Actual Wait Times Research Articles

Perceptions, Reality, and Tolerance for Voting Wait Times in American Elections and Their Consequences for Democracy

Abstract Since 2012, voters in U.S. presidential elections have faced increasingly long wait times, depressing turnout, and undermining public confidence in the vote. While past studies have quantified differing levels of actual wait times, voters' perceptions about these wait times, which could directly influence their decision to vote, remain a black box. Using a novel nationally representative survey of the U.S. population, this study measures voters' perceptions of wait times, actual wait times, and willingness to wait. We ask whether some groups of voters expect longer waits and whether some are willing to wait longer than others. We also examine whether Americans who expect longer waits are actually less likely to vote. Overall, there seems to be more than willing to wait at the polls: 78 percent say they would wait over 30 minutes, while only 17 percent said they actually waited that long. At the individual level, women and nonwhite voters expect longer wait times, while lower-education voters marginally do. Women and less-educated voters, but not nonwhite voters, are less willing to wait at the polls. And the longer voters expect to wait, the less likely they are to vote. A voter who expects to wait 3 hours as opposed to not at all is 2.6 percentage points less likely to turn out. These findings highlight discrepancies between who expects and who is willing to wait through longer lines to cast their ballot. They bring with them important implications for policymakers.

Using co-design to improve the client waiting experience at an outpatient mental health clinic

Prolonged wait times in healthcare are a complex issue that can negatively impact both clients and staff. Longer wait times are often caused by a number of factors such as...

Evaluating Real Time Ramp Meter Queue Length and Wait Time Estimation

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.

P008 A Novel Smartphone Application to Reduce Infusion Suite Wait Times.

Patients with inflammatory bowel disease (IBD) are often treated with biologic medications that require visits to an infusion suite. Due to the high cost of biologics, they are not mixed and released from the pharmacy until patient arrival. This typically leads to long wait times after patient check in. An investigator-developed novel smartphone application (app) titled Almost There was created to reduce wait times through the use of geofences to notify the infusion suite when patients are nearing their destination (the hospital). Our aim was to perform a pilot study to assess the feasibility of Almost There for patients with IBD coming for infusions at a rural academic medical center. Adult patients with Crohn's disease (CD) or ulcerative colitis (UC) currently receiving intravenous biologic therapy were recruited for this pilot study. Patients received instructions on how to download the app onto their smartphone. Patients activated the application prior to leaving their homes and answered standard screening questions to assess for active infections. Using geofences, the app automatically notified the infusion suite when the patient was within 10 miles of the hospital. The infusion suite alerted the pharmacy who immediately proceeded with medication preparation. Patients completed surveys upon arrival to the infusion suite collecting basic demographics and disease information, perceived wait times for that visit and previous visits prior to using the app. The infusion suite staff recorded actual wait times from check in to medication administration. The app was trialed successfully for 12 total visits among 9 patients (5 CD, 4 UC). 78% of app users were 25-44 years old and 22% were 45 years and older. 56% of patients were receiving infliximab and 44% were receiving vedolizumab. 11 patients were recruited and 9 patients out of 11 were able to successfully use the app. Reasons for app failure included one user who forgot their phone at home, one with loss of cell service, and one app malfunction. The median actual wait time from when patients arrived at the infusion suite to the start of the infusion was 38-40 minutes. In 67% of visits, there was decreased perceived wait time for patients using the app compared to their prior visits. When using the app, perceived wait times were shorter than actual wait times in 75% percent of patients. In this pilot feasibility study, the Almost There app led to decreased perceived wait times for IBD patients coming for biologic infusions. The application was not effective for 18% of patients for a variety of reasons. If this or a similar app was improved to allow for more consistent usability it should be studied compared to a control group to evaluate if it leads to decreased wait times for any medical infusion.

S3314 A Novel Smartphone Application to Reduce Infusion Suite Wait Times

Introduction: Patients with inflammatory bowel disease (IBD) are often treated with biologic medications that require visits to an infusion suite. Due to the high cost of biologics, they are not mixed and released from the pharmacy until patient arrival. This typically leads to long wait times after patient check in. A novel smartphone application (app) titled Almost There was created to reduce wait times through the use of geofences to notify the infusion suite when patients are nearing their destination. We performed a pilot study to assess the feasibility of Almost There for patients with IBD coming for infusions at a rural academic medical center. Methods: Adult patients with Crohn’s disease (CD) or ulcerative colitis (UC) currently receiving intravenous biologic therapy were recruited for the study. Patients received instructions on how to download the app onto their smartphone. Patients activated the application prior to leaving their homes and answered standard screening questions to assess for active infections. Using geofences, the app automatically notified the infusion suite when the patient was within 10 miles of their destination. The infusion suite alerted the pharmacy who immediately proceeded with medication preparation. Patients completed surveys upon arrival to the infusion suite collecting basic demographics and disease information, perceived wait times for that visit and previous visits prior to using the app. The infusion suite staff recorded actual wait times from check in to medication administration. Results: The app was trialed successfully for 12 total visits among 9 patients (5 CD, 4 UC). 78% of app users were 25-44 years old and 22% were 45 years and older. 56% of patients were receiving infliximab and 44% were receiving vedolizumab. 11 patients were recruited and 9 patients out of 11 were able to successfully use the app. Reasons for app failure included one user who forgot their phone at home, one with loss of cell service, and one app malfunction. In 67% of visits, there was decreased perceived wait times with the app (Figure 1). When using the app, perceived wait times were shorter than actual wait times in 75% percent of patients. Conclusion: The Almost There app led to decreased perceived wait times for IBD patients coming for biologic infusions. The application was not effective for 18% of patients for a variety of reasons. If this or a similar app was improved to allow for more consistent usability, it could allow for decreased wait times for any medical infusion.Figure 1.: Comparing Perceived Wait Times to Actual Infusion Suite Wait Times X axis: Patient Visits Y axis: Time (minutes) Legend: Blue - Perceived wait time without app Orange - Perceived wait time with app Gray - Actual wait time.

Donor and Recipient Matching in Facial Vascularized Composite Allotransplantation: A Closer Look at the Donor Pool.

Identifying a donor for facial vascularized composite allotransplant recipients can be a lengthy, emotionally challenging process. Little is known about the relative distribution of key donor characteristics among potential donors. Data on actual wait times of patients are limited, making it difficult to estimate wait times for future recipients. The authors retrospectively reviewed charts of nine facial vascularized composite allotransplant patients and provide data on transplant wait times and patient characteristics. In addition, they analyzed the United Network for Organ Sharing database of dead organ donors. After excluding donors with high-risk characteristics (e.g., active cancer or risk factors for blood-borne disease transmission), the authors calculated the distribution of relevant donor-recipient matching criteria (i.e., ethnicity, body mass index, age, ABO blood group, cytomegalovirus, Epstein-Barr virus, hepatitis C virus) among 65,201 potential donors. The median wait time for a transplant was 4 months (range, 1 day to 17 months). The large majority of United Network for Organ Sharing-recorded deaths from disease were white (63 percent) and male (58 percent). Female donors of black, Hispanic, or Asian descent are underrepresented, with 7, 5, and 1 percent of all recorded deaths from disease, respectively. Potential donors show cytomegalovirus and Epstein-Barr virus seropositivity of 65 and 95 percent, respectively. The number of annual hepatitis C-positive donors increased over time. Actual facial vascularized composite allotransplant wait times vary considerably. Although most patients experience acceptable wait times, some with underrepresented characteristics exceed acceptable levels. Cytomegalovirus-seropositive donors present a large portion of the donor pool, and exclusion for seronegative patients may increase wait time. Hepatitis C-seropositive donors may constitute a donor pool for underrepresented patient groups in the future.

Effects of hospital funding reform on wait times for hip fracture surgery: a population-based interrupted time-series analysis

BackgroundHealth care funding reforms are being used worldwide to improve system performance but may invoke unintended consequences. We assessed the effects of introducing a targeted hospital funding model, based on fixed price and volume, for hip fractures. We hypothesized the policy change was associated with reduction in wait times for hip fracture surgery, increase in wait times for non-hip fracture surgery, and increase in the incidence of after-hours hip fracture surgery.MethodsThis was a population-based, interrupted time series analysis of 49,097 surgeries for hip fractures, 10,474 for ankle fractures, 1,594 for tibial plateau fractures, and 40,898 for appendectomy at all hospitals in Ontario, Canada between April 2012 and March 2017. We used segmented regression analysis of interrupted monthly time series data to evaluate the impact of funding reform enacted April 1, 2014 on wait time for hip fracture repair (from hospital presentation to surgery) and after-hours provision of surgery (occurring between 1700 and 0700 h). To assess potential adverse consequences of the reform, we also evaluated two control procedures, ankle and tibial plateau fracture surgery. Appendectomy served as a non-orthopedic tracer for assessment of secular trends.ResultsThe difference (95 % confidence interval) between the actual mean wait time and the predicted rate had the policy change not occurred was − 0.46 h (-3.94 h, 3.03 h) for hip fractures, 1.46 h (-3.58 h, 6.50 h) for ankle fractures, -3.22 h (-39.39 h, 32.95 h) for tibial plateau fractures, and 0.33 h (-0.57 h, 1.24 h) for appendectomy (Figure 1; Table 3). The difference (95 % confidence interval) between the actual and predicted percentage of surgeries performed after-hours − 0.90 % (-3.91 %, 2.11 %) for hip fractures, -3.54 % (-11.25 %, 4.16 %) for ankle fractures, 7.09 % (-7.97 %, 22.14 %) for tibial plateau fractures, and 1.07 % (-2.45 %, 4.59 %) for appendectomy.ConclusionsWe found no significant effects of a targeted hospital funding model based on fixed price and volume on wait times or the provision of after-hours surgery. Other approaches for improving hip fracture wait times may be worth pursuing instead of funding reform.

Abstract P5-08-03: Creation of a high volume, proactive high-risk breast cancer prevention program

Abstract Introduction: Approximately one out of eight women get breast cancer in their lifetime, and the National Cancer Institute estimates 268,600 new diagnoses in 2019. About 5-10% of breast cancers can be attributed to a genetic predisposition. Identifying women with genetic predispositions is significant because they qualify for more intense breast cancer screening or risk reducing strategies. Unfortunately, genetic testing is often not offered until cancer is diagnosed. This traditional genetic testing model is reactive rather than proactive, and it fails to identify a large proportion of unaffected genetic mutation carriers in the United States. Purpose: Our goal was to create a proactive system for identifying women at high risk for breast cancer. We integrated an online family history software, Progeny, into our mammography center to screen unaffected women for high-risk status (Tyrer-Cuzick (TC) score ≥ 20%) and National Comprehensive Cancer Network (NCCN) genetic testing criteria. Methods: Patients undergoing breast imaging were given a computer tablet with a preprogrammed Progeny questionnaire. Progeny collects the patient’s gynecological and family history, calculates a TC risk score, and assesses NCCN genetic testing criteria. Progeny was integrated into the mammography center on 1/10/2019 and this preliminary data was collected through 6/6/2019. Summary statistics were tabulated. Results: A total of 3,129 patients were invited to complete the questionnaire. 2,474 (79.1%) completed the questionnaire and 540 (21.8%) met NCCN genetic testing criteria. The average age of those meeting NCCN criteria was 60.3 years (range: 21-94). Of the patients who completed the questionnaire, 148 (6.0%) patients were found to be high-risk due to a TC score ≥20%. Clinical Impact: We found 540 patients who qualified for genetic counseling/testing. Under the previous, reactive genetic testing model, most of these patients would not have been selected for testing unless they received a cancer diagnosis. We recommended pretest genetic counseling to all these high-risk patients. Typical oncology genetic counselors see 10.2 new patients a week and our clinic can see 15 new patients per week. It would take 36 weeks with our current clinic system for all the patients identified during the initial questionnaire trial period to see a genetic counselor for pretest counseling. This demand alone would require two full time genetic counselors to accommodate. Conclusion: Our initial experience integrating a genetic screening questionnaire into our mammography center shows that 22% of patients need genetic testing and 6% need follow-up in a high-risk breast cancer clinic. Given current staffing constraints, it would take about 9 months to genetic test and counsel the patients identified after just 6 months of screening. This estimate does not include the demand placed on genetic counselors from current referral sources, so actual wait times could be longer than 9 months. This long wait time could increase anxiety for patients and dissatisfaction for patients and providers. A new approach to offering genetic testing and counseling to these patients is needed. Future Directions: In response to this increased clinical demand, we are partnering with Progeny and Ambry Genetics to create a novel program that allows patients to receive preliminary genetic education by video and genetic testing the same day as their mammogram appointment. Ultimately, the hope is that this new, high volume, proactive high-risk prevention program will allow a larger proportion of women with a genetic predisposition to receive diagnoses of their mutations and to initiate risk reducing and life-saving treatments. With this partnership, we will better accommodate the increased patient demand for genetic testing and avoid long wait times for counseling. Citation Format: Rebecca C Luiten, Vilert A Loving, Jennifer Siettmann, Beverly Reyes, Lida A Mina. Creation of a high volume, proactive high-risk breast cancer prevention program [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-08-03.

Predicting Patient Wait Times in the Phlebotomy Unit

Patients frequently complain of long waiting times in phlebotomy units. Patients try to predict how long they will stay in the phlebotomy unit according to the number of patients in front of them. If it is not known how fast the queue is progressing, it is not possible to predict how long a patient will wait. The number of prior patients who will come to the phlebotomy unit is another important factor that changes the waiting time prediction. We developed an artificial intelligence (AI)-based system that predicts patient waiting time in the phlebotomy unit. The system can predict the waiting time with high accuracy by considering all the variables that may affect the waiting time. In this study, the blood collection performance of phlebotomists, the duration of the phlebotomy in front of the patient, and the number of prior patients who could come to the phlebotomy unit was determined as the main parameters affecting the waiting time. For two months, actual wait times and predicted wait times were compared. The wait time for 95 percent of the patients was predicted with a variance of ± 2 minutes. An AI-based system helps patients make predictions with high accuracy, and patient satisfaction can be increased.

Price Promotions Cause Impatience

In this research, the authors propose that incidental exposure to price promotions can cause downstream impatience in an unrelated domain. Specifically, price promotions trigger reward seeking—a general motivational state—and reward seeking, in turn, yields impatience. Seven experiments (N = 1,795) demonstrate how incidental exposure to price promotions can cause greater willingness to pay to avoid waiting (Experiments 1a and 1b), shorter actual wait times (Experiments 2, 3b, and 5), greater propensity to break a rule to save time (Experiment 3a), and greater discounting in a consequential intertemporal choice (Experiment 4). Consistent with this account, the effect is both more pronounced for people with greater reward sensitivity (Experiments 3a and 3b) and mediated by reward seeking (Experiment 4). Finally, a conceptual replication in a field setting underscores the external validity and managerial relevance of the findings (Experiment 5).

The psychology of the wait time experience \u2013 what clinics can do to manage the waiting experience for patients: a longitudinal, qualitative study

BackgroundWait time, defined as time spent in the waiting and exam rooms waiting to see a provider, is a key quality metric in a number of national patient experience surveys. However, the literature on wait time does not show a consistent correlation between long waits and worse overall patient care experiences. Herein, we examine contextual factors that can shape the manner in which patients may respond to different wait times. We also identify actions providers and clinics can take to promote positive wait experiences and mitigate negative ones.MethodsWe conducted over 130 h of semi-structured interviews with patients new to two HIV primary care clinics in Houston, Texas. We interviewed patients before the first provider visit, again within two weeks of the first visit, and again at 6–12 months. We analyzed the interviews using directed and conventional content analysis.ResultsOur study showed that patients’ “willingness to wait” is the product of the actual wait time, individual factors, such as the perceived value of the visit and cost of a long wait, and clinic and provider factors. Analyses revealed key steps providers and clinics can take to improve the wait time experience. These include: 1) proactively informing patients of delays, 2) explicitly apologizing for delays, and 3) providing opportunities for diversion. Patients noted the importance of these steps in curtailing frustrations that may result from a long wait.ConclusionsOur study highlights key steps cited by patients as having the potential to improve the wait time experience. These steps are practical and of particular interest to clinics, where waits are oftentimes inevitable.

Demographic-related variables impact subjective experiences of patient wait times and perceived attention afforded in surgical outpatient clinic encounters

Patient satisfaction remains a key component in successful delivery of high-quality healthcare. In this study, we attempted to better understand how patient demographics might influence perception of clinic wait times and determine factors that may positively influence perception of a clinic experience. A prospective study was conducted assessing patient satisfaction during outpatient surgical clinics in minimally invasive, breast, plastic/reconstructive, and orthopaedic surgery between May and September 2017. Patient demographics, subjective and objective assessments of wait time and physician encounter, and qualitative assessments of physician and patient interaction were collected. 150 patients were enrolled with median age between 45 and 54 years old. Patients perceived mean wait times of 22.5 min and contact with physician as 12.3 min. Objective measures of wait and physician-contact times were 30.8 min and 10.7 min. These trends persisted despite surgical specialty and new versus returning patient class. Widowed patients perceived receiving less attention by doctors (p<.05), retirees believed they spent less time with their physician (p<.05), and associate's degree holders as highest education status had greater differences in perceived-versus-actual contact time with their doctor (p<.05). Clinic patients reported high satisfaction scores (>96%) quantifying physician eye-contact (99.3%), attention (99.8%), clarity of clinical communication (98.7%), interest in answering questions (99.2%), and reasonability (98.2%) highly. Patients described their physicians as excellent (99.4%) and were likely to refer their provider to others (99.9%). Our findings suggest qualitative factors of patient encounters including eye contact, attention, communication, interest, and subjective perceptions of time bear more weight in the final assessment of patient satisfaction with care than quantitative factors such as actual wait time and duration of time with provider. This is irrespective of differences in perceived wait and contact times between different groups.

Estudo de filas e análise qualitativa do desempenho de um restaurante universitário

Verificar o desempenho de operações em serviço pode auxiliar na indicação de oportunidades de melhoria dessas operações. Neste contexto, o presente artigo tem o objetivo de verificar em uma escala de 0% a 100% o nível de serviço de uma operação de produção (restaurante universitário) segundo cinco objetivos de desempenho: qualidade do serviço, qualidade do produto, flexibilidade do produto, custo do produto e desempenho do sistema de filas. Para alcançar o objetivo proposto, estruturou-se um procedimento composto por quatro etapas. Essas etapas envolvem (1) a definição do local de aplicação e suas características, (2) a coleta de dados quantitativos em campo e definição das características operacionais da fila, (3) a definição e a coleta de dados qualitativos em campo e tabulação desses dados, além da (4) análise dos dados qualitativos e características operacionais da fila. De um modo geral, a qualidade do serviço, a qualidade do produto e a flexibilidade do produto se destacaram com baixo nível de insatisfação. No caso do desempenho do sistema de filas, há oportunidade de melhoria, pois o tempo real médio de espera é superior ao tempo que os respondentes acham adequado para esperar na fila. Outro ponto de melhoria é o custo do produto, com redução ideal deste valor em 33%, segundo os respondentes.

Patient Wait Time Recall Accuracy for Gastroenterology Specialty Consultation in Nova Scotia

BACKGROUND: Inflammatory Bowel Disease (IBD) is a chronic disease with lifelong health, social, and economic burden. The province of Nova Scotia (NS) has the highest age-adjusted incidence and prevalence rates of IBD in Canada, with Canada having the highest prevalence rates of IBD in the world. The Canadian Association of Gastroenterology guidelines suggest wait times between two and 16 weeks for those with active IBD symptoms. Despite these guidelines, a 2015 audit of the NS Health Authority revealed that 50% of IBD referrals were seen within 12 weeks and 90% of referrals waiting longer (up to two years). Long wait times can lead to increased anxiety, decreased quality of life, and reduction in patient satisfaction and overall health. Estimating wait times is complex but essential in order to evaluate access to specialty care. Aims: 1) To determine whether patients referred to GI specialty services in NS can accurately estimate the length of time between GP referral and first GI specialty appointment; 2) To examine demographic, disease-related, and system factors which may influence the accuracy of patient wait time estimates. METHODS: Questionnaires were distributed to patients following their appointment with a luminal GI or IBD nurse practitioner. Patients were asked to estimate their wait time for seeing a GI specialist. They were also asked to report on factors that could influence their wait time recall (geographic locale, age, employment status, completed education, disease severity, and relevant comorbidities). Completed questionnaires were returned and retrospective chart reviews were performed to validate the patients' responses. Descriptive analyses (means, standard deviations) on disease phenotype, complications and surgeries, current and past medication use, distance travelled to appointment, and perceived acceptability of wait times were completed. Spearman's correlations were run on wait time estimates and actual wait times to determine level of estimate accuracy. RESULTS: A total of 70 patients were prospectively enrolled as of July 2017. Forty-three (61.4%) patients were female, with an average age of 45.3 years (SD=20.3 years). When patients were asked to estimate their wait time between referral and seeing a GI specialist, they reported an average of 35.7 weeks. Following retrospective chart reviews, the patient estimates were shown to be conservative. In reality, records showed the average patient waited 39.8 weeks from the time the referral was sent to seeing a GI specialist. Spearman's correlation was used to determine the relationship between patient estimates and referral dates. There was a strong positive correlation between patient estimates and referral dates (rs=0.8, N=67, P<0.0001). CONCLUSION(S): This study is the first of its kind to look at the accuracy of patient recall of wait times for gastroenterology. The initial pilot data highlights the reality of excessive wait times in NS for patients seeking GI speciality care and suggests that patient wait time recall may be used to provide estimates of specialist wait times for patients with IBD. Future research will look at access to GI care using a healthcare systems mapping approach using patient recall to estimate perceived wait times to better inform clinical care pathways in the province.

What Wait Time in a Soiled Pad Is Acceptable to Older Patients and Their Direct Caregivers?

We sought to establish views on wait times and hypothesized that they may vary depending upon perspective, circumstance, and incontinence type. Cross-sectional, descriptive survey. Eligible patients were inpatients, 65 years and older, in a single tertiary acute care hospital. Eligible staff were regulated (licensed) and unregulated providers of direct care to patients. Patients may or may not have used absorbent continence products prior to their admission. We examined views on acceptable and actual wait times of elderly acute care inpatients and their direct care providers (DCPs). Participants were asked about wait times for pads soiled with urine or feces during the day and at night. Differences between patients and DCPs and acceptable and actual wait times were compared. Factors associated with tolerance to the presence of urine or stool in absorptive products were analyzed by logistic regression. There was patient-provider mismatch for daytime urinary incontinence: 90% of patients but only 44% of DCPs reported urinary soiling more than 1 hour in the daytime as unacceptable (38.0 vs 85.0 minutes; P < .0001). A significant majority (80%-90%) of both groups reported short acceptable wait times for fecal incontinence (<15 minutes). The odds of being tolerant to any soiling were significantly higher in patients who were prior residents of care facilities (odds ratio [OR] = 6.2; 95% confidence interval [CI], 1.3-28.1; P = .019), previously used incontinence products (OR = 2.0; 95% CI, 1.0-3.8; P = .036), or used walking aids (OR = 4.0; 95% CI, 1.1-14.7; P = .039). Actual wait times were significantly longer than deemed acceptable by either patients or DCPs. There are significant gaps in patient-provider perspectives on acceptable wait times in soiled incontinence products. Direct care providers need to take patient preference into account when managing continence.

Patient perspectives on wait times and the impact on their life: A waiting room survey in a chronic pain clinic

Chronic pain is a debilitating condition that requires prompt access to care for effective treatment. Wait times for care often exceed benchmark recommendations, with potential consequences to patient health outcomes. The goal of this paper is to gain the perspectives of patients attending a chronic pain clinic regarding the acceptability of current wait times and the impact of their experiences of waiting for chronic pain care. The study took place in a chronic pain clinic at an academic-affiliated teaching hospital in Ottawa, Canada, which housed seven clinicians at the time of the study. New patients attending the chronic pain clinic between July 14, 2014 and August 5, 2015 were eligible to participate based on the availability of the research and clerical staff who administered the survey on a variety of days over the course of the study. Patients completed a self-administered 29-item survey. The survey took approximately five to ten minutes to complete. Questions pertained to patients' socioeconomic factors, chronicity and burden of pain symptoms, and satisfaction with current wait times. Actual wait times were self-reported. Survey results were entered into an Excel spreadsheet, exported to SPSS, and coded numerically to facilitate descriptive analyses using comparative graphs and tables. Open-text responses were reviewed by the authors. Sixty-six patients completed the survey. While 83% of patients stated that their ideal wait time was less than three months, 32% reported receiving an appointment within this period, and 31% reported waiting a year or more. Only 37% of patients felt the wait time for their appointment was appropriate. During their wait, 41% of patients reported receiving written information about chronic pain and 47% were referred to a local chronic pain management group. 94% reported interference with social/recreational activities and normal activities of daily living, 31% had to miss work or school due to the frequency of ongoing symptoms, and 22% reported being unable to attend work or school altogether. Furthermore, 37% of patients reported visiting the emergency room within the previous year and 65% worried about having a serious undiagnosed disease. Our study found that wait times for chronic pain care, even those triaged as urgent cases, far exceeded what patients considered ideal. Only a third of patients received care within three months of making their appointment, while nearly another third waited over a year. During the waiting period, nearly all patients experienced some impact on their day-to-day activities and work or school attendance, half were unemployed, and nearly a quarter reported a complete inability to attend work or school because of pain. Wait times for chronic pain care exceed timelines deemed acceptable by patients, causing anxiety and reducing function. The patient perspective must be considered in initiatives attempting to improve access to care for this population with specific needs and goals. Innovative solutions, such as electronic consultation and shared care models, hold promise.

The relationship between emergency department wait times and inpatient satisfaction

ABSTRACTThis study examines patient perceptions of emergency department wait times and inpatient experiences. For many hospitals across the United States, the emergency department (ED) is now the “front door”; therefore, understanding the impact of ED experience on the inpatient experience is critical for leaders managing these complex settings today. Results showed statistically significant relationships between a very good ED experience and a very good inpatient experience. Perceived wait times in the ED, more so than actual ED wait times, served as a predictor of a very good ED rating as well as a very good rating of the inpatient experience.

Patient Satisfaction Is Associated With Time With Provider But Not Clinic Wait Time Among Orthopedic Patients

Clinic wait time is considered an important predictor of patient satisfaction. The goal of this study was to determine whether patient satisfaction among orthopedic patients is associated with clinic wait time and time with the provider. The authors prospectively enrolled 182 patients at their outpatient orthopedic clinic. Clinic wait time was defined as the time between patient check-in and being seen by the surgeon. Time spent with the provider was defined as the total time the patient spent in the examination room with the surgeon. The Consumer Assessment of Healthcare Providers and Systems survey was used to measure patient satisfaction. Factors associated with increased patient satisfaction included patient age and increased time with the surgeon (P=.024 and P=.037, respectively), but not clinic wait time (P=.625). Perceived wait time was subject to a high level of error, and most patients did not accurately report whether they had been waiting longer than 15 minutes to see a provider until they had waited at least 60 minutes (P=.007). If the results of the current study are generalizable, time with the surgeon is associated with patient satisfaction in orthopedic clinics, but wait time is not. Further, the study findings showed that patients in this setting did not have an accurate perception of actual wait time, with many patients underestimating the time they waited to see a provider. Thus, a potential strategy for improving patient satisfaction is to spend more time with each patient, even at the expense of increased wait time. [Orthopedics. 2017; 40(1):43-48.].

Accessibility and Barriers to Oncology Appointments at 40 National Cancer Institute–Designated Comprehensive Cancer Centers: Results of a Mystery Shopper Project

Patients turn to National Cancer Institute (NCI) -designated comprehensive cancer centers because of perceived better quality and more timely access to care. However, recent studies have found that patients at various institutions may struggle to gain access to an appointment or obtain consistent information from attendants. Our study employs a mystery shopper format to identify and quantify barriers faced by patients seeking to make a first consultation appointment across a homogenous sample of 40 NCI-designated comprehensive cancer centers. Five mystery shoppers used a standardized call script to inquire about first available appointment times and service offerings. When inquiring about a date for a first available appointment, 29% of callers were unable to secure an estimated date without registering into the center's database, 51% were able to secure an estimated date, and 20% were provided with an actual date. Of estimated or actual dates for a first available appointment, 74% were greater than 1 week away. There was no statistically significant variation between appointment availability across insurance type or US region. Our study highlights the difficulty of accessing information about appointment availability. Although not statistically significant, inquiries regarding first available appointments for Medicaid patients resulted in longer estimated or actual wait times than those for patients with private insurance, and Medicaid shoppers noted qualitative differences. Although our study was limited by small sample size and imperfect analytic methods, our results suggest the need for more efficient and accessible care for patients at our nation's top cancer centers.

Waiting for scheduled surgery: A complex patient experience.

The aim of this study was to understand experiences of wait time among patients awaiting scheduled orthopaedic or cardiac surgery. Using a qualitative approach, 32 patients completed two interviews each regarding their wait time experiences, including effects of waiting. Patient experiences of wait time varied regardless of actual wait time and included reports of restriction, uncertainty, resignation, coping and opportunity. Participants' waiting experiences indicate a complex relationship between greater symptom severity and less tolerance for wait time. We suggest healthcare resources focus on alleviating the deleterious effects of waiting for certain patients rather than reducing absolute wait times.