The Impact of COVID-19 on the Surgical Wait Times for Plastic and Reconstructive Surgery in Ontario

2021 ◽  
pp. 229255032110643
Author(s):  
Moaath M. Saggaf ◽  
Dimitri J. Anastakis
Keyword(s):  
Reconstructive Surgery ◽  
Wait Time ◽  
Baseline Period ◽  
Wait Times ◽  
Surgical Volume ◽  
Time Data ◽  
Plastic And Reconstructive Surgery ◽  
Percent Change ◽  
The Mean ◽  
The Impact

Purpose: The aim of this study was to assess the impact of COVID-19 on surgical wait times for Plastic and Reconstructive Surgery (PRS) in Ontario, Canada. Methods: Ontario's wait time data has fourteen reporting categories for PRS. For each category, the mean wait time for consultation and for surgery were reported. Each category was given a priority ranging from 1 to 4. Two periods, three-month and six-month, were selected and compared to the same calendar months of the previous year. Wait times, surgical volume and percent change to the provincial wait time target were reported and compared to the baseline data. Results: This study reviewed 9563 consults and 15,000 operative cases. There was a 50% reduction in the volume of surgical consults during the study period compared to the baseline period (P = 0.004). The reduction ranged from 46% to 75% based on the reporting category. The volume of surgical cases decreased by 43% during the study period compared to the baseline period (P = 0.005). A statistically significant increase in the mean wait times for surgery was observed, involving priorities 2 to 4 (overall mean = 32 days, P ≤ 0.01). There was a 15% decrease in the percentage of surgeries meeting the provincial target times (P < 0.0001). Conclusion: COVID-19 has caused a significant reduction in the volume of cases performed in the majority of PRS categories with an overall increase in the wait times for consultation and for surgery. Recovery following COVID-19 will require strategies to address the growing volume of cases and wait times for surgery across all PRS categories.

scholarly journals Effects of COVID-19 on Facial Plastic and Reconstructive Surgery Fellowship Training and Director Practices

OTO Open ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 2473974X2110141
Author(s):  
Parsa P. Salehi ◽  
Sina J. Torabi ◽  
Yan Ho Lee ◽  
Babak Azizzadeh
Keyword(s):  
Facial Nerve ◽  
Reconstructive Surgery ◽  
Trauma Surgery ◽  
The United States ◽  
Fellowship Training ◽  
Craniofacial Surgery ◽  
Worker Safety ◽  
Plastic And Reconstructive Surgery ◽  
Facial Plastic ◽  
The Impact

Objectives The objectives of this study include characterizing the practice patterns and testing strategies of facial plastic and reconstructive surgery (FPRS) fellowship directors (FDs) secondary to COVID-19 and to quantify the impact of COVID-19 on FPRS fellowship training. Study Design Cross-sectional survey. Setting Online. Methods A survey was sent to all American Academy of Facial Plastic and Reconstructive Surgery FDs and co-FDs in September 2020. Descriptive analyses were performed. Results Of 77 eligible FDs, 45 responded (58.4%) representing a diverse group across the United States. All but 1 FD routinely screened patients for COVID-19 in the preoperative setting. FDs largely believed that universal preoperative testing was cost-effective (66.7%), improved patient safety (80.0%) and health care worker safety (95.6%), and was not burdensome for patients (53.3%). With regard to volume of cosmetic/aesthetic, reconstructive, facial nerve, and trauma surgery, FDs indicated largely no change in volume (34.9%, 71.0%, 68.4%, and 80.0%, respectively) or fellow experience (67.4%, 80.6%, 84.2%, and 80.0%). Half (50.0%) of the FDs reported decreased volume of congenital/craniofacial surgery, but 75.0% did not believe that there was a change in fellow experience. Overall, of the 15 responses indicating “worsened training” across all domains of FPRS, 14 were located in the Northeast (93.33%). Conclusions The COVID-19 pandemic has had the least impact on the volume of reconstructive procedures, facial nerve operations, and trauma surgery and a negative impact on congenital/craniofacial surgery volume, and it has accelerated the demand for cosmetic/aesthetic operations. Overall, the majority of FDs did not feel as though their fellows’ trainings would be adversely affected by the ongoing pandemic.

scholarly journals The impact of seasonal operating room closures on wait times for oral cancer surgery

2018 ◽  
Vol 25 (1) ◽  
pp. 67 ◽  
Author(s):  
N. Mundi ◽  
J. Theurer ◽  
A. Warner ◽  
J. Yoo ◽  
K. Fung ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Cancer Patients ◽  
Operating Room ◽  
Oral Cavity Cancer ◽  
Tertiary Care ◽  
Wait Time ◽  
Disease Recurrence ◽  
Health Science ◽  
Wait Times ◽  
The Impact

Background Operating room slowdowns occur at specific intervals in the year as a cost-saving measure. We aim to investigate the impact of these slowdowns on the care of oral cavity cancer patients at a Canadian tertiary care centre.Methods A total of 585 oral cavity cancer patients seen between 1999 and 2015 at the London Health Science Centre (lhsc) Head and Neck Multidisciplinary Clinic were included in this study. Operating room hours and patient load from 2006 to 2014 were calculated. Our primary endpoint was the wait time from consultation to definitive surgery. Exposure variables were defined according to wait time intervals occurring during time periods with reduced operating room hours.Results Overall case volume rose significantly from 2006 to 2014 (p < 0.001), while operating room hours remained stable (p = 0.555). Patient wait times for surgery increased from 16.3 days prior to 2003 to 25.5 days in 2015 (p = 0.008). Significant variability in operating room hours was observed by month, with lowest reported for July and August (p = 0.002). The greater the exposure to these months, the more likely patients were to wait longer than 28 days for surgery (odds ratio per day [or]: 1.07, 95% confidence interval [ci]: 1.05 to 1.10, p < 0.001). Individuals seen in consultation preceding a month with below average operating room hours had a higher risk of disease recurrence and/or death (hazard ratio [hr]: 1.59, 95% ci: 1.10 to 2.30, p = 0.014).Conclusions Scheduled reductions in available operating room hours contribute to prolonged wait times and higher disease recurrence. Further work is needed to identify strategies maximizing efficient use of health care resources without negatively affecting patient outcomes.

scholarly journals Improving patient flow and timeliness in the diagnosis and management of breast abnormalities: the impact of a rapid diagnostic unit

2016 ◽  
Vol 23 (3) ◽  
pp. 260 ◽  
Author(s):  
J.M. Racz ◽  
C.M.B. Holloway ◽  
W. Huang ◽  
N.J. Look Hong
Keyword(s):  
Care Delivery ◽  
Patient Flow ◽  
Wait Time ◽  
Definitive Treatment ◽  
Wait Times ◽  
Fisher Exact Test ◽  
Patient Anxiety ◽  
Diagnostic Unit ◽  
Clinical Investigations ◽  
The Impact

Background Efforts to streamline the diagnosis and treatment of breast abnormalities are necessary to limit patient anxiety and expedite care. In the present study, we examined the effect of a rapid diagnostic unit (RDU) on wait times to clinical investigations and definitive treatment.Methods A retrospective before–after series, each considering a 1-year period, examined consecutive patients with suspicious breast lesions before and after initiation of the RDU. Patient consultations, clinical investigations, and lesion characteristics were captured from time of patient referral to initiation of definitive treatment. Outcomes included time (days) to clinical investigations, to delivery of diagnosis, and to management. Groups were compared using the Fisher exact test or Student t-test.Results The non-RDU group included 287 patients with 164 invasive breast carcinomas. The RDU group included 260 patients with 154 invasive carcinomas. The RDU patients had more single visits for biopsy (92% RDU vs. 78% non-RDU, p < 0.0001). The RDU group also had a significantly shorter wait time from initial consultation to delivery of diagnosis (mean: 2.1 days vs. 16.7 days, p = 0.0001) and a greater chance of receiving neoadjuvant chemotherapy (37% vs. 24%, p = 0.0106). Overall time from referral to management remained statistically unchanged (mean: 53 days with the RDU vs. 50 days without the RDU, p = 0.3806).Conclusions Introduction of a RDU appears to reduce wait times to definitive diagnosis, but not to treatment initiation, suggesting that obstacles to care delivery can occur at several points along the diagnostic trajectory. Multipronged efforts to reduce system-related delays to definitive treatment are needed.

scholarly journals Dietitian first gastroenterology clinic: an initiative to reduce wait lists and wait times for gastroenterology outpatients in a tertiary hospital service

2018 ◽  
Vol 10 (3) ◽  
pp. 229-235 ◽  
Author(s):  
Rumbidzai N Mutsekwa ◽  
Russell Canavan ◽  
Anthony Whitfield ◽  
Alan Spencer ◽  
Rebecca L Angus
Keyword(s):  
Patient Flow ◽  
Wait Time ◽  
Tertiary Hospital ◽  
University Hospital ◽  
Wait Times ◽  
Activity Data ◽  
Patient Journey ◽  
Service Activity ◽  
High Level ◽  
The Impact

ObjectiveThe demand for outpatient gastroenterology medical specialist consultations is above what can be met within budgetary and staffing constraints. This study describes the establishment of a dietitian first gastroenterology clinic to address this issue, the patient journey and its impact on wait lists and wait times in a tertiary gastroenterology service.DesignA dietitian first gastroenterology clinic model was developed and a mixed-methods approach used to evaluate the impact of the service over a 21-month period.SettingGold Coast University Hospital, Queensland, Australia (a public tertiary hospital).Patients658 patients were triaged to the clinic between June 2016 and March 2018.InterventionA dietitian first gastroenterology clinic for low-risk gastroenterology patients.Main outcome measuresWe examined demographic, referral, wait list, wait time and service activity data, patient satisfaction and patient journey.ResultsAt the time of audit, 399 new (67.9% female) and 307 review patients had been seen. Wait times for eligible patients reduced from 280 to 66 days and the percentage of those in breach of their recommended wait times reduced from 95% to zero. The average time from referral to discharge was 117.8 days with an average of 2.4 occasions of service. 277 patients (69.4%) had been discharged to the care of their general practitioner and 43 patients (10.7%) had an expedited specialist medical review. Patient surveys indicated a high level of satisfaction.ConclusionA dietitian first gastroenterology model of care helps improve patient flow, reduces wait times and may be useful elsewhere to address outpatient gastroenterology service pressures.

scholarly journals Wait Times for Sleep Apnea Care in Ontario: A Multidisciplinary Assessment

2010 ◽  
Vol 17 (4) ◽  
pp. 170-174 ◽  
Author(s):  
Brian W Rotenberg ◽  
Charles F George ◽  
Kevin M Sullivan ◽  
Eric Wong
Keyword(s):  
Sleep Apnea ◽  
Wait Time ◽  
Simplified Model ◽  
Wait Times ◽  
Sleep Laboratory ◽  
Time Data ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Survey Tool ◽  
Obstructive Sleep

BACKGROUND: Obstructive sleep apnea (OSA) is a highly prevalent disorder that is associated with significant patient morbidity and societal burden. In general, wait times for health care in Ontario are believed to be lengthy; however, many diseases lack specific corroborative wait time data.OBJECTIVE: To characterize wait times for OSA care in Ontario.METHODS: Cross-sectional survey. A survey tool was designed and validated to question physicians involved in OSA care about the length of the wait times their patients experience while traversing a simplified model of OSA care. The survey was sent to all otolaryngologists and respirologists in the province, as well as to a random sample of provincial family physicians.RESULTS: Patients waited a mean of 11.6 months to initiate medical therapy (continuous positive airway pressure), and 16.2 months to initiate surgical therapy. Sleep laboratory availability appeared to be the major restriction in the patient management continuum, with each additional sleep laboratory in a community associated with a 20% decrease in overall wait times. Smaller community sizes were paradoxically associated with shorter wait times for sleep studies (P<0.01) but longer wait times for OSA surgery (P<0.05). Regression analysis yielded an r2of 0.046; less than 5% of the wait time variance could be explained by the simplified model.CONCLUSION: Patients experienced considerable wait times when undergoing management for OSA. This has implications for both individual patient care and public health in general.

Wait time reduction for patients awaiting chemotherapy infusion using a formal process improvement approach and sequential.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 92-92
Author(s):  
Andrew David Norden ◽  
Lori A. Buswell ◽  
Meg Amorati ◽  
Lois Arthur ◽  
Antoinette Bernard ◽  
...  
Keyword(s):  
Wait Time ◽  
Baseline Data ◽  
Cancer Center ◽  
Wait Times ◽  
Time Data ◽  
Statistical Process ◽  
Effect Analysis ◽  
Process Improvements ◽  
Reminder System ◽  
Exam Room

92 Background: At a community hospital satellite of an academic cancer center, baseline data indicated that 49% of patients waited longer than 30 minutes from arrival in the treatment chair until treatment was started, resulting in dissatisfaction and decreased chair turnover. Methods: A team was assembled, including physicians, nurses, pharmacists, and administrative staff. The team constructed a detailed process flow map and performed a cause-and-effect analysis. Wait time data were collected using the electronic scheduling system and time sheets. Additionally, nurses used a structured data collection sheet to categorize the reasons for prolonged wait times. A p-type statistical process control chart was constructed to track the proportion of infusion visits per day with wait times longer than 30 minutes. The team brainstormed process improvements and selected ones to implement by employing a priority/pay-off matrix. Results: Baseline data were assessed for 403 visits over a 3 week period. Of 232 visits with wait times longer than 30 minutes, 98 (42%) involved excessive waiting for the physician to see the patient or write orders. One of 4 physicians was responsible for 56 (57%) of these. This physician’s patients were seen exclusively in the infusion room, while the other physicians saw patients in the exam room before sending them to the infusion area. Three PDSA cycles were conducted: (1) All physicians started seeing patients in the exam room before sending them to infusion chairs, (2) Specific treatments were selected that could be routinely administered without the physician seeing the patient, and (3) A reminder system prompted physicians to enter treatment orders within 24 hours of each patient’s visit. After 6 months, 29% of patients waited longer than 30 minutes, down from 49% at baseline. Conclusions: These interventions implemented using PDSA cycles successfully reduced wait times. Measurement and presentation of data were critical in persuading physicians to practice in a more homogeneous fashion.

Impact of room pooling and electronic health record on patient (pt) wait time, clinic work flow, and pts’/providers’ satisfaction.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18191-e18191
Author(s):  
Saroj Vadhan-Raj ◽  
Xiao Zhou ◽  
Meyyammai Narayanan ◽  
Shawn J Janarthanan ◽  
Mary Daniel ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Work Flow ◽  
Wait Times ◽  
Health Record ◽  
Allocation Model ◽  
Exam Room ◽  
Negative Effect ◽  
Electronic Health ◽  
The Impact

e18191 Background: Excessive pt wait time can have negative effect on clinic work flow and on pts/ providers satisfaction. Increasing pt volume and limited clinic capacity can lead to long wait times for pts. The purpose of this two-part study was to evaluate the impact of Room Pooling Model (RPM) instead of Room Allocation Model (Part 1) and Electronic Health Record (EHR) on pt wait times in clinic and pts’/providers’ satisfaction (Part 2). Methods: The time studies and pts’/providers’ wait time satisfaction surveys were carried out over 2 weeks before (baseline) and 8 weeks after the implementation of RPM (Part 1), prior to the new EHR system, and 6 months after the implementation of EHR (part 2). All times of when pts, mid-level providers (MLP), and doctors (MDs) entered and exited the exam rooms were collected for 887 pts seen during the clinic. Data was analyzed using JMP and SAS. Results: As described earlier (ASCO 2016, abst 6595), the RPM was associated with increase in the proportion of pts seen by MDs within 30 min from the time roomed in the exam room and improvement in pts’/provider’s satisfaction. Post EHR, there were delays with decrease in the proportion of pts seen within 30 min from the time roomed in. Although the pt satisfaction did not change significantly, the number of times MDs had to wait for an open exam room increased from 8% (5/65) to 24% (14/59, p=0.01). The impact of RPM and EHR on pt times are shown below. The delays to see MDs after EHR were associated with longer time spent with the nurse (from median 4 to 7 min) and delays in seeing MLPs (from 11 to 18 min). Conclusions: These findings indicate that RPM can improve pt wait times. During initial stages of EHR implementation, the increase in pt wait time and reduced clinical efficiencies can be related to learning, and adapting to the new system. These data can be useful to design interventions that can target the areas of delays such as training and redesigning workflow to improve the clinical efficiency. [Table: see text]

Optimizing infusion wait time: Redesigning medication order verification/approval at a comprehensive cancer center.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14042-e14042
Author(s):  
Neda Hashemi-Sadraei ◽  
Zoneddy R. Dayao ◽  
Shenthol Sasankan ◽  
Andrea Cox ◽  
Sandra Peacock ◽  
...  
Keyword(s):  
Wait Time ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Wait Times ◽  
Medication Order ◽  
Time Data ◽  
Safety Standards ◽  
University Of New Mexico ◽  
Flat Dose ◽  
Nurse Assessment

e14042 Background: Nationwide, many cancer centers experience challenges with infusion center efficiency while maintaining high safety standards. Many factors contribute to long wait times for patients on the day of their infusion appointments. At University of New Mexico Comprehensive Cancer Center (UNMCCC), a contributing factor is the delays in verification or approval of medications. We conducted a project to improve order verification/approval workflow within a Plan-Do-Study-Act (PDSA) framework with the objective to decrease the infusion wait time. Methods: A multidisciplinary working group was formed consisting of the infusion floor physician lead, nurse lead, pharmacy lead, and analytics and process improvement leads. Upon exploring the infusion workflow database, disruptions in verification or approval of orders had a large impact on wait times. Order verification workflow was broken down into 3 steps: 1) physician assessment of patient and approval of orders, 2) infusion nurse assessment of patient, 3) pharmacist verification of order. Beginning Feb 2019, the following interventions were implemented in each section: 1) once patient was assessed by physician and orders approved, the patient was marked as “ready-to-treat”. 2) Pharmacist verified the order once “ready-to-treat” was communicated and initiated preparation of medications prior to arrival of patient to the infusion suit. 3) Infusion nurse assessment occurred once patient was seated on infusion chair. 4) Physicians were encouraged to pre-approve selected injections by the morning of patient appointment. Results: Prospective wait time was gathered for May 2019 using the real-time data available in the electronic medical record. Wait times were analyzed for patients receiving chemotherapy or flat dose injections. By marking appropriate patients “ready-to-treat” and moving pharmacist verification prior to infusion nurse assessment, there was an immediate decrease in wait time from 79 to 60 min. Selected injections which did not require mixing were pre-approved by the physician and stored in the medication dispensing system (Pyxis). This resulted in decrease in the injection wait time by 8.5 minutes, without wasting of drugs. Conclusions: Redesigning the medication order verification/approval workflow resulted in reduced wait times for patients receiving infusions or injections. We aim to further refine our PDSA cycles and ensure sustainability of change.

scholarly journals The Impact of Outcomes Research in Plastic and Reconstructive Surgery

2021 ◽  
Vol 148 (4) ◽  
pp. 921-926
Author(s):  
Elizabeth Laikhter ◽  
Samuel M. Manstein ◽  
Andrea L. Pusic ◽  
Kevin C. Chung ◽  
Samuel J. Lin
Keyword(s):  
Reconstructive Surgery ◽  
Outcomes Research ◽  
Plastic And Reconstructive Surgery ◽  
The Impact

scholarly journals Specialist Wait Time Reporting Using Family Physicians’ Electronic Medical Record Data: A Mixed Method Study of Feasibility and Clinical Utility

2021 ◽  
Author(s):  
Michelle Naimer ◽  
Babak Aliarzadeh ◽  
Chaim M. Bell ◽  
Noah Ivers ◽  
Liisa Jaakkimainen ◽  
...  
Keyword(s):  
Primary Care ◽  
Mixed Method ◽  
Clinical Utility ◽  
Family Physicians ◽  
Wait Time ◽  
Mixed Method Study ◽  
Wait Times ◽  
Specialist Care ◽  
Time Data ◽  
Time Information

Abstract Background: More than 50% of Canadian patients wait longer than four weeks to see a specialist after referral from primary care. Access to accurate wait time information may help primary care physicians choose the timeliest specialist to address a patient’s specific needs. We conducted a mixed-methods study to assess if primary to specialist care wait times can be extracted from electronic medical records (EMR), analyzed the wait time information, and used focus groups and interviews to assess the potential clinical utility of the wait time information. Methods: Two family practices were recruited to examine primary care physician to specialist wait times between 2016 and 2017, using EMR data. The primary outcome was the median wait time from physician referral to specialist appointment for each specialty service. Secondary outcomes included the physician and patient characteristics associated with wait times as well as qualitative analyses of physician interviews about the resulting wait time reports.Results: Wait time data can be extracted from the primary care EMR and converted to a report format for family physicians and specialists to review. After data cleaning, there were 7141 referrals included from 4967 unique patients. The 5 most common specialties referred to were Dermatology, Gastroenterology, Ear Nose and Throat, Obstetrics and Gynecology and Urology. Half of the patients were seen by a specialist within 42 days, 75% seen within 80 days and all patients within 760 days. There were few patient or provider differences amongst the wait times for referrals. Overall, wait time reports were perceived to be important since they could help family physicians decide how to triage referrals and might lead to system improvements. Conclusions: Wait time information from primary to specialist care can aid in decision-making around specialist referrals, identify bottlenecks, and help with system planning. This mixed method study is a starting point to review the importance of providing wait time data for both family physicians and local health systems. Future work can be directed towards developing wait time reporting functionality and evaluating if wait time information will help increase system efficiency and/or improve provider and patient satisfaction.

Sign in / Sign up

Export Citation Format

Share Document