Challenges in Developing Effective Clinical Decision Support Systems

Sartipi, Kamran; Archer, Norm; Yarmand, Mohammad H.

doi:10.5772/17837

Cited by 6 publications

(3 citation statements)

References 48 publications

(44 reference statements)

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“…By concentrating on the model component of the DSSs developed in the healthcare area (i.e., either clinical DSSs or others), there are several models have been proposed for these systems. Sartipi, Archer, and Yarmand (2011) categorized these models into four main categories: deterministic models (e.g., linear programming), stochastic models (e.g., queuing models), artificial intelligence (e.g., artificial neural networks), and practical (e.g., simulation).…”

Section: Dsss In the Healthcare Areamentioning

confidence: 99%

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

Fadelelmoula

2022

International Journal of Decision Support System Technology

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This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.

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Section: Dsss In the Healthcare Areamentioning

confidence: 99%

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

Fadelelmoula

2022

International Journal of Decision Support System Technology

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“…In this subsection, we discuss the concept of analysis oriented decision support system (AODSS), which is a combination of the concept of the serviceoriented decision support system (SODSS) [46] and the clinic decision support system (CDSS) [14]. As the AODSS integrated in the MCC-based telerehabilitation platform, it contributes to the establishment of mobile CDSS mentioned in [14], which was considered a research challenge in CDSS. The contribution of the paper to AODSS is that data is analysed with different granularities, which can be collected from various types of sensors and stored in various databases in the cloud.…”

Section: Analysis Oriented Decision Support Systemmentioning

confidence: 99%

“…The former utilises the huge amount of data to analyse the performance of patients from different levels of granularities, while the latter protects the information of data spanning from acquisition, storing, and transferring throughout the platform. These two are research challenges in conventional clinical decision support system (CDSS) [14] and will be discussed in Sections 4.5 and 4.6, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

Pham

Karunarathne

et al. 2015

Mathematical Problems in Engineering

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Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

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Decision Analyses of Medical Resources for Disabled Elderly Home Care: The Hyper Aged District in Taiwan

Lin

Wang

2016

Lecture Notes in Electrical Engineering

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Challenges in Developing Effective Clinical Decision Support Systems

Cited by 6 publications

References 48 publications

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

Decision Analyses of Medical Resources for Disabled Elderly Home Care: The Hyper Aged District in Taiwan

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