scholarly journals Does real-time artificial intelligence-based visual pathology enhancement of three-dimensional optical coherence tomography scans optimise treatment decision in patients with nAMD? Rationale and design of the RAZORBILL study

2021 ◽  
pp. bjophthalmol-2021-319211
Author(s):  
Frank G Holz ◽  
Rodrigo Abreu-Gonzalez ◽  
Francesco Bandello ◽  
Renaud Duval ◽  
Louise O'Toole ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optical Coherence Tomography ◽  
Disease Activity ◽  
Clinical Care ◽  
Three Dimensional ◽  
Clinical Decision ◽  
Decision Support Tools ◽  
Routine Clinical Care ◽  
Support Tools ◽  
Clinical Decision Support Tools

Background/rationaleArtificial intelligence (AI)-based clinical decision support tools, being developed across multiple fields in medicine, need to be evaluated for their impact on the treatment and outcomes of patients as well as optimisation of the clinical workflow. The RAZORBILL study will investigate the impact of advanced AI segmentation algorithms on the disease activity assessment in patients with neovascular age-related macular degeneration (nAMD) by enriching three-dimensional (3D) retinal optical coherence tomography (OCT) scans with automated fluid and layer quantification measurements.MethodsRAZORBILL is an observational, multicentre, multinational, open-label study, comprising two phases: (a) clinical data collection (phase I): an observational study design, which enforces neither strict visit schedule nor mandated treatment regimen was chosen as an appropriate design to collect data in a real-world clinical setting to enable evaluation in phase II and (b) OCT enrichment analysis (phase II): de-identified 3D OCT scans will be evaluated for disease activity. Within this evaluation, investigators will review the scans once enriched with segmentation results (i.e., highlighted and quantified pathological fluid volumes) and once in its original (i.e., non-enriched) state. This review will be performed using an integrated crossover design, where investigators are used as their own controls allowing the analysis to account for differences in expertise and individual disease activity definitions.ConclusionsIn order to apply novel AI tools to routine clinical care, their benefit as well as operational feasibility need to be carefully investigated. RAZORBILL will inform on the value of AI-based clinical decision support tools. It will clarify if these can be implemented in clinical treatment of patients with nAMD and whether it allows for optimisation of individualised treatment in routine clinical care.

scholarly journals Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

2021 ◽  
Vol 61 (1) ◽  
pp. 225-245 ◽  
Author(s):  
Adam S. Darwich ◽  
Thomas M. Polasek ◽  
Jeffrey K. Aronson ◽  
Kayode Ogungbenro ◽  
Daniel F.B. Wright ◽  
...  
Keyword(s):  
Drug Therapy ◽  
Current Knowledge ◽  
Clinical Care ◽  
Clinical Decision ◽  
Decision Support Tools ◽  
Support Tools ◽  
Drug Regulations ◽  
The Common ◽  
Clinical Decision Support Tools ◽  
Practical Validation

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

scholarly journals 72. Improving Antibiotic Prescribing in Interventional Radiology Using Clinical Decision Support Tools to Assess Penicillin Allergies

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S54-S54
Author(s):  
Vidya Atluri ◽  
Paula Marsland ◽  
Luke M Johnson ◽  
Rupali Jain ◽  
Paul Pottinger ◽  
...  
Keyword(s):  
Interventional Radiology ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Prophylactic Antibiotics ◽  
Decision Support Tools ◽  
First Line ◽  
Support Tools ◽  
History Of ◽  
Clinical Decision Support Tools

Abstract Background Patients labeled with penicillin allergies often receive alternative antibiotics, leading to increased cost, higher risk of adverse events, and decreased efficacy of procedural prophylaxis. However, most of those patients can tolerate a cephalosporin. University of Washington Medical Center – Montlake (UWMC-ML) Interventional Radiology (IR) frequently administer a pre-procedure prophylactic cephalosporin. We worked with the clinicians in IR to develop tools to allow them to better assess penicillin allergies, make the most appropriate antibiotic choice, and update the patient’s allergy documentation. Methods We identified all patients who underwent procedures in IR between 2017–2019. Chart review was done to determine the procedures performed, patient demographic information, allergies, allergy documentation, and prophylactic antibiotics received. In May 2020 we implemented new Clinical Decision Support tools, including an online assessment app (https://tinyurl.com/IRPCNAllAssess) and handouts to guide antibiotic decision making to clinicians in IR. Results From 2017 to 2019, 381 patients underwent 958 procedures in IR. Of those, 379 patients underwent 496 procedures for which the recommended first line choice for antibiotic prophylaxis is a cephalosporin. Of patients who received pre-procedure prophylactic antibiotics for those procedures, 15.9% [n=11] of patients with penicillin allergies received the first line antibiotic, compared to 89.9% [n=319] of patients without a reported penicillin allergy. Since implementation, the online app has been used to evaluate 9 patients, of whom 8 had penicillin allergies. All 8 patients safely received the first line antibiotic (3 were delabeled, 4 reported a history of mild reactions, and 1 reported a history of an immediate IgE mediated response to penicillin but safely received cefazolin). Conclusion IR evaluates hundreds of patients who may receive prophylactic antibiotics each year. By providing tools to assess penicillin allergies, we were able to improve both their prescribing and de-label patients which will provide a much broader impact on their care than on just their current procedure. Our free tool can be accessed at the website above, and we will demonstrate in person. Disclosures All Authors: No reported disclosures

scholarly journals Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data

2017 ◽  
Vol 56 (S 01) ◽  
pp. e74-e83 ◽  
Author(s):  
Vaishnavi Kannan ◽  
Jason Fish ◽  
Jacqueline Mutz ◽  
Angela Carrington ◽  
Ki Lai ◽  
...  
Keyword(s):  
Data Collection ◽  
New Product Development ◽  
Data Model ◽  
New Product ◽  
Quality Measures ◽  
Clinical Decision ◽  
Decision Support Tools ◽  
Disease Registries ◽  
Support Tools ◽  
Clinical Decision Support Tools

SummaryBackground: Creation of a new electronic health record (EHR)-based registry often can be a “one-off” complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care.Objective: To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development.Methods: We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed - either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) - were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined “grains” from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-gener-ated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week “sprints” for rapid-cycle feedback and refinement.Results: Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends.Conclusions: This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.

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