Data-driven modeling of clinical pathways using electronic health records

Funkner, Anastasia A.; Yakovlev, A. N.; Kovalchuk, Sergey V.

doi:10.1016/j.procs.2017.11.108

Cited by 24 publications

(9 citation statements)

References 17 publications

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“…Based on the interpretation of the patient's status, clinicians should decide on whether: (1) observe the patient and wait until an event triggers the need for a decision; (2) collect more data to improve the odds of making the right decision; or (3) perform an intervention and monitor the outcome (see Figure 1 ). Machine learning methods can help clinicians to decide which pathway to follow ( 25 ), in a way that is cost-effective ( 26 ).…”

Section: Decision-making (Prediction)mentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.

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Section: Decision-making (Prediction)mentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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“…JCR Category MI [3, 11, 13, 14, 16, 17, 19, 32, 37, 42, 45-47, 52, 54, 56, 57, 66, 72, 73, 1-93, 95-97, 99-101, 108, 122, 124, 131, 140, 144, 149, 150, 157, 160, 164, 170, 178, 187, 196, 197, 206, 211] OR/MS [7,8,12,18,22,23,27,30,31,43,50,51,64,75,79,90,113,114,134,151,158,165,169,190,203,212] HPS [9, 10, 21, 33, 39, 40, 80, 102-104, 110, 116, 141, 143, 153, 163, 166, 209] IE [34,120] AN [53,133] No ISSN [15,24,36,60,70,71,82,127,138,139,142,161,183,[198][199][200] Table A4. Way in which information about the pathway was obtained.…”

Section: Fundingmentioning

confidence: 99%

Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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“…Based on the interpretation of the state of the patient, clinicians should take a decision, which either consists in: 1) observing the patient and waiting until an event triggers the need for a decision; 2) collecting more data to improve the odds of taking an informed and appropriate decision; or 3) performing an intervention, followed by monitoring to assess the outcome of the treatment. Machine learning methods can help the clinician to decide which pathway to follow (Funkner, Yakovlev, & Kovalchuk, 2017), in a way that is cost-effective (Morid, Kawamoto, Ault, Dorius, & Abdelrahman, 2017). Several studies have assessed the predictive power of ML.…”

Section: Decision-making (Prediction)mentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

Sanchez-Martinez¹,

Cámara²,

Piella³

et al. 2019

Preprint

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The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

show abstract

Data-driven modeling of clinical pathways using electronic health records

Cited by 24 publications

References 17 publications

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Clinical pathway modelling: a literature review

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

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