A Clinical Pathway Mining Approach to Enable Scheduling of Hospital Relocations and Treatment Services

Helbig, Karsten; Römer, Michael; Mellouli, Taïeb

doi:10.1007/978-3-319-23063-4_17

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Two journals not included in the JCR categories published multiple papers identified in the search, these are Lecture Notes in Computer Science [20,48,85,87,98,112,179] and Studies in Health Technology and Informatics [5,74,76,94,121,136,210].…”

Section: Publication Areamentioning

confidence: 99%

“…As presented in the selected papers, there are two common ways of obtaining this information: either data driven or through collaboration with those who interact with the pathway. There were many different ways of obtaining data described, including historic [143], billing [85], messages [107] and Electronic Medical Records [88]. Similarly, collaboration took on a number of different forms including, consulting with experts [17], staff [10], patients [136] and through observations [101].…”

Section: Obtaining the Pathwaymentioning

confidence: 99%

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Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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Section: Publication Areamentioning

confidence: 99%

Section: Obtaining the Pathwaymentioning

confidence: 99%

Clinical pathway modelling: a literature review

2019

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“…Gartner and Kolisch (2014) represent the possible schedules for each CP on a directed graph in which the arcs form minimum time lags between the clinical activities. The CP scheduling approach proposed in this work relies on the constraint-based CP representation introduced in (Helbig et al 2015). We give a brief overview and example of this representation in this section; for details regarding the representation as well as regarding the pathway-mining approach, we refer the reader to (Helbig et al 2015).…”

Section: Constraint-based Cp Representationmentioning

confidence: 99%

“…As explained above, we conduct our investigation with the real-world data for cases admitted in March 2011 to the department of urology of a German university hospital. For these cases, we applied the CP mining approach proposed in Helbig et al (2015) and shortly sketched in Sect. 3 of this paper.…”

Section: Case Data and Automated Pathway Extractionmentioning

confidence: 99%

“…First, we propose a data-driven approach to pathway scheduling. Our approach is based on scheduling relevant pathway information automatically extracted from standardized hospital billing data available for every hospital in Germany using the pathway-mining approach introduced by Helbig et al (2015). As a result, one of the most time-consuming data preparation steps necessary for pathway schedulingdefining pathways and their constraints-is automated to a large extent.…”

Section: Introductionmentioning

confidence: 99%

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A data-driven hierarchical MILP approach for scheduling clinical pathways: a real-world case study from a German university hospital

Schwarz

Römer

Mellouli

2019

Bus Res

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Facing economic pressure and case-based compensation systems, hospitals strive for effectively planning patient hospitalization and making efficient use of their resources. To support this endeavor, this paper proposes a flexible hierarchical mixed-integer linear programming (MILP)-based approach for the day-level scheduling of clinical pathways (CP). CP form sequences of ward stays and treatments to be performed during a patient's hospitalization under consideration of all relevant resources such as beds, operating rooms and clinical staff. Since in most hospitals CP-related information needed for planning is not readily available, we propose a data-driven approach in which the structure of the CP to be scheduled including all CP-related constraints is automatically extracted from standardized hospital billing data available in every German hospital. The approach uses a flexible multi-criteria objective function considering several patient-and hospitalrelated aspects which makes our approach applicable in various scenarios. Furthermore, in contrast to other approaches, it considers several practically relevant aspects ensuring the implementability of the scheduling results such as multiple ward stays per hospitalization and gender-separated room assignments. Regarding the treatment resources such as operation rooms and clinical staff, it considers the eligibility of resources for treatments based on information such as special

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A Two-Dimensional Categorization Scheme for Simulation/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility

Stoeck

Mellouli

2020

Operations Research and Simulation in Healthcare

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A Clinical Pathway Mining Approach to Enable Scheduling of Hospital Relocations and Treatment Services

Cited by 8 publications

References 13 publications

Clinical pathway modelling: a literature review

Clinical pathway modelling: a literature review

A data-driven hierarchical MILP approach for scheduling clinical pathways: a real-world case study from a German university hospital

A Two-Dimensional Categorization Scheme for Simulation/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility

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