Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation...

Area of Science:

• Health Informatics
• Health Services Research
• Digital Health

Background:

• Learning health care systems (LHS) leverage routinely collected data for continuous health care improvement.
• Implementation of the analytics crucial for LHS often lacks detailed reporting on challenges and methods.
• Digital hospitals are increasingly adopting real-time clinical analytics.

Purpose of the Study:

• To systematically review the literature on real-time clinical analytics implementation in digital hospitals.
• To identify challenges and methods associated with implementing these analytics.
• To synthesize a conceptual framework to guide LHS analytics implementation.

Main Methods:

• A systematic literature search was conducted across Embase, PubMed, and Web of Science (2015-2021).
• Studies focused on clinical analytics derived from electronic health records in adult inpatient and emergency departments.
• Extracted evidence on implementation challenges, methods to overcome them, and dashboard assessment was mapped to a digital health transformation model.

Main Results:

• 14 out of 238 articles met inclusion criteria, yielding 37 implementation challenges and 64 methods.
• Common approaches for evaluating clinical dashboard implementation were identified.
• Few studies assessed clinical process (6) or patient health outcomes (4).

Conclusions:

• Health care organizations face evolving challenges in real-time data analytics implementation, shifting towards post-pilot issues like user and workflow integration.
• Practical methods to overcome these challenges have been identified and incorporated into a novel conceptual framework.
• The proposed framework aims to support organizations in deploying near-real-time clinical dashboards and advancing toward a learning health care system.