Variation In Waiting Times Research Articles

The Impact of the COVID-19 Pandemic on Case Volume and Wait Times of Elective Hand Procedures: A Retrospective Chart Review Study

Introduction: The COVID-19 pandemic has decreased the number of surgeries performed in North America. The purpose of this study was to compare the number of elective hand surgeries performed during the pandemic to a corresponding pre-pandemic time period and to quantify the impact to the surgical backlog in hand surgery. Methods: Patient health records for individuals who underwent surgical management of carpal tunnel syndrome (CTS), Dupuytren's disease (DD) or stenosing tenosynovitis (time periods: March 11, 2018 to July 1, 2019 [pre-pandemic] and March 11, 2020 to July 1, 2021 [pandemic]) were retrieved from two academic institutions. The primary outcome was number of surgeries performed in each time period. Secondary outcomes included wait times for each time period; and variables as predictors of wait times, including a) age; b) gender; c) socioeconomic status; d) geographic location; and, e) comorbidities. Results: Seven-hundred-and-fifteen cases were included (447 CTR cases, 135 fasciotomy/subtotal palmar fasciectomy cases and 133 pulley release/tendon release cases). Two-hundred-and-sixty-four elective hand procedures were performed during the COVID-19 time period, compared to 451 in the pre-pandemic time period (n = 187 surgeries, 41.5%). Mean surgical wait times decreased for CTS and DD and increased for stenosing tenosynovitis during the pandemic compared to the corresponding pre-pandemic time period. No association or variation in wait times was found in regard to the aforementioned variables. Conclusions: During the pandemic, a decreased total number of elective hand surgeries were performed when compared to the corresponding pre-pandemic period. This contributes to a backlog of elective surgical procedures.

Wait Times for Scheduling Appointments for Prevention of Macrovascular and Microvascular Complications of Diabetes: Cross-Sectional Descriptive Study.

Diabetes is a chronic disease that requires lifelong management and care, affecting around 422 million people worldwide and roughly 37 million in the United States. Patients newly diagnosed with diabetes must work with health care providers to formulate a management plan, including lifestyle modifications and regular office visits, to improve metabolic control, prevent or delay complications, optimize quality of life, and promote well-being. Our aim is to investigate one component of system-wide access to timely health care for people with diabetes in New York City (NYC), namely the length of time for someone with newly diagnosed diabetes to obtain an appointment with 3 diabetes care specialists: a cardiologist, an endocrinologist, and an ophthalmologist, respectively. We contacted the offices of 3 different kinds of specialists: cardiologists, endocrinologists, and ophthalmologists, by telephone, for this descriptive cross-sectional study, to determine the number of days required to schedule an appointment for a new patient with diabetes. The sampling frame included all specialists affiliated with any private or public hospital in NYC. The number of days to obtain an appointment with each specialist was documented, along with "time on hold" when attempting to schedule an appointment and the presence of online booking capabilities. Of the 1639 unique physicians affiliated with (private and public) hospitals in the 3 subspecialties, 1032 (cardiologists, endocrinologists, and ophthalmologists) were in active practice and did not require a referral. The mean wait time for scheduling an appointment was 36 (SD 36.4; IQR 12-51.5) days for cardiologists; 82 (SD 47; IQR 56-101) days for endocrinologists; and 50.4 (SD 56; IQR 10-72) days for ophthalmologists. The median wait time was 27 days for cardiologists, 72 days for endocrinologists, and 30 days for ophthalmologists. The mean time on hold while attempting to schedule an appointment with these specialists was 2.6 (SD 5.5) minutes for cardiologists, 5.4 (SD 4.3) minutes for endocrinologists, and 3.2 (SD 4.8) minutes for ophthalmologists, respectively. Over 46% (158/341) of cardiologists enabled patients to schedule an appointment on the web, and over 55% (128/228) of endocrinologists enabled patients to schedule an appointment on the web. In contrast, only approximately 25% (117/463) of ophthalmologists offered web-based appointment scheduling options. The results indicate considerable variation in wait times between and within the 3 specialties examined for a new patient in NYC. Given the paucity of research on wait times for newly diagnosed people with diabetes to obtain an appointment with different specialists, this study provides preliminary estimates that can serve as an initial reference. Additional research is needed to document the extent to which wait times are associated with complications and the demographic and socio-economic characteristics of people served by different providers.

Surgeon- and hospital-level variation in wait times for scheduled non-urgent surgery in Ontario, Canada: A cross-sectional population-based study.

Canadian health systems fare poorly in providing timely access to elective surgical care, which is crucial for quality, trust, and satisfaction. We conducted a cross-sectional analysis of surgical wait times for adults receiving non-urgent cataract surgery, knee arthroplasty, hip arthroplasty, gallbladder surgery, and non-cancer uterine surgery in Ontario, Canada, between 2013 and 2019. We obtained data from the Wait Times Information System (WTIS) database. Inter- and intra-hospital and surgeon variations in wait time were described graphically with caterpillar plots. We used non-nested 3-level hierarchical random effects models to estimate variation partition coefficients, quantifying the proportion of wait time variance attributable to surgeons and hospitals. A total of 942,605 procedures at 107 healthcare facilities, conducted by 1,834 surgeons, were included in the analysis. We observed significant intra- and inter-provider variations in wait times across all five surgical procedures. Inter-facility median wait time varied between six-fold for gallbladder surgery and 15-fold for knee arthroplasty. Inter-surgeon variation was more pronounced, ranging from a 17-fold median wait time difference for cataract surgery to a 216-fold difference for non-cancer uterine surgery. The proportion of variation in wait times attributable to facilities ranged from 6.2% for gallbladder surgery to 23.0% for cataract surgery. In comparison, surgeon-related variation ranged from 16.0% for non-cancer uterine surgery to 28.0% for cataract surgery. There is extreme variability in surgical wait times for five common, high-volume, non-urgent surgical procedures. Strategies to address surgical wait times must address the variation between service providers through better coordination of supply and demand. Approaches such as single-entry models could improve surgical system performance.

Geographic Variation in Appointment Wait Times for US Military Veterans

Timely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied. To describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties. This is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022. Referral to either VHA or community-based clinicians. Total appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level. The final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care). This cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach.

BackgroundWait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers.ObjectiveThe first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers).MethodsWe analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments.ResultsThe average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models.ConclusionsThe random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

Inequity in Access to Transcatheter Aortic Valve Replacement: A Pan-Canadian Evaluation of Wait-Times

BackgroundThere has been an exponential increase in the demand for transcatheter aortic valve replacement (TAVR). Our goal was to examine trends in TAVR capacity and wait-times across Canada. MethodsAll TAVR cases were identified from April 1, 2014, to March 31, 2017. Wait-time was defined as the duration in days from the initial referral to the TAVR procedure. TAVR capacity was defined as the number of TAVR procedures per million population/province/fiscal year. We performed multivariable multilevel Cox proportional hazards modelling of the time to TAVR as the dependant variable and the effect of provinces as random effects. We quantified the variation in wait-times among provinces using the median hazard ratio. ResultsWe identified a total of 4906 TAVR procedures across 9 provinces. Despite a year over year increase in overall capacity, there was a greater than 3-fold difference in capacity between provinces. Crude median wait-times increased over time in all provinces, with marked variation from 71.5 days in Newfoundland to 190.5 and 203 days in Manitoba and Alberta, respectively. This suggests increasing demand outpaced the growth in capacity. We found a median hazard ratio of 1.62, indicating that in half of the possible pairwise comparisons, the time to TAVR for identical patients was at least 62% longer between different provinces. ConclusionWe found substantial geographic inequity in TAVR access. This calls for policy makers, clinicians, and administrators across Canada to address this inequity through revaluation of provincial funding mechanisms, as well as implementation of efficient care pathways.

Increased Interval From Initial Evaluation to Initial Voice Therapy Session is Associated With Missed Voice Therapy Appointments

Voice therapy (VT) is an essential component to a successful treatment of laryngological disorders and thus patient outcomes depend on patient compliance with VT. As volume of VT patients' ebb and flow, there is often variation in wait times for initial VT sessions. There is a paucity research evaluating VT latency and missed appointments. It is important to find associations between latency and patient compliance to assist with increasing the quality of patient care in all voice and swallowing clinics and decreasing missed appointments for VT following. Should a clinical setting be able to make changes to better care for the patients, it may show a substantial change in missed appointments and patient outcomes. To determine what factors contribute to missed initial VT session and specifically whether patients who have a time interval of >4 weeks from their initial interdisciplinary evaluation to their initial VT session are more likely to not attend. Retrospective analysis of a database, at a tertiary academic voice and swallowing center. A prospective voice and swallowing database was evaluated for new voice patient evaluations starting from January 1, 2014 to December 31, 2017. Patients with a primary diagnosis of dysphonia or dyspnea and scheduled for VT after the initial patient evaluation in an interdisciplinary laryngology clinic were included. Three hundred and eighteen patients (mean age = 5.27, standard deviation 16.0) met the inclusion criteria. Patients were 73% (n = 231) female, and 81% White/Caucasian. Univariate analysis shows that insurance type, profession, body mass index, interval to VT appointment, Reflux Severity Index (RSI), and Eating Assessment Tool-10 scores were associated with missed appointment rates. Multivariate logistic regression model demonstrated interval to VT, RSI, race, and profession were significant factors in missed appointments. Patients with an interval >4 weeks, were more likely to miss their appointment, as compared to patients <4 weeks (odds ratio 2.07 95% confidence interval 1.18-3.60). The data presented suggests that patients who have their first VT appointment greater than 4-weeks from the time of their initial interdisciplinary evaluation have an increased risk of missed appointment. Race, RSI, and professional voice use also appear to play a role in missed appointment rates.

Closing the Referral Loop: an Analysis of Primary Care Referrals to Specialists in a Large Health System.

Ideally, a referral from a primary care physician (PCP) to a specialist results in a completed specialty appointment with results available to the PCP. This is defined as "closing the referral loop." As health systems grow more complex, regulatory bodies increase vigilance, and reimbursement shifts towards value, closing the referral loop becomes a patient safety, regulatory, and financial imperative. To assess the ability of a large health system to close the referral loop, we used electronic medical record (EMR)-generated data to analyze referrals from a large primary care network to 20 high-volume specialties between July 1, 2015 and June 30, 2016. The primary metric was documented specialist appointment completion rate. Explanatory analyses included documented appointment scheduling rate, individual clinic differences, appointment wait times, and geographic distance to appointments. Of the 103,737 analyzed referral scheduling attempts, only 36,072 (34.8%) resulted in documented complete appointments. Low documented appointment scheduling rates (38.9% of scheduling attempts lacked appointment dates), individual clinic differences in closing the referral loop, and significant differences in wait times and distances to specialists between complete and incomplete appointments drove this gap. Other notable findings include high variation in wait times among specialties and correlation between high wait times and low documented appointment completion rates. The rate of closing the referral loop in this health system is low. Low appointment scheduling rates, individual clinic differences, and patient access issues of wait times and geographic proximity explain much of the gap. This problem is likely common among large health systems with complex provider networks and referral scheduling. Strategies that improve scheduling, decrease variation among clinics, and improve patient access will likely improve rates of closing the referral loop. More research is necessary to determine the impact of these changes and other potential driving factors.

Factors Associated With Waiting Time on the Liver Transplant List: An Analysis of the United Network for Organ Sharing (UNOS) Database

Introduction: Liver transplantation (LT) is an important treatment for acute liver failure and end-stage liver disease. The increasing demand on limited resource results in increased wait time on the LT list. More than 2000 candidates die each year waiting for a liver transplant. In 2002, the Model for End-stage Liver Disease (MELD) score was incorporated to prioritize patients awaiting LT. Although there is data on MELD score affecting wait times on the LT list, there continues to be paucity of literature about other factors that could affect the wait time. We aimed to evaluate the factors affecting LT waiting times in the United States. Methods: Using the UNOS database, patients aged 12 to 75 years listed for LT between the years of 2002 to 2015 were included. We excluded patients prior to implementation of MELD score in 2002. A generalized linear model to predict wait time in days after performing logistic transformation was used. Variables tested in the model included patient characteristics (age at listing, body mass index, presence of ascites, hepatoceullar carcinoma and need for mechanical ventilation), laboratory values (serum bilirubin, albumin, sodium, creatinine, international normalized ratio) as well as other factors (ABO blood type, region of listing, primary payer, ethnicity and listing for simultaneous transplantation). We used backward elimination to maintain significant predictors in our model. Results: A total of 75,771 patients were included in the final analysis. Factors that were associated with wait times on the LT list are summarized in Table 1. The components of the MELD score, i.e. serum bilirubin, INR and creatinine were associated with shorter wait times. Other factors associated with shorter wait times were the need of mechanical ventilation and region of transplantation (region 3). ABO blood type, primary payer and placement of transjugular porto-systemic shunt (TIPS) also influenced time on the LT wait list.Table: Factors affecting wait times on the liver transplantation list.Conclusion: As expected, the MELD score was important in predicting the wait time on the LT list. However, other factors also strongly influenced the wait times. ABO blood type, region of transplantation, primary payer type and placement of TIPS also influenced wait times on the LT list significantly. Further research is needed to address reasons for the variation in wait times between regions and payment systems in an attempt to decrease wait times, standardize the listing process and improve patient outcomes.

Lung cancer care trajectory at a Canadian centre: an evaluation of how wait times affect clinical outcomes.

Lung cancer continues to be one of the most common cancers in Canada, with approximately 28,400 new cases diagnosed each year. Although timely care can contribute substantially to quality of life for patients, it remains unclear whether it also improves patient outcomes. In this work, we used a set of quality indicators that aim to describe the quality of care in lung cancer patients. We assessed adherence with existing guidelines for timeliness of lung cancer care and concordance with existing standards of treatment, and we examined the association between timeliness of care and lung cancer survival. Patients with lung cancer diagnosed between 2010 and 2015 were identified from the Pulmonary Division Lung Cancer Registry at our centre. We demonstrated that the interdisciplinary pulmonary oncology service successfully treated most of its patients within the recommended wait times. However, there is still work to be done to decrease variation in wait time. Our results demonstrate a significant association between wait time and survival, supporting the need for clinicians to optimize the patient care trajectory. It would be helpful for Canadian clinicians treating patients with lung cancer to have wait time guidelines for all treatment modalities, together with standard definitions for all time intervals. Any reductions in wait times should be balanced against the need for thorough investigation before initiating treatment. We believe that our unique model of care leads to an acceleration of diagnostic steps. Avoiding any delay associated with referral to a medical oncologist for treatment could be an acceptable strategy with respect to reducing wait time.

Looking for Lithotripsy: Accessibility and Portability of Canadian Healthcare

Extracorporeal shock wave lithotripsy (ESWL) is a definitive, ambulatory and non-invasive modality for treating kidney stones. ESWL is not available in all urban centres and many Canadians must either travel, sometimes out of province, or wait to have this procedure performed. We sought to evaluate the variability in access to ESWL treatment. We compiled a comprehensive list of ESWL centres in Canada and contacted all centres in 2011 to assess their wait times, out-of-province patient fees, and roles and responsibilities of the referring physician. We contacted all 23 ESWL facilities across Canada (100% response rate). Wait times for elective ESWL procedures ranged from one day to over one year, with a mean of 8.4 weeks (SD, 16.76 weeks). No centres refused out-of-province patients, although five discouraged travel to their centre owing to their prolonged wait times. No facilities charged extra fees for out-of-province patients. Ten (43%) facilities required a secondary consultation by a urolo-gist at the centre before booking. Twelve (52%) of the centres indicated the waiting time could be shortened if the referring physician were to advocate on the patient's behalf. Contact was repeated one year later in 2012 with five centres, and the results were similar. There is marked variation in wait times across Canada for ESWL but there are few barriers to care. Patients' waits may be shortened by physician advocacy.

Wait Times for Women With Abnormal Uterine Bleeding in South-Western Ontario

ObjectiveWomen referred to an obstetrician-gynaecologist because of abnormal uterine bleeding (AUB) should be seen within 12weeks, according to the benchmarks recommended for medically acceptable wait times established by the Society of Obstetricians and Gynaecologists of Canada. Our study aimed to determine the proportion of patients having wait times that meet this recommendation, and to understand patient-level sources of variation in these waits. MethodsWe performed a secondary analysis of data from the Delivering Primary Healthcare Information (DELPHI) project database. Electronic medical record data from 10 family practices across south-western Ontario were used to study AUB wait times. ResultsOver a 30-month period, 223 referrals were made to obstetrician-gynaecologists because of AUB. The mean wait time for initial assessment was 70.4days (median=51days); the range among referring practices varied from 36 to 111days. Seventy-one percent of women were seen by the obstetrician-gynaecologist within the recommended time of 12weeks or less. Multi-level regression analysis indicated that older patients were more likely to have shorter waits. The intraclass correlation was 0.256, indicating that nearly a quarter of the variation in wait time was explained at the practice level. ConclusionNearly 30% of women referred to specialists because of AUB wait longer than the period of time recommended by the SOGC, with younger women more likely to wait longer. This difference may be due to the perception of an increased risk of malignancy in older women. Future research on correlates of AUB wait times should take factors such as severity and prior family physician work up into account.

Wait Times and Diagnostic Pathways among Women with Invasive Breast Cancer in a Population-Based Publicly Funded Cancer Centre, British Columbia, Canada.

Abstract Rationale and background: The Fraser Valley Cancer Center serves a large geographic population of urban and rural areas. Selected data on wait times and diagnostic pathways are available for mammography screening programs in British Columbia (BC), but not for patients presenting with clinical abnormalities such as a palpable breast lump. Diagnostic pathways in BC are managed by primary care physicians and community surgeons. Anecdotal evidence from oncology services suggests large variation in wait times and pathways.Objective: To examine time intervals and pathways between the detection of a breast abnormality and formal assessment by medical or radiation oncology for adjuvant treatment.Methods: Retrospective chart review of all patients with a new diagnosis of breast cancer first seen at the Fraser Valley Cancer Center in 2002, linked with demographic and other registry data. Data collected included type, location, date of investigations, surgical procedures, diagnosis, treatment and stage. Exclusion criteria were patients initially diagnosed with metastatic breast cancer, patients with recurrent breast cancer, male breast cancer patients, patients diagnosed or previously treated outside BC. Microsoft Access and SAS 9.2 were used to enter and analyse data.Results: From a total of 513 women diagnosed in 2002 with invasive breast cancer, 422 women (82%), without metastases (age 30 to 97 yrs, median 59 yrs), presented with abnormal screen (45%), breast lump (47%) or other findings. These women had up to 7 different radiological breast investigations and up to 5 surgical interventions. About 5% had 5 or more breast investigations. Median time (days) from presentation to first surgery was 41 days and varied by location of first investigation. Time to surgery was highest among women who presented with abnormal screening mammogram (median 49 days). Median time from first surgery consultation to first surgery was lowest for women presenting with breast lumps (15 days).Conclusions: These data indicate wait times beyond national and international targets and identified wide variability and some inefficient, redundant or unnecessary steps. Recognition and analysis of abnormal pathways will guide changes in breast health service delivery. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3083.

Non-Clinical Factors Associated with Variation in Cataract Surgery Waiting Times in Manitoba

Cataract surgery is the highest-volume surgical procedure in Canada, with over 200,000 performed annually, mostly (85%) on persons aged 65 or older. Concerns have been raised about wait times to access this procedure. This study explores the relationship between waiting times for cataract surgery in Manitoba and a variety of characteristics, including age, sex, socio-economic status, region of residence, health status, surgeon, and surgeon's caseload. The study included 6114 individuals who had first-eye cataract surgery between November 1, 1998, and March 31, 2000, in Winnipeg, MB. Significant predictors of variation in wait times were age, sex, having a hospitalization while waiting, and surgeon. The model explained 32.5 per cent of the variance in wait times; specific surgeon independently explained 29.5 per cent of the variance. Median waiting times varied widely by surgeon, ranging from 61 to 399 days. Differences in surgeons' expected wait times should be available to patients and referring clinicians.