Protocol: Value Stream Maping in Healthcare. A systematic literature review

Vidal-Carreras

García-Sabater

2021

IJERPH

Self Cite

Lean healthcare aims to manage and improve the processes in the healthcare sector by eliminating everything that adds no value by improving quality of services, ensuring patient safety and facilitating health professionals’ work to achieve a flexible and reliable organization. Value Stream Mapping (VSM) is considered the starting point of any lean implementation. Some papers report applications of VSM in healthcare services, but there has been less attention paid to their contribution on sustainability indicators. The purpose of this work is to analyze the role of VSM in this context. To do so, a scoping review of works from recent years (2015 to 2019) was done. The results show that most applications of VSM reported are in the tertiary level of care, and the United States of America (USA) is the country which leads most of the applications published. In relation with the development of VSM, a heterogeneity in the maps and the sustainability indicators is remarkable. Moreover, only operational and social sustainability indicators are commonly included. We can conclude that more standardization is required in the development of the VSM in the healthcare sector, also including the environmental indicators.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Role of Value Stream Mapping in Healthcare Services: A Scoping Review

Vidal-Carreras

García-Sabater

2021

IJERPH

Self Cite

“…In this sense, in settings characterized by a great need for coordination of decisions among the members of the healthcare system and by high levels of uncertainty and complexity, the forecast of the demand can be considered to reduce the uncertainty (generation of possible scenarios), as well as resource planning (Molina-Pariente et al, 2015) taking into account Theory Of Constraints, would facilitate complex decision-making in each scenario (Gupta & Andersen, 2018;Lin et al, 2004;Miclo et al, 2019;Wee et al, 2016). The current health management systems have evolved towards a concept that is similar to that of a chain or network (Bae et al, 2019;Choi et al, 2001;Pitt et al, 2016;Postacchini et al, 2016;Sholihah et al, 2018;Squires et al, 2016), in which its links (made up by different levels of care, but also different levels of severity of patient condition) (Marin-Garcia et al, 2019;Vidal-Carreras et al, 2015) have the function of managing resources and providing services in the most efficient way possible (Chase et al, 2004). However, for the management of the chain, a high level of predictability, stability and leveling of demand and stability of the production processes dedicated to supply is required (Doolen & Hacker, 2005).…”

Section: The Covid-19 Crisis From An Operations Management Perspectivementioning

confidence: 99%

Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak

García-Sabater

Ruiz³

et al. 2020

JIEM

Self Cite

Purpose: With this work, we intend to promote research on the application of Operations Management tools in order to assist with decision-making in health crisis situations. During the first six weeks of the COVID-19 crisis in Spain, we have contacted a large number of hospital and health department managers in the Valencian Community and other regions of Spain. The result is that very few, at least when contact was made and at the time of writing this article, had consulted staff members in the Operations Management area for advice on this situation, and they are quite reluctant to do so. This is in spite of the fact that some medical sources also consider this crisis to be one of resources, not merely a medical crisis. Our opinion is that Operations Management can make a useful and valuable contribution to anticipate and improve the management of scarce resources, even in times of crisis. If those responsible for public health or heads of hospitals do not see this usefulness, then there is a huge gap between research and practice in Operations Management and what is transmitted to the healthcare sector. Our aim is to help reduce this gap.Design/methodology/approach: In the first part of the article, we will justify that this crisis, besides being a medical crisis, is also a crisis in terms of resources. We will briefly review some of the Operations Management tools that we believe are appropriate for managing this crisis. We will focus on those that we will illustrate in the second part of the article as a practical example of the action research protocol (only the action planning phase), in which we are currently involved.Findings: Due to the peculiarities of the COVID-19 epidemic, it is reasonable to think that the evolution and duration of each outbreak, and the way it is dealt with, will be different in each hospital and/or each health department. Anticipating the number of patients who will require care and forecasting the consumption of resources over time is complex, due to the long and variable incubation times and the uncontrolled evolution of the treatments needed by each patient. Furthermore, since the evolution of new patient admissions is not homogeneous, the forecasting of requirements is difficult. Providing real-time support and detecting with a certain degree of anticipation the potential levels of demand for health care will enable hospitals to define what stages of the contingency plan to apply and how to (re)assign the available resources in the most efficient way possible.Research limitations/implications: Hardly any current data are available, not only publicly, but also through public health ministries and managers; those that are available are not very reliable and come from studies with non-representative samples. For this reason, all forecasts must be taken with a note of caution, and in particular, designing review mechanisms every time a new set of updated reliable data becomes available (something that occurs on a weekly basis).Originality/value: In this work, we will show an example of a proposal for the practical application of Operations Management tools. Our proposal may be useful to hospital managers and public administrators in the healthcare sector (regional ministries or their equivalents). Each health department, hospital or hospital group will generate the aggregate data that facilitate the calculation of needs as input parameters for the model (available and occupied beds, daily admissions, mean lengths of stay in each healing phase and the percent of patients who evolve from one stage to another in the progression of the disease/healing process) and will be able to analyze how the system responds and evaluate different strategies or mitigating actions. These tools have been shown to be useful and reliable in manufacturing and service contexts that show a strong seasonal demand (both occasional and recurring) requiring a great deal of resources. We believe these situations are analogous to those faced by hospitals and the healthcare system in an epidemic like COVID-19. With them, we could facilitate the aggregate planning of resources for hospital administrators in order to provide them operational support during the COVID-19 contingency plan. However, the array of useful tools for this crisis is not limited to those that we will illustrate. Our intent is to encourage the Operations Management academic community to demonstrate how they contribute to the service of society in this COVID-19 epidemic, providing knowledge that can be extrapolated to other similar situations in the future that we may be unfortunate enough to experience.

“…Hospital bed management is a concrete application of a generic problem of capacity management (Claudio et al, 2021;Garcia-Sabater et al, 2020;Lagarda-Leyva & Ruiz, 2019;Marin-Garcia et al, 2019;Nino et al, 2021;Xia & Sun, 2013). For this purpose, it is possible to use operations management tools, and more specifically design, planning, and control or process-improvement tools.…”

Section: Introductionmentioning

confidence: 99%

“…La gestión de camas de hospitales es una aplicación concreta de un problema genérico de gestión de la capacidad (Claudio et al, 2021;Garcia-Sabater et al, 2020;Lagarda-Leyva & Ruiz, 2019;Marin-Garcia et al, 2019;Nino et al, 2021;Xia & Sun, 2013). Para ello, es factible el intento de usar las herramientas de gestión de operaciones, y más concretamente las herramientas de diseño, planificación y control o mejora de procesos.…”

Section: Introductionunclassified

A data generator for covid-19 patients’ care requirements inside hospitals

Work. pap. oper. manag.

Ruiz²,

Maheut

et al. 2021

Self Cite

This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic.Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems.Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021).This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.