Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making
DOI:
https://doi.org/10.15662/IJEETR.2022.0405008Keywords:
Healthcare Data Infrastructure Engineering, Predictive Clinical Analytics, Evidence-Based Decision Support, Clinical Data Governance, Trustworthy Healthcare Analytics, Population Health Intelligence, Secure Health Data Integration, Ethical Clinical Data Management, Healthcare Data Quality Assurance, Regulatory-Compliant Medical AnalyticsAbstract
Engineering healthcare data infrastructures for predictive clinical analytics and evidence-based decision making is a complex task that affects several stakeholders and contributes to the growing health data ecosystem. Healthcare data infrastructures combine the people, processes, technologies, policies, standards, and products needed for the effective and efficient integration, sharing, and use of healthcare data. Modern healthcare relies heavily on data to support clinical analytics—computations that summarize, describe, or predict healthcare events—often on a large scale over aggregated population data. Clinical decisions are typically supported through evidence-based medicine, which urges decision-makers to rely chiefly on information from systematic reviews of randomized controlled trials and meta-analyses. However, reliable and timely predictive clinical analytics from trustworthy data still lack the same level of validation.
Completeness, accuracy, timeliness, and safety bear a direct relation to the data lifecycle and the quality of the data used. Data sources must be known and governed, and appropriate procedures must regulate the collection and processing of the data throughout their life to ensure sufficient quality for the intended use. Privacy, confidentiality, security, compliance, and ethical aspects must also be adequately addressed in relation to current legislation, organizational policies, and recognized best practices.
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