Computable knowledge: An imperative for Learning Health Systems

Friedman, Charles P.; Flynn, Allen J.

doi:10.1002/lrh2.10203

Cited by 28 publications

(36 citation statements)

References 9 publications

(9 reference statements)

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“… 29 Any translation, sharing, and reuse of text classifiers should seek to leverage the growing use of common data formats and platforms for machine-executable models. 30 …”

Section: Discussionmentioning

confidence: 99%

Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?

Wang

Coiera

Magrabi

2020

Journal of the American Medical Informatics Association

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Objective The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. Materials and Methods Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected from a state-wide reporting system. Generalizability was evaluated on different and independent hospital-level reporting system. Concepts were extracted from report narratives using the UMLS Metathesaurus, and their relevance and frequency were used as semantic features. Performance was evaluated by F-score, Hamming loss, and exact match score and was compared with SVM ensembles using bag-of-words (BOW) features on 3 testing datasets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent =6000/5950). Results SVMs using semantic features met or outperformed those based on BOW features to identify 10 different incident types (F-score [semantics/BOW]: benchmark = 82.6%/69.4%; original = 77.9%/68.8%; independent = 78.0%/67.4%) and extreme-risk events (F-score [semantics/BOW]: benchmark = 87.3%/87.3%; original = 25.5%/19.8%; independent = 49.6%/52.7%). For incident type, the exact match score for semantic classifiers was consistently higher than BOW across all test datasets (exact match [semantics/BOW]: benchmark = 48.9%/39.9%; original = 57.9%/44.4%; independent = 59.5%/34.9%). Discussion BOW representations are not ideal for the automated identification of incident reports because they do not account for text semantics. UMLS semantic representations are likely to better capture information in report narratives, and thus may explain their superior performance. Conclusions UMLS-based semantic classifiers were effective in identifying incidents by type and extreme-risk events, providing better generalizability than classifiers using BOW.

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“… 29 Any translation, sharing, and reuse of text classifiers should seek to leverage the growing use of common data formats and platforms for machine-executable models. 30 …”

Section: Discussionmentioning

confidence: 99%

Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?

Wang

Coiera

Magrabi

2020

Journal of the American Medical Informatics Association

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“…Translating data to knowledge, knowledge to performance, and performance to data is sometimes referred to as The Learning Cycle. 10 Until recently, however, such a model has remained mostly aspirational.…”

Section: Introductionmentioning

confidence: 99%

A collaborative learning health system agent‐based model: Computational and face validity

Seid

Bridgeland²,

Bridgeland

et al. 2021

Learning Health Systems

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Introduction: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) -network organizations that allow all healthcare stakeholders to collaborate at scale -are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity.Methods: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient-and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter -the degree to which patients influence other patients -and trace its effects on patient engagement, shared knowledge, and outcomes. Results:The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. Conclusions:We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.

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“…Biomedical knowledge is growing at a dramatic pace, and there is a pressing need to incorporate or provide access to this knowledge into a variety of systems, organizations, and applications. Increasingly, biomedical knowledge is represented in computable formats with potential for rapid dissemination . Computable biomedical knowledge (CBK), such as predictive models or executable rules, alerts, or data visualizations, can be integrated into health information systems to improve health care and the in‐context learning of trainees.…”

Section: Introductionmentioning

confidence: 99%

“…Computable biomedical knowledge (CBK), such as predictive models or executable rules, alerts, or data visualizations, can be integrated into health information systems to improve health care and the in‐context learning of trainees. CBK is essential for both the discovery and intervention components of learning health system activities . The movement to mobilize computable biomedical knowledge (MCBK) aims to represent knowledge in computable formats and make it widely available to achieve better health in different settings, contexts, and applications.…”

Section: Introductionmentioning

confidence: 99%

Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

Richesson

Bray

Dymek

et al. 2020

Learning Health Systems

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The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the

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Computable knowledge: An imperative for Learning Health Systems

Cited by 28 publications

References 9 publications

Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?

Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?

A collaborative learning health system agent‐based model: Computational and face validity

Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

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