Automated Diagnosis Through Ontologies and Logical Descriptions

2011 ◽  
Vol 3 (1) ◽  
pp. 21-39 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Real World ◽  
Information Age ◽  
Semantic Technologies ◽  
Automated Diagnosis ◽  
Real World Data ◽  
World Data ◽  
Degree Of Maturity ◽  
Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

Automated Diagnosis through Ontologies and Logical Descriptions

2013 ◽  
pp. 117-134
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Real World ◽  
Information Age ◽  
Semantic Technologies ◽  
Automated Diagnosis ◽  
Real World Data ◽  
World Data ◽  
Degree Of Maturity ◽  
Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

Quality criteria for Real-World Data in pharmaceutical research and healthcare decision making. An Austrian Expert Consensus (Preprint)

2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Real World ◽  
Pharmaceutical Medicine ◽  
Pharmaceutical Research ◽  
Quality Criteria ◽  
Clinical Decision ◽  
European Medicines Agency ◽  
Real World Data ◽  
World Data

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies

scholarly journals Building learning health systems on Real World Data

2019 ◽  
Vol 19 (4) ◽  
pp. 11
Author(s):  
Enrique Bernal-Delgado ◽  
Anibal García-Sempere ◽  
Ibai Tamayo-Rodríguez ◽  
Francisco Estupiñán-Romero ◽  
Felipe Aizupuru-Barandiaran ◽  
...  
Keyword(s):  
Health Systems ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

scholarly journals Workshop: Real-world data: are we ready to take up the challenge?

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Full Potential ◽  
Real World Data ◽  
World Data ◽  
General Data Protection Regulation ◽  
Health Technologies ◽  
Future Challenges ◽  
Relevant Role

Abstract Evidence provided by traditional clinical research often fails to answer patients’, physicians’ and healthcare decision-makers’ questions about real-world practice and outcomes; in fact, real-world effectiveness may considerably differ from efficacy assessed in clinical trial settings. The rise of interest in real-world data (RWD) - data routinely collected outside a controlled research environment - is driven by the increasing need for evidence in specific populations, such as comorbid or multi-treated people. RWD can also allow investigation of unanticipated, uncommon or long-term outcomes. In addition, RWD may represent the only source of information in some fields of special interest, e.g. rare diseases. Furthermore, current conditional reimbursement systems of drugs require dynamic evaluations of cost-effectiveness that have necessarily to take RWD into account. Indeed, RWD play a relevant role also in Health Technology Assessment (HTA) projects that should release the most up-to-date evidence and should be updated once new data are available. Nonetheless, common concerns about evidence derived from RWD include uncertainty about data quality, high possibility of bias, difficulties in managing a vast amount of data coming from different sources as well as legal and privacy requirements, in particular in the light of the new General Data Protection Regulation. However, HTA cannot exempt using RWD as it is entrusted to assess short- and long-term consequences of the application of health technologies, in terms of both health outcomes and costs. This workshop is an opportunity to discuss current and future challenges in the use of RWD in HTA and concerns preventing researchers from exploiting RWD full potential. Key messages Real-world evidence and randomized control trial data are considered mutually complementary in generating evidence and supporting decision-making. Real-world data have proved to be crucial as instrument to gather data in HTA and to support decision-making.

scholarly journals Do existing real-world data sources generate suitable evidence for the HTA of medical devices in Europe? Mapping and critical appraisal

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Benedetta Pongiglione ◽  
Aleksandra Torbica ◽  
Hedwig Blommestein ◽  
Saskia de Groot ◽  
Oriana Ciani ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Devices ◽  
Real World ◽  
Real World Data ◽  
Data Accessibility ◽  
World Data ◽  
Transcatheter Aortic Valve ◽  
Real World Evidence ◽  
Hip And Knee Arthroplasty ◽  
Valve Implantation

Abstract Aim Technological and computational advancements offer new tools for the collection and analysis of real-world data (RWD). Considering the substantial effort and resources devoted to collecting RWD, a greater return would be achieved if real-world evidence (RWE) was effectively used to support Health Technology Assessment (HTA) and decision making on medical technologies. A useful question is: To what extent are RWD suitable for generating RWE? Methods We mapped existing RWD sources in Europe for three case studies: hip and knee arthroplasty, transcatheter aortic valve implantation (TAVI) and mitral valve repair (TMVR), and robotic surgery procedures. We provided a comprehensive assessment of their content and appropriateness for conducting the HTA of medical devices. The identification of RWD sources was performed combining a systematic search on PubMed with gray literature scoping, covering fifteen European countries. Results We identified seventy-one RWD sources on arthroplasties; ninety-five on TAVI and TMVR; and seventy-seven on robotic procedures. The number, content, and integrity of the sources varied dramatically across countries. Most sources included at least one health outcome (97.5%), with mortality and rehospitalization/reoperation the most common; 80% of sources included resource outcomes, with length of stay the most common, and comparators were available in almost 70% of sources. Conclusions RWD sources bear the potential for the HTA of medical devices. The main challenges are data accessibility, a lack of standardization of health and economic outcomes, and inadequate comparators. These findings are crucial to enabling the incorporation of RWD into decision making and represent a readily available tool for getting acquainted with existing information sources.

Contributions of Real-World Evidence and Real-World Data to Decision-Making in the Management of Soft Tissue Sarcomas

Oncology ◽  
2021 ◽  
Vol 99 (Suppl. 1) ◽  
pp. 3-7
Author(s):  
George D. Demetri ◽  
Silvia Stacchiotti
Keyword(s):  
Decision Making ◽  
Soft Tissue ◽  
Real World ◽  
Soft Tissue Sarcomas ◽  
Clinical Settings ◽  
Disease Entity ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence

Real-world data are defined as data relating to any aspect of a patient’s health status collected in the context of routine health surveillance and medical care delivery. Sources range from insurance billing claims through to electronic surveillance data (e.g., activity trackers). Real-world data derive from large populations in diverse clinical settings and thus can be extrapolated more readily than clinical trial data to patients in different clinical settings or with a variety of comorbidities. Real-world data are used to generate real-world evidence, which might be regarded as a “meta-analysis” of accumulated real-world data. Increasingly, regulatory authorities are recognizing the value of real-world data and real-world evidence, especially for rare diseases where it may be practically unfeasible to conduct randomized controlled trials. However, the quality of real-world evidence depends on the quality of the data collected which, in turn, depends on a correct pathological diagnosis and the homogeneous behaviour of a reliably defined and consistent disease entity. As each of the more than 80 varieties of soft tissue sarcoma (STS) types represents a distinct disease entity, the situation is exceedingly complicated. Discordant diagnoses, which affect data quality, present a major challenge for use of real-world data. As real-world data are difficult to collect, collaboration across sarcoma reference institutions and sophisticated information technology solutions are required before the potential of real-world evidence to inform decision-making in the management of STS can be fully exploited.

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