Developing an efficient scheduling template of a chemotherapy treatment unit: A case study

Evaluating the effects of triage education on triage accuracy within the emergency department: An integrative review

INTRODUCTION

Triage accuracy can affect patient outcomes. Education to ensure nurses provide the most accurate triage scores is paramount for patient safety.The objective was to investigate whether ongoing triage education increases triage accuracy, knowledge or behaviour.

METHOD

An integrative review was conducted by searching five databases to identify studies that included triage-based education. A systematic search strategy was completed followed by analysis with critical appraisal using the Critical Appraisal Skills Programme, a TIDieR Checklist and thematic analysis.

FINDINGS

Four thousand five hundred seventy-six studies were retrieved, with 34 studies selected for inclusion. Thirty-one studies were quantitative, and three were mixed methods. 18 out of 34 studies showed improvement in triage accuracy. Seven showed increased knowledge. Six studies showed no improvement in triage accuracy. Sixteen studies assessed triage behaviour and showed improvement post-intervention, with five showing no changes. Only three studies compared interventions. Fifty-three opportunities for changes to triage accuracy, knowledge or behaviour were found, 41 showed improvements.

CONCLUSION

Triage education interventions can improve accuracy, knowledge and behaviour, but whether improvements are sustained needs further research.

Improving Utilization of the Chemotherapy Unit through Implementing the Medication Early Release Project

Introduction

The outpatient oncology infusion unit is very busy, serving 60 to 70 patients per day. Due to a limited number of nurses, treatment chairs, only one pharmacy hood for bio-hazardous drug preparation, and other factors, patients wait a long time before starting their treatment, which affects the patient experience negatively. We conducted a quality improvement project to reduce the waiting time before starting the treatment, improve the patients' experience, and allow the unit to work more effectively through better resource utilization and accommodating more patients.

Methods

A committee was formed with representatives from oncology nursing and the quality specialist, chemotherapy pharmacy supervisor, data manager, and a medical consultant (team leader). We studied baseline data of patient waiting times from January to March 2019 and the factors that contributed to delays before starting the treatment. The charge nurse identified patients who could safely have their medication released early in the morning at 7 am, enabling the pharmacy to dispense at 8 am without their actual presence being required in the infusion suite (i.e., medication early release program or MERP). Multiple plan-do-study-act (PDSA) cycles were implemented to achieve a wait time from check-in to medication administration of less than 60 minutes. Data collected included check-in time, chair time, vital signs time, administration time, and discharge time. Additionally, reasons for drug wastage were assessed for patients who did not receive the prepared medication. A patient satisfaction survey was conducted with the patients before and after being enrolled in the program.

Results

At baseline, average waiting time for patients receiving similar medications in the MERP was 2 hours and 27 minutes. After the first intervention, average waiting time was reduced to 1 hour and 24 minutes, and small improvements were observed after each PDSA cycl. A major breakthrough occurred after an intensive patient education program and enforcement of strict compliance with the criteria in selecting the patients appropriate for theMERP. Average waiting time wasreduced to ≤ 60 minutes, and in November 2022, it was 30 minutes on average. Drug wastage was identified as a balancing measure. We were successful in reducing drug wastage by implementing several changes and patient education measures and achieved zero wastage. The patient satisfaction survey showed better satisfaction with the new changes.

Conclusion

A positive impact was achieved in this quality improvement project, with a significant reduction in the average waiting time for patients to start receiving chemotherapy. The outcome of this project has been maintained for 4 years and is still ongoing.

A configurable computer simulation model for reducing patient waiting time in oncology departments

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare managers. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilised, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.

Patient Flow Analysis Using Real-Time Locating System Data: A Case Study in an Outpatient Oncology Center

PURPOSE:

Electronic health records (EHRs) have been mainly used to analyze bottlenecks in care processes of outpatient oncology clinics. However, EHR data lead to some limitations in understanding patient flow because they are manually entered and not updated in real time. Data generated from a real-time location system (RTLS) can supplement EHR data. This study aims to demonstrate how RTLS data combined with EHR data can be used to evaluate potential interventions to improve patient flow in an outpatient cancer center.

METHODS:

EHR and RTLS data obtained from a large cancer center in central Virginia were analyzed to estimate process times and determine the various patient paths patients follow during their visit for infusion. Using the input data, we developed a discrete-event simulation (DES) model and assessed 5 what-if scenarios involving changes in staff scheduling and care processes.

RESULTS:

Raw RTLS data including > 3.5 million observations were preprocessed to remove noise and extract meaningful information. The DES results showed that new nursing schedules for the infusion center and improved pharmacy processes have positive impacts on reducing patient waiting times by approximately 20% and overall length of stay by approximately 3.4% to 4.6%, compared with the current system.

CONCLUSION:

Combining EHR and RTLS data, we were able to capture dynamic aspects of patient flow more realistically. DES models that represent a complex system based on accurate input data can help decision making on determining operational changes to improve patient flow.

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach

Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time.

Evaluating waiting time with real-world health information in a high-volume cancer center

Wait time and scheduling for outpatient chemotherapy administration depends on various factors including infusion room hours of operation, availability of oncologists, nursing and pharmacy staffing, and physical space limitations. The aim of this study was to use the electronic event log of patients on health information system (HIS) to map and analyze patient flow in advanced metastatic colorectal patients at an academic cancer center. From January 2009 to December 2014, patients who were diagnosed with metastatic colorectal cancer and received outpatient chemotherapy confined to FOLFIRI (fluorouracil, leucovorin, and irinotecan) or FOLFOX (folinic acid, fluorouracil, and oxaliplatin) were identified. From the HIS, patient flow was mapped by collection of event records including blood collection and pretreatment laboratory test, arrival to outpatient clinics, outpatient session (interview, drug accountability and appointment scheduling), and initiation of chemotherapy. A total of 10,638 patients were analyzed for 136,281 outpatient visits. The total office stay time from outpatient registration to initiation of chemotherapy was 92.58 ± 87.96 (mean ± standard deviation) minutes. Each outpatient session lasted 23.75 ± 51.55 minutes. After completing the outpatient session, patients waited 1,657.23 ± 3,027.65 minutes before chemotherapy and 46.66 ± 75.94 minutes within infusion room. Compared to the prior first come first serve rule, the new reservation system showed an improvement in overall waiting time from 2,432.3 ± 4,822.9 to 2,386.7 ± 143.4 minutes; however, waiting time within infusion room slightly increased from 36.68 ± 49.33 to 48.13 ± 46.32 minutes. Our findings indicate that transaction data analytics from HIS can be used to evaluate patient flow within oncology outpatient practice based on real-world hospital data.

Efficiency Model of Cladribine Tablets Versus Infusion-Based Disease-Modifying Drugs for Patients with Relapsing-Remitting Multiple Sclerosis

INTRODUCTION

To develop a simulation model assessing the efficiency of using cladribine tablets versus infusion-based disease-modifying drugs (DMDs) for the treatment of relapsing-remitting multiple sclerosis (RRMS) from a facility perspective in the UK.

METHODS

A scheduling algorithm was developed to simulate day-case admissions and calculate the mean changes to resource use and time burden for patients in a facility that transitions from infusion-based treatments to cladribine tablets over 1 year. Model inputs and assumptions were based on previous research and expert opinion. Model validation and quality checks were performed and additional scenario analyses were also conducted.

RESULTS

The model successfully scheduled all infusion treatments in the base case and no patients were left off the schedule as a result of lack of capacity. Modeled base-case outcomes increased in future scenarios owing to a 35% increase in demand. The introduction of cladribine tablets reduced these impacts. Specifically, the difference in mean daily utilization was reduced in the future scenario from 13% to 3% as 8% of patients moved to cladribine tablets; annual administration costs decreased by 96% and annual time burden decreased by 90%. Results from additional scenarios showed the largest benefits from switching current infusion patients to cladribine tablets were realized in facilities having moderate to high resource utilization.

CONCLUSIONS

This model provides facility decision-makers the ability to assess the efficiency of using cladribine tablets rather than an infusion-based DMD. The simulation quantified the benefits gained from reducing the burden on facility resources by switching some patients with RRMS from infusion-based DMDs to cladribine tablets. Overall, modeled outcomes increased in future scenarios owing to an increase in demand, although the introduction of cladribine tablets reduced this impact.

Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study

Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements.

National Comprehensive Cancer Network Infusion Efficiency Workgroup Study: Optimizing Patient Flow in Infusion Centers

PURPOSE:

The National Comprehensive Cancer Network (NCCN) formed an Infusion Efficiency Workgroup to determine best practices for operating efficient and effective infusion centers.

METHODS:

The Workgroup conducted three surveys that were distributed to NCCN member institutions regarding average patient wait time, chemotherapy premixing practices, infusion chair use, and premedication protocols. To assess chair use, the Workgroup identified and defined five components of chair time.

RESULTS:

The average patient wait time in infusion centers ranged from 25 to 102 minutes (n = 23; mean, 58 minutes). Five of 26 cancer centers (19%) routinely mix chemotherapy drugs before patient arrival for patients meeting specified criteria. Total planned chair time for subsequent doses of the same drug regimens for the same diseases varied greatly among centers, as follows: Administration of doxorubicin and cyclophosphamide ranged from 85 to 240 minutes (n = 22); of FOLFIRINOX (folinic acid, fluorouracil, irinotecan hydrochloride, and oxaliplation) ranged from 270 to 420 minutes (n = 22); of rituximab ranged from 120 to 350 minutes (n = 21); of paclitaxel plus carboplatin ranged from 255 to 380 minutes (n = 21); and of zoledronic acid ranged from 30 to 150 minutes (n = 22) for planned total chair time. Cancer centers were found to use different premedication regimens with varying administration routes that ranged in administration times from zero to 60 minutes.

CONCLUSION:

There is a high degree of variation among cancer centers in regard to planned chair time for the same chemotherapy regimens, providing opportunities for improved efficiency, increased revenue, and more standardization across centers. The NCCN Workgroup demonstrates potential revenue impact and provides recommendations for cancer centers to move toward more efficient and more standard practices.

Alternative Outpatient Chemotherapy Scheduling Method to Improve Patient Service Quality and Nurse Satisfaction

INTRODUCTION

Optimal scheduling and calendar management in an outpatient chemotherapy unit is a complex process that is driven by a need to focus on safety while accommodating a high degree of variability. Primary constraints are infusion times, staffing resources, chair availability, and unit hours.

METHODS

We undertook a process to analyze our existing management models across multiple practice settings in our health care system, then developed a model to optimize safety and efficiency. The model was tested in one of the community chemotherapy units. We assessed staffing violations as measured by nurse-to-patient ratios throughout the workday and at key points during treatment. Staffing violations were tracked before and after the implementation of the new model.

RESULTS

The new model reduced staffing violations by nearly 50% and required fewer chairs to treat the same number of patients for the selected clinic day. Actual implementation results indicated that the new model leveled the distribution of patients across the workday with an 18% reduction in maximum chair utilization and a 27% reduction in staffing violations. Subsequently, a positive impact on peak pharmacy workload reduced delays by as much as 35 minutes. Nursing staff satisfaction with the new model was positive.

CONCLUSION

We conclude that the proposed optimization approach with regard to nursing resource assignment and workload balance throughout a day effectively improves patient service quality and staff satisfaction.

Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency

Outpatient cancer treatment centers can be considered as complex systems in which several types of medical professionals and administrative staff must coordinate their work to achieve the overall goals of providing quality patient care within budgetary constraints. In this article, we use analytical methods that have been successfully employed for other complex systems to show how a clinic can simultaneously reduce patient waiting times and non-value added staff work in a process that has a series of steps, more than one of which involves a scarce resource. The article describes the system model and the key elements in the operation that lead to staff rework and patient queuing. We propose solutions to the problems and provide a framework to evaluate clinic performance. At the time of this report, the proposals are in the process of implementation at a cancer treatment clinic in a major metropolitan hospital in Montreal, Canada.

Acuity-Based Scheduling: Outcomes in Ambulatory Oncology Centers

With an increase in the number of patients with cancer receiving treatment in the ambulatory setting, a need exists to evaluate lean approaches to provide safe, effective, and timely care delivery. Acuity-based scheduling (ABS) was implemented across the regional ambulatory care centers of a National Cancer Institute-designated comprehensive cancer center. ABS involved templates and a reconfiguration of clinical space and staff according to acuity levels. Results suggest improvement in wait times, capacity, infusion hours, chair use rate, patient visits, chair turns, average infusion length, and patient satisfaction. 
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Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.

Triage of Rheumatology Referrals Facilitates Wait Time Benchmarks

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.

Reducing barriers to timely MR imaging scheduling

Scheduling a magnetic resonance (MR) imaging study at the authors' large health system in 2011 required considerable preparation before an appointment time was given to a patient. Difficulties in promptly scheduling appointments resulted from the varying time required for examinations, depending on the requested protocol, availability of appropriate MR imaging equipment, examination timing, prior insurance authorization verification, and proper patient screening. These factors contributed to a backlog of patients to schedule that regularly exceeded 300. A multidisciplinary process-improvement team was assembled to improve the turnaround time for scheduling an outpatient MR imaging examination (the interval between the time when the order was received and the time when the patient was informed about the MR imaging appointment). Process improvements targeted by the team included protocol turnaround time, schedule standardization, schedule intervals, examination timing, service standards, and scheduling redesign. Using lean methods and multiple plan-do-check-act cycles, the time to schedule an outpatient MR imaging examination improved from 117 hours to 33 hours, a 72% reduction, during the 9-month study period in 2011-2012. The number of patients in the scheduling queue was reduced by 90%. Overall MR imaging examinations within the specific patient population studied increased from 773 patient studies during the first month of intervention to 1444 studies the following month and averaged over 1279 patient studies per month throughout the study.

Patients prefer chemotherapy on the same day as their medical oncology outpatient appointment

PURPOSE

Numerous oncology units have separated outpatient appointments and chemotherapy delivery to another day (TOD) to improve efficiency. This survey assessed patient preferences for scheduling medical oncology outpatient appointments and chemotherapy delivery for either treatment delivered on the same day as the outpatient appointment (TSD) or TOD.

PATIENTS AND METHODS

Patients (N = 198) from two major metropolitan tertiary centers in Perth-Sir Charles Gairdner Hospital (n = 110) and Royal Perth Hospital (n = 88)-completed surveys from April 15 to May 24, 2013. Eligibility criteria included any adult patient with cancer receiving an intravenous chemotherapy or targeted agent who had completed ≥ two cycles of treatment or attended ≥ two chemotherapy appointments on a concurrent chemoradiotherapy program.

RESULTS

The majority of patients preferred TSD (85%) versus TOD. Convenience (50%) and distance or difficulty in transportation to hospital (25%) were the most common reasons for TSD preference. Current treatment schedule (odds ratio [OR], 59.2; 95% CI, 18.7 to 265.2) was significantly associated with treatment schedule preference. Younger age (58.3 v 65.2 years; P = .01) and presence of household dependents (OR, 4.2; 95% CI, 1.2 to 27.1) were also associated with TSD preference. Scheduling preference was not influenced by time prepared to wait for chemotherapy (χ(2) (2) = 3.86; P = .14), with 44% and 39% of patients willing to wait up to 60 and 120 minutes, respectively. Almost all patients preferred chemotherapy delivery before 2 pm (99%).

CONCLUSION

Patients preferred to receive chemotherapy on the same day as their medical oncology outpatient appointment. Morning delivery of chemotherapy was preferred. Meeting patients' expectations will present significant challenges to efficient service provision as caseloads increase.