scholarly journals Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Mathijs P. Hendriks ◽  
Xander A.A.M. Verbeek ◽  
Thijs van Vegchel ◽  
Maurice J.C. van der Sangen ◽  
Luc J.A. Strobbe ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Decision Trees ◽  
Breast Imaging ◽  
Clinical Decision ◽  
Data Item ◽  
Data Driven ◽  
Clinical Implementation ◽  
Coding Systems ◽  
National Breast

PURPOSE The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems. METHODS All recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations. RESULTS In total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations. CONCLUSION By defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.

scholarly journals Best Paper Selection

2020 ◽  
Vol 29 (01) ◽  
pp. 158-158
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Knowledge Engineering ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Data Driven ◽  
Evidence Based ◽  
National Breast ◽  
Multi Level

Hendriks MP, Verbeek XAAM, van Vegchel T, van der Sangen MJC, Strobbe LJA, Merkus JWS, Zonderland HM, Smorenburg CH, Jager A, Siesling S. Transformation of the National Breast Cancer Guideline into data-driven clinical decision trees. JCO Clin Cancer Inform 2019 May;3:1-14 https://ascopubs.org/doi/full/10.1200/CCI.18.00150 Kamišalić A, Riaño D, Kert S, Welzer T, Nemec Zlatolas L. Multi-level medical knowledge formalization to support medical practice for chronic diseases. Data & Knowledge Engineering 2019; 119:36–57 https://www.sciencedirect.com/science/article/abs/pii/S0169023X16303937 Khalifa M, Magrabi F, Gallego B. Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support. BMC Med Inform Decis Mak 2019 Oct 29;19(1):207 https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0940-7

scholarly journals Application of Clinical Decision Support System to Assist Breast Cancer Patients with Lifestyle Modifications during the COVID-19 Pandemic: A Randomised Controlled Trial

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2115
Author(s):  
Panos Papandreou ◽  
Aristea Gioxari ◽  
Frantzeska Nimee ◽  
Maria Skouroliakou
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Control Group ◽  
Healthy Weight ◽  
Lifestyle Modifications ◽  
Randomised Controlled

Clinical decision support systems (CDSS) are data aggregation tools based on computer technology that assist clinicians to promote healthy weight management and prevention of cardiovascular diseases. We carried out a randomised controlled 3-month trial to implement lifestyle modifications in breast cancer (BC) patients by means of CDSS during the COVID-19 pandemic. In total, 55 BC women at stages I-IIIA were enrolled. They were randomly assigned either to Control group, receiving general lifestyle advice (n = 28) or the CDSS group (n = 27), to whom the CDSS provided personalised dietary plans based on the Mediterranean diet (MD) together with physical activity guidelines. Food data, anthropometry, blood markers and quality of life were evaluated. At 3 months, higher adherence to MD was recorded in the CDSS group, accompanied by lower body weight (kg) and body fat mass percentage compared to control (p < 0.001). In the CDSS arm, global health/quality of life was significantly improved at the trial endpoint (p < 0.05). Fasting blood glucose and lipid levels (i.e., cholesterol, LDL, triacylglycerols) of the CDSS arm remained unchanged (p > 0.05) but were elevated in the control arm at 3 months (p < 0.05). In conclusion, CDSS could be a promising tool to assist BC patients with lifestyle modifications during the COVID-19 pandemic.

Building an information system to facilitate pharmacogenomics clinical translation with clinical decision support

2021 ◽  
Author(s):  
Weifeng Qin ◽  
Xudong Lu ◽  
Qiang Shu ◽  
Huilong Duan ◽  
Haomin Li
Keyword(s):  
Information System ◽  
Decision Support ◽  
Electronic Health Record ◽  
Precision Medicine ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Clinical Translation ◽  
Health Record ◽  
Clinical Implementation ◽  
Novel Approach

Pharmacogenomics clinical decision support (PGx-CDS) is an important tool to incorporate PGx information into existing clinical workflows and facilitate PGx clinical translation. However, due to the lack of a computable formalization to represent the primary PGx knowledge, the complexity of genomics information and the lag of current commercial electronic health record (EHR) system for precision medicine, it is difficult to develop computerized PGx-CDS. Therefore, we explored a novel approach to build an information system, named the Pharmacogenomics Clinical Translation Platform (PCTP), for PGx clinical implementation. The PCTP can represent, store, and manage the primary PGx knowledge in a structured and computable format. Moreover, it has the potential to provide various PGx-CDS services and simplify the integration of PGx-CDS into EHRs.

scholarly journals Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

2020 ◽  
Vol 21 (6) ◽  
pp. 375-386 ◽  
Author(s):  
Christina L Aquilante ◽  
David P Kao ◽  
Katy E Trinkley ◽  
Chen-Tan Lin ◽  
Kristy R Crooks ◽  
...  
Keyword(s):  
Decision Support ◽  
Health System ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Support Tool ◽  
University Of Colorado ◽  
The University ◽  
The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

scholarly journals Contributions on Clinical Decision Support from the 2018 Literature

2019 ◽  
Vol 28 (01) ◽  
pp. 135-137 ◽  
Author(s):  
Vassilis Koutkias ◽  
Jacques Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Data Driven ◽  
Editorial Team ◽  
Cancer Center ◽  
Bibliographic Databases ◽  
Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

scholarly journals Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 369 ◽  
Author(s):  
Claudia Mazo ◽  
Cathriona Kearns ◽  
Catherine Mooney ◽  
William M. Gallagher
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Major Advance

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

Sign in / Sign up

Export Citation Format

Share Document