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Current indications point to a future where only a fraction of digital health technologies are subject to regulatory review prior to market entry. Many other sophisticated, unproven solutions will continue to proliferate, frustrating end-users looking for a way to improve their well-being or disease self-management. Thus, the onus is shifted to the clinician and patient to identify effective and useful digital health technologies, bearing the risk of ineffective, or even potentially harmful, solutions. The need for new accessible tools to assist in informed decision-making is clear for all domains of the digital health spectrum illustrated in Fig.

For lower-risk technologies where there is a trend toward lower regulatory oversight, new tools may provide the only independent insight into the performance of a digital health technology for consideration by the clinician and patient end-users. A summary of existing representative resources and approaches are detailed in Table 1. Within industry, the Personal Connected Health Alliance provides design guidance based on the Continua Alliance standards and design model. The newly formed Xcertia group is creating broad criteria and guidelines specifically for health mobile app curation, but leaves the task of evaluation and validation based on the guidelines to industry participants.

The Xcertia guidelines presently do not include clinical outcomes validation, though others such as NODE. Health and the Digital Therapeutic Alliance are attempting to fill this gap. The NHS specifically solicits from industry app development for particular areas of health, including maternity, social care, chronic conditions, cancer, and mental health. A host of additional app curation sites also exist, 21 , 22 with a varying range of analytic methods compared with the NHS.

These initiatives are relatively new with overlapping standards. They will likely advance the field, but may not provide sufficiently clear and robust direction for patients and providers on the most effective products that meet specific requirements to best integrate into a particular healthcare context. In addition, there is a lack of objective, comprehensive, transparent, and standards-based evaluation, which also limits confidence and practical application of existing approaches. Outside digital health, there are analogous consumer product evaluation organizations, such as UL formerly Underwriters Laboratory 24 and Consumer Reports, 25 which provide potentially useful models for digital health.

UL focuses on safety-related topics by certifying product compliance with international standards. UL does not, however, ensure a product meets end-user needs. Although safety is one end-user need, needs extend beyond safety, including portability, interoperability, and usability. Consumer Reports, on the other hand, represents the opinions of objective reviewers regarding the ability of a product to serve a particular function, which is end-user focused—is the product right for the job?

Together, UL and Consumer Reports evaluations steer product developers toward the development of solutions that best serve end users. There are two important differences between UL and Consumer Reports. First, the standards UL certifies are well-known to the product developers. Second, UL conducts pre-market certification testing.

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In aviation or motor vehicle manufacturing, there is a straightforward path from a perceived market need to product commercialization Fig. The product lifecycle begins with establishing requirements. A representative depiction of the steps within a traditional product development lifecycle is presented in the top half of the figure. The role of an independent evaluator and its relationship with the broader marketplace and product lifecycle is also presented. Furthermore, requirements could dictate standards, such as electrical or physical safety standards.

Importantly, these requirements and standards are thoroughly documented and may even lead to initial prototypes, which may be virtual simulations or physical models of the eventual end product to aid in refining the design ensuring it meets the specified requirements.

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The former term determines whether the product was designed and developed in accordance with the upfront requirements e. Presently, much of the digital health industry lacks this rigor, including several steps along the traditional product development cycle Fig. The current digital health product lifecycle often focuses on high-level requirements, if at all, which limits what can be verified or validated.

We believe a Digital Health Scorecard will promote requirements-driven development to the benefit of all stakeholders. Specifically, in our proposed lifecycle Fig. In this approach, an objective independent entity or authorized entities undertake the evaluation of digital health pre-market products emerging from developers.

The independent evaluation would address both verification and validation and would be based upon a set of well-defined i. Developers can use feedback from the Digital Health Scorecard to refine existing products or create new ones. The broader marketplace could use the Scorecard to make informed decisions regarding which products are most applicable for the intended use and which products perform best. The development of requirements will vary across types of digital health solutions based on functionality diagnostics, monitoring, care coordination, etc.

It is critical to incorporate the preferences of the clinicians and patients impacted by the digital health solution into the requirement development process. Once requirements are established, the proposed framework that could form the basis for evaluation includes the following domains: technical, clinical, usability, and cost Fig.

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Components of Digital Health Scorecard. The four domains of a digital health scorecard with example considerations are detailed in this figure. Their relationship to an assessment of stakeholder requirements is also presented. Technical validation is the most traditional type of evaluation in product testing. Does the solution actually perform to its self-proclaimed functionality with accuracy and precision? For example, how accurately and reliably does a wearable measure heart rate compared with a gold standard?

Other elements of technical validation could also include security and interoperability assessments. Applications that claim to perform the functions of established medical devices, such as those reflecting biological processes like heart rate, blood pressure, or respiratory rate, should be able to demonstrate equivalence according to rigorous standards set for other non-smartphone based novel devices. Is there a need for back-end monitoring of user engagement or daily calibration to ensure appropriate system performance? Do certain processors or sensors fail to meet stringent minimum standards required for reliable system outputs?

A system architecture, the sum of structure, behavior, and components of a technology, has unique considerations in digital health. Developers must consider the privacy and security requirements of handling patient data that may be confidential and even linked to larger electronic health record systems. Robust, enterprise architecture standards exist to guide developers on issues, such as the levels of encryption and user authentication necessary to safeguard patient information.

This requirement may vary with the degree of inherent confidentiality of the health condition in focus— ranging from low, when dealing with step-counters or accelerometers—to high if managing a socially stigmatized disease or storing test results. Clinical validation to demonstrate efficacy is generally well understood and considered vitally necessary in the context of traditional clinical or translational research.

However, analogous studies for digital health products is uncommon. Particularly for studies aiming to demonstrate the clinical impact of the a product, these may take the form of accepted care quality metrics, such as measures of clinical outcomes e.

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A digital health product that aims to prevent diabetes mellitus would be measured by standard clinical quality measures, such as clinically validated disease diagnostic criteria, 29 glycemic control, or diabetes-specific complications like stroke and retinal disease.

An integral validation step is standardized and critical appraisal of any existing evidence e. Existing digital health review resources may provide subjective assessments of the quality of clinical evidence by technical experts, but generally do not provide systematic and objective analysis. More advanced validation efforts would require external testing within a simulated or actual trial settings to determine if results can be duplicated. Furthermore, such testing would determine how the product performs across the relevant provider systems, clinical settings, and integrated technologies, in which it is likely to be deployed.

For example, the data that flows from an app that monitors cardiac function should actually assess the intended cardiac measure or function, assess it in the targeted population e. The data must flow across emergency, intensive care, and ambulatory settings to electronic health records, provider alert systems, and ambulances of these hospital systems. Without meeting such system requirements, deployment of such an app, even if shown to be clinically effective in a research setting, may have no meaning in the real healthcare system for patients.

Formal usability assessment of traditional healthcare goods and services e.

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When considering digital technology, there is no assurance or protection that the technology will align with user needs or preferences. Usability is arguably among the most important considerations with patient-oriented mobile- and digital-based solutions. These technologies are frequently literally in the hands of patients and consequently demand a more patient-centered approach to usability.

Existing digital health qualitative reviews address only some aspects of usability. All stakeholders would benefit from a standardized approach, unlike the status quo, which is often ad hoc, qualitative, or dependent on the volume of reviews. Digital health apps must be easy to use for their intended purpose, require minimal effort to complete tasks, have minimal data entry burden, and allow the user to control preferences when appropriate e.

Since systems can be designed for users with different requirements e. These considerations, not surprisingly, play an important role in patient engagement—an often neglected, yet essential aspect of digital health; having a unused medical device is tantamount to not having one at all. At a minimum, a best-practice evaluation framework should be considered, 37 , 38 but these frameworks establish a lowest common denominator, and do not necessarily incorporate the principles of user centered design into the development process.

There are multiple efforts underway to codify standards for design principles specifically to digital health solutions, most notably Xcertia and Node. Some criteria are easier to objectively specify than others. For example, criteria such as number of steps required to complete essential tasks, consistency of navigation, visual legibility, and use of recognizable iconography can be objectively developed.

More work is needed to formalize subjective aspects of usability, such as utility and user delight and satisfaction. Usability viewed in this way, along with clinical relevance, creates the opportunity to impact patient engagement. To maximize impact, digital health solutions will likely require clinician input as part of solution development, thereby accounting for UCD on at least two fronts. Just as early electronic health record implementations increased clinician burden by not adequately considering clinician workflow, 39 digital health solutions designers need must pay attention to ease of accomplishing the expected tasks.

At face value, cost—defined as the price a consumer must pay to gain access—may be an inadequate differentiator of digital health solutions—many are free or low priced, particularly in the mobile app arena. When integrated in a composite assessment, however, true cost may provide greater discrimination of overall value. Here, cost estimation becomes more complex by incorporating broader considerations, such as costs of the technology lifecycle and those to integrate technology into the clinical workflow.

Furthermore, the long-term cost implications from outcomes improvements are also difficult to calculate but should be taken into consideration, leveraging metrics from pharmaceutical and device industries. While determination and attribution of financial benefit derived from mobile health apps is challenging, 40 real value may be derived from increased personal health engagement, improved patient—clinician engagement, or patient and clinician satisfaction.

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New ways of quantifying and measuring these types of attributes will provide a more comprehensive picture overall cost-benefit. In the consumer financial industry, the FICO score represents a global score as an amalgamation of credit information to approximate borrower quality and lending risk. Correspondingly, by aggregating the individual domain assessments from the Digital Health Scorecard and contextualizing the degree to which a product satisfies end-user requirements, a composite Global Digital Health Score can be created.

As a result, consumers and other users would be provided a high-level synthesis of quality and risk for digital health products. This aggregate score allows gross initial selection of digital solutions. Individual scores allow finer discrimination of particular products. Such scores also allow digital health companies to identify where improvements are needed and inform stakeholders on what gaps will exist when the products are deployed.

The scores could become benchmarks and establish thresholds for particular types of digital solutions. These scores would also need to highlight and prioritize how well the product ultimately met the end-user requirements. Android applications are published through Google Play Store which is an official marketplace for Android.

If we have to define the current security policy implemented by Google Play Store for publishing Android applications in one sentence then we can write it as " all are suspect but innocent until proven guilty ".

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It means an application does not have to go through rigorous security review to be accepted for publication. It is assumed that all the applications are benign which does not mean it will remain so in future. If any application is found doing suspicious activities then the application will be categorized as malicious and it will be removed from the Play Store. In addition to your end users auto sizing a field, the DataPresenter controls can automatically resize all fields within a field layout to fit within the bounds of the control.

The fixed records will not scroll out of view when your end users scroll new records into view. However, if you programmatically fix a record in a xamDataCarousel control, the record will move to the beginning of the record list and xamDataCarousel will scroll it into view. Unfixing the record will move it back into its original position in the record list and xamDataCarousel will scroll to the beginning of the record list.

If your DataPresenter control contains nested records, i. If your end users fix multiple records to the top and they expand one of the fixed records, subsequent fixed records closest to the scrollable area will become scrollable. If your end users collapse the expanded record, the subsequent fixed records will be fixed again. If your end users fix multiple records to the bottom and they expand one of the fixed records, the expanded record and preceding fixed record closest to the scrollable area will become scrollable.

Since the DataPresenter controls append fixed records to the bottom of the fixed-records list records fixed to the top or the top of the fixed-records list records fixed to the bottom , any records that your end users fix after expanding a fixed record will be scrollable see previous rules. Your end users can only fix a child record or a data record within a group-by record to the top of its respective record island.

Fixed child records are only fixed relative to its siblings. For example, if your end users fix a child record that has five siblings, the fixed child record will remain in view as long as your end users do not scroll the last sibling record out of view. Once your end users scroll the last sibling record out of view, the fixed child record will become scrollable. This also means that if your end users fix all child records in a record island, all of them will be scrollable.

Your end users can group records from multiple field layouts by dragging field headers into the group-by area. If your data items implement the IDataErrorInfo interface found in the. NET Framework, the DataPresenter family of controls can display an error message when your end users enter invalid data in a cell. In addition to enabling support for data errors, the SupportDataErrorInfo property determines whether the DataPresenter control displays the data error in a cell, record selector, or both. If you enable support for the IDataErrorInfo interface on a FieldLayoutSettings object, any fields in the affected field layout will automatically report data errors.

Setting the DataErrorDisplayMode property determines whether a data error is presented as an error icon, a highlight, or both. Value constraints will force your end users to correct an error before they can move focus to a new cell. Instead of using multiple nested panels to arrange its records, the GridView object uses a single root-level panel and indents records appropriately. Hierarchical Records and Performance. For example, if your end users sort a field by clicking on a field header, they can undo it.