DSS in Healthcare: Advances and Opportunities

Kohli, Rajiv; Piontek, Frank

doi:10.1007/978-3-540-48716-6_23

Cited by 13 publications

(11 citation statements)

References 37 publications

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“…Beyond the well-established general behavior to performance relationship, prior work, mostly based on anecdotal evidence, has suggested that e-healthcare system use can contribute positively to overall patient satisfaction (see Kohli and Piontek 2007). Several mechanisms are cited in the trade press for such an effect-e.g., e-healthcare systems reduce costs and errors (e.g., Anderson 1997, Jensen andAanestad 2007).…”

Section: Baseline Modelmentioning

confidence: 96%

“…Several mechanisms are cited in the trade press for such an effect-e.g., e-healthcare systems reduce costs and errors (e.g., Anderson 1997, Jensen andAanestad 2007). As noted earlier, despite limited systematic empirical evidence, patient satisfaction is emerging as a critical metric that reflects quality and value of care (Kohli and Piontek 2007). Beyond the useperformance relationship, network centrality itself can yield direct performance benefits given that central individuals are more likely to gain access to resources, such as advice and social support unrelated to the system, that contribute positively to their performance (Sparrowe et al 2001).…”

Section: Baseline Modelmentioning

confidence: 98%

“…Beyond system use, the IS success model calls for a study of net benefits and/or broader personal and organizational outcomes of interest. In the context of health care, one such metric is patient satisfaction-in fact, recent work has suggested that patient satisfaction is key because it is not only an important metric to healthcare providers in its own right but also is key to insurance companies as a consequence of quality of care (Kohli and Piontek 2007). Therefore, research explaining patient satisfaction is particularly important in the domain of health care and technology.…”

Section: Introductionmentioning

confidence: 97%

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“Doctors Do Too Little Technology”: A Longitudinal Field Study of an Electronic Healthcare System Implementation

Venkatesh

Zhang

Sykes

2011

Information Systems Research

176

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W ith the strong ongoing push toward investment in and deployment of electronic healthcare (e-healthcare) systems, understanding the factors that drive the use of such systems and the consequences of using such systems is of scientific and practical significance. Elaborate training in new e-healthcare systems is not a luxury that is typically available to healthcare professionals-i.e., doctors, paraprofessionals (e.g., nurses) and administrative personnel-because of the 24 × 7 nature and criticality of operations of healthcare organizations, especially hospitals, thus making peer interactions and support a key driver of or barrier to such e-healthcare system use. Against this backdrop, using social networks as a theoretical lens, this paper presents a nomological network related to e-healthcare system use. A longitudinal study of an e-healthcare system implementation, with data gathered from doctors, paraprofessionals, administrative personnel, patients, and usage logs lent support to the hypotheses that: (1) ingroup and outgroup ties to doctors negatively affect use in all user groups; (2) ingroup and outgroup ties to paraprofessionals and administrative personnel positively affect use in both those groups, but have no effect on doctors' use; and (3) use contributes positively to patient satisfaction mediated by healthcare quality variables-i.e., technical quality, communication, interpersonal interactions, and time spent. This work contributes to the theory and practice related to the success of e-healthcare system use in particular, and information systems in general.

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Section: Baseline Modelmentioning

confidence: 96%

Section: Baseline Modelmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

“Doctors Do Too Little Technology”: A Longitudinal Field Study of an Electronic Healthcare System Implementation

Venkatesh

Zhang

Sykes

2011

Information Systems Research

176

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“…DSSs have been widely used in multiple disciplines, for example, in the financial environment [ 21 ]; marketing [ 22 ], business intelligence [ 23 ], commercial stock [ 24 ], agriculture [ 25 ], vehicle fleet management [ 26 ], health [ 27 , 28 ], security systems [ 29 ], risk monitoring [ 30 ], and psychology [ 31 ]. They have also been applied to social and urban sciences issues, such as in this study, including urban and regional planning [ 20 ], linked to a multi-layer multi-criteria analysis [ 32 ], with geographic information systems (GIS) to support decision making on transport policies [ 33 ], water resources [ 30 ], to support participatory processes at the political level [ 6 ], and to provide knowledge and facilitate the resolution or mitigation of conflicts by providing visual and spatial representations of data and different scenarios and policies in relation to coastal environments [ 34 ].…”

Section: State-of-the-art Dss and Som Applicationsmentioning

confidence: 99%

Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children’s Perception of Urban School Environments

Abarca-Álvarez

Campos-Sánchez

Mora-Esteban

2019

Entropy

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The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied in many fields, facilitating the graphical representation and the simple interpretation of high-dimensionality data. This clustering method, based on unsupervised learning, also allows obtaining profiles of respondents without the need to provide additional information for the creation of these clusters. In this work, we propose the identification of profiles using SOM for evaluating opinion surveys. Subsequently, non-parametric chi-square tests were first conducted to contrast whether answer was independent of each profile found, and in the case of statistical significance (p ≤ 0.05), the odds ratio was evaluated as an indicator of the effect size of such dependence. Finally, all results were displayed in an odds and cluster heat map so that they could be easily interpreted and used to make decisions regarding the survey results. The methodology was applied to the analysis of a survey based on forms administered to children (N = 459) about their perception of the urban environment close to their school, obtaining relevant results, facilitating results interpretation, and providing support to the decision-process.

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“…Objective data are obtained from a spectrum of resources, of which Decision Support Systems (DSS) are a major example for the integration of evidence‐based literature and its assimilation at the point of care (Finnegan & Hamid, ; Kohli & Piontek, ). DSS assist medical staff in making informed clinical decisions based on prevailing standards, guidelines, and best practice data, yet lack the adaptive ability to respond to unprecedented events (Hall, ; Kohli & Piontek, ; Schoen, 2011). Unprecedented events are characterized by instances of multiple co‐morbidities as well as the interprofessional perspective (Finnegan & Hamid, ).…”

Section: Decision Making For the Complex Patientmentioning

confidence: 99%

Towards a framework for untangling complexity: The interprofessional decision‐making model for the complex patient

Weinberger

Cohen

Tadmor

et al. 2014

Asso for Info Science & Tech

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Decision making for the complex patient is challenging for doctors because of increased complexity, such as multiple co-morbidities and interprofessionality for which evidence-based literature and guidelines are currently lacking. The consequent uncertainty causes vagueness, threatening patient safety and the quality of care. This article is motivated by the design science paradigm and describes the interprofessional decisionmaking model for the complex patient, namely, INDECO along with an example instantiation. Drawing on our experience in an intensive care unit of a tertiary hospital in Israel, the bi-dimensional view of this model includes the medical-and the interprofessional perspective. Retrospective assessment of 3 case studies of complex patients is used for assessing the usefulness of INDECO in decision making. The study reported here draws support from relevant literature, including the information science, information systems, and the medical domains. The findings resonate with emerging research developments focusing on healthcare decision making.

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DSS in Healthcare: Advances and Opportunities

Cited by 13 publications

References 37 publications

“Doctors Do Too Little Technology”: A Longitudinal Field Study of an Electronic Healthcare System Implementation

“Doctors Do Too Little Technology”: A Longitudinal Field Study of an Electronic Healthcare System Implementation

Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children’s Perception of Urban School Environments

Towards a framework for untangling complexity: The interprofessional decision‐making model for the complex patient

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