scholarly journals Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs) (Preprint)

2019 ◽  
Author(s):  
Jennifer Goldsack ◽  
Andrea Coravos ◽  
Jessie Bakker ◽  
Brinnae Bent ◽  
Ariel V. Dowling ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Evidence Base ◽  
Evaluation Framework ◽  
Clinical Validation ◽  
Analytical Validation ◽  
Science Data ◽  
Interdisciplinary Field ◽  
Digital Medicine ◽  
Monitoring Technologies

UNSTRUCTURED Digital medicine is an interdisciplinary field, drawing together stakeholders with expertise in engineering, manufacturing, clinical science, data science, biostatistics, regulatory considerations, ethics, patient advocacy, and healthcare policy, to name a few. While this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes 1) verification, 2) analytical validation, and 3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

BioMeT and algorithm measures: A proposed standardized evaluation framework (Preprint)

2019 ◽  
Author(s):  
Alan Godfrey ◽  
Jennifer Goldsack ◽  
Pamela Tenaerts ◽  
Clara Aranda ◽  
Azad Hussain ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Evaluation Framework ◽  
Audit Trail ◽  
Digital Medicine ◽  
Fit For Purpose ◽  
Monitoring Technologies

UNSTRUCTURED Technology is advancing at extraordinary rates with novel data being generated which could potentially revolutionary different therapeutic areas of medicine. However, adoption is medicine is hampered by a lack of trust, particularly for biometric monitoring technologies (BioMeTs) where a key question facing frontline healthcare professionals is are BioMeTs fit for purpose? Here, we discuss pragmatic barriers and guidance regarding BioMeTs, cumulating in a proposed guidance framework to better inform their development and deployment in digital medicine. Furthermore, the framework proposes a process to establish an audit trail of BioMeTs (hardware and algorithms), to instil trust amongst multidisciplinary users.

scholarly journals COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248128
Author(s):  
Mark Stewart ◽  
Carla Rodriguez-Watson ◽  
Adem Albayrak ◽  
Julius Asubonteng ◽  
Andrew Belli ◽  
...  
Keyword(s):  
Best Practices ◽  
Adverse Events ◽  
Data Science ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Parallel Analysis ◽  
Science Data ◽  
Systems Research ◽  
Cox Proportional Hazards Models ◽  
Global Threat

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

scholarly journals Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems

2021 ◽  
Vol 3 ◽  
Author(s):  
Usman Mahmood ◽  
Robik Shrestha ◽  
David D. B. Bates ◽  
Lorenzo Mannelli ◽  
Giuseppe Corrias ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Best Practices ◽  
Learning System ◽  
Clinical Validation ◽  
Efficacy And Safety ◽  
Analytical Validation ◽  
Validation Data ◽  
Machine Perception ◽  
Computed Tomography Scans ◽  
Development Data

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.

scholarly journals Resistance to Adoption of Best Practices

2019 ◽  
Author(s):  
Dan Sholler ◽  
Sara Stoudt ◽  
Chris J. Kennedy ◽  
Fernando Hoces de la Guardia ◽  
Francois Lanusse ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Everyday Practice ◽  
Science Data ◽  
Intensive Research ◽  
Data Intensive ◽  
New Ways Of Working ◽  
Group Members

There are many recommendations of "best practices" for those doing data science, data-intensive research, and research in general. These documents usually present a particular vision of how people should work with data and computing, recommending specific tools, activities, mechanisms, and sensibilities. However, implementation of best (or better) practices in any setting is often met with resistance from individuals and groups, who perceive some drawbacks to the proposed changes to everyday practice. We offer some definitions of resistance, identify the sources of researchers' hesitancy to adopt new ways of working, and describe some of the ways resistance is manifested in data science teams. We then offer strategies for overcoming resistance based on our group members' experiences working alongside resistors or resisting change themselves. Our discussion concluded with many remaining questions left to tackle, some of which are listed at the end of this piece.

scholarly journals Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

2017 ◽  
Author(s):  
Julie A McMurry ◽  
Nick Juty ◽  
Niklas Blomberg ◽  
Tony Burdett ◽  
Tom Conlin ◽  
...  
Keyword(s):  
Best Practices ◽  
Large Scale ◽  
Data Science ◽  
Life Science ◽  
Core Component ◽  
Science Data ◽  
Web Based ◽  
Large Scale Data ◽  
Online Databases ◽  
Scale Data

AbstractIn many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

The sharing economy: a hedge against recession setbacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Reza Habibi ◽  
Chiranjeev S. Kohli
Keyword(s):  
Best Practices ◽  
Design Methodology ◽  
Data Science ◽  
Science And Technology ◽  
Sharing Economy ◽  
Economic Sector ◽  
Content Type ◽  
Key Characteristics ◽  
The Future ◽  
Conceptual Paper

Purpose This paper aims to provide lessons from the emergence of the sharing economy after the 2008 recession and helps managers prepare more effectively for recessions in the future. Design/methodology/approach In this conceptual paper, the authors build on research on the sharing economy and study the best practices contributing to the sharing economy’s emergence and growth after the 2008 recession. The authors identify the key characteristics of this new economic sector and share lessons that can be used by other companies. Findings The authors recommend five major takeaways: seeking a more flexible supply; actively watching the trends; leveraging customers like employees; using advanced data science and technology like the sharing economy companies; and proactively avoiding panicked responses. This will help companies succeed during recessionary times – and the boom times that follow. Originality/value This is the first paper that, to the best of the authors’ knowledge, investigates the interplay between the sharing economy and recession and highlights practical lessons.

Sign in / Sign up

Export Citation Format

Share Document