Utilizing a Human-Computer Interaction Approach to Evaluate the Design of Current Pharmacogenomics Clinical Decision Support.
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ABSTRACT:
SUBMITTER: Elchynski AL
PROVIDER: S-EPMC8618963 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Clinical pharmacogenomics (PGx) has the potential to make pharmacotherapy safer and more effective by utilizing genetic patient data for drug dosing and selection. However, widespread adoption of PGx depends on its successful integration into routine clinical care through clinical decision support tools, which is often hampered by insufficient or fragmented infrastructures. This paper describes the setup and implementation of a unique multimodal, multilingual clinical decision support intervention consisting of digital, paper-, and mobile-based tools that are deployed across implementation sites in seven European countries participating in the Ubiquitous PGx (U-PGx) project.
| S-EPMC6016647 | biostudies-literature
Project description:Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola-Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola-Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.
| S-EPMC8773857 | biostudies-literature
Project description:BACKGROUND:A pharmacogenomic clinical decision support tool (PGx-CDS) for thiopurine medications can help physicians incorporate pharmacogenomic results into prescribing decisions by providing up-to-date, real-time decision support. However, the PGx-CDS user interface may introduce errors and promote alert fatigue. The objective of this study was to develop and evaluate a prototype of a PGx-CDS user interface for thiopurine medications with user-centered design methods. METHODS:This study had two phases: In phase I, we conducted qualitative interviews to assess providers' information needs. Interview transcripts were analyzed through a combination of inductive and deductive qualitative analysis to develop design requirements for a PGx-CDS user interface. Using these requirements, we developed a user interface prototype and evaluated its usability (phase II). RESULTS:In total, 14 providers participated: 10 were interviewed in phase I, and seven providers completed usability testing in phase II (3 providers participated in both phases). Most (90%) participants were interested in PGx-CDS systems to help improve medication efficacy and patient safety. Interviews yielded 11 themes sorted into two main categories: 1) health care providers' views on PGx-CDS and 2) important design features for PGx-CDS. We organized these findings into guidance for PGx-CDS content and display. Usability testing of the PGx-CDS prototype showed high provider satisfaction. CONCLUSION:This is one of the first studies to utilize a user-centered design approach to develop and assess a PGx-CDS interface prototype for Thiopurine Methyltransferase (TPMT). This study provides guidance for the development of a PGx-CDS, and particularly for biomarkers such as TPMT.
| S-EPMC6798472 | biostudies-literature
Project description:BackgroundActive clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care.ObjectiveWe describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively.Materials and methodsClinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge.ResultsThrough November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued.ConclusionsOur experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.
| S-EPMC3957400 | biostudies-literature
Project description:Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children's Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians' adherence to 4783 pharmacogenomically guided CDS alerts that triggered for 12 genes and 60 drugs. Clinicians adhered to the therapeutic recommendations provided in 4392 alerts (92%). In our population of pediatric patients with catastrophic illnesses, the most frequently presented gene/drug CDS alerts were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications (n = 51), and CYP2C19 and proton pump inhibitors (omeprazole and pantoprazole; n = 50). The high adherence rate was facilitated by our team approach to prescribing and our collaborative CDS design and delivery.
| S-EPMC9748527 | biostudies-literature
Project description:BackgroundIncreasing use of electronic health records offers the potential to incorporate computer decision support systems (CDSSs) to prompt evidence-based actions within routine consultations.AimTo synthesise the evidence for the use of CDSSs by professionals managing people with asthma.Materials and methodsWe systematically searched Medline, Embase, Health Technology Assessment, Cochrane and Inspec databases (1990 to April 2012, no language restrictions) for trials, and four online repositories for unpublished studies. We also wrote to authors. Eligible studies were randomised controlled trials of CDSSs supporting professional management of asthma. Studies were appraised (Cochrane Risk of Bias Tool) and findings synthesised narratively.ResultsA total of 5787 articles were screened, and eight trials were found eligible, with six at high risk of bias. Overall, CDSSs for professionals were ineffective. Usage of the systems was generally low: in the only trial at low risk of bias the CDSS was not used at all. When a CDSS was used, compliance with the advice offered was also low. However, if actually used, CDSSs could result in closer guideline adherence (improve investigating, prescribing and issuing of action plans) and could improve some clinical outcomes. The study at moderate risk of bias showed increased prescribing of inhaled steroids.ConclusionsThe current generation of CDSSs is unlikely to result in improvements in outcomes for patients with asthma because they are rarely used and the advice is not followed. Future decision support systems need to align better with professional workflows so that pertinent and timely advice is easily accessible within the consultation.
| S-EPMC4373260 | biostudies-literature
Project description:Changes in behavior are necessary to apply genomic discoveries to practice. We prospectively studied medication changes made by providers representing eight different medicine specialty clinics whose patients had submitted to preemptive pharmacogenomic genotyping. An institutional clinical decision support (CDS) system provided pharmacogenomic results using traffic light alerts: green = genomically favorable, yellow = genomic caution, red = high risk. The influence of pharmacogenomic alerts on prescribing behaviors was the primary endpoint. In all, 2,279 outpatient encounters were analyzed. Independent of other potential prescribing mediators, medications with high pharmacogenomic risk were changed significantly more often than prescription drugs lacking pharmacogenomic information (odds ratio (OR) = 26.2 (9.0-75.3), P < 0.0001). Medications with cautionary pharmacogenomic information were also changed more frequently (OR = 2.4 (1.7-3.5), P < 0.0001). No pharmacogenomically high-risk medications were prescribed during the entire study when physicians consulted the CDS tool. Pharmacogenomic information improved prescribing in patterns aimed at reducing patient risk, demonstrating that enhanced prescription decision-making is achievable through clinical integration of genomic medicine.
| S-EPMC5636653 | biostudies-literature
Project description:ObjectiveProvider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care.Material and methodsA narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems.ResultsFourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features.DiscussionFeatures are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS.ConclusionThis review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.
| S-EPMC6018977 | biostudies-literature
Project description:BackgroundThe increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.PurposeTo design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.MethodsDesign science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.ResultsThe analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.ImpactOur work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.ConclusionAugmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
| S-EPMC9705761 | biostudies-literature
Project description:Molecular biology has been revolutionized by the miniaturization and parallelization of DNA sequencing assays previously performed on bulk samples. Many of these technologies rely on biomolecular reagents to facilitate detection, synthesis, or labeling of samples. To aid in the construction of analogous experimental approaches for proteins and peptides, we have used computer-aided design to engineer an enzyme capable of catalyzing the cleavage step of the Edman degradation. We exploit the similarity between the sulfur nucleophile on the Edman reagent and the catalytic cysteine in a naturally occurring protease to adopt a substrate-assisted mechanism for achieving controlled, step-wise removal of N-terminal amino acids. The ability to expose amino acids iteratively at the N-terminus of peptides is a central requirement for protein sequencing techniques that utilize processive degradation of the peptide chain. While this can be easily accomplished using the chemical Edman degradation, achieving this activity enzymatically in aqueous solution removes the requirement for harsh acid catalysis, improving compatibility with low adsorption detection surfaces, such as those used in single molecule assays.
| S-EPMC4380987 | biostudies-literature