scholarly journals Accompanying the Prostate Cancer Patient Pathway: Evaluation of Novel Clinical Decision Support Software

2021 ◽  
Author(s):  
Maurice Henkel ◽  
Tobias Horn ◽  
Francois Leboutte ◽  
Pawel Trotsenko ◽  
Sarah G. Dugas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Data Quality ◽  
Clinical Decision Support ◽  
User Satisfaction ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Process

Abstract Introduction Physicians spend more than half of their workday interacting with health information systems to care for their patients. Effective data management that provides physicians with comprehensive patient information from various information systems is required to ensure high quality clinical decision making.Objectives We evaluated the impact of a novel, CE-certified clinical decision support tool on physician’s effectiveness and satisfaction in the clinical decision-making process.Methods Using pre-therapeutic prostate cancer management cases, we compared physician’s expenditure of time, data quality, and user satisfaction in the decision-making process comparing the current standard with the software. Ten urologists from our department conducted the diagnostic work-up to the treatment decision for a total of 10 patients using both approaches.Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9 % (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated data quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).Conclusion The software demonstrated that effective data management can improve physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Prostate-specific membrane antigen-based imaging in prostate cancer: Impact on clinical decision making process

The Prostate ◽  
2015 ◽  
Vol 75 (7) ◽  
pp. 748-757 ◽  
Author(s):  
Mehmet Onur Demirkol ◽  
Ömer Acar ◽  
Burcu Uçar ◽  
Sultan Rana Ramazanoğlu ◽  
Yeşim Sağlıcan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Prostate Specific Membrane Antigen ◽  
Specific Membrane

scholarly journals A Clinical Decision Support System based on Ontology and Causal Reasoning Models

2019 ◽  
Vol 14 (2) ◽  
pp. 187-197
Author(s):  
Nur Raidah Rahim ◽  
Sharifalillah Nordin ◽  
Rosma Mohd Dom
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Causal Reasoning ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Graphical Modeling

  Clinical decision support system (CDSS) is promising in assisting physicians for improving decision-making process and facilitates healthcare services. In medicine, causality has become the main concern throughout healthcare and decision-making. Causality is necessary for understanding all structures ofscientific reasoning and for providing a coherent and sufficient explanation for any event. However, thereare lack of existing CDSS that provide causal reasoning for the presented outcomes or decisions. Theseare necessary for showing reliability of the outcomes, and helping the physicians in making properdecisions. In this study, an ontology-based CDSS model is developed based on several key concepts andfeatures of causality and graphical modeling techniques. For the evaluation process, the Pellet reasoneris used to evaluate the consistency of the developed ontology model. In addition, an evaluation toolknown as Ontology Pitfall Scanner is used for validating the ontology model through pitfalls detection.The developed ontology-based CDSS model has potentials to be applied in clinical practice and helpingthe physicians in decision-making process. Keywords: clinical decision support system, ontology, causality, causal reasoning, graphical modeling

scholarly journals Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipesh Niraula ◽  
Jamalina Jamaluddin ◽  
Martha M. Matuszak ◽  
Randall K. Ten Haken ◽  
Issam El Naqa
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Dose Response ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Specific Information ◽  
Clinical Decisions ◽  
Nsclc Patients

AbstractSubtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 390-409
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Mental Health Clinical Decision Support Exploiting Big Data

2021 ◽  
pp. 983-1000
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

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