Joe Viana is a Postdoctoral Fellow on the MIA - Measures for Improved Availability of Medicines and Vaccines project at BI Norwegian Business School. He received his PhD and MSc from the Southampton Business School, University of Southampton, UK, in Operational Research and Management.
Prior to starting at BI in May 2020, Joe was a Research Fellow at the Health Services Research Unit (HØKH), Akershus University Hospital on the Centre for Connected Care (C3) project, a Norwegian Research Council Centre for Research-based Innovation project. Prior to that he was a Research Fellow at the Southampton Business School, University of Southampton on the Care Life Cycle (CLC) project, an Engineering and Physical Science Research Councils, Complexity Science for the Real World project.
Research areas
Joe uses analytical approaches especially simulation modelling, with system stakeholders, to improve the understanding and operation of health and social care systems. During his career he has built a variety of models in Excel, Vensim, iThink, Stella, AnyLogic, Simul8 and R to support decision making primarily in the health sector but also in other sectors.
He specialises in applying Operational Research approaches in the healthcare sector, in particular: agent based, discrete event, and system dynamics simulation modelling. He has developed many models collaboratively including but not limited to: i) Operational level models of hospitals and departments within hospitals, ii) Disease progression models and Infection transmission models, iii) Cost effectiveness analysis, iv) Strategic level modelling and whole system modelling, v) Provided modelling support for projects at: DSTL, London Fire Brigade, Ford, Dorset County Council, Boeing, British Airways, RBS Insurance, Virgin Media and Hampshire County Council.
Joe was the co-organiser of the 2018 EURO Working Group on Operational Research Applied to Health Services (ORAHS) conference that was held at Oslo Science Park, which is an important academic community for him. His work been has published in such journals as The European Journal of Operational Research, BMC Health Services Research, Journal of Neurology, Health & Social Care in the Community, and The Proceedings of the Winter Simulation Conference.
Joe is a Board member of EURO Working Group on Operational Research Applied to Health Services (ORAHS).
Teaching areas
At present Joe does not teach at BI. He focuses on research.
At the Southampton Business School, he taught: Simulation, Healthcare Modelling and Business Simulation to MSc students.
Conventional simulation models used in Operations Research and Management Science (OR/MS) use historical data. With the increasing availability of real-time data, technologies commonly associated with applied computing, such as Data Acquisition Systems (DAS), may need to be integrated with conventional OR/MS models to develop Hybrid Models (HMs). We distinguish between HMs that use only real-time data – we refer to them as Digital Twins (DTs) – and those using a combination of historical and real-time data – called Real-time Simulation (RtS). Our previous contribution focused on the challenges of such integration, a concept referred to as information fusion, and presented a conceptualization of DT/RtS. This paper focuses on DT/RtS data synchronization and methods that could be employed from Parallel and Distributed Simulation (PADS). The conceptualizations and discussions reflect on the authors' experience implementing an RtS of a network of Emergency Departments and Urgent Care Centers in the UK.
Mustafee, Navonil; Harper, Alison & Viana, Joe (2023)
Hybrid Models with Real-time Data: Characterising Real-time Simulation and Digital Twins
Currie, Christine & Rhodes-Leader, Luke (red.). Proceedings of the Operational Research Society Simulation Workshop 2023 (SW23)
Real-time Simulation (RtS) and Digital Twins (DT) are terms generally associated with hybrid models that use real-time data to drive computational models. Additionally, in the case of DTs, real-time data is often used to create virtual replicas of the physical system as it progresses through real-time. There is an increasing volume of literature on RtS and DT; however, the field of OR/MS is yet to coalesce on accepted definitions and conceptualisations. This has arguably led to the cascading usage of these terms. The objective of the paper is threefold: (1) distinguish between RtS and DT, (2) present RtS-DT conceptualisation in four dimensions, and (3) present methodological and technical insights on developing RtS with limited data. We argue that the evolution of conventional simulation models to fully-fledged hybrid DTs may necessitate a focus on a transitional stage; namely, RtS models primarily driven using historical distributions with limited real-time data feeds.
Khadri, Ines Julia & Viana, Joe (2022)
Simulation of IT Data Integration to Optimize an Antibiotics Supply Chain with System Dynamics
Supply chain (SC) optimization is essential for a firm to cope with everchanging market conditions and disruptions. New technologies have allowed for more advanced supply chain optimization. This paper uses system dynamics (SD) simulation to model the effects of data integration technologies on an antibiotic (AB) SC operation. The study aims to improve the AB SC to benefit all relevant stakeholders including the patient population. We evaluate how IT integration technologies can improve communication across the SC to mitigate or reduce the impact of the of disruptions on AB users. The presented model is under development and is subject to structural and parametric changes as discussions continue with stakeholders about the system structure and what data can be used and disclosed. Despite extensive SC optimization literature there has been a growing call of an evidence base to support decision making relating to national medicine policies.
Optimal material resource planning is crucial to run safe and cost-efficient hospital services. In this article, we investigate a real problem in hospitals, motivated by an environmental and economically inefficient use of disposable, single-use, endoscopes. We develop a mathematical model and create a decision support tool to determine when reusable, multi-use, bronchoscopes should be sent for inspection including information to what extent single-use bronchoscopes can cover the remaining demand. The results show that the proposed approach can contain operational costs which consist of costs for buying single-use devices, inspection costs and reprocessing costs, i.e., sterilization of reusable devices. Our tool can assist hospitals to predict when reusable bronchoscopes should undergo inspection and whether the current inventory of reusable devices is sufficient to cover the demand. Finally, we evaluate the impact of variation in demand on total costs.
Ahlqvist, Victoria; Dube, Nonhlanhla, Jahre, Marianne, Lee, Jin Soo, Melaku, Tsegaye, Moe, Andreas Farstad, Olivier, Max, Selviaridis, Kostas, Viana, Joe & Årdal, Christine Oline (2022)
Supply chain risk management strategies in normal and abnormal times: policymakers' role in reducing generic medicine shortages
This paper links supply chain risk management to medicine supply chains to explore the role of policymakers in employing supply chain risk management strategies (SCRMS) to reduce generic medicine shortages. Using secondary data supplemented with primary data, we map and compare seven countries’ SCRMS for handling shortage risks in their paracetamol supply chains before and during the first two waves of the COVID-19 pandemic. Consistent with recent research, the study finds that policymakers had implemented few SCRMS specifically for responding to disruptions caused by COVID-19. However, shortages were largely avoided since multiple strategies for coping with business-as-usual disruptions had been implemented prior to the pandemic. We did find that SCRMS implemented during COVID-19 were not always aligned with those implemented pre-pandemic. We also found that policymakers played both direct and indirect roles. Combining longitudinal secondary data with interviews sheds light on how, regardless of the level of preparedness during normal times, SCRMS can be leveraged to avert shortages in abnormal times. However, the problem is highly complex, which warrants further research Supply chain professionals and policymakers in the healthcare sector can use the findings when developing preparedness and response plans. The insights developed can help policymakers improve the availability of high-volume generic medicines in (ab)normal times. We contribute to prior SCRM research in two ways. First, we operationalize SCRMS in the medicine supply chain context in (ab)normal times, thereby opening avenues for future research on SCRM in this context. Second, we develop insights on the role policymakers play and how they directly implement and indirectly influence the adoption of SCRMS. Based on our findings, we develop a framework that captures the diverse roles of policymakers in SCRM.
Viana, Joe; van Oorschot, Kimball Elizabeth & Årdal, Christine Oline (2021)
Assessing Resilience of Medicine Supply Chain Networks to Disruptions: A Proposed Hybrid Simulation Modeling Framework
Kim, Sojung; Feng, Ben, Smith, Katy, Masoud, Sara, Zheng, Zeyu, Szabo, Claudia & Loper, Margaret (red.). Proceedings of the 2021 Winter Simulation Conference
Viana, Joe; van Oorschot, Kimball Elizabeth & Årdal, Christine Oline (2021)
Assessing Resilience Of Medicine Supply Chain Networks To Disruptions: A Proposed Hybrid Simulation Modeling Framework
Kim, Sojung; Feng, Ben, Smith, Katy, Masoud, Sara, Zheng, Zeyu, Szabo, Claudia & Loper, Margaret (red.). Proceedings of the 2021 Winter Simulation Conference
The objective of the proposed hybrid simulation modeling framework is to improve the understanding and operation of medicine supply chains, to strengthen their resilience to ensure the availability of medicines. The framework draws upon hybrid simulation, supply chain resilience and medicine supply chain literature. The utility of the proposed framework is presented through the development of a case model of a generic (off-patent) case medicine in the Norwegian system to perform scenario-based experiments on disruption events and interventions. Two disruption scenarios are evaluated a demand shock e.g., hoarding, and a supply shock, e.g., a major disruption at a key supplier. The effect of these disruptions on the system without interventions is compared with proactive and reactive interventions, namely prepositioned stock, and flexible ordering. Future directions for framework development have been identified.
Viana, Joe; Simonsen, Tone Breines, Faraas, Hildegunn E., Schmidt, Nina, Dahl, Fredrik Andreas & Flo, Kari (2020)
Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study
Background: The demand for a large Norwegian hospital’s post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital’s catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times. Methods: A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations. Results: Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women’s length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins. Conclusions: The simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic. Keywords: Simulation, Post-term pregnancies, Patient flow, Capacity planning, Optimisation, Outpatient
Rand, Kim; Dahl, Fredrik Andreas, Viana, Joe, Rønning, Ole M., Faiz, Kashif Waqar & Barra, Mathias (2019)
Fewer ischemic strokes, despite an ageing population: stroke models from observed incidence in Norway 2010–2015
Barra, Mathias; Labberton, Angela Susan, Faiz, Kashif Waqar, Lindstrøm, Jonas Christoffer, Rønning, Ole M., Viana, Joe, Dahl, Fredrik Andreas & Rand, Kim (2019)
Stroke incidence in the young: evidence from a Norwegian register study
Rand, Kim; Viana, Joe & Dahl, Fredrik Andreas (2017)
Combining bootstrap-based stroke incidence models with discrete event modeling of travel-time and stroke treatment: non-normal input and non-linear output
Viana, Joe; Ziener, Vigdis Margrethe, Ponton, Irene Gynnild, Holhjem, Marita Sommer, Thøgersen, Lisa Johanne & Simonsen, Tone Breines (2017)
Optimizing home hospital health service delivery in norway using a combined geographical information system, agent based, discrete event simulation model
Mustafee, Navonil; Harper, Alison & Viana, Joe (2023)
Hybrid Models with Real-time Data: Characterising Real-time Simulation and Digital Twins
[Academic lecture]. The Operational Research Society 11th Simulation Workshop (SW23).
Real-time Simulation (RtS) and Digital Twins (DT) are terms generally associated with hybrid models that use real-time data to drive computational models. Additionally, in the case of DTs, real-time data is often used to create virtual replicas of the physical system as it progresses through real-time. There is an increasing volume of literature on RtS and DT; however, the field of OR/MS is yet to coalesce on accepted definitions and conceptualisations. This has arguably led to the cascading usage of these terms. The objective of the paper is threefold: (1) distinguish between RtS and DT, (2) present RtS-DT conceptualisation in four dimensions, and (3) present methodological and technical insights on developing RtS with limited data. We argue that the evolution of conventional simulation models to fully-fledged hybrid DTs may necessitate a focus on a transitional stage; namely, RtS models primarily driven using historical distributions with limited real-time data feeds.
Proactive and Reactive Interventions to Mitigate Medicine Shortages in (Ab)Normal Times
[Academic lecture]. NOFOMA Iceland 2022.
Viana, Joe (2022)
Assessing resilience of medicine supply chain networks to disruptions: A proposed hybrid simulation modelling framework
[Academic lecture]. Invited seminar.
The objective of the proposed hybrid simulation modeling framework is to improve the understanding and operation of medicine supply chains, to strengthen their resilience to ensure the availability of medicines. The framework draws upon hybrid simulation, supply chain resilience and medicine supply chain literature. The utility of the proposed framework is presented through the development of a case model of a generic (off-patent) case medicine in the Norwegian system to perform scenario-based experiments on disruption events and interventions. Two disruption scenarios are evaluated: a demand shock e.g., hoarding, and a supply shock, e.g., a major disruption at a key supplier. The effect of these disruptions on the system without interventions is compared with proactive and reactive interventions, namely prepositioned stock, and flexible ordering. Future directions for framework development have been identified.
Discrete event simulation model to analyse the impact of COVID-19 on radiotherapy practice
[Academic lecture]. Operational Research Applied to Health Services [ORAHS] conference.
Radiotherapy cancer treatment is resource intensive and process optimisation to improve patient flow, whilst guaranteeing quality and safety. Process deviations must be well tested. Operational changes in radiotherapy centres were necessary to protect patients and staff from COVID-19. The aim of this simulation study was to quantify the impact of pandemic-related staff absence and selected protection measures on radiotherapy centre patient waiting times and throughput. A generic discrete event simulation model was developed to analyse patient flow changes when implementing COVID-19 control measures. A private German radiotherapy centre case is presented to investigate three sets of scenarios 1) the effects of health care staff and equipment shortfalls without COVID-19 measures, 2) set 1, with COVID-19 measures and a low 7-day COVID-19 incidence, and 3) set 1, with COVID-19 measures and high COVID-19 incidence. Staff absence increases average patient waiting time and reduces patient throughput which can lead to negative patient outcomes, independent of the COVID-19 incidence. The greatest waiting time increase occurs when two radiation therapists are on duty. The absence of a linear accelerator for treatment leads to long average waiting times . Model results suggest that centre administrators are a potential bottleneck if they must perform COVID-19 protection measures in addition to their administrative tasks. Outsourcing COVID-19 related tasks could mitigate this effect.
Viana, Joe; Jahre, Marianne, Årdal, Christine Oline & van Oorschot, Kim E. (2021)
Medicine supply chain resilience: A hybrid simulation cost effectiveness analysis study of disruption intervention strategies,
[Academic lecture]. European Conference on Operational Research [EURO].
Risk, Resilience and Sustainability in Paracetamol Supply Chain Networks: A Norwegian Case Study
[Academic lecture]. Production and Operations Management Society [POMS] conference.
Viana, Joe; Jahre, Marianne, Årdal, Christine Oline & van Oorschot, Kim E. (2021)
Defining measures, identifying, and obtaining data to conduct (cost) effectiveness analysis of medicine supply chains
[Academic lecture]. Operational Research Applied to Health Services [ORAHS] conference.
Jahre, Marianne & Viana, Joe (2021)
Presentation of MIA and COVID-19 Task Force projects for Indian Institute of Management Kozhikode
[Academic lecture]. Indian Institute of Management Kozhikode.
Viana, Joe (2020)
Reflections on healthcare applications of hybrid simulation modelling in the UK and Norway
[Academic lecture]. Complex Systems Modelling Group Seminar Series.
The combination or mixing of methods in Operational Research (OR) is not new. The argument for combing OR methods is that as each has different strengths and weaknesses, and mixing methods offers the potential to overcome some of the drawbacks of using a single approach. Hybrid simulation (HS), defined as a modelling approach that combines two or more of the following methods: discrete-event simulation (DES), system dynamics (SD), and agent-based simulation (ABS), follows the same rationale for combining or mixing OR methods. It has been argued that HS are better suited to capture the complexity of systems that need to be better understood or improved. The use of HS and number of HS publications have increased over the last 20 years. The main HS application areas are healthcare, supply chain management and manufacturing, and most published models combine discrete-event simulation and system dynamics. I will introduce and reflect on three health related HS models 1) a DES-SD HS model of Chlamydia transmission and treatment in a UK county to assess effectiveness of screening programs, 2) a DES-SD-ABM HS model of age related macular degeneration, to understand and illustrate the implication to and interactions between the social care and health care systems in a UK county and, 3) a DES-ABM HS model to improve the operation of a post-term pregnancy outpatient clinic in a Norwegian hospital.
Brailsford, Sally; Evenden, Dave C. & Viana, Joe (2020)
Hybrid simulation modeling in population health
Apostolopoulos, Yorghos; Hassmiller Lich, Kristen & Lemke, Michael K. (red.). Complex Systems and Population Health: A Primer
Viana, Joe; Simonsen, Tone Breines, Rand, Kim, Barra, Mathias & Dahl, Fredrik Andreas (2019)
The development of workshops to introduce computer simulation modelling health care decision-makers in familiar contexts
[Academic lecture]. Den tredje nasjonale konferansen i helsetjenesteforskning.
Title: The development of workshops to introduce computer simulation modelling health care decision-makers in familiar contexts. Authors: Joe Viana, Tone Breines Simonsen, Kim Rand, Mathias Barra and Fredrik Dahl Abstract (300 word limit) Background Computer simulation modelling (modelling) provides a virtual “safe” environment to evaluate potential changes to systems, before implemented those changes in reality. Many sectors use modelling in the decision-making process, including but not limited to finance, engineering, manufacturing, space and aviation, military and healthcare. Purpose The other industries use modelling more than healthcare. This paper presents three case studies to illustrate modelling applied to health and social care systems. Case 1: Chlamydia (UK) - a model of screening linked with an operational level model of a sexual health clinic to examine the cost effectiveness of different screening programmes. Case 2: Age Related Macular Degeneration (UK) – individual eye based physiological models of the progression of the disease, linked with models of that individual’s social care needs, and the operation of a hospital eye unit to examine how the health and social care systems interactions affect each other’s resources and the impact on the sufferers. Case 3: Post Term Pregnancy clinic (NO) – a model of the operation of a post-term pregnancy clinic, to improve the flow of women through the clinic, to make better use of resources and improve the women’s experience. Method & Results The case studies utilise three main simulation paradigms, Discrete Event Simulation (DES), Agent Based Modelling/Simulation (ABM/ABS) and System Dynamics (SD). We will summarise the method(s) and results for each case study. Utilising the authors’ experience and existing modelling and problem structuring frameworks and literature, we are developing a workshop approach to introduce modelling to potential users through eliciting a problem of particular interest to them. We will discuss the proposed workshop design. Conclusions We introduced simulation case studies that have informed health/social care decision making. To disseminate the use of modelling we present a workshop approach to engage with the healthcare sector.
Viana, Joe (2019)
Research governance and ethical challenges for simulation studies: Should we develop guidelines?
[Academic lecture]. EURO working group Operational Research Applied to Health Services (ORAHS).
This talk focuses on the Research Ethics Committee (REC), and Data Privacy and Governance (data access) challenges researchers face who apply Operational Research (OR) techniques, such as simulation, to better understand and improve Health and Social care systems. It is not uncommon for a REC to judge OR research as service improvement, quality assurance, or other research outside their mandate. Protection of data, privacy and exemption from consent are the main concerns that need to be carefully considered by researchers and Data Privacy and Governance specialists. A selection of guidelines and review papers including those from the health care economic guidelines that can support the application process will be presented and assessed e.g. ISPOR-SMDM (International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making) and STRESS (Strengthening the reporting of empirical simulation studies). The author will reflect on experiences from the UK and Norway. It is hoped that the session will prompt a discussion between colleagues from different sectors in different countries about application strategies, to share guidelines, literature, and advice.
Simonsen, Tone Breines; Viana, Joe, Faraas, Hildegunn E., Schmidt, Nina, Dahl, Fredrik Andreas & Flo, Kari (2019)
Driftsplanlegging i en kvinneklinikk ved hjelp av simuleringsmodellering
[Academic lecture]. Jordmordagene 2019.
Barra, Mathias; Labberton, Angela Susan, Faiz, Kashif Waqar, Lindstrøm, Jonas Christoffer, Rønning, Ole M., Viana, Joe, Dahl, Fredrik Andreas, Rand, Kim & Næss, Halvor (2019)
Slaginsidens i alle aldre – tidstrender og fremskrivninger
[Academic lecture]. Hjerte- og karregisterseminaret 2019.
Vogt, Yngve; Häikiö, Kristin, Rand, Kim, Labberton, Angela Susan, Cheng, Socheat, Nyttingnes, Olav, Saddiqui, Tahreem, Kristvik, Ellen Elisabeth, Beddari, Henriette Høyer, Viana, Joe, Bråten, Beret, Barra, Mathias, Lindstrøm, Jonas Christoffer, Kakad, Meetali & Menichetti, Julia (2019)
Helse-Norge kan spare mye penger - se de 15 ideene
[Popular scientific article]. Apollon : Forskningsmagasin for Universitetet i Oslo
Viana, Joe; Rødøy, Lise Raanes, Rojahn, Tone, Simonsen, Tone Breines & Dahl, Fredrik Andreas (2018)
Improving chemotherapy drug production at Radium Hopsital Oslo
The preparation of drugs is a crucial logistical challenge for health services. Each drug has its own recipe, requiring different raw ingredients, each with potentially different shelf lives. Patients require different drug courses which follow specific protocols in terms of when the drugs are required, how much is required and how frequently. The drug preparation facility at the Radium Hospital produces primarily chemotherapy drugs for the Radium hospital and other public and private hospitals in Oslo. The facility has invested in a robot to prepare drugs alongside pharmacists. The pharmacy is interested in exploring the best way to allocate requests to the pharmacists and the robot given their constraints and operating procedures, subject to the following: the urgency of request, e.g. children and other more vulnerable patients such as urgent requests from outpatients, allocation of drug requests between different pharmacists and to the robot, the pick-up schedule for completed drugs, the number of hospitals they produce drugs for, the pharmacy opening hours, the procedures and pathways, staff schedules and the robot’s maintenance schedule. A Discrete Event Simulation model has been combined with an Agent Based Model to evaluate suggested quality improvements, and evaluate specific research questions defined by the model stakeholders. Data was collected from January 2016 to May 2017. The data collected was a mixture of manually collected observations and data available from various hospital systems. The model will be validated against historical data and used to evaluate the effectiveness of alternative procedures, prior to implementation, to predict what affect these proposed changes would have on operations. The model will project forward over a 3-year time horizon. Three scenarios of interest to model stakeholders are evaluated in the model, with the data currently available. The modelling work identified several areas where better data could be collected and/or estimated.
Rand, Kim; Dahl, Fredrik Andreas, Barra, Mathias, Viana, Joe & Faiz, Kashif Waqar (2018)
Rand, Kim; Viana, Joe, Barra, Mathias & Dahl, Fredrik Andreas (2017)
Using bootstrapping to reflect variance in resource demand in simulation modeling of patient flow; the case of stroke treatment in Norway
[Academic lecture]. Operational Research Applied to Health Services 2017.
Dahl, Fredrik Andreas; Rand, Kim, Viana, Joe, Barra, Mathias & Simonsen, Tone Breines (2017)
Effects of stroke on labour force participation for patients and family caregivers
[Academic lecture]. Operational Research Applied to Health Services.
Viana, Joe; Rødøy, Lise Raanes, Rojahn, Tone, Simonsen, Tone Breines & Dahl, Fredrik Andreas (2017)
Using a combined discrete event simulation agent based model to improve drug production at the Radium Hospital, Norway
[Academic lecture]. Operational Research Applied to Health Services.
Viana, Joe; Simonsen, Tone Breines, Schmidt, Nina, Rand, Kim & Dahl, Fredrik Andreas (2017)
Simulation modelling of patient flow in the Obstetrics department at Akershus Universitetssykehus.
[Academic lecture]. Nasjonal forskningskonferanse i helsetjenesteforskning.
Barra, Mathias; Rand, Kim, Faiz, Kashif Waqar, Viana, Joe & Dahl, Fredrik Andreas (2017)
Survival of the fittest? Frailty modelling of stroke incidence.
[Academic lecture]. ORAHS2017.
Viana, Joe; Rand-Hendriksen, Kim, Simonsen, Tone Breines, Dahl, Fredrik Andreas & Barra, Mathias (2016)
Flexible healthcare hybrid simulation modeling
[Academic lecture]. Operational Research Applied to Health Services 2016.
Dahl, Fredrik Andreas; Viana, Joe & Rand-Hendriksen, Kim (2016)
Association between hospital occupancy and mortality
[Academic lecture]. Operational Research Applied to Health Services 2016.
Rand-Hendriksen, Kim; Viana, Joe & Dahl, Fredrik Andreas (2016)
Too obvious to mention? Some simple ways in which running empirical data directly through simulation models can be used to identify flaws in models and data
[Academic lecture]. Operational Research Applied to Health Services 2016.
Simulation models of hospital activities have often required manual data collection, observation, and other forms of activity sampling. Due to the increasingly pervasive automatic and semi-automatic data collection in electronic journals and other computer systems, many model parameters (arrival times, transition probabilities, lengths of stay, patient characteristics) can now be estimated based on complete empirical records. Interestingly, while the literature on simulation modeling has much to say about how such models can be validated and compared to empirical data, there is a curious lack of mention of a verification procedure that should be obvious: models built to reflect and represent a reality in the form of a complete empirical record should be able to run the empirical records in question without encountering problems. Consider a maternity ward with delivery rooms, regular patient rooms, and a patient hotel (hotel-like rooms with nursing staff for low-risk patients). Most women arrive shortly before birth, and move to the patient hotel quickly after. Other patients move between rooms several times, and some occupy regular hospital both prior to and after delivery. Our aim is to model the impact of expected changes in the demographic makeup within the catchment area, and investigate the possible benefits of altering the schedules for when patients are discharged, and how empty rooms are made ready for new patients. We create a simulation model with rooms, beds, and staff based on information from the ward administration. Distributions for patient admission, length of stay, and transition between rooms are all estimated based on complete empirical records from the electronic journal system. Before going any further, we alter the model in such a way as to allow the generation of agents (patients) directly based on the electronic record. That is, each patient in the electronic record is set to appear in the model at a time matching the record, and is set to move around and occupy resources exactly as recorded. This procedure is deterministic. If contradictions occur, such as the occupancy reaching levels beyond what is available, we need to look for problems in the model or in the data. If the model behaves well, we can sequentially «turn on» assumed model parameters one at a time, for example enforcing the restriction on number of beds, in order to verify that all assumed parameters are able to accommodate the real record. This procedure rests on the availability of full empirical records, and on the model in question being built to accommodate those records. We assume that the lack of mention of these kinds of verification procedure in the literature is caused by the historic rarity of models in which this is possible have been relatively rare, and that procedure has been considered too obvious to mention. We present a real-world example based on models of the maternity ward in a university hospital in Norway, and show how procedures such as these have been surprisingly useful in identifying flaws in the model and errors in the empirical records.
Akademisk grad
År
Akademisk institusjon
Grad
2011
University of Southampton, UK
PhD
2006
University of Southampton
MSc in Management Science
Arbeidserfaring
År
Arbeidsgiver
Tittel
2020 - Present
BI Norwegian Business School
Assistant Professor
2015 - 2020
Health Services Research Unit (HØKH), Akershus University Hospital
Researcher
2010 - 2015
Southampton Business School, University of Southampton