scholarly journals Influence of publicly available online wait time data on emergency department choice in patients with noncritical complaints

CJEM ◽  
2012 ◽  
Vol 14 (04) ◽  
pp. 237-246 ◽  
Author(s):  
Amelia Yip ◽  
Shelley McLeod ◽  
Andrew McRae ◽  
Bin Xie
Keyword(s):  
Emergency Department ◽  
Patient Satisfaction ◽  
Public Awareness ◽  
Wait Time ◽  
Future Research ◽  
Wait Times ◽  
Time Data ◽  
Logistic Regression Models ◽  
Willingness To Use ◽  
Time Information

ABSTRACTObjectives:Increased emergency department (ED) wait times lead to more patients who leave without being seen and decreased patient satisfaction. Many EDs post estimated wait times either online or in the ED to guide patient expectations. The objectives of this study were to assess patients' awareness of online wait time data and to investigate patients' willingness to use this information when choosing between two academic EDs in London, Ontario.Methods:A prospective study was conducted over a 2-month period in a tertiary ED with online available wait times. Patients over 18 years of age assigned a Canadian Triage and Acuity Scale (CTAS) score of 3, 4, or 5 were approached by trained research assistants to complete a 15-item paper-based questionnaire. Multivariable logistic regression models were used to determine factors independently associated with the outcomes.Results:A total of 1,211 patients completed the survey. Of these, 109 (9%) were aware that ED wait time information was available on the Internet; 544 (45%) reported that they would use the available data to make a decision on which ED to visit, and 536 (44%) indicated that they were more likely to go to the ED with a shorter wait time. Age, gender, household income, education, and Internet access were not associated with awareness of online ED wait times. Participants less than 40 years of age were more likely to use online wait time information.Conclusion:There is low awareness of the availability of ED wait time data published online in the study locaton. Future research may include the delivery of a public awareness strategy for ED wait time data and a re-evaluation of ED use and patient satisfaction following this.

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

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

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

scholarly journals The Effect of Bedside Presentations in the Emergency Department on Patient Satisfaction

2011 ◽  
Vol 3 (4) ◽  
pp. 481-486 ◽  
Author(s):  
Craig I. Schranz ◽  
Robert J. Sobehart ◽  
Kiva Fallgatter ◽  
Robert H. Riffenburgh ◽  
Michael J. Matteucci
Keyword(s):  
Emergency Department ◽  
Patient Satisfaction ◽  
Resident Education ◽  
Statistical Significance ◽  
Future Research ◽  
First Year ◽  
Convenience Sample ◽  
Visual Analog Scales ◽  
Significant Difference ◽  
The Difference

Abstract Background Due to increasing time constraints, the use of bedside presentations in resident education has declined. We examined whether patient satisfaction in the emergency department is affected when first-year residents present at the bedside with attendings. Methods We performed an observational, prospective, nonblinded study in the emergency department of a military teaching hospital. We alternately assigned first-year residents to present a convenience sample of 248 patients to the attending physician at the patient's bedside or away from the patient. We measured patient satisfaction by using the Patient Satisfaction Questionaire-18 (PSQ-18), a validated survey instrument that utilizes a Likert scale, and additional nonvalidated survey questions involving Likert and visual analog scales. Results While the median PSQ-18 score of 74 (95% confidence interval [CI], 72–76) was higher for patient satisfaction when residents made bedside presentations than that for standard presentations, 72 (95% CI, 70–74), the difference did not reach statistical significance (P  =  .33). Conclusion There was no significant difference in overall patient satisfaction between residents' bedside presentations and presentations to attendings away from the patient. Although not significant, the differences noted in PSQ-18 subscales of communication, general satisfaction, and interpersonal manner warrant further investigation. Patients did not appear to be uncomfortable with having their care discussed and with having subsequent resident education at the bedside. Future research on patient satisfaction after implementation of standardized bedside teaching techniques may help further elucidate this relationship.

scholarly journals P140: Investigating volunteer perspectives on leading patient-centred practices in the emergency department

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S114-S115
Author(s):  
L. Witt ◽  
T. Oyedokun ◽  
D. Goodridge ◽  
J. Stempien ◽  
T. Graham
Keyword(s):  
Emergency Department ◽  
Patient Satisfaction ◽  
Quality Care ◽  
Waiting Times ◽  
Physical Space ◽  
Cultural Safety ◽  
Wait Times ◽  
Two Phase ◽  
Survey Responses ◽  
Current Practices

Introduction: Patient satisfaction is an essential component of effective delivery of quality care in the emergency department (ED). Frequent reflection on current practices is required to detect areas in need of improvement. The Ontario Hospital Association (OHA) outlined five ‘Leading Practices’ (LPs) targeted to increase patient satisfaction in this setting. The ED volunteers are a group of individuals who have unique perspectives on ED practices that are unbiased by confounders affecting patients and staff. The goal of this study was to explore the unique perspectives of ED volunteers involving what they believe will improve the delivery of patient-centered care, as well as to examine to what extent Saskatoon EDs are embracing the principles outlined in the OHA LPs. Methods: A two-phase mixed methods approach, with a survey followed by interviews that allowed participants to expand on survey findings was used. The pool of 45 ED volunteers was extended the opportunity to participate resulting in 36 survey responses and 6 interviews. The 13 Likert-grade survey questions were generated to align to each of the LPs and allowed room for qualitative feedback. Interview questions were generated following 15 survey responses to expand on the LPs that were rated below average. Results: Analysis of responses identified inefficient ED processes leading to increased waiting times, inefficient patient location, inadequate signage, a lack of physical space, unclean environments, and a lack of staff and volunteer awareness regarding spiritual care and interpreter services, perceptions of received care by patients due to long wait times and level of cultural safety training of ED staff. Themes reduced from interviews yielded common themes such as patient frustration, disorganization, uncomfortable environment, overcrowding, prolonged wait times, and patient misconception of ED processes at Site 1. Themes common to Site 2 included organization, patient-friendly environment, patient misconception of ED processes, and prolonged wait times. Additionally, the volunteers suggested a plethora of interventions that could improve the current processes in Saskatoon's EDs to make them more patient friendly. Conclusion: Saskatoon EDs comply reasonably well to the OHA Leading practices. Surveying ED volunteers provides important insight into current practices and areas for improvement, and should be considered at other sites to improve adherence to the OHA LPs.

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

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

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

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

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

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

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

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

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

scholarly journals A comparative study of patient characteristics, opinions, and outcomes, for patients who leave the emergency department before medical assessment

CJEM ◽  
2016 ◽  
Vol 19 (5) ◽  
pp. 347-354 ◽  
Author(s):  
Jacqueline Fraser ◽  
Paul Atkinson ◽  
Audra Gedmintas ◽  
Michael Howlett ◽  
Rose McCloskey ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Department ◽  
Performance Indicator ◽  
Medical Condition ◽  
Demographic Data ◽  
Family Doctor ◽  
Wait Time ◽  
Wait Times ◽  
Control Group ◽  
Fisher Exact Test

AbstractObjectiveThe emergency department (ED) left-without-being-seen (LWBS) rate is a performance indicator, although there is limited knowledge about why people leave, or whether they seek alternate care. We studied characteristics of ED LWBS patients to determine factors associated with LWBS.MethodsWe collected demographic data on LWBS patients at two urban hospitals. Sequential LWBS patients were contacted and surveyed using a standardized telephone survey. A matched group of patients who did not leave were also surveyed. Data were analysed using the Fisher exact test, chi-square test, and student t-test.ResultsThe LWBS group (n=1508) and control group (n=1504) were matched for sex, triage category, recorded wait times, employment and education, and having a family physician. LWBS patients were younger, more likely to present in the evening or at night, and lived closer to the hospital. A long wait time was the most cited reason for leaving (79%); concern about medical condition was the most common reason for staying (96%). Top responses for improved likelihood of waiting were shorter wait times (LWBS, 66%; control, 31%) and more information on wait times (41%; 23%). A majority in both groups felt that their condition was a true emergency (63%; 72%). LWBS patients were more likely to seek further health care (63% v. 28%; p<0.001) and sooner (median time 1 day v. 2-4 days; p=0.002). Among patients who felt that their condition was not a true emergency, the top reason for ED attendance was the inability to see their family doctor (62% in both groups).ConclusionLWBS patients had similar opinions, experiences, and expectations as control patients. The main reason for LWBS was waiting longer than expected. LWBS patients were more likely to seek further health care, and did so sooner. Patients wait because of concern about their health problem. Shorter wait times and improved communication may reduce the LWBS rate.

Time stamps used to measure the patient's clinic experience.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 150-150 ◽  
Author(s):  
Terry Jensen ◽  
Roy Brown ◽  
Gay Riegel ◽  
Lalan S. Wilfong ◽  
John Russell Hoverman
Keyword(s):  
Patient Satisfaction ◽  
Wait Time ◽  
Office Visit ◽  
Site Quality ◽  
Satisfaction Survey ◽  
Wait Times ◽  
Time Stamp ◽  
Patient Satisfaction Survey ◽  
Patient Reported ◽  
Time Stamps

150 Background: In 2013, a patient reported satisfaction survey indicated 19% of patients waited 20-40 minutes, 8% 40-60 minutes and 4% over 1 hour. We initiated a project to objectively quantify the components of wait times to investigate opportunities for improvement. Methods: Utilizing existing technology in the practice management system, clinic staff use the Day List feature to capture time stamps as patients move through the clinic. We focused on provider appointments but these visits could also include business office, labs, infusion and diagnostics. It was important to define where the wait(s) occurred. The Time Stamp durations measured are as follows: Arrival to Depart – duration of each appointment; Arrival to site to Exam Start – duration of activity until ready to be seen by the provider, includes rooming, labs and business office activity. Used to compare to the patient satisfaction survey responses; Exam Start to Depart – the provider portion of the office visit, includes patient wait plus exam time. Three reports are generated: Time Stamp Error Report indicating the completeness of data collection; Average Wait Times Report with appointment counts by physician by site and average durations; Provider Wait Times Report with office visit counts, Wait Time Category counts ( < 10 min, 10-20, 20-40, 40-60, and > 1 hour ) and average durations. Results: There was a correlation calculation to the patient satisfaction survey of .779, with long wait times more likely to be underreported by patients. Site and physician data were available for review at site Quality Committees. The data can be used by the site to improve processes, such as lab and infusion room scheduling. Time stamps are used to communicate patient readiness for next steps in the office visit. The time stamps provide objective data to discuss patient complaints with staff. Conclusions: Patient wait times are a valued measure of patient satisfaction and quality. Full utilization of the Day List and supporting technology allows us to objectively monitor and improve this aspect of patient care. Table 1: Sample Provider Report [Table: see text]

The next generation of quality improvement at MSK: Turning chemotherapy process delays into chemo express.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18230-e18230
Author(s):  
Jennifer Tota ◽  
Kathleen Levine ◽  
Jeanine Gordon ◽  
Abigail Baldwin ◽  
Jodi Wald ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Patient Satisfaction ◽  
Patient Experience ◽  
Wait Time ◽  
Wait Times ◽  
Pilot Program ◽  
Treatment Results ◽  
Average Wait Time ◽  
Major Bottleneck ◽  
Work Done

e18230 Background: Chemotherapy wait times can dramatically affect patient experience. MSK’s largest outpatient facility has 76 infusion spaces and 250-300 daily visits. A retrospective review of the facility’s infusion area wait times suggested that the lab (where all patients go to get their vitals and blood drawn) was a major bottleneck leading to process delays in infusion. Methods: We conducted a pilot program using a multi-pronged approach. Our goal was to decrease wait time from 40 minutes to an average of 15 minutes. Our initiative was defined as follows: (1) to redefine lab parameters that are relevant for toxicity and to only consider drawing those necessary labs; additionally, we created guidelines for timing of the labs prior to infusion treatment, (2) to introduce a program known as “ChemoExpress” which offers patients the opportunity to get blood work done prior to the day of their infusion appointment. After the labs result, the outpatient RN calls the patient, assesses symptoms and “clears them” for treatment cueing the pharmacy to prepare and “premix” the drug on the day of treatment. Results: 150 patients have enrolled in ChemoExpress. Patient satisfaction was high based on patient satisfaction surveys (n = 20). Average wait time was 9 minutes (76% less) in ChemoExpress participants as compared to an average wait of 39 minutes for those who did not participate in ChemoExpress. Conclusions: Implementing a process that enables patients to have their bloodwork drawn prior to the day of treatment and drugs prepared in advance of their treatment appointment results in greater efficiency in the overall workflow. It also offers the patient a lower wait time and a more efficient and satisfying experience.

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