Treatment Wait Time Research Articles

Retrospective study of factors associated with late detection of oral cancer in alberta: A qualitative study.

Oral cancer continues to be diagnosed in advanced stages, giving patients lower chances of survival. The objective of this study was to explore reasons for delayed diagnosis of oral cancer in Alberta. A retrospective qualitative design was implemented through seven steps suggested for conducting a narrative clinical document. Data was retrieved from the Alberta Cancer Registry database between 2005 and 2017. A sample of initial consultation notes (ICN) of oral and oropharyngeal cancer patients were identified through a purposeful sampling method and added to the study until saturation was achieved. A deductive analysis approach inspired by the model pathways to treatment health care provider (HCP) was employed. From the 34 ICN included in our analysis, five main categories were identified: appraisal interval, help-seeking interval, diagnosis interval, pre-treatment interval, and other contributing factors such as health-related behaviours, system delay, and tumor characteristics. These factors negatively contributed to early detection of oral and oropharyngeal cancers and affect treatment wait time with patients, providers, and the healthcare system. Patient’s lack of awareness, provider’s oversight and prolonged access to care were the main reasons of delay in cancer diagnosis and management in our study. A sustainable plan for public awareness interventions and implementation of a solid curriculum for medical and dental students is needed to enhance their related knowledge, competence in clinical judgement, and treatment managements.

The first wave of COVID-19 did not cause longer wait times in head and neck cancer. Experience of a French expert center

BackgroundHead and neck cancers (HNC) have poor survival prognosis, as tumors are often diagnosed at advanced stages in patients consulting late. The first lockdown linked to the 1st wave of COVID-19 (Coronavirus Disease 2019) disrupted consultation schedules in France. ObjectiveThe principal aim of the present study was to analyze consultation wait time in HNC during and after lockdown, in our university expert oncology reference center, to disclose any increase in treatment wait time. MethodsA single-center retrospective study included patients with a first diagnosis of HNC. Three groups were distinguished: “lockdown”, “post-lockdown”, and a “control” group (corresponding to a reference period 1 year earlier). Intervals between first oncologic consultation and multidisciplinary tumor board (FC-MTB) and between MTB and first treatment (MTB-T) were assessed. ResultsOne hundred and seven patients were included in the control group, 60 in the lockdown group and 74 in the post-lockdown group. There was no increase in median FC-MTB interval (respectively 35, 29 and 28 days) between the lockdown and post-lockdown groups compared to the control group (respectively P=0.2298 and P=0.0153). Likewise, there was no increase in MTB-T interval (27, 20 and 26 days respectively) (P=0.4203). ConclusionNo increase in wait times was observed during the lockdown and post-lockdown periods in our center.

The impact of primary human papillomavirus screening on negative loop excision histology following biopsy-proven high-grade cervical intra-epithelial lesions: A review from a large tertiary colposcopy unit.

The renewed National Cervical Screening Program incorporating primary human papillomavirus (HPV) screening was implemented in Australia in December 2017. In a previous study conducted in the UK, primary HPV screening was found to be associated with a 25% reduction in the incidence of negative histology following loop electrosurgery excision procedure (LEEP). To examine the change in incidence and associated risk factors for a negative LEEP with introduction of primary HPV screening. A retrospective review of the records of all patients undergoing a LEEP excision for biopsy-proven high-grade cervical intra-epithelial lesions between 1 January 2014 and 30 June 2019 in a specialised centre. There were 1123 patients who underwent a LEEP included in the analysis. The incidence of a negative LEEP specimen was 7.5% (59/784) and 5.3% (18/339) in the pre- and post-HPV screening cohort. More patients in the post-HPV screening group had low-grade cytology on referral (P<0.001), smaller cervical lesions on colposcopy (P=0.012) and longer biopsy to treatment interval (P=0.020). Primary HPV screening was associated with a significant reduction in the incidence of a negative LEEP specimen in a propensity matched cohort (11.2% to 5.1%, P=0.006) and a 41% (P=0.045) decreased relative risk of a negative LEEP on multivariate analysis. Primary HPV screening results in a lower incidence of negative LEEP histology, despite a longer biopsy to treatment wait time and higher proportion of low-grade cytology at triage.

Reduce Patient Treatment wait time in a Proton Beam Facility – A Gatekeeper Approach

Patient wait time can negatively impact treatment quality in a proton therapy center, where multiple treatment rooms share one proton beam. Wait time increases patient discomfort that can lead to patient motion, dissatisfaction, and longer treatment delay. This study was to develop a patient call-back model that reduced patient wait while efficiently utilizing the proton beam. A "Gatekeeper" logic allowing therapists to adjust the time of a patient's call-back to the treatment room was developed. It uses a two-pronged approach to minimize overlap of long treatment and the possibility of excessive wait in the queue to receive the proton beam. The goal was to reduce the maximum wait time to less than eight minutes per field for a four-room facility. The effectiveness of this logic was evaluated through simulation, and five scenarios were compared. Four scenarios implementing various levels of gatekeeper logic were compared with the original scenario without the logic. The best performing model provided a reduction of the maximum field wait by 26% and met the predefined goal. Adjusting call-back extended the treatment day length by an average of 6min and a maximum of 12min in total. The use of this gatekeeper logic significantly reduces patient field wait with minimal impact on treatment day length for a four-room proton facility. A sample interface that adopts this logic for therapists to make informed decision on patient call-back time is demonstrated.

Disparities in Opioid Use Disorder Treatment Admissions in the United States

ObjectivesThe Cincinnati region has been at the epicenter of the nation’s unfolding opioid epidemic. The objectives of this study were twofold: (1) to compare the Cincinnati region to the United States in length of time to obtain treatment and planned medication-assisted therapy for the treatment for opioid use disorder (OUD); and (2) to assess racial disparities within the Cincinnati region in wait time and type of treatment.MethodsThe 2017 Treatment Episode Data Set: Admissions (TEDS-A) from the Substance Abuse and Mental Health Services Administration (SAMHSA) was used to identify a cohort of eligible individuals with a primary substance use of opioids, including opioid derivatives. Logistic regression models were performed to assess the differences for treatment wait time and type of planned treatment. Model covariates included patient demographics and socioeconomic characteristics. Three different models were performed to assess the influence of covariates of the outcomes.ResultsThere were 678 766 US and 3298 Cincinnati region individuals admitted for OUD treatment in 2017. The rate per 1000 for treatment admissions was 2.08 and 1.51 (P value < 0.0001) for the United States and Cincinnati, respectively. The fully saturated regression results found that the odds of Cincinnati individuals receiving planned medication-assisted therapy were 0.497 (95% CI, 0.451–0.546; P value < 0.001). The odds of waiting longer for treatment in Cincinnati were higher than in the United States as a whole: 2.33 (95% CI, 2.19–2.48; P value < 0.001). In Cincinnati, there were 3102 Caucasian, 123 African American, and 73 Other admissions. The fully saturated model results found that Caucasians and Other had an increased likelihood of receiving planned medicationassisted therapy (OR 1.89, P value 0.039; OR 7.07, P value 0.002, respectively) compared to African Americans. Within Cincinnati, there was not a statistically significant difference in the likelihood of waiting time to receive treatment by race.ConclusionIndividuals seeking treatment for OUD in Cincinnati were less likely to receive planned medication-assisted therapy and were more likely to wait longer than individuals in the United States as a whole. These results suggest that the demand for treatment is greater than the supply in Cincinnati. Within Cincinnati, there does not appear to be a racial disparity in treatment type or length of time to receive treatment for OUD.

Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples

Background: Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its ability to accurately segment pelvic OAR.Material and methods: We combined two network models, Dense Net and V-Net, to establish the Dense V-Network algorithm. For the training model, we adopted 100 kV computed tomography (CT) images of patients with cervical cancer, including 80 randomly selected as training sets, by which to adjust parameters of the automatic segmentation model, and the remaining 20 as test sets to evaluate the performance of the convolutional neural network model. Three representative parameters were used to evaluate the segmentation results quantitatively.Results: Clinical results revealed that Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 mm; and Jaccard distance was within 2.3 mm. Except for the small intestine, the Hausdorff distance of other organs was less than 9.0 mm. Comparison of our approaches with those of the Atlas and other studies demonstrated that the Dense V-Network had more accurate and efficient performance and faster speed.Conclusions: The Dense V-Network algorithm can be used to automatically segment pelvic OARs accurately and efficiently, while shortening patients’ waiting time and accelerating radiotherapy workflow.

PMH45 DISPARITIES IN TREATMENT ADMISSIONS FOR OPIOID USE DISORDER IN CINCINNATI BY RACE

The objective of this study was to assess racial disparities within the Cincinnati region with respect to the length of time to obtain treatment, and planned medication-assisted therapy for the treatment for opioid use disorder (OUD). The Treatment Episode Data Set – Admissions from the Substance Abuse and Mental Health Administration was used to identify a cohort of eligible individuals. All 2017 admissions with a primary substance use of opioids, including opioid derivatives, were included. The data were stratified by ethnicity: Caucasian, African-American, and Other. Logistic regression models were performed to assess the differences by race for treatment wait time and type of planned treatment. Model covariates included patient demographics and socioeconomic characteristics. A total of 3,298 individuals were identified with 3,102 Caucasian, 123 African-Americans, and 73 Other. There were statistically significant differences between Caucasians, African-Americans, and Other for all patient characteristics and socio-economic factors with the exception of unemployment. The rate of treatment admissions per 1,000 for Caucasians, African-Americans, and Other was 1.71, 0.42, and 0.73, respectively. The overwhelming majority of individuals did not have planned medication assisted therapy at the time of admission (range: 73% to 89%). The percentage of individuals waiting 0-7 days for treatment was 70% for Caucasians, 89% for African-Americans, and 71% for Other. One logistic regression model found that Caucasians and Other had an increased likelihood of receiving planned medication-assisted therapy (OR 1.89, p-value 0.039; OR 7.07, p-value 0.002, respectively) compared to African-Americans. However, these results were sensitive to model specification. There was not a statistically significant difference in the likelihood of waiting time to receive treatment by race. Based on one-year of data there does not appear to be a disparity in treatment type or length of time to receive treatment for OUD in the Cincinnati region.

Group Therapy Treatment for Food Selectivity (FS17-05-19)

ObjectivesThe purpose of this quality improvement project was to implement a group exposure therapy intervention to more efficiently serve the patients that are referred to our hospital due to severe food selectivity. MethodsThe intervention used evidenced-based practices such as systematic desensitization, a token economy, counterconditioning, and parent training to help patients expand their food repertoire. The intervention is a 12-week multi-family group therapy for children that range in age from 4–14. Patients are placed into groups of no more than six members of similar age. In 11 of the sessions, therapists direct patients through an exposure hierarchy with novel foods and record the number of bites patients take of each food. In 4–5 sessions, a psychologist meets with parents separately to discuss how to use exposure therapy to treat food selectivity. We examined data from the groups that ran from March 2016 to June 2018. ResultsUsing a Wilcoxon Signed-Ranks Test, we compared the number of bites of novel foods that patients took in the first and last food exposure sessions. Results indicated that patients took significantly more bites of novel foods in the last group (median = 34) compared to the first group (median = 8.5), z = 4.79, P < .001. ConclusionsThe outcomes of this project suggest that time-limited group interventions may help children who struggle with severe food selectivity. Time-limited groups also may also decrease treatment wait time for patients with severe food selectivity because patients can be seen simultaneously and with clear start and end date for the service. Funding SourcesN/A. Supporting Tables, Images and/or Graphs▪

Do Moderate Surgical Treatment Delays Influence Survival in Colon Cancer?

Studies examining treatment delay and survival after surgical treatment of colon cancer have varied in quality and outcome, with little evidence available regarding the safety of longer surgical treatment wait times. Our study examined the effect of surgical treatment wait times on survival for patients with stage I to III colon cancer. A subset cohort analysis was performed using data from a prospectively maintained database. The study was conducted at a tertiary referral center. Data on all of the patients undergoing elective surgery for stage I to III colon cancer from 2006 to 2015 were collected from a prospectively maintained clinical and administrative database. We examined the impact of prolonged wait time to surgery on disease-free and overall survival. Patients were divided into 2 groups based on a treatment wait time of ≤30 or >30 days and were compared using a Cox proportional hazards model. A subgroup analysis was performed using alternative treatment delay cutoffs of 60 and 90 days. There were 908 patients with stage I to III colon cancer treated over the study period, with a median treatment wait time of 38 days (interquartile range, 21-61 days); 368 patients were treated within 30 days, and 540 were treated beyond 30 days from diagnosis. In adjusted multivariate analysis, a treatment delay of >30 days was not associated with decreased disease-free survival (HR = 0.89 (95% CI, 0.61-1.3); p = 0.52) or overall survival (HR = 0.82 (95% CI, 0.63-1.1); p = 0.16). Likewise, subgroup analysis using alternative treatment delay cutoffs of 60 and 90 days did not demonstrate an adverse effect on survival. This study was limited by retrospective analysis. Despite longer median treatment wait times from diagnosis to surgery, with the majority of patients exceeding 30 days and many experiencing delays of 2 to 3 months, no adverse impact on survival was observed. Patients who require additional consultations or investigations preoperatively may safely have their surgery moderately delayed to minimize their perioperative risk without any evidence that this will compromise treatment outcomes. See Video Abstract at http://links.lww.com/DCR/A397.

SU‐F‐P‐20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

Purpose:Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient's waiting experience.Methods:In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested.Results:We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. This means that the majority of our estimates are within 6.5 minutes of the actual wait time.Conclusion:The goal of this project was to define an appropriate machine learning algorithm to estimate waiting times based on the collective knowledge and experience learned from previous patients. Our results offer an opportunity to improve the information that is provided to patients and family members regarding the amount of time they can expect to wait for radiotherapy treatment at our centre.AJ acknowledges support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and from the 2014 Q+ Initiative of the McGill University Health Centre.

Monitoring treatment wait times for cancer patients in Japan.

289 Background: Treatment wait times for cancer patients is among the key performance indicators of cancer policy in Japan, but has never been measured across a large number of patients. We aimed to measure treatment wait times for cancer patients across a large number of hospitals in Japan using health claims data. Methods: We developed a database of claims data (Jan 2011 - Dec 2012) linked to 2011 hospital-based cancer registry data of breast, cervical, colorectal, liver, lung, prostate and stomach cancer patients from 126 designated cancer hospitals in Japan. Treatment wait time was defined as the number of days between the date of diagnosis (the date on which a pathological test or imaging exam that contributed the most to the diagnosis was performed) and initiation of treatment. Dates of diagnostic tests, therapeutic procedures, and chemotherapy (chemo) administration were obtained from health claims data. We calculated wait times by cancer type, treatment, and stage, and investigated if these factors were significantly associated with wait times even after adjusting for age using multiple linear regression. Results: Among 44,102 cancer patients who received cancer treatment, the median wait time was 31 days (IQR=18-50) for all patients, and 32, 42, 31, 22, 22, 55 and 31 days for breast, cervical, colon, liver, lung, prostate and stomach cancer patients, respectively. Wait times were longer for surgery (36 days) compared to radiation (22 days) and chemo (21 days), and shorter for later stage patients: 48, 36, 35, 23, and 14 days for stages 0, I, II, III, and IV, respectively. Cancer type, treatment type, and stages were all independently and significantly associated with treatment wait times even after adjusting for age (p&lt;0.05). Conclusions: This is the first study to investigate treatment wait times across a large number of cancer hospitals in Japan. Treatment wait times were particularly longer for cervical and prostate cancer patients. While the effect of prolonged treatment wait times on clinical outcomes is unknown, longer treatment wait time increases the psychosocial stresses of cancer patients. Continuous monitoring and feedback of study findings to hospitals are required for improvements in the future.

Longer waiting times for patients undergoing colorectal cancer surgery are not associated with decreased survival

Wait times are a growing concern in Canada's publicly-funded healthcare system. We sought to determine if increased wait times for colorectal cancer (CRC) treatments resulted in worse outcomes. A population-based retrospective cohort analysis of wait times for CRC patients undergoing major surgical resections in Manitoba, Canada, between 2004 and 2006 was undertaken. Administrative records were utilized to estimate total wait time (TWT), defined as the sum of time from index contact with the healthcare system to diagnosis of CRC (diagnostic wait time [DWT]) and the time from diagnosis to first cancer treatment (treatment wait time [TxWT]). Multivariate Cox regression analysis of 5-year overall survival was performed to determine the effect of TWT quartiles on survival. One thousand six hundred twenty eight patients with stage I-IV CRC underwent major surgery with a median TWT of 95 days. Predictors of lower 5-year survival included advanced age, higher stage, lower economic status, increased medical comorbidity, urgent presentation, living between 101 and 500 km from the Provincial cancer center, and not receiving adjuvant chemotherapy. After controlling for these variables, TWT quartiles were not associated with survival (P = 0.4898). On a population basis, increased TWT was not associated with worse survival, while controlling for important confounders.

Radiation oncology whiteboard: Data and workflow manager for enhanced communication and task management.

304 Background: Patient care within the radiation oncology clinic consists of several interdependent data driven procedures which must be executed in concert to ensure the best possible patient care. State of the art treatment in radiation oncology is largely computer driven, where patient care relies on the use of a typically vendor dependent, heterogeneous server environment. Traditional, peer-to-peer communication in such an environment introduces a fault intolerant workflow, where failed communication and data inconsistency issues may result in delayed or inappropriate patient treatment. Methods: The MGH Radiation Oncology Whiteboard is an intranet based application allowing for live monitoring of these patient care tasks available across the entire radiation oncology clinical domain. Patient events are recorded through interaction from various care-givers and data sources within the department, changing state as well as collecting event data from the accomplished task. Personnel may be alerted of state changes through automatic, customizable paging, email and live monitoring notification, triggering subsequent steps in the workflow chain. Currently the Whiteboard leverages data from multiple points in the clinic including: CT Simulation and data (4D), treatment planning written directive, dvh plan results, imrt and sbrt plan QA, daily and monthly linac QA, IORT coordination and calculation check, linear accelerator engineering log, patient treatment wait time (linac), remote treatment plan review. Results: Since introduction of the Whiteboard into the MGH clinic in 2008, task compliance in key areas, such as contour definition, radiation dose prescription, imrt QA measurement completion and pre-treatment plan checking has risen to levels well above 95%. Conclusions: Monitoring of patient care events improves QA by eliminating peer-to-peer communication and introducing complete transparency to the workflow communication chain. This approach enables all members of the staff to become active participants in the quality control process, every individual, through interaction with the Whiteboard, acting as an additional QC filter to improve patient care.

Longer waiting times for colorectal cancer surgery are not associated with decreased survival

METHODS: A population-based retrospective cohort analysis of wait times for colorectal cancer patients in the Province of Manitoba, Canada, between 2004 and 2006 was undertaken. Administrative records were utilized to estimate total wait time (TWT), defined as the sum of the time from index contact with the healthcare system to diagnosis of CRC (diagnostic wait time) and the time from diagnosis to definitive cancer surgery (treatment wait time). Multivariate Cox regression analysis of 5-year overall survival was performed to control for potential confounding variables including, age, gender, stage, emergency/urgent presentation, adjuvant treatments, and TWT.

The Utility of Publicly Reported Cancer Treatment Wait Time Information for Patients and Health Care Providers

Aims To determine the utility of web-based radiation wait time information for patients and health care providers in decision-making. To revise the information using a simulated laboratory environment and to re-evaluate the new web-based information. Materials and methods An online ‘pop-up’ survey on the Cancer Care Ontario (CCO) website measured user satisfaction. Qualitative data were gathered through patient focus groups and physician interviews. On the basis of the analysis, the website was revised and usability testing conducted. The information was re-evaluated by end-users through survey methodology. Results The majority accessing the wait time website were patients and family members. The modal age of use of the website was 31–50 years. Patients found the information more helpful after redesign than health professionals, but both found the language less easy to understand, highlighting the need to continuously evaluate the effectiveness of the website. Patients did not identify themselves as consumers of wait time information. Their expectation was that physicians would determine the urgency for treatment and would ensure timely access to care. Physicians reported that they did not use the CCO website on wait times and would not use the data for decision-making. Referrals were based on urgency of care and usual referral patterns. Referral patterns did not shift to centres with shorter wait times. Conclusions The results of this study did not confirm the usefulness of the web-based wait time information for patients and physicians as a resource on how to obtain timely access to radiation treatment. Patients relied on their physician to manage their access to treatment according to the urgency of their clinical condition. Physicians preferred their established referral process rather than referring their patients to centres with shorter wait times. As patients become more computer savvy, it will be interesting to see if they increasingly become consumers of web-based wait time information.