departments (EDs) worldwide are confronted with rising patient volumes causing significant strains on both Emergency Medicine and entire healthcare systems. Consequently, many EDs are in a situation where the number of patients in the ED is temporarily beyond the capacity for which the ED is designed and resourced to manage-a phenomenon called Emergency Department (ED) crowding. ED crowding can impair the quality of care delivered to patients and lead to longer patient waiting times for ED doctor's consult (time to provider) and admission to the hospital ward. In Singapore, total ED attendance at public hospitals has grown significantly, that is, roughly 5.57% per year between 2005 and 2016 and, therefore, emergency physicians have to cope with patient volumes above the safe workload. The purpose of this study is to create a virtual ED that closely maps the processes of a hospital-based ED in Singapore using system dynamics, that is, a computer simulation method, in order to visualize, simulate, and improve patient flows within the ED. Based on the simulation model (virtual ED), we analyze four policies: (i) co-location of primary care services within the ED, (ii) increase in the capacity of doctors, (iii) a more efficient patient transfer to inpatient hospital wards, and (iv) a combination of policies (i) to (iii). Among the tested policies, the co-location of primary care services has the largest impact on patients' average length of stay (ALOS) in the ED. This implies that decanting non-emergency lower acuity patients from the ED to an adjacent primary care clinic significantly relieves the burden on ED operations. Generally, in Singapore, there is a tendency to strengthen primary care and to educate patients to see their general practitioners first in case of non-life threatening, acute illness.

Improving the wait time to admission by reducing bed rejections.

Congestion at the emergency department (ED) is associated with increased wait times, morbidity and mortality. We have identified prolonged wait time to admission as a significant contributor to ED congestion. One of the main contributors to prolonged wait time to admission was due to rejections by ward staff for the beds allocated to newly admitted patients by the Bed Management Unit (BMU). We have identified this as a systemic issue and through this quality improvement effort, seek to reduce the incidence of bed rejections for all admitted patients by 50% from 9% to 4.5% within 6 months. We used PDSA (Plan, Do, Study, Act) cycles to implement a series of interventions, such as updating legacy categorisation of wards, instituting a 'no rejects' policy and performing ward level audits. Compared with baseline, there was reduction in rejected BMU allocation requests from 9% to 5% (p<0.01). The monthly percentage of patients with at least one rejection dropped from an average of 7% to 4% (p<0.01). With reduction in the number of rejections, the average wait time to the final request acknowledged by the ward for all admission sources decreased from 2 hours 19 min to 1 hour (p<0.01), thereby allowing the overall wait time to admission to decrease by 68 min, from 5 hours 13 min to 4 hours 5 min. Improvements in the absolute duration and variance of wait times were sustained. Although the team's initial impetus was to improve ED wait times, this hospital-wide effort improved wait times across all admission sources. There has been a resultant increase in ownership of the admissions process by both nursing and BMU staff. With the conclusion of this effort, we are looking to further reduce the wait time to admission by optimising the current bed allocation logic through another quality improvement effort.

Improving the wait time to consultation at the emergency department.

Prolonged wait times at the emergency department (ED) are associated with increased morbidity and mortality, and decreased patient satisfaction. Reducing wait times at the ED is challenging. The objective of this study is to determine if the implementation of a series of interventions would help decrease the wait time to consultation (WTC) for patients at the ED within 6 months. Interventions include creation of a common board detailing work output, matching manpower to patient arrivals and adopting a team-based model of care. A retrospective analysis of the period from January 2015 to May 2016 was undertaken to define baseline duration for WTC. Rapid PDSA (Plan, Do, Study, Act) cycles were used to implement a series of interventions, and changes in wait time were tracked, with concurrent patient load, rostered manpower and number of admissions from ED. Results of the interventions were tracked from 1 October 2016 to 30 April 2017. There was improvement in WTC within 6 months of initiation of interventions. The improvements demonstrated appeared consistent and sustained. The average 95th centile WTC decreased by 38 min to 124 min, from the baseline duration of 162 min. The median WTC improved to 21 min, compared with a baseline timing of 24 min. The improvements occurred despite greater patient load of 4317 patients per month, compared with baseline monthly average of 4053 patients. There was no increase in admissions from ED and no change in the amount of ED manpower over the same period. We demonstrate how implementation of low-cost interventions, enabling transparency, equitable workload and use of a team-based care model can help to bring down wait times for patients. Quality improvement efforts were sustained by employing a data-driven approach, support from senior clinicians and providing constant feedback on outcomes.