scholarly journals P.017 Results of a Pilot feasibility study to develop reduce wait times strategy in the evaluation of children with new onset epilepsy

2018 ◽  
Vol 45 (s2) ◽  
pp. S20-S20
Author(s):  
JA Mailo ◽  
M Diebold ◽  
E Mazza ◽  
P Guertjens ◽  
H Gangam ◽  
...  
Keyword(s):  
Patient Flow ◽  
Waiting Times ◽  
Wait Time ◽  
Febrile Seizures ◽  
Limiting Factors ◽  
Wait Times ◽  
Timely Access ◽  
Flow Through ◽  
Pilot Feasibility Study ◽  
New Onset

Background: The goal was to understand factors leading to prolonged wait times for neurological assessment of children with new onset seizures. A second objective was to develop an innovative approach to patient flow through and achieve a reduction in waiting times utilizing limited resources.Methods:Audit of the referrals, flow through, wait timesIdentification of bottlenecksDevelopment of triaging strategy:Suspected Febrile seizures and non-epileptic events;Suspected benign and absence epilepsies;Suspected other Focal epilepsies, generalized epilepsies, epilepsy under 2 yearsInitiation of early telephone contact and supportDevelopment of a ketogenic dietResults: Using a triaging strategy and focusing on timely access to investigations, wait times for clinic evaluations were shortened despite larger numbers of referrals (mean wait time reductions from 179 to 91 days). Limiting factors such increase in referral numbers, attrition in support staff, interfered with sustainability of reduced wait times achieved in the initial phase of the program. Conclusions: This pilot study highlights the effectiveness of an innovative triaging strategy and improvements in patient flow through in achieving the goals of reduction in wait times for clinical evaluation and timely investigations to improve care for children with new onset seizures. Insights into limitations of such strategies and factors determining sustainability are discussed.

scholarly journals Online Ratings of Primary Care Physicians: Comparison of Gender, Training, and Specialty

2021 ◽  
Vol 8 ◽  
pp. 237437352110077
Author(s):  
Daliah Wachs ◽  
Victoria Lorah ◽  
Allison Boynton ◽  
Amanda Hertzler ◽  
Brandon Nichols ◽  
...  
Keyword(s):  
Primary Care ◽  
Primary Care Physicians ◽  
Waiting Times ◽  
Wait Time ◽  
Primary Care Providers ◽  
Wait Times ◽  
Care Providers ◽  
Female Physicians ◽  
Online Ratings ◽  
And Gender

The purpose of this study was to explore patient perceptions of primary care providers and their offices relative to their physician’s philosophy (medical degree [MD] vs doctorate in osteopathic medicine [DO]), specialty (internal medicine vs family medicine), US region, and gender (male vs female). Using the Healthgrades website, the average satisfaction rating for the physician, office parameters, and wait time were collected and analyzed for 1267 physicians. We found female doctors tended to have lower ratings in the Midwest, and staff friendliness of female physicians were rated lower in the northwest. In the northeast, male and female MDs were rated more highly than DOs. Wait times varied regionally, with northeast and northwest regions having the shortest wait times. Overall satisfaction was generally high for most physicians. Regional differences in perception of a physician based on gender or degree may have roots in local culture, including proximity to a DO school, comfort with female physicians, and expectations for waiting times.

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

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

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

scholarly journals An Audit of Wait Times for Service Offered by the Gynaecology Oncology Unit at the University Hospital in Jamaica: Are We Meeting the Standards?

2016 ◽  
Vol 2 (3_suppl) ◽  
pp. 74s-74s
Author(s):  
Ian Bambury ◽  
Christopher Fletcher ◽  
Carole Rattray ◽  
Matthew Taylor ◽  
Charmaine Mitchell ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Conflicts Of Interest ◽  
Waiting Times ◽  
Wait Time ◽  
International Standards ◽  
University Hospital ◽  
Wait Times ◽  
Oncology Unit ◽  
The University ◽  
Mean Time

Abstract 53 Background: The European Board and College of Obstetrics and Gynaecology recommends that time from referral of suspected or proven gynaecological cancers to consultation should be within two weeks and that initiation of treatment should occur within six weeks. It is has been shown that a delay in waiting times beyond these international standards results in significantly increased morbidity and mortality. Methods: An audit of wait times was performed for all patients who presented to the gynaecology oncology unit at the University Hospital in Jamaica for consultation between January 1, 2013 and December 31, 2013. Wait time for consultation was calculated as the date of first referral to the date of initial consultation. Wait time for treatment was calculated as time from consultation to either surgery or initiation of radiation therapy or chemotherapy. Primary site, stage, and the region from which the referrals came were abstracted from the medical record. Results: A total of 1,289 unique patients were seen at least once during the audit period; of these, 108 were new consultations and 1,219 were patients seen for follow-up. 72% were from the greater metropolitan area (Kingston & St. Andrew), while the others were from the surrounding parishes of Jamaica. Of the 108 new patients, malignancy was confirmed in 70 (65%). Case make-up included 23 cases of endometrial cancer (33%); 20 cases of cervical cancer (29%); 16 cases of ovarian cancer (23%); and 11 cases of other gynecologic cancers (vulvar, vaginal, choriocarcinoma) (15%). At presentation, there were 23 patients with stage 1 disease (33%), 16 patients with stage 2 disease (23%), 27 patients with stage 3 disease (38%); and 4 patients with stage 4 disease (6%). 39 out of 70 patients with malignancy (56%) underwent surgery; 11 (15.7%) were treated with radiation therapy; and 22 (31%) were treated with chemotherapy. Among patients with a cancer diagnosis, the mean time from referral to consultation was 2.1 weeks. Mean time from consultation to surgery was 7.6 weeks; mean time from consultation to start of radiotherapy was 16 weeks; and mean time from consultation to start of chemotherapy was 11.6 weeks. 66% of patients underwent surgery within the international standard of six weeks from referral. Only 36% initiated radiation therapy and 14% initiated chemotherapy within six weeks from referral. Conclusion: While the majority of patients met international standards for time to consultation to surgery, wait times for initiation of radiation and chemotherapy were sub-standard. This audit has provided information that will help us to assess the inadequacy of available services and could potentially inform national cancer policies in Jamaica. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST: No COIs from the authors.

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

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

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

scholarly journals Waiting for Treatment for Chronic Pain – a Survey of Existing Benchmarks: Toward Establishing Evidence-Based Benchmarks for Medically Acceptable Waiting Times

2007 ◽  
Vol 12 (4) ◽  
pp. 245-248 ◽  
Author(s):  
Mary E Lynch ◽  
Fiona A Campbell ◽  
Alexander J Clark ◽  
Michael J Dunbar ◽  
David Goldstein ◽  
...  
Keyword(s):  
Systematic Review ◽  
Chronic Pain ◽  
Time Management ◽  
Task Force ◽  
International Association ◽  
Waiting Times ◽  
Wait Time ◽  
Wait Times ◽  
Pain Clinics ◽  
The World

As medical costs escalate, health care resources must be prioritized. In this context, there is an increasing need for benchmarks and best practices in wait time management. In December 2005, the Canadian Pain Society struck a Task Force to identify benchmarks for acceptable wait times for treatment of chronic pain. The task force mandate included a systematic review and survey to identify national or international wait time benchmarks for chronic pain, proposed or in use, along with a review of the evidence upon which they are based. An extensive systematic review of the literature and a survey of International Association for the Study of Pain Chapter Presidents and key informants has identified that there are no established benchmarks or guidelines for acceptable wait times for the treatment of chronic pain in use in the world. In countries with generic guidelines or wait time standards that apply to all outpatient clinics, there have been significant challenges faced by pain clinics in meeting the established targets. Important next steps are to ensure appropriate additional research and the establishment of international benchmarks or guidelines for acceptable wait times for the treatment of chronic pain. This will facilitate advocacy for improved access to appropriate care for people suffering from chronic pain around the world.

scholarly journals Innovative Strategies for Decreasing Blood Collection Wait Times for Patients in Early-Phase Cancer Clinical Trials

2016 ◽  
Vol 12 (7) ◽  
pp. e784-e791 ◽  
Author(s):  
Bayabel Mengistu ◽  
Dina Ray ◽  
Passion Lockett ◽  
Vivian Dorsey ◽  
Ron A. Phipps ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Patient Flow ◽  
Care Facility ◽  
Cost Savings ◽  
Primary Source ◽  
Wait Time ◽  
Turnover Time ◽  
Wait Times ◽  
Blood Collection

Purpose: Long wait times are a primary source of dissatisfaction among patients enrolled in early-phase clinical trials. We hypothesized that an automated patient check-in system with readily available display for increasing awareness of waiting intervals would improve patient flow and use of our rooms, with decreased turnover time and increased throughput. Methods: We recorded in-room wait times for patients seen in our clinic and observed the logistics involved in the blood collection process to delineate causes for delays. We then implemented a three-step strategy to alleviate the causes of these delays: (1) changing the collection of materials and the review of faxed orders, (2) improving our LabTracker automated database system that included wait time calculators and real-time information regarding patient status, and (3) streamlining lower complexity appointments. Results: After our intervention, we observed a 19% decrease in mean wait times and a 30% decrease in wait times among patients waiting the longest (95th percentile). We also observed an increase in staff productivity during this process. Modifications in LabTracker provided the biggest reduction in mean wait times (17%). Conclusion: We observed a significant decrease in mean wait times after implementing our intervention. This decrease led to increased staff productivity and cost savings. Once wait times became a measurable metric, we were able to identify causes for delays and improve our operations, which can be performed in any patient care facility.

The Impact of Diagnostic Imaging Wait Times on the Prognosis of Lung Cancer

2015 ◽  
Vol 66 (1) ◽  
pp. 53-57 ◽  
Author(s):  
Suzanne C. Byrne ◽  
Brendan Barrett ◽  
Rick Bhatia
Keyword(s):  
Lung Cancer ◽  
Diagnostic Imaging ◽  
Lung Biopsy ◽  
Patient Flow ◽  
Tumour Size ◽  
Wait Time ◽  
Wait Times ◽  
Eastern Health ◽  
Ct Guided ◽  
Initial Imaging

Objective This study was performed to determine whether gaps in patient flow from initial lung imaging to computed tomography (CT) guided lung biopsy in patients with non–small cell lung cancer (NSCLC) was associated with a change in tumour size, stage, and thus prognosis. Methods All patients who had a CT-guided lung biopsy in 2009 (phase I) and in 2011 (phase II) with a pathologic diagnosis of primary lung cancer (NSCLC) at Eastern Health, Newfoundland, were identified. Dates of initial abnormal imaging, confirmatory CT (if performed), and CT-guided biopsy were recorded, along with tumour size and resulting T stage at each time point. In 2010, wait times for diagnostic imaging at Eastern Health were reduced. The stage and prognosis of NSCLC in 2009 was compared with 2011. Results In phase 1, there was a statistically significant increase in tumour size (mean difference, 0.67 cm; P < .0001) and stage ( P < .0001) from initial image to biopsy. There was a moderate correlation between the time (in days) between the images and change in size ( r = 0.33, P = .008) or stage ( r = 0.26, P = .036). In phase II, the median wait time from initial imaging to confirmatory CT was reduced to 7.5 days (from 19 days). At this reduced wait time, there was no statistically significant increase in tumour size (mean difference, 0.02; P > .05) or stage ( P > .05) from initial imaging to confirmatory CT. Conclusions Delays in patient flow through diagnostic imaging resulted in an increase in tumour size and stage, with a negative impact on prognosis of NSCLC. This information contributed to the hiring of additional CT technologists and extended CT hours to decrease the wait time for diagnostic imaging. With reduced wait times, the prognosis of NSCLC was not adversely impacted as patients navigated through diagnostic imaging.

Formative Exploration of the Relationship Between Waiting Times and Attendance at General Adult Psychiatry Clinics, at a Hospital in Pennine Care NHS Foundation Trust

2017 ◽  
Vol 41 (S1) ◽  
pp. S607-S607
Author(s):  
M. Gan ◽  
S. Salujha
Keyword(s):  
Waiting Times ◽  
Wait Time ◽  
Wait Times ◽  
Clinic Attendance ◽  
Outpatient Psychiatry ◽  
Adult Psychiatry ◽  
Adult Outpatient ◽  
Competing Interest ◽  
The Relationship ◽  
Patient Referrals

IntroductionAn audit was done to assess new patient wait times. As part of this project we reviewed possible relationships between wait times and clinic attendance.ObjectiveTo examine available data, for possible explanations of patients’ attendance behaviour at outpatient clinics.AimsTo identify possible correlation between length of waiting time for adult psychiatry appointments and status of attendance.MethodService Line: New patient referrals to adult outpatient psychiatry (January–December 2015)Sample size: 401.ResultsFig. 1 and Table 1 show wait times compared with clinic attendance outcome. Percentage attendance appeared to gradually fall as wait times increased; while cancellation (%) by the NHS, and DNAs (did not attend) by the patient, appeared to rise over time.ConclusionsThis review has demonstrated a possible correlation between wait time for a clinic appointment and how patients behave. The shorter a patient has to wait; it appears they are more likely to actually attend clinic. If so, this potentially has implications for discussions around possible reorganization of services, to improve engagement and outcomes, by coming up with innovative ways of reducing wait times.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Predicting Patient Wait Times in the Phlebotomy Unit

2020 ◽  
pp. 1-5
Author(s):  
Dilek Orbatu ◽  
Oktay Yıldırım ◽  
Eminullah Yaşar ◽  
Ali Rıza Şişman ◽  
Süleyman Sevinç
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
High Accuracy ◽  
Wait Times ◽  
Blood Collection ◽  
Time Prediction ◽  
Number Of Patients ◽  
Patient Will ◽  
Patient Waiting Time

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.

Wait Times and Bed Capacity for Hematopoietic Cell Transplantation in Ontario: A System Dynamics Model

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2378-2378
Author(s):  
Jonathan Wang ◽  
Saba Vahid ◽  
Hertz Sherrie ◽  
C. Tom Kouroukis
Keyword(s):  
System Dynamics ◽  
Research Funding ◽  
Hematopoietic Cell Transplantation ◽  
Patient Flow ◽  
Wait Time ◽  
Flow Diagram ◽  
Wait Times ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Bed Capacity

Abstract Objectives: Cancer Care Ontario (CCO) is the provincial governmental organization responsible for planning hematopoietic cell transplantation (HCT) services in Ontario, Canada. The objective of this project is to develop a capacity planning model to investigate the effects on wait times of adding extra bed capacity for allogeneic transplant (ALLOHCT) in HCT centers. Approach: A high-level process flow diagram was generated to understand patient flow at a 6-bed HCT unit within a hospital in Ontario and validated through consultation. This flow diagram was used to construct a system dynamics model to simulate patient flow. The model was parameterized with data from CCO, Discharge Abstract Database, and with hospital and clinical expert input. The effects at six months were projected for five scenarios: 1) current state; 2) increase bed capacity by 1 bed; or 3) increase bed capacity by 2 beds; 4) increasing patient demand by 20 patients per year; 5) combination of scenarios 3 and 4. Provincial clinical consensus established a benchmark wait time of 42 days for ALLOHCT from ready to transplant to the transplant date. In addition, the estimated number of beds required to reduce the wait times to the provincial benchmark within 1 year was calculated. Results:The addition of 1 ALLOHCT bed resulted in a reduction of 22% and 11% to the ALLOHCT wait times and wait lists, respectively. The addition of 2 beds resulted in a reduction of 38% and 22% to the wait times and wait lists, respectively. If the demand increases by 20 patients per year, the addition of 2 beds resulted in a reduction of 16% in the wait times and while the wait list may experience a brief reduction, after 6 months, the wait list size will have increased by 9% as a result of the increased demand. In order to reduce the wait times to the provincial benchmark within 1 year, an additional 8 beds are needed. Considerations: Concurrent planning for additional health human resources (physicians, nurses, etc…) needs to be done to ensure the additional beds are adequately staffed. This model also only considers the effects of adding beds within 1 year. There may be instances where bed space cannot be immediately opened and new capital is required. Additionally, the demand for ALLOHCT continues to increase, which in turn drives up the number of arrivals to the queue. A multi-year model will be built to account for timing of bed openings and increasing demand for ALLOHCT. Conclusion:Using a system dynamics model, we are able to quantify the relationship between ALLOHCT bed capacity and wait times at an HCT center. This model can be used to estimate the ALLOHCT bed requirements for sites in other jurisdictions where ALLOHCT demand and wait time benchmarks are known. Disclosures Kouroukis: Janssen: Research Funding; Karyopharm: Research Funding.

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