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 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.

Minimizing wait time between chemotherapy and radiotherapy treatments.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 82-82
Author(s):  
James J. Sauerbaum ◽  
Gina DeMaio ◽  
Bradley Geiger ◽  
Regina Cunningham ◽  
Marianna Holmes ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Task Force ◽  
Wait Time ◽  
Support Staff ◽  
Cancer Center ◽  
Wait Times ◽  
Average Wait Time ◽  
Scheduling Method ◽  
Reduce Wait Time ◽  
E Mail

82 Background: Members of the scheduling teams at the Abramson Cancer Center observed prolonged delays between chemotherapy and radiation therapy treatments scheduled by staff from 2 independent departments leading to inconvenience for patients receiving concurrent chemo- and radiation therapy (CRpts). Methods: An analysis of baseline data over 6 weeks revealed that for 157 unique consecutive patients undergoing daily chemotherapy and radiation (a total of 353 encounters), the mean time between scheduled treatments was 122 minutes. For 39% of encounters the wait time was greater than 120 minutes. To improve the adjacency of chemotherapy and radiation appointments and to consistently reduce wait time between treatments to less than 120 minutes, we formed a Chemotherapy/Radiation Scheduling Task Force consisting of patient service representatives, practice managers, and physician and nurse advisors. We determined that CRpts should be scheduled using a “huddle” strategy whereby prospectively identified CRpts are simultaneously scheduled for both treatments in a coordinated manner. Identifying CRpts for coordinated scheduling was facilitated by the creation of a chemo-radiation scheduling inbox to which clinicians and support staff e-mail names of new CRpts in order to alert the scheduling team. Our two lead schedulers meet 2-3 times per week to coordinate patient schedules. A weekly scorecard of the wait times for CRpts patients is distributed via e-mail to the clinicians and support staff. Results: Over the past 6 months, we have used the huddle method for 80% of 986 consecutive CRpt encounters. Our average wait time for huddle-scheduled encounters has been reduced to 62.5 minutes with only 9% of encounters having wait times over 120 minutes. For non-huddle-scheduled encounters, the average wait time is 129 minutes with 57% having wait times over 120 minutes. Conclusions: Utilization of a huddle scheduling method has successfully reduced wait time for CRpts. Use of the huddle method continues to grow with staff training and awareness of the new process.

Impact of room pooling and electronic health record on patient (pt) wait time, clinic work flow, and pts’/providers’ satisfaction.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18191-e18191
Author(s):  
Saroj Vadhan-Raj ◽  
Xiao Zhou ◽  
Meyyammai Narayanan ◽  
Shawn J Janarthanan ◽  
Mary Daniel ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Work Flow ◽  
Wait Times ◽  
Health Record ◽  
Allocation Model ◽  
Exam Room ◽  
Negative Effect ◽  
Electronic Health ◽  
The Impact

e18191 Background: Excessive pt wait time can have negative effect on clinic work flow and on pts/ providers satisfaction. Increasing pt volume and limited clinic capacity can lead to long wait times for pts. The purpose of this two-part study was to evaluate the impact of Room Pooling Model (RPM) instead of Room Allocation Model (Part 1) and Electronic Health Record (EHR) on pt wait times in clinic and pts’/providers’ satisfaction (Part 2). Methods: The time studies and pts’/providers’ wait time satisfaction surveys were carried out over 2 weeks before (baseline) and 8 weeks after the implementation of RPM (Part 1), prior to the new EHR system, and 6 months after the implementation of EHR (part 2). All times of when pts, mid-level providers (MLP), and doctors (MDs) entered and exited the exam rooms were collected for 887 pts seen during the clinic. Data was analyzed using JMP and SAS. Results: As described earlier (ASCO 2016, abst 6595), the RPM was associated with increase in the proportion of pts seen by MDs within 30 min from the time roomed in the exam room and improvement in pts’/provider’s satisfaction. Post EHR, there were delays with decrease in the proportion of pts seen within 30 min from the time roomed in. Although the pt satisfaction did not change significantly, the number of times MDs had to wait for an open exam room increased from 8% (5/65) to 24% (14/59, p=0.01). The impact of RPM and EHR on pt times are shown below. The delays to see MDs after EHR were associated with longer time spent with the nurse (from median 4 to 7 min) and delays in seeing MLPs (from 11 to 18 min). Conclusions: These findings indicate that RPM can improve pt wait times. During initial stages of EHR implementation, the increase in pt wait time and reduced clinical efficiencies can be related to learning, and adapting to the new system. These data can be useful to design interventions that can target the areas of delays such as training and redesigning workflow to improve the clinical efficiency. [Table: see text]

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.

Improving efficiencies in a hospital-based hematology outpatient service.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 165-165
Author(s):  
Brittany A. Rask ◽  
Steven R. Zeldenrust ◽  
Jessica Brandt ◽  
Heather Hagan ◽  
Elizabeth Witty ◽  
...  
Keyword(s):  
Process Analysis ◽  
Outpatient Service ◽  
Value Added ◽  
Wait Time ◽  
Wait Times ◽  
Process Data ◽  
Implementation Phase ◽  
Final Destination ◽  
Exam Room ◽  
Before And After

165 Background: The Hematology Hospital Outpatient service at Rochester Methodist Hospital provides ambulatory outpatient care to patients who would normally be hospitalized on our inpatient services due to cytopenias and need for infusion/transfusion support. A quality project was conducted to streamline our process and decrease wait times. Methods: This project was a part of a Mayo Lean Collaborative initiative to increase customer satisfaction and eliminate waste. The first step was to collect baseline measurements to capture the non-value added waiting time of our entire outpatient process. These benchmark times were collected from 65 patients over 4 days. We evaluated these wait times to identify gaps in care and factors contributing to delays in our process. Data collected "before and after" each implementation phase determined if the change was beneficial. Results: Through our data collection, one major area of opportunity we identified was the wait time for CBC results. At baseline, patients were waiting in a room for 61.9 minutes for lab results. A major contributor to delay was the lack of carriers to send blood samples through the tube system being stocked in the outpatient area. We also discovered that the time from when the sample was placed in the carrier to the time it arrived in our lab was significant. Through a process analysis it was identified that the CBC tubes were being sent to a central lab prior to their final destination in the Hematology lab. We were able to re-route our CBCs directly to the hem lab. Data collected after the intervention showed we were receiving results at 51 minutes, therefore, eliminating 10.9 minutes off of an appointment. We also identified additional opportunities such as pre-assigning rooms, scheduling, exam room organization, and communication that were able to be addressed. Conclusions: As a result of this lean collaborative project we reduced patient wait time by 20.1 minutes and increased patient satisfaction. We were able to order interventions sooner thereby increasing patient safety. By process analysis, we identified multiple areas in our process that could be shortened with no additional cost.

Reducing infusion wait time: An initiative to increase early signing of medication orders by providers at a comprehensive cancer center.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 123-123
Author(s):  
Jing Jing Wang Yakowec ◽  
Michael S. Rabin ◽  
Ursula A. Matulonis ◽  
Shital Shukla ◽  
Michael D Kearney ◽  
...  
Keyword(s):  
Cancer Patient ◽  
Process Improvement ◽  
Working Group ◽  
Wait Time ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Wait Times ◽  
Cancer Group ◽  
Dana Farber Cancer Institute ◽  
Routine Basis

123 Background: Many factors contribute to long wait times for oncology patients on the day of their infusion appointment. At Dana-Farber Cancer Institute (DFCI), one of the main causes of delay to infusion start is providers not signing medication orders in advance of patients checking in for their infusion appointment. We conducted a project to improve provider order signing behavior on the gynecology cancer patient infusion floor at DFCI. Methods: A data working group was formed which consisted of the infusion floor medical leads, nurse lead, pharmacy lead, and analytics and process improvement leads. Starting in February 2018, the working group shared baseline order signing data from September 2017 through January 2018 with the Gynecology Cancer Group. Descriptive and timestamp data from Epic were extracted and cleaned via Tableau to analyze the percentage of non-investigational medication orders, including chemotherapy, that were signed after a patient checked into infusion and the distributions of late order signing times. Results: Gynecology cancer patient providers had higher late order signing percentages at baseline (September 2017 through January 2018) than after sharing those data, which occurred from February through May 2018. The table below provides medication order counts and late order signing percentages by month. Although late signing percentages decreased after sharing the baseline data, the distribution of how late the late orders were signed did not show improvement, staying at an average of 20 minutes late. Conclusions: Sharing late order signing data with providers on a routine basis reduced late signing percentages. Initiating this process with all disease groups is crucial so that downstream workflows can start sooner and patient wait times reduced.[Table: see text]

Barriers to accessing healthcare services: A multidisciplinary approach towards improving pancreatic cancer survival in a Canadian province.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18018-e18018
Author(s):  
Elizabeth Faour ◽  
Bruce Colwell ◽  
Nathan William Dana Lamond ◽  
Stephanie Leann Snow ◽  
Alwin S. Jeyakumar ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Survival Data ◽  
Multidisciplinary Team ◽  
Barriers To Care ◽  
Healthcare Delivery ◽  
Wait Time ◽  
Cancer Center ◽  
Time Data ◽  
Access To Treatment ◽  
Improvement Project

e18018 Background: Pancreatic cancer (PC) is associated with the highest death rate among common malignancies and is the fourth leading cause of cancer-related death in North America. Despite similar access to treatment options across Canada, the province of Nova Scotia (NS) has the lowest 5-year survival rate for PC. To investigate reasons behind the poor PC outcomes in NS, a multidisciplinary team was created to investigate barriers to care and streamline patient flow. In 2016, initial data informed the reorganization of the hepatopancreaticobiliary (HPB) multidisciplinary team towards the goal of identifying and reducing barriers to care and, ultimately, improving survival. Methods: This quality improvement project included a retrospective chart review of PC patient data from a single institution (The NS Cancer Center), where over 80% of PC patients from this province are seen. A review of PC diagnosis, referrals patterns, and wait time data was undertaken. Results: Data was extracted on 365 patients with a diagnosis of PC between 2011 and 2014. During that period, only 40.4% of patients diagnosed with PC had a tissue diagnosis and just over 71% had a baseline CA19-9. Referral rate to Medical Oncology (MO) was 53%, mean wait time to see MO was 37.2 days and only 23% of patients received systemic treatment. Initiatives to improve access to care included standardization of diagnostic procedures, early triaging of referrals, transfer of port-a-cath (PAC) insertions from interventional radiology to the HPB surgeons, and the creation of provincial guidelines, which were implemented in 2016. Positive Improvements were observed in all identified barriers to care. Conclusions: Barriers to accessing care for PC patients in NS were identified, and a multidisciplinary team proposed provincial guidelines were implemented to expedite care. Preliminary results show improvement in all aspects of healthcare delivery. Survival data will be available in late 2019. [Table: see text]

scholarly journals Wait times for psychiatric care in Ontario

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.

scholarly journals Development and implementation of technology to measure truck crossing time at international land ports of entry

2015 ◽  
Vol 34 (2) ◽  
pp. 75-86
Author(s):  
Juan Carlos Villa
Keyword(s):  
Supply Chain ◽  
Wait Time ◽  
The United States ◽  
Border Crossing ◽  
Wait Times ◽  
Border Crossings ◽  
Time Data ◽  
Public And Private ◽  
Crossing Time ◽  
Mexico Border

Public and private stakeholders that operate land border crossings are increasingly concerned about long wait times for trucks crossing from Mexico into the United States. Long wait times are detrimental to the regional competiveness, supply chain operations, the environment in the region adjacent to the border crossings, and to the overall economic development. In order to have reliable and systematic information on border crossing time and delay, a system to measure travel time through the border is required. This paper describes the basic border crossing operations at the Texas/Mexico border that serves as the foundation to identify a technology that could be used to collect border crossing information. The design and deployment processes that were used for the implementation of the border crossing time measurement system for U.S.-bound commercial vehicles are described. The paper also presents the results of the system that was developed to disseminate border crossing and wait time data. Benefits to supply chain operators at land border crossings and next steps in the development of more border-related performance measures are described.

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