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      Call for Papers: Digital Diagnostic Techniques

      Submit here before August 31, 2024

      About Pathobiology: 3.5 Impact Factor I 8.5 CiteScore I 1.088 Scimago Journal & Country Rank (SJR)

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      Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health.

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          Abstract

          With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research.

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          Most cited references16

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          Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

          Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although 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 focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. 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.
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            Clinical Outcome Assessments: Conceptual Foundation-Report of the ISPOR Clinical Outcomes Assessment - Emerging Good Practices for Outcomes Research Task Force.

            An outcome assessment, the patient assessment used in an endpoint, is the measuring instrument that provides a rating or score (categorical or continuous) that is intended to represent some aspect of the patient's health status. Outcome assessments are used to define efficacy endpoints when developing a therapy for a disease or condition. Most efficacy endpoints are based on specified clinical assessments of patients. When clinical assessments are used as clinical trial outcomes, they are called clinical outcome assessments (COAs). COAs include any assessment that may be influenced by human choices, judgment, or motivation. COAs must be well-defined and possess adequate measurement properties to demonstrate (directly or indirectly) the benefits of a treatment. In contrast, a biomarker assessment is one that is subject to little, if any, patient motivational or rater judgmental influence. This is the first of two reports by the ISPOR Clinical Outcomes Assessment - Emerging Good Practices for Outcomes Research Task Force. This report provides foundational definitions important for an understanding of COA measurement principles. The foundation provided in this report includes what it means to demonstrate a beneficial effect, how assessments of patients relate to the objective of showing a treatment's benefit, and how these assessments are used in clinical trial endpoints. In addition, this report describes intrinsic attributes of patient assessments and clinical trial factors that can affect the properties of the measurements. These factors should be considered when developing or refining assessments. These considerations will aid investigators designing trials in their choice of using an existing assessment or developing a new outcome assessment. Although the focus of this report is on the development of a new COA to define endpoints in a clinical trial, these principles may be applied more generally. A critical element in appraising or developing a COA is to describe the treatment's intended benefit as an effect on a clearly identified aspect of how a patient feels or functions. This aspect must have importance to the patient and be part of the patient's typical life. This meaningful health aspect can be measured directly or measured indirectly when it is impractical to evaluate it directly or when it is difficult to measure. For indirect measurement, a concept of interest (COI) can be identified. The COI must be related to how a patient feels or functions. Procedures are then developed to measure the COI. The relationship of these measurements with how a patient feels or functions in the intended setting and manner of use of the COA (the context of use) could then be defined. A COA has identifiable attributes or characteristics that affect the measurement properties of the COA when used in endpoints. One of these features is whether judgment can influence the measurement, and if so, whose judgment. This attribute defines four categories of COAs: patient reported outcomes, clinician reported outcomes, observer reported outcomes, and performance outcomes. A full description as well as explanation of other important COA features is included in this report. The information in this report should aid in the development, refinement, and standardization of COAs, and, ultimately, improve their measurement properties.
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              Digital Medicine: A Primer on Measurement

              Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast “clinical research” versus routine “clinical care,” outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures throughout that we hope readers will borrow from liberally. This primer is the first in a series that will accelerate the safe and effective advancement of the field of digital medicine.
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                Author and article information

                Journal
                Digit Biomark
                Digital biomarkers
                S. Karger AG
                2504-110X
                2504-110X
                2020
                : 4
                : 3
                Affiliations
                [1 ] Digital Medicine Society, Boston, Massachusetts, USA.
                [2 ] Elektra Labs, Boston, Massachusetts, USA.
                [3 ] Evidation Health, Inc., San Mateo, California, USA.
                Article
                dib-0004-0069
                10.1159/000509725
                7548919
                33083687
                701e89fc-7012-4e80-bd8f-5377ef9a49c1
                History

                Digital measures,Patient engagement,Digital medicine

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