Optimizing Outpatient Phlebotomy Staffing: Tools to Assess Staffing Needs and Monitor Effectiveness

Context.—Short patient wait times are critical for patient satisfaction with outpatient phlebotomy services. Although increasing phlebotomy staffing is a direct way to improve wait times, it may not be feasible or appropriate in many settings, particularly in the context of current economic pressures in health care. Objective.—To effect sustainable reductions in patient wait times, we created a simple, data-driven tool to systematically optimize staffing across our 14 phlebotomy sites with varying patient populations, scope of service, capacity, and process workflows. Design.—We used staffing levels and patient venipuncture volumes to derive the estimated capacity, a parameter that helps predict the number of patients a location can accommodate per unit of time. We then used this parameter to determine whether a particular phlebotomy site was overstaffed, adequately staffed, or understaffed. Patient wait-time and satisfaction data were collected to assess the efficacy and accuracy of the staffing tool after implementing the staffing changes. Results.—In this article, we present the applications of our approach in 1 overstaffed and 2 understaffed phlebotomy sites. After staffing changes at previously understaffed sites, the percentage of patients waiting less than 10 minutes ranged from 88% to 100%. At our previously overstaffed site, we maintained our goal of 90% of patients waiting less than 10 minutes despite staffing reductions. All staffing changes were made using existing resources. Conclusions.—Used in conjunction with patient wait-time and satisfaction data, our outpatient phlebotomy staffing tool is an accurate and flexible way to assess capacity and to improve patient wait times.

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P012: Why did you leave? Contacting left without being seen patients to understand their emergency department experience

CJEM ◽

10.1017/cem.2018.210 ◽

2018 ◽

Vol 20 (S1) ◽

pp. S61-S61

Author(s):

B. Brar ◽

J. Stempien ◽

D. Goodridge

Keyword(s):

Health Care ◽

Wait Time ◽

Wait Times ◽

Average Wait Time ◽

Left Without Being Seen ◽

Care Processes ◽

Time Period ◽

Initial Interaction ◽

Improving Health Care

Introduction: As experienced in Emergency Departments (EDs) across Canada, Saskatoon EDs have a percentage of patients that leave before being assessed by a physician. This Left Without Being Seen (LWBS) group is well documented and we follow the numbers closely as a marker of quality, what happens after they leave is not well documented. In Saskatoon EDs, if a CTAS 3 patient that has not been assessed by a physician decides to leave the physician working in the ED is notified. The ED physician will: try to talk to the patient and convince them to stay, can assess the patient immediately if required, or discuss other appropriate care options for the patient. In spite of this plan patients with a CTAS score of 3 or higher (more acute) still leave Saskatoon EDs without ever being seen by a physician. Our desire was to follow up with the LWBS patients and try to understand why they left the ED. Methods: Daily records from one of the three EDs in Saskatoon documenting patients with a CTAS of 3 or more acute who left before being seen by a physician were reviewed over an eight-month period. A nurse used a standardized questionnaire to call patients within a few days of their ED visit to ask why they left. If the patients declined to take part in the quality initiative the interaction ended, but if they agreed a series of questions was asked. These included: how long they waited, reasons why they left, if they went somewhere else for care and suggestions for improvement. Descriptive statistics were obtained and analyzed to answer the above questions. Results: We identified 322 LWBS patients in an eight-month time period as CTAS 3 or more acute. We were able to contact 41.6% of patients. The average wait time was 2 hours and 18 minutes. The shortest wait time was 11 minutes, whereas the longest wait time was 8 hours and 39 minutes. It was found that 49.1% of patients went to another health care option (Medi-Clinic or another ED in Saskatoon) within 24hrs of leaving the ED. Long wait times were cited as the number one reason for leaving. Lack of better communication from triage staff regarding wait time expectations was cited as the top response for perceived roadblocks to care. Reducing wait times was cited as the number one improvement needed to increase the likelihood of staying. Conclusion: The Saskatoon ED LWBS patient population reports long wait times as the main reason for leaving. In order to improve the LWBS rates, improving communication and expectations regarding perceived wait times is necessary. The patient perception of the ED experience is largely intertwined with wait times, their initial interaction with triage staff, and how easily they navigate our very busy departments. Therefore, it is vital that we integrate the patient voice in future initiatives geared towards improving health care processes.

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P021: A novel way of hiding beds: manipulating wait time predictions to alter future patient flows into the ED

CJEM ◽

10.1017/cem.2020.229 ◽

2020 ◽

Vol 22 (S1) ◽

pp. S72-S72

Author(s):

S. Strobel

Keyword(s):

Mean Square Error ◽

Administrative Data ◽

Wait Time ◽

Care Physician ◽

Unintended Consequences ◽

Prediction Algorithm ◽

Wait Times ◽

Mean Square ◽

Online Data ◽

Number Of Patients

Introduction: Wait time predictions have become more common in emergency departments in Canada. These estimate the wait times a patient faces to see providers and they are usually provided in an accessible way such as through an online interface. One purpose of these trackers is to improve ED system efficiency. Patients can self-triage to alternative care such as their primary care physician, defer care until a later time or could move from oversubscribed to undersubscribed EDs. However, these mechanisms could also be abused. If providers can artificially influence the wait time this may provide a possible lever to change patients flows to an ED. I investigate whether there is evidence suggestive of manipulation of online wait time trackers at an ED system in Ontario. Methods: Inputs into the wait time prediction algorithm, like patient volumes are taken from the ED EMR. This is the most likely place where staff can manipulate the wait time tracker by retaining patients in the EMR system even after they are discharged. I examine two sets of data to assess whether the online tracker displays differences in patient volumes from “true” data. The first is scraped data of patient volumes from the wait times website. The second are the accurate patient volumes from administrative data which includes when a physician discharged patients from the ED. I compare values of the true patient volumes to the online values and plot distributions of these differences. I also employ measures of accuracy such as mean square error and root mean square error to provide a value of how accurate the online data is compared to the true data. I examine these by ED and over time. Results: There are differences between the number of patients that are posted online and those in the administrative data. The distributions of these differences are skewed towards positive values suggesting that the online data more often overcounts rather than undercounts patients. Measures of accuracy increase during times when EDs are congested but do not decrease when EDs become less congested. This inaccuracy persists for a period after EDs cease to be busy. Conclusion: ED wait time trackers have the potential to be manipulated. When staff have incentive to reduce patient volumes, online data becomes more inaccurate relative to true data. This suggests that wait time trackers may have unintended consequences and that the information that they provide may not be entirely accurate.

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Triage of Rheumatology Referrals Facilitates Wait Time Benchmarks

The Journal of Rheumatology ◽

10.3899/jrheum.151235 ◽

2016 ◽

Vol 43 (11) ◽

pp. 2064-2067 ◽

Cited By ~ 5

Author(s):

Chandra Farrer ◽

Liza Abraham ◽

Dana Jerome ◽

Jacqueline Hochman ◽

Natasha Gakhal

Keyword(s):

Quality Improvement ◽

Connective Tissue ◽

Patient Outcomes ◽

Inflammatory Arthritis ◽

Wait Time ◽

Wait Times ◽

Referral Letter ◽

Advanced Practice ◽

Priority Ranking ◽

Improve Patient

Objective.In 2014 the Canadian Rheumatology Association published wait time benchmarks for inflammatory arthritis (IA) and connective tissue disease (CTD) to improve patient outcomes. This study’s aim was to determine whether centralized triage and the introduction of quality improvement initiatives would facilitate achievement of wait time benchmarks.Methods.Referrals from September to November 2012 were retrospectively triaged by an advanced practice physiotherapist (APP) and compared to referrals triaged by an APP from January to March 2014. Each referral was assigned a priority ranking and categorized into one of 2 groups: suspected IA/CTD, or suspected non-IA/CTD. Time to initial consult and time to notification from receipt of referral were assessed.Results.A total of 558 (n = 227 and n = 331 from 2012 and 2014, respectively) referrals were evaluated with 35 exclusions. In 2012, there were 96 (42.5%) suspected IA/CTD and 124 (54.9%) suspected non-IA/CTD patients at the time of the initial consult. Mean wait times in 2012 for patients suspected to have IA was 33.8 days, 95% CI 27.8–39.8, compared to 37.3 days, 95% CI 32.9–41.7 in suspected non-IA patients. In 2014, there were 131 patients (43%) with suspected IA based on information in the referral letter. Mean wait times in 2014 for patients suspected to have IA was 15.5 days, 95% CI 13.85–17.15, compared to 52.2 days, 95% CI 46.3–58.1 for suspected non-IA patients. Time to notification of appointment improved from 17 days to 4.37 days.Conclusion.Centralized triage of rheumatology referrals and quality improvement initiatives are effective in improving wait times for priority patients as determined by paper referral.

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

CJEM ◽

10.1017/cem.2016.375 ◽

2016 ◽

Vol 19 (5) ◽

pp. 347-354 ◽

Cited By ~ 6

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.

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Measurement of surgical wait times in a universal health care system

Canadian Urological Association Journal ◽

10.5489/cuaj.974 ◽

2008 ◽

Vol 2 (6) ◽

pp. 597 ◽

Cited By ~ 6

Author(s):

Jun Kawakami ◽

Wilma M. Hopman ◽

Rachael Smith-Tryon ◽

D. Robert Siemens

Keyword(s):

Health Care ◽

Multivariate Analysis ◽

Real Time ◽

Access To Health Care ◽

A Priori ◽

Wait Time ◽

Administrative Databases ◽

Wait Times ◽

Data Set ◽

Care Indicator

Introduction: Reported increases in surgical wait times for cancer have intensified the focus on this quality of health care indicator and have created a very public, concerted effort by providers to decrease wait times for cancer surgeryin Ontario. Delays in access to health care are multifactorial and their measurement from existing administrative databases can lack pertinent detail. The purpose of our study was to use a real-time surgery-booking software program to examine surgical wait times at a single centre.Methods: The real-time wait list management system Axcess.Rx has been used exclusively by the department of urology at the Kingston General Hospital to book all nonemergency surgery for 4 years. We reviewed the length of time from the decision to perform surgery to the actual date of surgery for patients in our group urological practice. Variables thought to be potentially important in predicting wait time were also collected, including the surgeon’s assessment of urgency, the type of procedure (i.e., diagnostic, minor cancer, major cancer, minor benign, major benign), age and sex of the patient, inpatient versus outpatient status and year of surgery. Analysis was planned a priori to determine factors that affected wait time by using multivariate analysis to analyze variables that were significant in univariate analysis.Results: There were 960 operations for cancer and 1654 for benign conditions performed during the evaluation period. The overall mean wait time was 36 days for cancer and 47 days for benign conditions, respectively. The mean wait time for cancer surgery reached a nadir in 2004 at 29.9 days and subsequently increased every year, reaching 56 days in 2007. In comparison, benign surgery reached a nadir wait time of 33.7 days in 2004 and in 2007 reached 74 days at our institution. Multivariate analysis revealed that the year of surgery was still a significant predictor of wait time. Urgency score, type of procedure and inpatient versus outpatient status were also predictive of wait time.Conclusion: The application of a prospectively collected data set is an effective and important tool to measure and subsequently examine surgical wait times. This tool has been essential to the accurate assessment of the effect of resource allocation on wait times for priority and nonpriority surgical programs within a discipline. Such tools are necessary to more fully assess and follow wait times at an institution or across a region.

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Improving the wait time to consultation at the emergency department

BMJ Open Quality ◽

10.1136/bmjoq-2017-000131 ◽

2018 ◽

Vol 7 (1) ◽

pp. e000131 ◽

Cited By ~ 12

Author(s):

Yuzeng Shen ◽

Lin Hui Lee

Keyword(s):

Emergency Department ◽

Low Cost ◽

Wait Time ◽

Data Driven ◽

Wait Times ◽

Model Of Care ◽

Care Model ◽

Patient Load ◽

Data Driven Approach ◽

Team Based Care

Prolonged wait times at the emergency department (ED) are associated with increased morbidity and mortality, and decreased patient satisfaction. Reducing wait times at the ED is challenging. The objective of this study is to determine if the implementation of a series of interventions would help decrease the wait time to consultation (WTC) for patients at the ED within 6 months. Interventions include creation of a common board detailing work output, matching manpower to patient arrivals and adopting a team-based model of care. A retrospective analysis of the period from January 2015 to May 2016 was undertaken to define baseline duration for WTC. Rapid PDSA (Plan, Do, Study, Act) cycles were used to implement a series of interventions, and changes in wait time were tracked, with concurrent patient load, rostered manpower and number of admissions from ED. Results of the interventions were tracked from 1 October 2016 to 30 April 2017. There was improvement in WTC within 6 months of initiation of interventions. The improvements demonstrated appeared consistent and sustained. The average 95th centile WTC decreased by 38 min to 124 min, from the baseline duration of 162 min. The median WTC improved to 21 min, compared with a baseline timing of 24 min. The improvements occurred despite greater patient load of 4317 patients per month, compared with baseline monthly average of 4053 patients. There was no increase in admissions from ED and no change in the amount of ED manpower over the same period. We demonstrate how implementation of low-cost interventions, enabling transparency, equitable workload and use of a team-based care model can help to bring down wait times for patients. Quality improvement efforts were sustained by employing a data-driven approach, support from senior clinicians and providing constant feedback on outcomes.

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Predicting Patient Wait Times in the Phlebotomy Unit

Global Journal of Medical Research ◽

10.34257/gjmrkvol20is1pg1 ◽

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.

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Wait times for psychiatric care in Ontario

University of Western Ontario Medical Journal ◽

10.5206/uwomj.v86i2.2027 ◽

2017 ◽

Vol 86 (2) ◽

pp. 48-50

Author(s):

Rachel Loebach ◽

Sasha Ayoubzadeh

Keyword(s):

Mental Health ◽

Health Care ◽

Mental Health Care ◽

Psychiatric Inpatient ◽

Wait Time ◽

Mandatory Reporting ◽

Psychiatric Services ◽

Wait Times ◽

Time Data ◽

Developed Nations

Mental illness is a prevalent and costly health care issue. Lengthy wait times for psychiatric services in Ontario are a barrier to adequate mental health care for adults, children and youth. The objective of this paper is to highlight the current state of psychiatric wait times in Ontario by looking at provincial policies and comparing data to physical health services, as well as between provinces and other developed nations. The Ontario government has successfully implemented mandatory reporting of wait-time data for many medical and surgical services. However, such policies have yet to be implemented for psychiatric services. As a result, availability of current data for comparison is limited. Nova Scotia is currently the only province to government mandate reporting of wait times for mental health. Furthermore, The Organisation for Economic Co-operation and Development ranks Canada below average on measures related to accessibility of psychiatric inpatient services compared to other developed nations. While Ontario has implemented new initiatives to address the issue of timely mental health care, there is still insufficient evidence to determine if they are effective. Continued advocacy for mandatory wait-time reporting at the provincial level and further analysis of current initiatives worldwide are essential steps toward reducing wait times.

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Impact of an Electronic Health Record-Integrated Personal Health Record on Patient Participation in Health Care: Development and Randomized Controlled Trial of MyHealthKeeper (Preprint)

10.2196/preprints.8867 ◽

2017 ◽

Author(s):

Borim Ryu ◽

Nari Kim ◽

Eunyoung Heo ◽

Sooyoung Yoo ◽

Keehyuck Lee ◽

...

Keyword(s):

Health Care ◽

Clinical Trial ◽

Personal Health Record ◽

Intervention Group ◽

Data Driven ◽

Personal Health ◽

Control Group ◽

Health Record ◽

Study Participants ◽

Improve Patient

BACKGROUND Personal health record (PHR)–based health care management systems can improve patient engagement and data-driven medical diagnosis in a clinical setting. OBJECTIVE The purpose of this study was (1) to demonstrate the development of an electronic health record (EHR)–tethered PHR app named MyHealthKeeper, which can retrieve data from a wearable device and deliver these data to a hospital EHR system, and (2) to study the effectiveness of a PHR data-driven clinical intervention with clinical trial results. METHODS To improve the conventional EHR-tethered PHR, we ascertained clinicians’ unmet needs regarding PHR functionality and the data frequently used in the field through a cocreation workshop. We incorporated the requirements into the system design and architecture of the MyHealthKeeper PHR module. We constructed the app and validated the effectiveness of the PHR module by conducting a 4-week clinical trial. We used a commercially available activity tracker (Misfit) to collect individual physical activity data, and developed the MyHealthKeeper mobile phone app to record participants’ patterns of daily food intake and activity logs. We randomly assigned 80 participants to either the PHR-based intervention group (n=51) or the control group (n=29). All of the study participants completed a paper-based survey, a laboratory test, a physical examination, and an opinion interview. During the 4-week study period, we collected health-related mobile data, and study participants visited the outpatient clinic twice and received PHR-based clinical diagnosis and recommendations. RESULTS A total of 68 participants (44 in the intervention group and 24 in the control group) completed the study. The PHR intervention group showed significantly higher weight loss than the control group (mean 1.4 kg, 95% CI 0.9-1.9; P<.001) at the final week (week 4). In addition, triglyceride levels were significantly lower by the end of the study period (mean 2.59 mmol/L, 95% CI 17.6-75.8; P=.002). CONCLUSIONS We developed an innovative EHR-tethered PHR system that allowed clinicians and patients to share lifelog data. This study shows the effectiveness of a patient-managed and clinician-guided health tracker system and its potential to improve patient clinical profiles. CLINICALTRIAL ClinicalTrials.gov NCT03200119; https://clinicaltrials.gov/ct2/show/NCT03200119 (Archived by WebCite at http://www.webcitation.org/6v01HaCdd)

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General Anesthetic Versus Light Sedation: Effect on Pediatric Endoscopy Wait Times

Canadian Journal of Gastroenterology ◽

10.1155/2013/201025 ◽

2013 ◽

Vol 27 (9) ◽

pp. 519-522

Author(s):

Christine Edwards ◽

Vikram Kapoor ◽

Christopher Samuel ◽

Robert Issenman ◽

Herbert Brill

Keyword(s):

Health Care ◽

Emergency Room ◽

General Anesthesia ◽

Clinical Outcomes ◽

Hospital Admissions ◽

Wait Time ◽

Wait Times ◽

Pediatric Gastroenterology ◽

Emergency Room Visits ◽

Light Sedation

BACKGROUND: Wait times are an important measure of health care system effectiveness. There are no studies describing wait times in pediatric gastroenterology for either outpatient visits or endoscopy. Pediatric endoscopy is performed under light sedation or general anesthesia. The latter is hypothesized to be associated with a longer wait time due to practical limits on access to anesthesia in the Canadian health care system.OBJECTIVE: To identify wait time differences according to sedation type and measure adverse clinical outcomes that may arise from increased wait time to endoscopy in pediatric patients.METHODS: The present study was a retrospective review of medical charts of all patients <18 years of age who had been assessed in the pediatric gastroenterology clinic and were scheduled for an elective outpatient endoscopic procedure at McMaster Children’s Hospital (Hamilton, Ontario) between January 2006 and December 2007. The primary outcome measure was time between clinic visit and date of endoscopy. Secondary outcome measures included other defined waiting periods and complications while waiting, such as emergency room visits and hospital admissions.RESULTS: The median wait time to procedure was 64 days for general anesthesia patients and 22 days for patients who underwent light sedation (P<0.0001). There was no significant difference between the two groups with regard to the number of emergency room visits or hospital admissions, both pre- and postendoscopy.CONCLUSIONS: Due to the lack of pediatric anesthetic resources, patients who were administered general anesthesia experienced a longer wait time for endoscopy compared with patients who underwent light sedation. This did not result in adverse clinical outcomes in this population.

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