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Clinicians experience this as ) /Pg 9 0 R /K [14] >> endobj 48 0 obj << /Type /StructElem /S /P /P 31 0 R /E (fragmented patient journeys; informatics professionals see it as disconnected datasets. At the center of both perspectives lies a shared question: what can we truly learn from the data we collect? Learning Health Systems \(LHS\) offer a compelling answer: creating a continuous cycle where data becomes knowledge, knowledge informs practice, and ) /Pg 9 0 R /K [15] >> endobj 49 0 obj << /Type /StructElem /S /P /P 31 0 R /E (practice generates new data.) /Pg 9 0 R /K [16] >> endobj 50 0 obj << /Type /StructElem /S /P /P 31 0 R /E (This presentation traces how learning unfolds across the layers of healthcare. At one end, data reflects biological processes that inform disease risk and personalized medicine. At the bedside, it captures symptoms, diagnoses, and treatments that guide individual care. Beyond that, it reveals how cohorts of patients move through complex care pathways, and ultimately how populations experience health and healthcare systems. As we move across these layers, context becomes increasingly important to interpreting and acting on data.) /Pg 9 0 R /K [17] >> endobj 51 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Scroll below to view the full abstract) /Pg 9 0 R /K [18] >> endobj 52 0 obj << /Type /StructElem /S /H5 /P 31 0 R /T (Advancing Learning Health Systems: What Machine Learning Can Learn from Healthcare Data - Challenges and Opportunities) /E (Advancing Learning Health Systems: What Machine Learning Can Learn from Healthcare Data - Challenges and Opportunities) /Pg 9 0 R /K [19] >> endobj 53 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Health Information Science) /Pg 9 0 R /K [20] >> endobj 54 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Jonas Bambi, PhD is an Assistant Professor in the School of Health Information Science at the ̽ϵ. With over 20 years of experience in healthcare Information Management and Information Technology \(IMIT\), he has held roles ranging from clinical application analyst to systems integration architect, project manager, and co-director of Applied Health Data Analytics. This breadth of experience enables him to bridge technical, clinical, and organizational perspectives to support health system transformation.) /Pg 9 0 R /K [21] >> endobj 55 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Scroll below to view full Biography) /Pg 9 0 R /K [22] >> endobj 56 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [117 0 R 0] /K [23] >> endobj 57 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [118 0 R 0] /K [24] >> endobj 58 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [119 0 R 0] /K [25] >> endobj 59 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [120 0 R 0] /K [26] >> endobj 60 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [121 0 R 0] /K [27] >> endobj 61 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [122 0 R 0] /K [28] >> endobj 62 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [123 0 R 0] /K [29] >> endobj 63 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Jonas Bambi is an Assistant Professor in the School of Health Information Science at the ̽ϵ. With over 20 years of experience in healthcare Information Management and Information Technology \(IMIT\), he has held roles ranging from clinical application analyst to systems integration architect, project manager, and co-director of Applied Health Data Analytics. This breadth of experience enables him to bridge technical, clinical, and organizational perspectives to support health system transformation.) /Pg 10 0 R /K [0] >> endobj 64 0 obj << /Type /StructElem /S /P /P 32 0 R /E (His research focuses on the intersection of health informatics, machine learning, and health systems improvement, with a particular emphasis on advancing Learning Health Systems \(LHS\). He studies how complex healthcare data can be leveraged to uncover patterns of service utilization across the continuum of care, especially for individuals with complex conditions. His doctoral work developed novel methodologies for analyzing longitudinal healthcare data, contributing to the design of feedback loops that transform data into knowledge, knowledge into practice, and practice into improved outcomes.) /Pg 10 0 R /K [1] >> endobj 65 0 obj << /Type /StructElem /S /P /P 32 0 R /E (A key dimension of his current work is the development of target information architectures \(TIA\) that enable machine learning to learn effectively from healthcare data within context. This includes designing systems that integrate cross-continuum data and support continuous learning and quality improvement in real-world settings.) /Pg 10 0 R /K [2] >> endobj 66 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Jonas also explores how digital technologies can support safe aging at home, guided by the Ubuntu philosophy emphasizing community and interdependence. Through collaborations with health authorities, academia, and industry, he emphasizes knowledge translation and real-world impact.) /Pg 10 0 R /K [3] >> endobj 67 0 obj << /Type /StructElem /S /P /P 32 0 R /E (He holds an MSc in Computer Science, an MBA in Technology Management, and a PhD in Health Informatics) /Pg 10 0 R /K [4] >> endobj 68 0 obj << /Type /StructElem /S /H1 /P 32 0 R /T (Biography) /E (Biography) /Pg 10 0 R /K [5] >> endobj 69 0 obj << /Type /StructElem /S /H1 /P 32 0 R /T (Abstract) /E (Abstract) /Pg 10 0 R /K [6] >> endobj 70 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Healthcare is often described as data-rich but insight-poor. Clinicians experience this as fragmented patient journeys; informatics professionals see it as disconnected datasets. At the center of both perspectives lies a shared question: what can we truly learn from the data we collect? Learning Health Systems \(LHS\) offer a compelling answer: creating a continuous cycle where data becomes knowledge, knowledge informs practice, and practice generates new data.) /Pg 10 0 R /K [7] >> endobj 71 0 obj << /Type /StructElem /S /P /P 32 0 R /E (This presentation traces how learning unfolds across the layers of healthcare. At one end, data reflects biological processes that inform disease risk and personalized medicine. At the bedside, it captures symptoms, diagnoses, and treatments that guide individual care. Beyond that, it reveals how cohorts of patients move through complex care pathways, and ultimately how populations experience health and healthcare systems. As we move across these layers, context becomes increasingly important to interpreting and acting on data.) /Pg 10 0 R /K [8] >> endobj 72 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Yet, one critical layer remains underexplored: the cohort level, where quality of care is shaped by how real-world patient journeys align with clinical practice guidelines. 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