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Ngwa W, Ngoma T. Spillover benefits of workforce capacity building in radiotherapy. Lancet Oncol 2024:S1470-2045(24)00482-0. [PMID: 39362234 DOI: 10.1016/s1470-2045(24)00482-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Accepted: 08/22/2024] [Indexed: 10/05/2024]
Affiliation(s)
- Wilfred Ngwa
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Twalib Ngoma
- Department of Clinical Oncology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
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Petit S, Holtzer-Hoffmans N, Smolenaers L, Balvert M. Operations research to improve the sustainability of radiotherapy departments. Phys Med Biol 2024; 69:020301. [PMID: 38217480 DOI: 10.1088/1361-6560/ad0faf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 01/15/2024]
Affiliation(s)
- Steven Petit
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, The Netherlands
| | - Nienke Holtzer-Hoffmans
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, The Netherlands
| | | | - Marleen Balvert
- Zero Hunger Lab/Department of Econometrics & Operations Research, Tilburg School of Economics & Management, Tilburg University, The Netherlands
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Hoffmans-Holtzer N, Smolenaers L, Olofsen-van Acht M, Hoogeman M, Balvert M, Petit S. Robust optimization of a radiotherapy pretreatment preparation workflow. Phys Med Biol 2024; 69:025022. [PMID: 37625421 DOI: 10.1088/1361-6560/acf437] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/25/2023] [Indexed: 08/27/2023]
Abstract
Objective. Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time (Fmean) with the risk of working overtime (RoO). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy.Approach. The OSS at our institute sees, scans and treats 3-5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorterFmeanand lowerRoO, a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parametersFmeanandRoO. For every generation, fitness of sequences was determined by the distance to the estimated Pareto front ofFmeanandRoO. Experiments were run in various OSS-settings.Main results. According to our approach, the expectedFmeanof the current clinical schedule could be reduced with 37%, without increasingRoO. Additional experiments provided insights in trade-offs betweenFmean,RoO, working shift length, number of patients treated on a single day and staff composition.Significance. Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting.
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Affiliation(s)
- Nienke Hoffmans-Holtzer
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Luuk Smolenaers
- Tilburg School of Economics and Management, Department of Econometrics and Operations Research, Tilburg University, Tilburg, The Netherlands
| | - Manouk Olofsen-van Acht
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Mischa Hoogeman
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Marleen Balvert
- Tilburg School of Economics and Management, Department of Econometrics and Operations Research, Tilburg University, Tilburg, The Netherlands
| | - Steven Petit
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
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Redondo E, Nicoletta V, Bélanger V, Garcia-Sabater JP, Landa P, Maheut J, Marin-Garcia JA, Ruiz A. A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2023; 3:100197. [PMID: 37275436 PMCID: PMC10212597 DOI: 10.1016/j.health.2023.100197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/09/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.
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Affiliation(s)
- Eduardo Redondo
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Vittorio Nicoletta
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Valérie Bélanger
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
- Department of Logistics and Operations Management, HEC Montréal, 3000 chemin de la Cote Sainte-Catherine, Montreal (Quebec), H3T 2A7, Canada
| | - José P Garcia-Sabater
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Paolo Landa
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
| | - Julien Maheut
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Juan A Marin-Garcia
- ROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, Spain
| | - Angel Ruiz
- Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada
- Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada
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Gurjar M, Lindberg J, Björk-Eriksson T, Olsson C. Automated data extraction tool (DET) for external applications in radiotherapy. Tech Innov Patient Support Radiat Oncol 2022; 25:100194. [PMID: 36659909 PMCID: PMC9842687 DOI: 10.1016/j.tipsro.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/18/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. METHODS AND MATERIALS We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015-16). Extracted data included patients' referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. RESULTS From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. CONCLUSION We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.
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Affiliation(s)
- Mruga Gurjar
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Corresponding author
| | - Jesper Lindberg
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden,Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Caroline Olsson
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
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Raman S, Jia F, Liu Z, Wenz J, Carter M, Dickie C, Liu FF, Letourneau D. Forecasting Institutional LINAC Utilization in Response to Varying Workload. Technol Cancer Res Treat 2022; 21:15330338221123108. [PMID: 36285543 PMCID: PMC9608060 DOI: 10.1177/15330338221123108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
ObjectivesPandemics, natural disasters, and other unforeseen circumstances can cause short-term variation in radiotherapy utilization. In this study, we aim to develop a model to forecast linear accelerator (LINAC) utilization during periods of varying workloads. Methods: Using computed tomography (CT)-simulation data and the rate of new LINAC appointment bookings in the preceding week as input parameters, a multiple linear regression model to forecast LINAC utilization over a 15-working day horizon was developed and tested on institutional data. Results: Future LINAC utilization was estimated in our training dataset with a forecasting error of 3.3%, 5.9%, and 7.2% on days 5, 10, and 15, respectively. The model identified significant variations (≥5% absolute differences) in LINAC utilization with an accuracy of 69%, 62%, and 60% on days 5, 10, and 15, respectively. The results were similar in the validation dataset with forecasting errors of 3.4%, 5.3%, and 6.2% and accuracy of 67%, 60%, and 58% on days 5, 10, and 15, respectively. These results compared favorably to moving average and exponential smoothing forecasting techniques. Conclusions: The developed linear regression model was able to accurately forecast future LINAC utilization based on LINAC booking rate and CT simulation data, and has been incorporated into our institutional dashboard for broad distribution. Advances in knowledge: Our proposed linear regression model is a practical and intuitive approach to forecasting short-term LINAC utilization, which can be used for resource planning and allocation during periods with varying LINAC workloads.
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Affiliation(s)
- Srinivas Raman
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada,Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada,Srinivas Raman MD, FRCPC, Department of Radiation Oncology, University of Toronto, 700 University Avenue, Room 7-610, Toronto, Ontario, Canada M5G 2M9.
| | - Fan Jia
- Department of Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Zhihui Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Julie Wenz
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada,Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Michael Carter
- Department of Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Colleen Dickie
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada,Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Fei-Fei Liu
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada,Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Daniel Letourneau
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada,Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada,Daniel Letourneau PhD, DABR, Department of Radiation Oncology, University of Toronto, 700 University Avenue, Room 7-424, Toronto, Ontario, Canada M5G 2M9.
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Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study. BMC Health Serv Res 2022; 22:1059. [PMID: 35986322 PMCID: PMC9388987 DOI: 10.1186/s12913-022-08447-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Results Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. Conclusions With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-08447-8.
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Barbosa B, Bravo I, Oliveira C, Antunes L, Couto JG, McFadden S, Hughes C, McClure P, Dias AG. Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum. Radiography (Lond) 2022; 28:955-963. [PMID: 35842952 DOI: 10.1016/j.radi.2022.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/14/2022] [Accepted: 06/23/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists (TR/RTTs) have a key role to play in patient management. It is essential for TR/RTTs to keep abreast with new technologies and continuously develop the digital skills necessary for safe RT practice. The RT profession and education is not regulated at European Union level, which leads to heterogeneity in the skills developed and practised among countries. This study aimed to explore the white and grey literature to collate data on the relevant digital skills required for TR/RTTs practice. METHODS An exhaustive systematic search was conducted to identify literature discussing digital skills of TR/RTTs; relevant grey literature was also identified. A thematic analysis was performed to identify and organise these skills into themes and sub-themes. RESULTS 195 digital skills were identified, organised in 35 sub-themes and grouped into six main themes: (i) Transversal Digital Skills, (ii) RT Planning Image, (iii) RT Treatment Planning, (iv) RT Treatment Administration, (v) Quality, Safety and Risk Management, and (vi) Management, Education and Research. CONCLUSION This list can be used as a reference to close current gaps in knowledge or skills of TR/RTTs while anticipating future needs regarding the rapid development of new technologies (such as Artificial Intelligence or Big Data). IMPLICATIONS FOR PRACTICE It is imperative to align education with current and future RT practice to ensure that all RT patients receive the best care. Filling the gaps in TR/RTTs skill sets will improve current practice and provide TR/RTTs with the support needed to develop more advanced skills.
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Affiliation(s)
- B Barbosa
- Radiotherapy Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Escola Internacional de Doutoramento, Universidad de Vigo, Circunvalación ao Campus Universitario, 36310 Vigo, Pontevedra, Spain; Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
| | - I Bravo
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
| | - C Oliveira
- Radiotherapy Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Escola Internacional de Doutoramento, Universidad de Vigo, Circunvalación ao Campus Universitario, 36310 Vigo, Pontevedra, Spain.
| | - L Antunes
- School of Health, Polytechnic Institute of Porto, Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal.
| | - J G Couto
- Radiography Department, Faculty of Health Sciences, University of Malta, Msida MSD2080, Malta.
| | - S McFadden
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - C Hughes
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - P McClure
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - A G Dias
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Medical Physics Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
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Hulzen G, Martin N, Depaire B, Souverijns G. Supporting Capacity Management Decisions in Healthcare using Data-Driven Process Simulation. J Biomed Inform 2022; 129:104060. [DOI: 10.1016/j.jbi.2022.104060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/04/2022] [Accepted: 03/26/2022] [Indexed: 10/18/2022]
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Hadid M, Elomri A, El Mekkawy T, Kerbache L, El Omri A, El Omri H, Taha RY, Hamad AA, Al Thani MHJ. Bibliometric analysis of cancer care operations management: current status, developments, and future directions. Health Care Manag Sci 2022; 25:166-185. [PMID: 34981268 DOI: 10.1007/s10729-021-09585-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 10/05/2021] [Indexed: 01/31/2023]
Abstract
Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.
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Affiliation(s)
- Majed Hadid
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Adel Elomri
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
| | | | - Laoucine Kerbache
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Halima El Omri
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Ruba Y Taha
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Anas Ahmad Hamad
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
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Lindberg J, Gurjar M, Holmström P, Hallberg S, Björk-Eriksson T, Olsson CE. Resource planning principles for the radiotherapy process using simulations applied to a longer vacation period use case. Tech Innov Patient Support Radiat Oncol 2021; 20:17-22. [PMID: 34703909 PMCID: PMC8524937 DOI: 10.1016/j.tipsro.2021.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/02/2021] [Accepted: 10/04/2021] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Radiotherapy (RT) resources need to be used wisely to balance workload and patient throughput. There are no known strategies on how to plan resource use around longer vacation periods to avoid patient waiting times. We created a simulation model over the RT workflow to evaluate different scenarios for this purpose. MATERIALS AND METHODS The simulation model mimics a large modern RT department in Sweden. It was based on real data on patient referral patterns and resource use extracted from clinical systems (3666 treatment courses). Workshops with managers and staff were held to investigate nine different scenarios for the summer vacation period including one scenario to validate the model. Different capacity reductions, vacation period lengths and timing of the vacation periods between the preparatory part of the RT workflow and the treatment part were evaluated. RESULTS For an eight-week period, resource utilization was predicted to be high both before and after the vacation period regardless of timing. However, more patients would be waiting with completed preparations with simultaneous vacation periods than when the preparatory part started one-two weeks prior to the treatment part. With shorter vacation periods, treatment would require overtime during the vacation period with higher levels of patients waiting compared to an eight-week scenario. CONCLUSIONS Our proposed strategy aided managers to identify a preferred scenario for the summer vacation period with the preparatory part starting one-two weeks prior to the treatment part for an eight-week vacation period. This can help other RT departments to plan for similar situations.
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Affiliation(s)
- Jesper Lindberg
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Mrugaja Gurjar
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Paul Holmström
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Stefan Hallberg
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Caroline E Olsson
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
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Lindberg J, Björk-Eriksson T, Olsson CE. Linear accelerator utilization: Concept and tool to aid the scheduling of patients for radiotherapy. Tech Innov Patient Support Radiat Oncol 2021; 20:10-16. [PMID: 34722940 PMCID: PMC8531843 DOI: 10.1016/j.tipsro.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/25/2021] [Accepted: 09/22/2021] [Indexed: 10/31/2022] Open
Abstract
Background and purpose Resources in radiotherapy (RT) need to be used effectively to meet the current clinical demand. The aim of this data-driven study is to identify temporal trends in the scheduling of patients for RT and to develop a tool for a visual overview of future scheduling levels. Material and methods Scheduling data at an eight-linac modern RT department in Sweden were collected twice daily for planned and observed linac use in 2018-2020. Information was retrieved each day for the present (Day 0) and the forthcoming 100 weekdays with total linac utilization rates (LURs) calculated for two activity categories: treatment and non-treatment. An in-house tool based on the LUR concept, database queries from the oncology information system (OIS)/automatic calculations was developed and evaluated by RT managers and scheduling staff (n = 10). Results Overall median LURs were 87%/89% (planned/observed; p < 0.01) with more frequent and larger daily increase for non-treatment activities compared with treatment activities. LUR increased with shorter planning horizons and reached 100% for fully-operating linacs ≈3 weeks before Day 0. The tool was reported by 88% to ease the work and to contribute towards an even scheduling of patients (responses: 8/10). Conclusion Alterations from a planned RT schedule occurs frequently. Having a tool that helps to reduce the abundance of booking information into clinically relevant overviews promise to increase the understanding of present and future scheduling levels. Our proposed concept and tool suggest that this is a feasible approach to schedule patients for RT more evenly.
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Affiliation(s)
- Jesper Lindberg
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45 Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45 Gothenburg, Sweden.,Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
| | - Caroline E Olsson
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45 Gothenburg, Sweden
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Lindberg J, Holmström P, Hallberg S, Björk-Eriksson T, Olsson CE. An analytical approach to aggregate patient inflows to a simulation model over the radiotherapy process. BMC Health Serv Res 2021; 21:207. [PMID: 33685475 PMCID: PMC7938525 DOI: 10.1186/s12913-021-06162-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 02/09/2021] [Indexed: 11/18/2022] Open
Abstract
Background In meeting input data requirements for a system dynamics (SD) model simulating the radiotherapy (RT) process, the number of patient care pathways (RT workflows) needs to be kept low to simplify the model without affecting the overall performance. A large RT department can have more than 100 workflows, which results in a complex model structure if each is to be handled separately. Here we investigated effects on model performance by reducing the number of workflows for a model of the preparatory steps of the RT process. Methods We created a SD model sub-structure capturing the preparatory RT process. Real data for patients treated in 2015-2016 at a modern RT department in Sweden were used. RT workflow similarity was quantified by averaged pairwise utilization rate differences (%) and the size of corresponding correlation coefficients (r). Grouping of RT workflows was determined using two accepted strategies (80/20 Pareto rule; merging all data into one group) and a customized algorithm with r≥0.75:0.05:0.95 as criteria for group inclusion by two strategies (A1 and A2). Number of waiting patients for each grouping strategy were compared to the reference of all workflows handled separately. Results There were 128 RT workflows for 3209 patients during the studied period. The 80/20 Pareto rule resulted in 14/8/21 groups for curative/palliative/disregarding treatment intent. Correspondingly, A1 and A2 resulted in 7-40/≤4-36/7-82 groups depending on r cutoff. Results for the Pareto rule and A2 at r≥85 were comparable to the reference. Conclusions The performance of a simulation model over the RT process will depend on the grouping strategy of patient input data. Either the Pareto rule or the grouping of patients by resource use can be expected to better reflect overall departmental effects to various changes than when merging all data into one group. Our proposed approach to identify groups based on similarity in resource use can potentially be used in any setting with variable incoming flows of objects which go through a multi-step process comparable to RT where the aim is to reduce the complexity of associated model structures without compromising with overall performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-021-06162-4.
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Affiliation(s)
- Jesper Lindberg
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden. .,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden. .,Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden.
| | - Paul Holmström
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
| | - Stefan Hallberg
- Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden.,Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden
| | - Caroline E Olsson
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45, Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden
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Vieira B, Demirtas D, van de Kamer JB, Hans EW, Jongste W, van Harten W. Radiotherapy treatment scheduling: Implementing operations research into clinical practice. PLoS One 2021; 16:e0247428. [PMID: 33606831 PMCID: PMC7894882 DOI: 10.1371/journal.pone.0247428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/05/2021] [Indexed: 12/24/2022] Open
Abstract
Background Every week, radiotherapy centers face the complex task of scheduling hundreds of treatment sessions amongst the available linear accelerators. With the increase in cancer patient numbers, manually creating a feasible and efficient schedule has shown to be a difficult, time-consuming task. Although operations research models have been increasingly reported upon to optimize patient care logistics, there is almost no scientific evidence of implementation in practice. Methods A mathematical operations research model was adapted to generate radiotherapy treatment schedules in two Dutch centers. The model was iteratively adjusted to fulfill the technical and medical constraints of each center until a valid model was attained. Patient data was collected for the planning horizon of one week, and the feasibility of the obtained schedules was verified by the staff of each center. The resulting optimized solutions are compared with the ones manually developed in practice. Results The weekly schedule was improved in both centers by decreasing the average standard deviation between sessions’ starting times from 103.0 to 50.4 minutes (51%) in one center, and the number of gaps in the schedule from 18 to 5 (72%) in the other. The number of patients requiring linac switching between sessions has also decreased from 71 to 0 patients in one center, and from 43 to 2 in the other. The automated process required 5 minutes and 1.5 hours of computation time to find an optimal weekly patient schedule, respectively, as opposed to approximately 1.5 days when performed manually for both centers. Conclusions The practical application of a theoretical operations research model for radiotherapy treatment scheduling has provided radiotherapy planners a feasible, high-quality schedule in an automated way. Iterative model adaptations performed in small steps, early engagement of stakeholders, and constant communication proved to facilitate the implementation of operations research models into clinical practice.
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Affiliation(s)
- Bruno Vieira
- Department of Radiation Oncology, Netherlands Cancer Institute—Antoni van Leeuwenhoek, Amsterdam, The Netherlands
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands
| | - Derya Demirtas
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands
- Faculty of Behavioural Management and Social Sciences, Department Industrial Engineering and Business Information Systems, University of Twente, Enschede, The Netherlands
| | - Jeroen B. van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute—Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Erwin W. Hans
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands
- Faculty of Behavioural Management and Social Sciences, Department Industrial Engineering and Business Information Systems, University of Twente, Enschede, The Netherlands
| | | | - Wim van Harten
- Department of Radiation Oncology, Netherlands Cancer Institute—Antoni van Leeuwenhoek, Amsterdam, The Netherlands
- Department of Health Technology and Services Research, School of Governance and Management, University of Twente, Enschede, The Netherlands
- * E-mail:
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15
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Vieira B, Demirtas D, van de Kamer JB, Hans EW, Rousseau LM, Lahrichi N, van Harten WH. Radiotherapy treatment scheduling considering time window preferences. Health Care Manag Sci 2020; 23:520-534. [PMID: 32594285 PMCID: PMC7676074 DOI: 10.1007/s10729-020-09510-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 06/10/2020] [Indexed: 11/26/2022]
Abstract
External-beam radiotherapy treatments are delivered by a linear accelerator (linac) in a series of high-energy radiation sessions over multiple days. With the increase in the incidence of cancer and the use of radiotherapy (RT), the problem of automatically scheduling RT sessions while satisfying patient preferences regarding the time of their appointments becomes increasingly relevant. While most literature focuses on timeliness of treatments, several Dutch RT centers have expressed their need to include patient preferences when scheduling appointments for irradiation sessions. In this study, we propose a mixed-integer linear programming (MILP) model that solves the problem of scheduling and sequencing RT sessions considering time window preferences given by patients. The MILP model alone is able to solve the problem to optimality, scheduling all sessions within the desired window, in reasonable time for small size instances up to 66 patients and 2 linacs per week. For larger centers, we propose a heuristic method that pre-assigns patients to linacs to decompose the problem in subproblems (clusters of linacs) before using the MILP model to solve the subproblems to optimality in a sequential manner. We test our methodology using real-world data from a large Dutch RT center (8 linacs). Results show that, combining the heuristic with the MILP model, the problem can be solved in reasonable computation time with as few as 2.8% of the sessions being scheduled outside the desired time window.
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Affiliation(s)
- Bruno Vieira
- Department of Radiation Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
| | - Derya Demirtas
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.,Department of Industrial Engineering and Business Information Systems, Faculty of Behavioural Management and Social Sciences, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Jeroen B van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erwin W Hans
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.,Department of Industrial Engineering and Business Information Systems, Faculty of Behavioural Management and Social Sciences, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Louis-Martin Rousseau
- Mathematics and Industrial Engineering, Polytechnique Montreal, 2900 Edouard Montpetit Blvd, Montreal, Quebec, H3T 1J4, Canada
| | - Nadia Lahrichi
- Mathematics and Industrial Engineering, Polytechnique Montreal, 2900 Edouard Montpetit Blvd, Montreal, Quebec, H3T 1J4, Canada
| | - Wim H van Harten
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Rijnstate General Hospital, Arnhem, The Netherlands
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16
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Vieira B, Demirtas D, B van de Kamer J, Hans EW, van Harten W. Improving workflow control in radiotherapy using discrete-event simulation. BMC Med Inform Decis Mak 2019; 19:199. [PMID: 31651304 PMCID: PMC6814107 DOI: 10.1186/s12911-019-0910-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 09/06/2019] [Indexed: 11/22/2022] Open
Abstract
Background In radiotherapy, minimizing the time between referral and start of treatment (waiting time) is important to possibly mitigate tumor growth and avoid psychological distress in cancer patients. Radiotherapy pre-treatment workflow is driven by the scheduling of the first irradiation session, which is usually set right after consultation (pull strategy) or can alternatively be set after the pre-treatment workflow has been completed (push strategy). The objective of this study is to assess the impact of using pull and push strategies and explore alternative interventions for improving timeliness in radiotherapy. Methods Discrete-event simulation is used to model the patient flow of a large radiotherapy department of a Dutch hospital. A staff survey, interviews with managers, and historical data from 2017 are used to generate model inputs, in which fluctuations in patient inflow and resource availability are considered. Results A hybrid (40% pull / 60% push) strategy representing the current practice (baseline case) leads to 12% lower average waiting times and 48% fewer first appointment rebooks when compared to a full pull strategy, which in turn leads to 41% fewer patients breaching the waiting time targets. An additional scenario analysis performed on the baseline case showed that spreading consultation slots evenly throughout the week can provide a 21% reduction in waiting times. Conclusions A 100% pull strategy allows for more patients starting treatment within the waiting time targets than a hybrid strategy, in spite of slightly longer waiting times and more first appointment rebooks. Our algorithm can be used by radiotherapy policy makers to identify the optimal balance between push and pull strategies to ensure timely treatments while providing patient-centered care adapted to their specific conditions.
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Affiliation(s)
- Bruno Vieira
- Department of Radiation Oncology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands. .,Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
| | - Derya Demirtas
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.,Department of Industrial Engineering and Business Information Systems, Faculty of Behavioural Management and Social Sciences, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Jeroen B van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands
| | - Erwin W Hans
- Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.,Department of Industrial Engineering and Business Information Systems, Faculty of Behavioural Management and Social Sciences, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Wim van Harten
- Department of Radiation Oncology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.,Department of Health Technology and Services Research, School of Governance and Management, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands.,Rijnstate General Hospital, Arnhem, The Netherlands
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17
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Campi LB, Lopes FC, Soares LES, de Queiroz AM, de Oliveira HF, Saquy PC, de Sousa-Neto MD. Effect of radiotherapy on the chemical composition of root dentin. Head Neck 2018; 41:162-169. [PMID: 30552849 DOI: 10.1002/hed.25493] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 02/23/2018] [Accepted: 07/06/2018] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The radiotherapy can directly affect the bond strength of the adhesive materials, interfering in the prognosis of restorative treatments, which may be caused by chemical changes in dentin structure. METHODS Twenty inferior homologues premolars were distributed in 2 groups (in vitro study) (n = 10): nonirradiated and irradiated. The specimens were submitted to the analysis of phosphate (ν1 PO4 3- ;ν2 PO4 3- ;ν4 PO4 3- ), carbonate (ν3 CO3 2- ), amide I, CH2 , amide III, and amide I/III ratio by confocal Raman spectroscopy. Data were submitted to statistical analysis (T test, P < .05). RESULTS In intracanal dentin, the irradiated group had lower ν4 PO4 3- values (1.23 ± 0.06) compared to nonirradiated group (1.40 ± 0.18) (P < .05), with no difference for ν1 PO4 3- and ν2 PO4 3 peaks (P > .05). The irradiated (1.56 ± 0.06) had lower carbonate, amide III (1.05 ± 0.19), and amide I/III ratio values (0.19 ± 0.06) compared to nonirradiated group (1.42 ± 0.10, 1.28 ± 0.24, and 0.31 ± 0.10, respectively) (P < .05). For medium dentin irradiated group (1.30 ± 0.12) had lower phosphate values compared to nonirradiated group (1.48 ± 0.22) (P < .05). In cementum, there was no statistical difference between the groups. CONCLUSION The radiotherapy was able to cause changes in ν4 PO4 3- , carbonate, and amide III peaks of root dentin.
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Affiliation(s)
- Lívia Bueno Campi
- Department of Restorative Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Fabiane Carneiro Lopes
- Department of Restorative Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Luís Eduardo Silva Soares
- Laboratory of Dentistry and Applied Materials (LDAM), Research and Development Institute (IP&D), Universidade do Vale do Paraíba, Univap, São José dos Campos, São Paulo, Brazil
| | - Alexandra Mussolino de Queiroz
- Department Children's Clinic, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Harley Francisco de Oliveira
- Medical Clinic Department, Medical School of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Paulo César Saquy
- Department of Restorative Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Manoel Damião de Sousa-Neto
- Department of Restorative Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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18
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Saville CE, Smith HK, Bijak K. Operational research techniques applied throughout cancer care services: a review. Health Syst (Basingstoke) 2018; 8:52-73. [PMID: 31214354 PMCID: PMC6507866 DOI: 10.1080/20476965.2017.1414741] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 12/01/2017] [Accepted: 12/05/2017] [Indexed: 01/22/2023] Open
Abstract
Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.
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Affiliation(s)
| | - Honora K Smith
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Katarzyna Bijak
- Southampton Business School, University of Southampton, Southampton, UK
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Ponsard C, De Landtsheer R, Guyot Y, Roucoux F, Lambeau B. Quality of Care Driven Scheduling of Clinical Pathways Under Resource and Ethical Constraints. ENTERP INF SYST-UK 2018. [DOI: 10.1007/978-3-319-93375-7_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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20
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Niroumandrad N, Lahrichi N. A stochastic tabu search algorithm to align physician schedule with patient flow. Health Care Manag Sci 2017; 21:244-258. [DOI: 10.1007/s10729-017-9427-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 11/23/2017] [Indexed: 11/30/2022]
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Salleh S, Thokala P, Brennan A, Hughes R, Booth A. Simulation Modelling in Healthcare: An Umbrella Review of Systematic Literature Reviews. PHARMACOECONOMICS 2017; 35:937-949. [PMID: 28560492 DOI: 10.1007/s40273-017-0523-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Numerous studies examine simulation modelling in healthcare. These studies present a bewildering array of simulation techniques and applications, making it challenging to characterise the literature. OBJECTIVE The aim of this paper is to provide an overview of the level of activity of simulation modelling in healthcare and the key themes. METHODS We performed an umbrella review of systematic literature reviews of simulation modelling in healthcare. Searches were conducted of academic databases (JSTOR, Scopus, PubMed, IEEE, SAGE, ACM, Wiley Online Library, ScienceDirect) and grey literature sources, enhanced by citation searches. The articles were included if they performed a systematic review of simulation modelling techniques in healthcare. After quality assessment of all included articles, data were extracted on numbers of studies included in each review, types of applications, techniques used for simulation modelling, data sources and simulation software. RESULTS The search strategy yielded a total of 117 potential articles. Following sifting, 37 heterogeneous reviews were included. Most reviews achieved moderate quality rating on a modified AMSTAR (A Measurement Tool used to Assess systematic Reviews) checklist. All the review articles described the types of applications used for simulation modelling; 15 reviews described techniques used for simulation modelling; three reviews described data sources used for simulation modelling; and six reviews described software used for simulation modelling. The remaining reviews either did not report or did not provide enough detail for the data to be extracted. CONCLUSION Simulation modelling techniques have been used for a wide range of applications in healthcare, with a variety of software tools and data sources. The number of reviews published in recent years suggest an increased interest in simulation modelling in healthcare.
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Affiliation(s)
- Syed Salleh
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
| | - Praveen Thokala
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Alan Brennan
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Ruby Hughes
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Andrew Booth
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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