Improving the wait time to triage at the emergency department

Triaging of patients at the emergency department (ED) is one of the key steps prior to initiation of doctor consult. To improve the overall wait time to consultation, we have identified the need to reduce the wait time to triage for ED patients. We seek to determine if the implementation of a series of plan, do, study, act (PDSA) cycles would improve the wait time to triage within 1 year. The interventions related to the PDSA cycles include the refining of triage criteria, ‘eyeball’ triage by senior nurses to facilitate direct bedding of patients, formation of a triage nurse clinician role, and a needs analysis of required nursing manpower. The baseline period for this study was from January 2017 to April 2017, with the results following implementation of the respective PDSA cycles sequentially tracked from May 2017 to March 2019. There was an improvement in the wait time to triage from a baseline duration of 18 min to the postimplementation period duration of 13 min, with a 25% decrease in variance from 16 to 12 min. The improvements were sustained. Strategies to further reduce wait time to triage at the ED are discussed. We also highlight the importance of adequate triage manpower, data-driven decision making and continued engagement of stakeholders in enabling positive outcomes from this quality improvement effort.

Reducing overcrowding in an emergency department: a pilot study

SUMMARY OBJECTIVE Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times. RESULTS Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients’ wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients. CONCLUSION Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs’ can be used to generate and test solutions to decrease overcrowding.

Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems that handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user’s task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential, and 3) learn online from the human agent’s responses to reduce human agents’ load further. We evaluate our proposed method on a modified-bAbI dialog task, 1 which simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.

Chemotherapy wait times in a network of pediatric oncology clinics.

127 Background: Optimizing care delivery is a satisfier for patients and providers alike. Inadequate clinic flow may also drive up costs, as staff are more likely to utilize overtime hours. We noted in our network of outpatient pediatric oncology clinics that the lowest scores in patient satisfaction surveys were the category of waiting time in the chemotherapy area. We aimed to reduce wait time in the chemotherapy area for patients receiving outpatient, lab-dependent, intravenous push chemotherapy by 5% within 9 months. Methods: A team consisting of a nurse team leader and core members (physician, nurse and pharmacist) from affiliate clinics in 3 states (AL, MO, OK) obtained baseline data over 2 weeks. Data included 1) patient arrival time, 2) lab collection time, 3) lab result time, 4) chemotherapy order time, 5) chemotherapy delivery time to clinic, 6) chemotherapy administration time. Each clinic created their individual process map and cause/effect diagram. Additional measures collected were patient satisfaction scores, parent and staff surveys before and after the intervention. Each clinic site met weekly and the network of the 3 clinics met monthly to review all results. Using the baseline data, each clinic identified points in care where interventions could reduce chemotherapy wait time based on reviewing their own and other clinics’ data. Interventions included moving lab collection earlier in the visit, additional pharmacy staff to deliver chemotherapy and placing an electronic monitor to alert providers when lab resulted. Results: Within 4 months of the interventions all sites had a reduction in chemotherapy wait times (Site A 144m-pre, 134m-post; Site B 163m-pre, 140m-post; Site C 137m-pre, 116m-post). Parent and staff surveys are in process. Conclusions: Each clinic was able to reduce chemotherapy times using different interventions depending on their internal process, moreover each clinic learned how to improve from each other’s processes.

Propellant Slosh Force and Mass Measurement

We have used electrical capacitance tomography (ECT) to instrument a demonstration tank containing kerosene and have successfully demonstrated that ECT can, in real time, (i) measure propellant mass to better than 1% of total in a range of gravity fields, (ii) image propellant distribution, and (iii) accurately track propellant centre of mass (CoM). We have shown that the ability to track CoM enables the determination of slosh forces, and we argue that this will result in disruptive changes in a propellant tank design and use in a spacecraft. Ground testing together with real-time slosh force data will allow an improved tank design to minimize and mitigate slosh forces, while at the same time keeping the tank mass to a minimum. Fully instrumented Smart Tanks will be able to provide force vector inputs to a spacecraft inertial navigation system; this in turn will (i) eliminate or reduce navigational errors, (ii) reduce wait time for uncertain slosh settling, since actual slosh forces will be known, and (iii) simplify slosh control hardware, hence reducing overall mass. ECT may be well suited to space borne liquid measurement applications. Measurements are independent of and unaffected by orientation or levels of g. The electronics and sensor arrays can be low in mass, and critically, the technique does not dissipate heat into the propellant, which makes it intrinsically safe and suitable for cryogenic liquids. Because of the limitations of operating in earth-bound gravity, it has not been possible to check the exact numerical accuracy of the slosh force acting on the vessel. We are therefore in the process of undertaking a further project to (i) build a prototype integrated “Smart Tank for Space”, (ii) undertake slosh tests in zero or microgravity, (iii) develop the system for commercial ground testing, and (iv) qualify ECT for use in space.

Minimizing wait time between chemotherapy and radiotherapy treatments.

82 Background: Members of the scheduling teams at the Abramson Cancer Center observed prolonged delays between chemotherapy and radiation therapy treatments scheduled by staff from 2 independent departments leading to inconvenience for patients receiving concurrent chemo- and radiation therapy (CRpts). Methods: An analysis of baseline data over 6 weeks revealed that for 157 unique consecutive patients undergoing daily chemotherapy and radiation (a total of 353 encounters), the mean time between scheduled treatments was 122 minutes. For 39% of encounters the wait time was greater than 120 minutes. To improve the adjacency of chemotherapy and radiation appointments and to consistently reduce wait time between treatments to less than 120 minutes, we formed a Chemotherapy/Radiation Scheduling Task Force consisting of patient service representatives, practice managers, and physician and nurse advisors. We determined that CRpts should be scheduled using a “huddle” strategy whereby prospectively identified CRpts are simultaneously scheduled for both treatments in a coordinated manner. Identifying CRpts for coordinated scheduling was facilitated by the creation of a chemo-radiation scheduling inbox to which clinicians and support staff e-mail names of new CRpts in order to alert the scheduling team. Our two lead schedulers meet 2-3 times per week to coordinate patient schedules. A weekly scorecard of the wait times for CRpts patients is distributed via e-mail to the clinicians and support staff. Results: Over the past 6 months, we have used the huddle method for 80% of 986 consecutive CRpt encounters. Our average wait time for huddle-scheduled encounters has been reduced to 62.5 minutes with only 9% of encounters having wait times over 120 minutes. For non-huddle-scheduled encounters, the average wait time is 129 minutes with 57% having wait times over 120 minutes. Conclusions: Utilization of a huddle scheduling method has successfully reduced wait time for CRpts. Use of the huddle method continues to grow with staff training and awareness of the new process.