A Grounded Theory of Patient Flow Management within the Emergency Department

Background: Emergency department (ED) crowding is an urgent threat to patient safety and negatively impacts healthcare staff and institutions. Patient flow researchers have employed a range of methods to address this crisis, including an increase in the use of operations research and operations management strategies. However, identified patient flow solutions are inadequate. Research describing the complexities of patient flow processes and investigating the work and contributions of ED nurses is needed. Purposes: The purposes of this study were to explore how ED nurses perform patient flow management and to develop a constructivist grounded theory of patient flow management within the ED. Methods: A conceptual foundation for patient flow management was first established using evolutionary concept analysis and expanded concept analysis approaches. This study then employed constructivist grounded theory and situational analysis methodologies to examine the work of ED nurses. Data was collected through 29 focus groups and interviews with 27 participants and 64 hours of participant observations across four EDs. Data analysis relied on coding, constant comparative analysis, and memo-writing to identify emergent themes and develop a substantive theory. Findings: Concept analyses defined patient flow management as the application of ED experience, holistic perspectives, dynamic data, and complex considerations of multiple priorities by ED nurses to promote patient safety within their scope of responsibility. The study offers three main contributions: a theoretical model of the work of ED patient flow management, a theoretical framework to describe holistic considerations of factors that impact departmental capacity and nurse engagement in patient flow management, and a grounded theory of patient flow management capacity and engagement that describes how ED nurses adapt patient flow management strategies according to patient burden. Conclusion: This study offers a new conceptual and theoretical foundation to understand the work of patient flow management. This novel perspective centralizes the work of ED nurses as active agents in patient flow processes and describes their strategies and contributions to meet patient care needs. Several practical considerations are offered to engage and support nurses in their roles as patient flow managers, improve patient flow processes, and further investigate ED nurse patient flow management

Triage Nursing Practice in Australian Emergency Departments 2002-2004: An Ethnography

This ethnographic study provides insight and understanding, which is needed to educate and support the Triage Nursing role in Australian Emergency Departments (EDs). The triage role has emerged to address issues in providing efficient emergency care. However, Triage Nurses and educators have found the role challenging and not well understood. Method: Sampling was done first by developing a profile of 900 nurses who undertake the triage role in 50 NSW EDs through survey techniques. Purposive sampling was then done with data collected from participant observation in four metropolitan EDs (Level 4 and 6), observations and interviews with 10 Triage Nurses and the maintenance of a record of secondary data sources. Analysis used standard content and thematic analysis techniques. Findings: An ED culture is reflected in a standard geography of care and embedded beliefs and rituals that sustain a cadence of care. Triage Nurses to accomplish their role and maintain this rhythm of care used three processes: gatekeeping, timekeeping and decision-making. When patient overcrowding occurred the three processes enabled Triage Nurses to implement a range of practices to restore the cadence of care to which they were culturally oriented. Conclusion: The findings provide a framework that offers new ways of considering triage nursing practice, educational programs, policy development and future research

Community-based palliative care is associated with reduced emergency department use by people with dementia in their last year of life: A retrospective cohort study

Objective: To describe patterns in the use of hospital emergency departments in the last year of life by people who died with dementia and whether this was modified by use of community-based palliative care. Design: Retrospective population-based cohort study of people in their last year of life. Time-to-event analyses were performed using cumulative hazard functions and flexible parametric proportional hazards regression models. Setting/participants: All people living in Western Australia who died with dementia in the 2-year period 1 January 2009 to 31 December 2010 (dementia cohort; N = 5261). A comparative cohort of decedents without dementia who died from other conditions amenable to palliative care (N = 2685). Results: More than 70% of both the dementia and comparative cohorts attended hospital emergency departments in the last year of life. Only 6% of the dementia cohort used community-based palliative care compared to 26% of the comparative cohort. Decedents with dementia who were not receiving community-based palliative care attended hospital emergency departments more frequently than people receiving community-based palliative care. The magnitude of the increased rate of emergency department visits varied over the last year of life from 1.4 (95% confidence interval: 1.1–1.9) times more often in the first 3 months of follow-up to 6.7 (95% confidence interval: 4.7–9.6) times more frequently in the weeks immediately preceding death. Conclusions: Community-based palliative care of people who die with or of dementia is relatively infrequent but associated with significant reductions in hospital emergency department use in the last year of life

Proactive Coordination In Healthcare Service Systems Through Near Real-Time Analytics

The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved \u27work-flow\u27 coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time operational intelligence of service networks that promote efficiency and quality of operations in a proactive manner. In particular, we leverage operations research and predictive analytics techniques to develop proactive coordination mechanisms and decision methods to improve the operational efficiency of bed management service in the network spanning the emergency department (ED) to inpatient units (IUs) in a hospital, a key component of healthcare in most hospitals. The purpose of this study is to deepen our knowledge on proactive coordination empowered by predictive analytics in dynamic healthcare environments populated by clinically heterogeneous patients with individual information changing throughout ED caregiving processes. To enable proactive coordination for improved resource allocation and patient flow in the ED-IU network, we address two components of modeling/analysis tasks, i.e., the design of coordination mechanisms and the generation of future state information for ED patients. First, we explore the benefits of early task initiation for the service network spanning the emergency department (ED) and inpatient units (IUs) within a hospital. In particular, we investigate the value of proactive inpatient bed request signals from the ED to reduce ED patient boarding. Using data from a major healthcare system, we show that the EDs suffer from severe crowding and boarding not necessarily due to high IU bed occupancy but due to poor coordination of IU bed management activity. The proposed proactive IU bed allocation scheme addresses this coordination requirement without requiring additional staff resources. While the modeling framework is designed based on the inclusion of two analytical requirements, i.e., ED disposition decision prediction and remaining ED length of stay (LoS) estimation, the framework also accounts for imperfect patient disposition predictions and multiple patient sources (besides ED) to IUs. The ED-IU network setting is modeled as a fork-join queueing system. Unlike typical fork-join queue structures that respond identically to a transition, the proposed system exhibits state-dependent transition behaviors as a function of the types of entities being processed in servers. We characterize the state sets and sequences to facilitate analytical tractability. The proposed proactive bed allocation strategy can lead to significant reductions in bed allocation delay for ED patients (up to ~50%), while not increasing delays for other IU admission sources. We also demonstrate that benefits of proactive coordination can be attained even in the absence of highly accurate models for predicting ED patient dispositions. The insights from our models should give confidence to hospital managers in embracing proactive coordination and adaptive work flow technologies enabled by modern health IT systems. Second, we investigate the quantitative modeling that analyzes the patterns of decreasing uncertainty in ED patient disposition decision making throughout the course of ED caregiving processes. The classification task of ED disposition decision prediction can be evaluated as a hierarchical classification problem, while dealing with temporal evolution and buildup of clinical information throughout the ED caregiving processes. Four different time stages within the ED course (registration, triage, first lab/imaging orders, and first lab/imaging results) are identified as the main milestone care stages. The study took place at an academic urban level 1 trauma center with an annual census of 100,000. Data for the modeling was extracted from all ED visits between May 2014 and April 2016. Both a hierarchical disposition class structure and a progressive prediction modeling approach are introduced and combined to fully facilitate the operationalization of prediction results. Multinomial logistic regression models are built for carrying out the predictions under three different classification group structures: (1) discharge vs. admission, (2) discharge vs. observation unit vs. inpatient unit, and (3) discharge vs. observation unit vs. general practice unit vs. telemetry unit vs. intensive care unit. We characterize how the accumulation of clinical information for ED patients throughout the ED caregiving processes can help improve prediction results for the three-different class groups. Each class group can enable and contribute to unique proactive coordination strategies according to the obtained future state information and prediction quality, to enhance the quality of care and operational efficiency around the ED. We also reveal that for different disposition classes, the prediction quality evolution behaves in its own unique way according to the gain of relevant information. Therefore, prediction and resource allocation and task assignment strategies can be tailored to suit the unique behavior of the progressive information accumulation for the different classes of patients as a function of their destination beyond the ED

An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

The adaptive capability of the operational team to respond to challenges in the Emergency Centre. A SenseMaker® study in Emergency Centres within Cape Town

Background Emergency centres (ECs) serve as a main entry point for patients into hospitals, and patients that present here are undifferentiated with varying levels of acuity. Uncertainty, interruptions, multiple – often conflicting – priorities, and gaps in information flow are inherent to EC work practices, making it a high-risk environment for operational failure. The EC team, the core of which is formed by doctors and nurses, needs the ability to collaboratively and reliably sense and respond to the constant change and flux of information. This depends on the interactions and sense-making of the EC team. Objectives People give meaning to situations through the process of sense-making; they then subjectively construct their reality and share it via plausible stories regarding their situation and environment. The main objective of this study was to explore the collective team-based sense-making of the operational challenges and decisions within the EC. This interprofessionalstudy focused on the dynamics and negotiations within the EC as a complex adaptive system. Methods This exploratory study used narrative-based inquiry with abductive reasoning to meet the objectives. It was divided into two sections. The first was a thick description of the EC context, daily operations and processes. Then, using the SenseMaker® tool, we captured stories about a situation that stood out to participants, and thus mattered to them. Using this novel method, once they told their story, the storytellers self-analysed their stories within a specially designed framework. The results were then explored to find patterns based on the perspectives of sense-making. Results There is no proof of interprofessional sense-making in the EC, and if it occurs it is due to the informal networks between doctors and nurses, and despite formal structure. There is an operational disconnect between doctors, nurses and management, which is caused by information asymmetry, poor feedback loops and disparate communication channels. Because there is no collective sense-making, the EC team is vulnerable to operational failure and crises. Currently, they respond to operational challenges via quick fixes that result in constant firefighting, the impact of which could be seen by the extensive use of war-related metaphors in their stories

An exploratory study of priority setting in gynaecology nursing practice

ABSTRACT This study explored how nurses in acute and nurse-led gynaecology wards prioritised patient caseloads ranging in diversity and number of patient conditions. Statistics show that since the introduction of medical termination of pregnancy (MTOP) procedures into the National Health Service (NHS) in 1991, the number of women having this procedure is increasing year on year. To date very little is known about the impact this procedure may have had on nursing practice. The focus of this study was to explore the nursing care when this included, and did not include, caring for women having MTOP. The study was conducted in two parts. The first qualitative study employed non-participant observation and semi-structured interviews of nurses in gynaecology and surgical wards at two hospital sites to examine the external context in which nursing decisions were made. This found that nurses in gynaecology focused on emotional or psychosocial aspects more so than surgical nurses who focused on physical aspects of patient care. The second quantitative study involved a cross-sectional survey of nurses from both ward types in two hospitals sites in Scotland. Internal constructs were examined using personality and thinking styles measures. Nurses were assessed on their emotionality, that is, the numbers of times an emotional care aspect was prioritised. This found that nurses who prioritised the emotional aspects of the task tended to be more conscientious and elected preference for a ‘people-centred’ thinking style. The context in which women have TOP is also important since the findings suggest women may benefit from being cared for in nurse-led rather than in acute wards. Knowing how a person thinks about emotional and physical aspects of care also has implications for those involved in education, and career planning.N& M departmental scholarshi

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512