Using quality improvement methodology and tools to reduce patient wait time in a paediatric subspecialty rheumatology clinic

Our paediatric rheumatology clinic has experienced inefficient patient flow. Our aim was to reduce mean wait time and minimise variation for patients. Baseline data showed that most waiting occurs after a patient has been roomed, while waiting for the physician. Wait time was not associated with a patient’s age, time of day, day of the week or individual physician. We implemented a checkout sheet and staggered start times. After a series of plan–do–study–act cycles, we observed an initial 26% reduction in the variation of wait time and a final 17% reduction in the mean wait time. There was no impact on patient–physician contact time. Overall, we demonstrate how process improvement methodology and tools were used to reduce patient wait time in our clinic, adding to the body of literature on process improvement in an ambulatory setting.

Improving Outpatient Infusion Clinic Wait Times at a Comprehensive Cancer Center

PURPOSE: Many factors contribute to long wait times for patients on the day of their chemotherapy infusion appointments. Longer wait time leads to nonoptimal care, increased costs, and decreased patient satisfaction. We conducted a quality improvement project to reduce the infusion wait times at a Comprehensive Cancer Center. METHODS: A multidisciplinary working group of physicians, infusion center nurses, pharmacists, information technology analysts, the Chief Medical Officer, and patient advocates formed a working group. Wait times were analyzed, and the contributing factors to long wait time were identified. Plan-Do-Study-Act cycles were implemented and included labeling patients ready to treat earlier, loading premedications into the medication dispensing system, increasing the number of pharmacy staff, and improving communication using a secure messaging system. The outcome measure was time from patient appointment to initiation of first drug at the infusion center. The secondary outcome measure was patient wait time satisfaction on the basis of Press Ganey score. RESULTS: Postintervention, the mean time from appointment to initiation of first drug decreased 17.6 minutes ( P < .001; 95% CI, 16.3 to 18.9), from 58.1 minutes to 40.5 minutes (43.5% decrease). Patient wait time satisfaction score increased 8.9 points ( P < .001; 95% CI, 6.0 to 11.82), from 76.2 to 85.1 (11.7% increase). CONCLUSION: Exploring real-time data and using a classic quality improvement methodology allowed a Comprehensive Cancer Center to identify deficiencies and prevent delays in chemotherapy initiation. This significantly improved patient wait time and patient satisfaction.

Robotic Pharmacy Implementation and Outcomes in Saudi Arabia. A Twenty-One Month Review. (Preprint)

BACKGROUND We describe the introduction, use and evaluation of an automation and integration pharmacy development program in a private facility in Saudi Arabia. The project was undertaken to meet specific challenges of increasing throughput, reducing medication dispensing error, increasing patient satisfaction, and freeing up pharmacists’ time for increased face-to-face consultations with patients. OBJECTIVE To reduce outpatient waiting times for dispensing of medications, to help to free up time to meet patient expectations for pharmacy services including medication education, to reduce the volume of non-value-added pharmacist tasks, to reduce dispensing error rates, and to aid with the rapid development of a reputation in the served community for patient-centred care for a new facility. METHODS Pre-implementation data for patient wait-time for dispensing of prescribed medications as one measure of patient satisfaction, pharmacist activity and productivity in terms of patient interaction time were gathered. Reported and discovered dispensing errors per 1,000 prescriptions were also aggregated. All pre-implementation data was gathered over an eleven- month period. Initial project goals were set as a 50% reduction in the average patient wait-time, a 15% increase in patient satisfaction regarding pharmacy waiting time and pharmacy services, a 25% increase in pharmacist productivity and zero dispensing errors. This was expected to be achieved within ten months of go-live. RESULTS From go-live, data was gathered on the above metrics in one-month increments. At the 10-month point there had been a 53% reduction in the average waiting time, a 20% increase in patient satisfaction regarding pharmacy waiting time, with a 22% increase in overall patient satisfaction regarding pharmacy services, and a 33% increase in pharmacist productivity. There was a zero-rate dispensing error reported. CONCLUSIONS The robotic pharmacy solution studied was highly effective, but upstream supply chain is vital to throughput maintenance, particularly when automated filling is planned. The automation solution must also be seamlessly and completely integrated into the facility’s software systems for appointments, medication records and prescription in order to garner its full benefits. Patient overall satisfaction with pharmacy services is strongly influenced by waiting time and follow up studies ae required to identify how to use this positive effect and how to optimally use ‘freed-up’ pharmacist time. The extra time spent with patients by pharmacists, and the complete overview of the patient’s medication history, that full integration gives, creates opportunities for tackling challenging issues such as medication nonadherence. Reduced waiting times may also allow for smaller prescription fill volumes, and more frequent outpatient department visits, allowing increased contact time with pharmacists.

Behind the clock: elucidating factors contributing to longer clinic appointment duration and patient wait time

Background Longer wait time in ambulatory clinics can disrupt schedules and decrease satisfaction. We investigated factors associated with patient wait time (WT, check-in to examination room placement), approximate clinician time (ACT, completion of nurse assessment to check-out), and total appointment length (TAL, check-in to check-out). Methods A single-institution retrospective study was conducted of breast surgery clinic patients, 2017–2019, using actual encounter times. A before/after analysis compared a five-day 8 hour/day (from a four-day 10 hour/day) advanced practice provider (APP) work-week. Non-parametric tests were used, and medians with interquartile ranges (IQRs) reported. Results 15,265 encounters were identified. Overall WT was 15.0 minutes (IQR:6.0–32.0), ACT 49.0 minutes (IQR:31.0–79.0) and TAL 84.0 minutes (IQR:57.0-124.0). Trainees were associated with 30.0 minutes longer ACT (p < 0.0001); this increased time was greatest for follow-up appointments, least for new patients. Patients arriving > 5 minutes late (versus on-time) experienced shorter WT (11.0 vs. 15.0 minutes, p < 0.0001) and ACT (43.0 vs. 53.0 minutes, p < 0.0001). Busier days (higher encounter volume:APP ratios) demonstrated increased encounter times. After transitioning to a five-day APP work-week, ACT decreased. Conclusions High-volume clinics and trainee involvement prolong ambulatory encounters. Increasing APP assistance, altering work schedules, and assigning follow-up appointments to non-trainees may decrease encounter time.

A Review on the Contribution of Emergency Department Simulation Studies in Reducing Wait Time

Background: Because of the important role of hospital emergency departments (EDs) in providing urgent care, EDs face a constantly large demand that often results in long wait times. Objective: To review and analyze the existing literature in ED simulation modeling and its contribution in reducing patient wait time. Methods: A literature review was conducted on simulation modeling in EDs. Results: A total of 41 articles have met the inclusion criteria. The papers were categorized based on their motivations, modeling techniques, data collection processes, patient classification, recommendations, and implementation statuses. Real impact is seldom measured; only four papers (~10%) have reported the implementation of their recommended changes in the real world. Conclusion: The reported implementations contributed significantly to wait time reduction, but the proportion of simulation studies that are implemented is too low to conclude causality. Researchers should budget resources to implement their simulation recommendations in order to measure their impact on patient wait time.

Predictors of Wait Time in the Orthopaedic Foot and Ankle Clinic

Category: Health Sciences Research Introduction/Purpose: Wait times represent a critical component of the patient experience, and prolonged waits are correlated with decreased patient satisfaction. We hypothesized that time spent waiting for radiology is the largest contributor to total patient wait time in our orthopedic foot and ankle clinic. Methods: A prospective, observational study was conducted in the outpatient orthopaedic foot and ankle clinic at a tertiary medical center. A total of 210 new and follow-up adult patients were enrolled. Patients were tracked from arrival until checkout with multiple time points being recorded by a trained observer. The time between patient arrival and first contact with the orthopaedic surgeon was broken down into five distinct categories. Total time between patient arrival and first contact with the orthopaedic surgeon was tested for association with patient and appointment characteristics using Student’s t-test. Results: The average total time spent waiting for first contact with the orthopaedic surgeon was 57.1±30.4 minutes. The largest contributor was time spent waiting for an exam room (33.1±25.5 minutes), followed by time spent waiting for radiologic imaging (21.7±19.9 minutes), time spent waiting for resident/PA (12.2±10.9 minutes), and time spent waiting for attending surgeon after seeing resident/PA (11.7±9.3 minutes). Factors contributing to a longer overall wait included obtaining x-rays at the visit (+15.4±4.2 minutes, 95% confidence interval [CI]=+7.0 to +23.8, p<0.001) and failure to complete patient paperwork beforehand (+36.9±5.3 minutes, CI=+26.4 to +47.4, p<0.001; Table 1). In contrast, overall wait time was not associated with age≥50 years, female sex, late arrival, or outside medical records needing review. Conclusion: Time spent waiting for assignment to an exam room was the largest contributor to the time between patient arrival and first contact with the attending surgeon. In order, the other contributors were time spent waiting for radiology, time spent waiting for the resident/PA, and time spent waiting for the attending surgeon after seeing the resident/PA. Obtaining x-rays increased patient wait time and completing patient paperwork beforehand decreased patient wait time. Orthopaedic foot and ankle surgeons should work to avoid unnecessary x-rays and encourage completion of patient paperwork before arrival in order to optimize clinic flow and decrease patient wait times.

National Comprehensive Cancer Network Infusion Efficiency Workgroup Study: Optimizing Patient Flow in Infusion Centers

PURPOSE: The National Comprehensive Cancer Network (NCCN) formed an Infusion Efficiency Workgroup to determine best practices for operating efficient and effective infusion centers. METHODS: The Workgroup conducted three surveys that were distributed to NCCN member institutions regarding average patient wait time, chemotherapy premixing practices, infusion chair use, and premedication protocols. To assess chair use, the Workgroup identified and defined five components of chair time. RESULTS: The average patient wait time in infusion centers ranged from 25 to 102 minutes (n = 23; mean, 58 minutes). Five of 26 cancer centers (19%) routinely mix chemotherapy drugs before patient arrival for patients meeting specified criteria. Total planned chair time for subsequent doses of the same drug regimens for the same diseases varied greatly among centers, as follows: Administration of doxorubicin and cyclophosphamide ranged from 85 to 240 minutes (n = 22); of FOLFIRINOX (folinic acid, fluorouracil, irinotecan hydrochloride, and oxaliplation) ranged from 270 to 420 minutes (n = 22); of rituximab ranged from 120 to 350 minutes (n = 21); of paclitaxel plus carboplatin ranged from 255 to 380 minutes (n = 21); and of zoledronic acid ranged from 30 to 150 minutes (n = 22) for planned total chair time. Cancer centers were found to use different premedication regimens with varying administration routes that ranged in administration times from zero to 60 minutes. CONCLUSION: There is a high degree of variation among cancer centers in regard to planned chair time for the same chemotherapy regimens, providing opportunities for improved efficiency, increased revenue, and more standardization across centers. The NCCN Workgroup demonstrates potential revenue impact and provides recommendations for cancer centers to move toward more efficient and more standard practices.