Supporting capacity management decisions in healthcare using data-driven process simulation

Hulzen, Gerhardus van; Martin, Niels; Depaire, Benoît; Souverijns, Geert

doi:10.1016/j.jbi.2022.104060

Cited by 14 publications

(6 citation statements)

References 77 publications

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“…While the case study clearly demonstrates the potential of data-driven process simulation in healthcare, van Hulzen et al [84] also highlight challenges such as data quality issues, as well as the lack of support to interactively involve domain experts during the development of a simulation model.…”

Section: Predictive Process Miningmentioning

confidence: 99%

“…van Hulzen et al [84] use data-driven process simulation to explore potential future scenarios to support capacity management decisions for the radiology department of a Belgian hospital. Within the context of the construction of new facilities, which involves a centralisation of different geographically separated campuses, department management needs to provide input regarding the required number of radiological devices (X-ray, CT scanner, etc.…”

Section: Predictive Process Miningmentioning

confidence: 99%

“…A first step in the direction of action-oriented process mining is ensuring that process mining endeavors start from specific questions put forward by healthcare professionals [55]. Several research papers explicitly report on this matter, such as the work by van Hulzen et al [84] on data-driven process simulation for capacity management at the radiology department. In a similar vein, Agostinelli et al [2] explicitly devote attention to defining the questions of healthcare professionals in a process mining project in cooperation with the San Carlo di Nancy hospital.…”

Section: Action-oriented Process Miningmentioning

confidence: 99%

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Using Process Mining in Healthcare

Martin

Wittig²,

Muñoz-Gama

2022

Lecture Notes in Business Information Processing

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This chapter introduces a specific application domain of process mining: healthcare. Healthcare is a very promising domain for process mining given the significant societal value that can be generated by supporting process improvement in a data-driven way. Within a healthcare organisation, a wide variety of processes is being executed, many of them being highly complex due to their loosely-structured and knowledge-intensive nature. Consequently, performing process mining in healthcare is challenging, but can generate significant societal impact. To provide more insights in process mining in healthcare, this chapter first provides an overview of healthcare processes and healthcare process data, as well as their particularities compared to other domains. Afterwards, an overview of common use cases in process mining in healthcare research is presented, as well as insights from a real-life case study. Subsequently, an overview of open challenges to ensure a widespread adoption of process mining in healthcare is provided. By tackling these challenges, process mining will become able to fully play its role to support evidence-based process improvement in healthcare and, hence, contribute to shaping the best possible care for patients in a way that is sustainable in the long run.

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Section: Predictive Process Miningmentioning

confidence: 99%

Section: Predictive Process Miningmentioning

confidence: 99%

Section: Action-oriented Process Miningmentioning

confidence: 99%

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Using Process Mining in Healthcare

Martin

Wittig²,

Muñoz-Gama

2022

Lecture Notes in Business Information Processing

Self Cite

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“…Five [56] Support from experts with required digital health and health informatics expertise is provided with complementary information to explain difficult digital health concepts and understand digitized data for decision-making.…”

Section: Three [54]mentioning

confidence: 99%

A Systematic Approach in Developing Management Workforce Readiness for Digital Health Transformation in Healthcare

Brommeyer¹,

Liang²

2022

Preprint

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Background: The COVID-19 pandemic has sped up digital health transformation across the health sectors to enable innovative health service delivery. Such transformation relies on competent managers with the capacity to lead and manage. However, the health system has not adopted a holistic approach in addressing the health management workforce development needs, with many hurdles to overcome. The objectives of this paper are to present the findings of a three-step approach in understanding the current hurdles in developing a health management workforce that can enable and maximise the benefits of digital health transformation, and to explore ways of overcoming such hurdles. Methods: A three-step, systematic approach was undertaken, including an Australian digital health policy documentary analysis, an Australian health service management postgraduate program analysis, and a scoping review of international literatures. Results: The main findings will guide the formulation of strategies in developing a digitally enabled health management workforce in the digital health era. Conclusions: With the ever-changing landscape of digital health, being able to lead and manage in times of system transformation requires a holistic approach to develop the necessary health management workforce capabilities and system-wide capacity. The evidence would support that this can be achieved with the required system, policy, educational and professional organizational enablers, which drive a digital health focused approach across all the healthcare sectors, in a coordinated and contextual manner.

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“…Hospitals and health services are facing increasing demand that has led to overcrowded EDs and increases in the time patients spend waiting to access treatment. 1 There are pressures facing all hospitals to match this demand with available beds in order to get unwell patients into hospital beds in a timely manner. Optimising patient flow is becoming increasingly critical in a health system with finite capacity to deal with this rapid growth.…”

Section: Introductionmentioning

confidence: 99%

New bed configurations and discharge timing policies: A hospital‐wide simulation

Hassanzadeh

Khanna

Boyle

et al. 2022

Emerg Medicine Australasia

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Objective Optimising patient flow is becoming an increasingly critical issue as patient demand fluctuates in healthcare systems with finite capacity. Simulation provides a powerful tool to fine‐tune policies and investigate their impact before any costly intervention. Methods A hospital‐wide discrete event simulation is developed to model incoming flow from ED and elective units in a busy metropolitan hospital. The impacts of two different policies are investigated using this simulation model: (i) varying inpatient bed configurations and a load sharing strategy among a cluster of wards within a medical department and (ii) early discharge strategies on inpatient bed access. Several clinically relevant bed configurations and early discharge scenarios are defined and their impact on key performance metrics are quantified. Results Sharing beds between wards reduced the average and total ED length of stay (LOS) by 21% compared to having patients queue for individual wards. The current baseline performance level could be maintained by using fewer beds when the load sharing approach was imposed. Earlier discharge of inpatients resulted in reducing average patient ED LOS by approximately 16% and average patient waiting time by 75%. Specific time‐based discharge targets led to greater improvements in flow compared to blanket approaches of discharging all patients 1 or 2 hours earlier. Conclusions ED access performance for admitted patients can be improved by modifying downstream capacity or inpatient discharge times. The simulation model was able to quantify the potential impacts of such policies on patient flow and to provide insights for future strategic planning.

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Supporting capacity management decisions in healthcare using data-driven process simulation

Cited by 14 publications

References 77 publications

Using Process Mining in Healthcare

Using Process Mining in Healthcare

A Systematic Approach in Developing Management Workforce Readiness for Digital Health Transformation in Healthcare

New bed configurations and discharge timing policies: A hospital‐wide simulation

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