Introduction: Bed boarding of admitted patients in the Emergency Department (ED) is widely recognized as a major contributor to overcrowding, particularly in very high-volume hospitals. The issue of bed boarding is directly tied to hospital-wide capacity, flow and operations. Early morning discharge from inpatient units has been identified as a low-cost intervention to decrease bed boarding, as it allows earlier transfer of admitted patients from the ED. Several hospitals have instituted discharge before noon, or discharge before 10AM policies, practices and targets. Our objectives were 1) to assess the current status of flow within 3 high-volume community hospitals with respect to time of day of discharges from the in-patient units and time of day of transfers from the ED to in-patient units, and 2) to assess the association between time of transfer from the ED and total ED Length of Stay (EDLOS) of admitted patients. Methods: We conducted a retrospective multi-centre observational study during the period of January 1, 2015 to December 31, 2015 at three high-volume community hospitals within Ontario, Canada. All patients admitted to the Medicine service were identified. Time of discharge from the in-patient units and time of transfer from the ED were collected for all patients. EDLOS was calculated for all patients as a function of time of transfer from the ED. Results: Preliminary findings show that, for the three community hospitals, only 11.7% - 19.6% of admitted patients were discharged from the in-patient units during the period between 6AM and 12PM, with a peak discharge time of 2PM in all three hospitals. A concurrent lag was observed in the time of transfer of patients from the ED, with peak transfer times occurring the late afternoon between 3PM and 9PM, and coinciding with a peak in patient volume in the ED. Patients transferred out of the ED earlier in the day (between 12AM 11:59AM) had between 1.4 hours to 8.0 hours lower mean EDLOS when compared to those patients transferred later in the day (between 12PM 11:59PM). Conclusion: Hospital-wide flow and operational issues have a significant impact on ED bed boarding, and potential efficiencies seem at the current time to be underutilized. Interventions aimed at optimizing flow must be implemented alongside those aimed at increasing capacity in order to improve bed boarding. ** These findings are best communicated in graphic form to better represent the dynamics of the flow in and out of the ED over a 24-hour period, and will be presented in graphic format if selected for the conference.

Introduction: Bed boarding of admitted patients in the Emergency Department (ED) is one of the major contributors to ED overcrowding, and an indicator of hospital-wide deficiencies in capacity and flow. Most indicators of ED overcrowding have measured either counts or percentages of patient subgroups (e.g. number/percentage of patients waiting in triage or number/percentage of admitted patients as compared to full ED census), or specific process time intervals related to patient movement through the hospital (e.g. Physician to Initial Assessment (PIA) time or total ED Length of Stay (EDLOS)). We sought to 1) devise an alternative measure of ED overcrowding that captured the dynamic and disproportionate resource utilization of admitted versus non-admitted patients in the ED, and to 2) determine the association of this measure with selected ED quality metrics for non-admitted patients. Methods: We conducted a retrospective multi-centre observational study at three very high-volume community hospitals in the Greater Toronto Area. Data on all patients visiting the ED during the period between January 1, 2015 and December 31, 2016 were included in the study. We calculated the total daily cumulative boarding time - or time to bed (TTB) - for each day of the study duration. The daily cumulative TTB was calculated as the time from decision to admit to transfer from the ED for all admitted patients within a 24-hour period. We conducted linear regression analysis to determine the association between our measured daily cumulative TTB and daily median and 90th percentile PIA and EDLOS times for non-admitted patients. Results: Preliminary results for 2015 indicate a total cumulative TTB time ranging from 50,973 hours to 191,093 patient-hours for the year, with daily mean cumulative TTB ranging from 140 524 patient-hours/day among the three hospitals. In all three hospitals, there was a statistically significant (p<0.01) positive association between daily cumulative TTB and both median and 90th percentile PIA times for all patients, and median EDLOS times for non-admitted CTAS 1 -3 patients. There was a statistically significant (p<0.05) positive association between daily cumulative TTB and 90th percentile EDLOS for non-admitted CTAS 1-3 patients in two of the three hospitals, with the third hospital showing a positive but non-significant association. Conclusion: Bed boarding constitutes a significant resource cost for EDs, and has a negative impact on timeliness of ED care for the general ED population, particularly more complex (CTAS 1-3) non-admitted patients.

Introduction: When countermeasures are taken against an avian influenza (AI) pandemic in a hospital, it is essential to know the potential number of staff who would choose to be absent. The purpose of this study was to clarify how many medical staff would be willing to work during a pandemic, and requirements to secure adequate human resources.

Methods: From September to December 2008, a total of 3,152 questionnaires were sent to five private hospitals and one public hospital, which represent the core hospitals in the regions of Kyoto, Osaka, and Hyogo Prefectures. Participants consisted of hospital staff including: (1) physicians; (2) nurses; (3) pharmacists; (4) radiological technologists (RTs); (5) physical therapists (PTs); (6) occupational therapists (OTs); (7) clinical laboratory technologists (CLTs); (8) caregivers; (9) office clerks; and (10) others. They were queried about their attitude toward pandemics, including whether they would come to the hospital to work, treat patients, and what kinds of conditions they required in order to work.

Results: A total of 1,975 persons (62.7%) responded. A total of 204 persons (10.6%) would not come to the hospitals during a pandemic, 363 (18.8%) would perform their duties as usual, unconditionally, 504 (26.1%) would come to hospitals but not treat AI patients, and 857 (44.5%) would report to the hospital and treat AI patients with some essential conditions. These essential conditions were: (1) personal protective equipment (PPE) (80.0%); (2) receipt of workmen's compensation (69.3%); (3) receipt of anti-virus medication (58.2%); and (3) receipt of pre-pandemic vaccination (57.8%).

Conclusion: During a pandemic, all types of health professionals would be lacking, not only physicians and nurses. This study indicates that ensuring sufficient medical human resources would be difficult without the provision of adequate safety and compensation measures.

Objectives: Health technology assessment (HTA) programs influence practice on a broad scale through reimbursement decisions or national guidelines. Hospital-based HTA programs inform clinical decisions at the local level. Typically, they do this by adapting general HTA to their local setting, or by creating new HTA. However, unlike payer-based HTA organizations, hospital-based HTA organizations can also integrate local data into their reports.

Methods: We describe two examples of local data integrated into hospital-based HTA. In the first, qualitative data were used to select a new cardiac catheterization lab. In the second, quantitative data was used to inform a decision on whether to continue telemedicine services to critical care units. Local evidence sources included equipment service records, and interviews with physicians, technicians, and administrative staff in the first example, and the hospital's administrative and claims databases in the second example.

Results: In each case, there was little evidence from the peer-reviewed literature that could be applied to the decision. In the first example, staffing patterns and local preferences had considerable bearing on technology choices. In the second example, local outcomes data from administrative records were decisive.

Conclusions: Hospital-based HTA using local data can fill gaps in the published evidence, and also improve the generalizability of evidence to the local setting. To take advantage of local evidence, health systems should encourage the development of hospital-based HTA centers, seek out local preference data, and maintain databases of patient outcomes and utilization of services.