IT support for healthcare processes – premises, challenges, perspectives

Lenz, Richard; Reichert, Manfred

doi:10.1016/j.datak.2006.04.007

Cited by 425 publications

(318 citation statements)

References 69 publications

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“…4; each component will be described in detail in the following. On the left hand side TDMS provides a graphical editor for editing testcases (1). To the right, a graphical editor allows for designing the process model (2).…”

Section: Software Componentsmentioning

confidence: 99%

“…Process-Aware Information Systems (PAISs) offer a promising perspective on shaping this capability, resulting in growing interest to align information systems in a process-oriented way [2]. Yet, a critical success factor in applying PAISs is the possibility of flexibly dealing with process changes [1]. To address the need for flexible PAISs, competing paradigms enabling process changes and process flexibility have been developed, e.g., adaptive processes [3], declarative processes [4] and late binding and modeling [5].…”

Section: Introductionmentioning

confidence: 99%

“…In today's dynamic business environment the economic success of an enterprise depends on its ability to react to various changes like shifts in customer's attitudes or the introduction of new regulations and exceptional circumstances [1]. Process-Aware Information Systems (PAISs) offer a promising perspective on shaping this capability, resulting in growing interest to align information systems in a process-oriented way [2].…”

Section: Introductionmentioning

confidence: 99%

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Creating Declarative Process Models Using Test Driven Modeling Suite

Zugal

Pinggera

Weber

2012

Lecture Notes in Computer Science

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Abstract. Declarative approaches to process modeling promise a high degree of flexibility. However, current declarative state-of-the-art modeling notations are, while sound on a technical level, hard to understand. To cater for this problem, in particular to improve the understandability of declarative process models as well as the communication between domain experts and model builders, Test Driven Modeling (TDM) has been proposed. In this tool paper we introduce Test Driven Modeling Suite (TDMS) which provides operational support for TDM. We show how TDMS realizes the concepts of TDM and how Cheetah Experimental Platform is used to make TDMS amenable for effective empirical research. Finally, we provide a brief example to illustrate how the adoption of TDMS brings out the intended positive effects of TDM for the creation of declarative process models.

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Section: Software Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Creating Declarative Process Models Using Test Driven Modeling Suite

Zugal

Pinggera

Weber

2012

Lecture Notes in Computer Science

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“…Patient careflow are typically executed according to a diagnostic-therapeutic cycle, comprising observation, reasoning and action [6,[11][12][13][14]. The diagnostic-therapeutic cycle heavily depends on medical knowledge to deal with case-specific decisions that are made by interpreting patient-specific information [7,8,11]. During CP execution, the patient state might be changed dynamically, such as complications, infections, or poisonings, which in turn leads to various clinical activities occurred in CPs, and urges the process to be a mixture of latent treatment patterns as well.…”

mentioning

confidence: 99%

“…To this end, the methods using data mining and machine learning technologies to analyze CPs based on associated careflow logs are receiving gradual attentions in medical informatics [7,10,11]. These techniques are also called process mining [20][21][22][23].…”

mentioning

confidence: 99%

Latent Treatment Pattern Discovery for Clinical Processes

2013

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A clinical process is typically a mixture of various latent treatment patterns, implicitly indicating the likelihood of what clinical activities are essential/critical to the process. Discovering these hidden patterns is one of the most important components of clinical process analysis. What makes the pattern discovery problem complex is that these patterns are hidden in clinical processes, are composed of variable clinical activities, and often vary significantly between patient individuals. This paper employs Latent Dirichlet Allocation (LDA) to discover treatment patterns as a probabilistic combination of clinical activities. The probability distribution derived from LDA surmises the essential features of treatment patterns, and clinical processes can be accurately described by combining different classes of distributions. The presented approach has been implemented and evaluated via real-world data sets.

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