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Gertner, Abigail S.

doi:10.1023/a:1008220013693

Cited by 15 publications

(2 citation statements)

References 23 publications

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“…The use of the term critiquing to describe a system that criticises the solution provided by a human can be attributed to Miller [9], who developed his ATTENDING system for critiquing anaesthesia management. The earliest systems relied heavily on user interaction, resulting in rejection [10]. To avoid this problem, we followed in this paper the approach originally proposed by Lei [11], of gathering all information from computer-stored medical records.…”

Section: Related Workmentioning

confidence: 99%

Using model checking for critiquing based on clinical guidelines

Groot

Hommersom

Lucas

et al. 2009

Artificial Intelligence in Medicine

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Section: Related Workmentioning

confidence: 99%

Using model checking for critiquing based on clinical guidelines

Groot

Hommersom

Lucas

et al. 2009

Artificial Intelligence in Medicine

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“…Efforts had been made to develop automated systems that can perform some form of evidence-based assessment of the quality of care, the major examples include the HyperCritic [ 8 ], Trauma-AID [ 9 ], Trauma-TIQ [ 10 ], AsthmaCritic [ 11 ], IGR [ 12 ], RoMA [ 13 ], and the model checking for critiquing [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Design of a bi-directional methodology for automated assessment of compliance to continuous application of clinical guidelines, and its evaluation in the type 2 diabetes domain

Hatsek,

Hochberg,

Daoud Naccache

et al. 2024

PLoS ONE

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We introduce a new approach for automated guideline-based-care quality assessment, the bidirectional knowledge-based assessment of compliance (BiKBAC) method, and the DiscovErr system, which implements it. Our methodology compares the guideline’s Asbru-based formal representation, including its intentions, with the longitudinal medical record, using a top-down and bottom-up approach. Partial matches are resolved using fuzzy temporal logic. The system was evaluated in the type 2 Diabetes management domain, comparing it to three expert clinicians, including two diabetes experts. The system and the experts commented on the management of 10 patients, randomly selected from 2,000 diabetes patients. On average, each record spanned 5.23 years; the data included 1,584 medical transactions. The system provided 279 comments. The experts made 181 different unique comments. The completeness (recall) of the system was 91% when the gold standard was comments made by at least two of the three experts, and 98%, compared to comments made by all three experts. The experts also assessed all of the 114 medication-therapy-related comments, and a random 35% of the 165 tests-and-monitoring-related comments. The system’s correctness (precision) was 81%, compared to comments judged as correct by both diabetes experts, and 91%, compared to comments judged as correct by one diabetes expert and at least as partially correct by the other. 89% of the comments were judged as important by both diabetes experts, 8% were judged as important by one expert, and 3% were judged as less important by both experts. Adding the validated system comments to the experts’ comments, the completeness scores of the experts were 75%, 60%, and 55%; the expert correctness scores were respectively 99%, 91%, and 88%. Thus, the system could be ranked first in completeness and second in correctness. We conclude that systems such as DiscovErr can effectively assess the quality of continuous guideline-based care.

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