Parenteral Protein Decision Support System Improves Protein Delivery in Preterm Infants: A Randomized Clinical Trial

Patients often want to participate in relevant clinical trials for new or more effective alternative treatments. The clinical search system made available by the NIH is a step forward to support the patient's decision making, but, it is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. In addition, patients deciding whether to pursue a given trial often want more information, such as drug information. The authors' overall aim is to develop an intelligent patient-centered clinical trial decision support system. Their approach is to integrate Open Data sources related to clinical trials using the Semantic Web's Linked Data framework. The linked data representation, in terms of RDF triples, allows the development of a clinical trial knowledge base that includes entities from different open data sources and relationships among entities. The authors consider Open Data sources such as clinical trials provided by NIH as well as the drug side effects dataset SIDER. The authors use UMLS (Unified Medical Language System) to provide consistent semantics and ontological knowledge for clinical trial related entities and terms. The authors' semantic approach is a step toward a cognitive system that provides not only patient-centered integrated data search but also allows automated reasoning in search, analysis and decision making using the semantic relationships embedded in the Linked data. The authors present their integrated clinical trial knowledge base development and a prototype, patient-centered Clinical Trial Decision Support System that include capabilities of semantic search and query with reasoning ability, and semantic-link browsing where an exploration of one concept leads to other concepts easily via links which can provide visual search for the end users.

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Design-a-Trial: A Rule-Based Decision Support System for Clinical Trial Design

Applications and Innovations in Intelligent Systems XI ◽

10.1007/978-1-4471-0643-2_1 ◽

2004 ◽

pp. 3-17

Author(s):

Kushan Nammuni ◽

Claire Pickering ◽

S. Modgil ◽

Alan Montgomery ◽

P. Hammond ◽

...

Keyword(s):

Clinical Trial ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Trial Design ◽

Clinical Trial Design ◽

Rule Based

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Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost–Utility Study

Cancers ◽

10.3390/cancers13112687 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2687

Author(s):

Yvonka van Wijk ◽

Bram Ramaekers ◽

Ben G. L. Vanneste ◽

Iva Halilaj ◽

Cary Oberije ◽

...

Keyword(s):

Prostate Cancer ◽

Clinical Trial ◽

Decision Support ◽

Radical Prostatectomy ◽

Decision Support System ◽

Elderly Patients ◽

Support System ◽

In Silico ◽

External Beam Radiotherapy ◽

External Beam

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.

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Implications for the design of a Diagnostic Decision Support System (DDSS) to reduce time and cost to diagnosis in paediatric shoulder instability

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01446-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fraser Philp ◽

Alice Faux-Nightingale ◽

Sandra Woolley ◽

Ed de Quincey ◽

Anand Pandyan

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Shoulder Instability ◽

Clinical Decision Making ◽

Perceived Usefulness ◽

Novel Technologies ◽

Diagnostic Decision

Abstract Background Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis and improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability. Methods A modified nominal focus group technique, involving three clinical vignettes, was used to elicit physiotherapists decision-making processes. Results Twenty-five physiotherapists, (18F:7 M) from four separate clinical sites participated. The themes identified related to ‘Variability in diagnostic processes and lack of standardised practice’ and ‘Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making’. Conclusion No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed. Trial Registraty This was not a clinical trial and so no clinical trial registry is needed.

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Performance of an Electronic Decision Support System as a Therapeutic Intervention During a Multicenter PICU Clinical Trial: Heart and Lung Failure-Pediatric Insulin Titration trial (HALF-PINT)

CHEST Journal ◽

10.1016/j.chest.2021.04.049 ◽

2021 ◽

Author(s):

Eliotte L. Hirshberg ◽

Jamin L. Alexander ◽

Lisa A. Asaro ◽

Kerry Coughlin-Wells ◽

Garry M. Steil ◽

...

Keyword(s):

Clinical Trial ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Therapeutic Intervention ◽

Electronic Decision Support ◽

Electronic Decision Support System ◽

Lung Failure

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Design-a-trial: a rule-based decision support system for clinical trial design

Knowledge-Based Systems ◽

10.1016/j.knosys.2004.03.007 ◽

2004 ◽

Vol 17 (2-4) ◽

pp. 121-129 ◽

Cited By ~ 7

Author(s):

K Nammuni ◽

C Pickering ◽

S Modgil ◽

A Montgomery ◽

P Hammond ◽

...

Keyword(s):

Clinical Trial ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Trial Design ◽

Clinical Trial Design ◽

Rule Based

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Developing A Diagnostic Decision Support System (DDSS) to Reduce Time To Diagnosis in Paediatric Shoulder Instability

10.21203/rs.3.rs-111914/v1 ◽

2020 ◽

Author(s):

Fraser Philp ◽

Alice Faux-Nightingale ◽

Sandra Wooley ◽

Ed De Quincey ◽

Anand Pandyan

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Shoulder Instability ◽

Clinical Decision Making ◽

Perceived Usefulness ◽

Diagnostic Decision ◽

Time To Diagnosis

Abstract Background: Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability.Methods: A modified nominal focus group technique, involving three clinical vignettes was used to elicit information physiotherapists decision-making processes.Results: Twenty-five physiotherapists, (18F:7M) from four separate clinical sites participated. The themes identified related to ‘Variability in diagnostic processes and lack of standardised practice’ and ‘Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making’. Conclusion: No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed.Trial Registration: This was not a clinical trial and so no clinical trial registry is needed.

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