Role this area should play in the mHealth space |
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The amount of health data being generated across care organisations is growing exponentially. These data are collected not only by healthcare professionals, but increasingly also by (mobile) devices and patients themselves. Health data are generally collected with the primary goal of providing high-quality patient care. However, they are also often re-used to further improve patient outcomes and to foster innovation; for example to enable personalised medicine, to feed into artificial intelligence algorithms or decision support systems, or to drive clinical research. To allow valid and reliable decision making based on health data, it is of utmost importance to ensure the data are of high quality. |
Current challenges and limitations |
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Stakeholders within the health ecosystem are insufficiently aware of the often suboptimal quality of their data and the implications this may have. In addition, limited incentives and resources are available to make sure healthcare providers, systems and patients document health data completely and accurately. |
What benefit could this bring to adopters of this innovation? |
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When data are missing or incorrect, advisory and decision support systems, or standard analytic procedures, might provide erroneous advice. Ensuring data is of high quality hence significantly improves the quality of output generated by mHealth apps, such as personalised feedback, decision support and alerts. |
How does it contribute to major EU policy priorities? (e.g. EHDS, COVID-19, DTHC etc.) |
Data quality is an increasingly recognised issue, and its assessment is proposed to be one of the features of the EHDS.
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What is on the horizon? |
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After performing a manual data quality assessment, these tools and functionalities can be implemented into a real-time monitoring dashboard on mHealth system interfaces.
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Keywords |
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Data Quality; Electronic Health Data |