Hospital Stockpile for Influenza Pandemic Preparedness Planning

Increasing threat of the next influenza pandemic H5N1 in wild birds & poultry Confirmed human cases: 21 (year-to-date); deaths 18 (85.7%) Past experiences suggest severe impact Surge demand Social disruption Nations With Confirmed Cases H5N1 Avian Influenza P d i Fl Economic los

Using No-Show Modeling to Improve Clinic Performance

‘No-shows’ or missed appointments result in under-utilized clinic capacity. We develop a logistic regression model using electronic medical records to estimate patients’ no-show probabilities and illustrate the use of the estimates in creating clinic schedules that maximize clinic capacity utilization while maintaining small patient waiting times and clinic overtime costs. This study used information on scheduled outpatient appointments collected over a three-year period at a Veterans Affairs medical center. The call-in process for 400 clinic days was simulated and for each day two schedules were created: the traditional method that assigned one patient per appointment slot, and the proposed method that scheduled patients according to their no-show probability to balance patient waiting, overtime and revenue. Combining patient no-show models with advanced scheduling methods would allow more patients to be seen a day while improving clinic efficiency. Clinics should consider the benefits of implementing scheduling software that includes these methods relative to the cost of no-shows

Planning for Pandemic Influenza: Lessons from the Experiences of Thirteen Indiana Counties

Significant concerns exist over the ability of the healthcare and public health systems to meet the surge demands that would result from an event such as an influenza pandemic. Current guidance for public health planners is largely based on expert opinion and may lack connection to the problems of street-level public health practice. To identify the problems of local planners and prepare a state-level planning template for increasing health care surge capacity that accounted for these issues,a study was conducted of local pandemic planning efforts in thirteen counties, finding that cognitive biases, coordination problems, institutional structures in the healthcare system, and resource shortfalls are significant barriers to preparing and implementing a surge capacity plan. In addition, local planners identify patient demand management through triage and education efforts as a viable means of ensuring adequate capacity, in contrast to guidance proposing an increased supply of care as a primary tool

Effects of clinical characteristics on successful open access scheduling

Many outpatient clinics are experimenting with open access scheduling. Under open access, patients see their physicians within a day or two of making their appointment request, and long term patient booking is very limited. The hope is that these short appointment lead times will improve patient access and reduce uncertainty in clinic operations by reducing patient no-shows. Practice shows that successful implementation can be strongly influenced by clinic characteristics, indicating that open access policies must be designed to account for local clinical conditions. The effects of four variables on clinic performance are examined: (1) the fraction of patients being served on open access, (2) the scheduling horizon for patients on longer term appointment scheduling, (3) provider care groups, and (4) overbooking. Discrete event simulation, designed experimentation, and data drawn from an intercity clinic in central Indiana are used to study the effects of these variables on clinic throughput and patient continuity of care. Results show that, if correctly configured, open access can lead to significant improvements in clinic throughput with little sacrifice in continuity of care. Copyright Springer Science+Business Media, LLC 2007Open access, Appointment scheduling, Patient no-show, Outpatient clinic, Simulation,