Analyzing interactions on combining multiple clinical guidelines

Zamborlini, Veruska; Silveira, Marcos Da; Pruski, Cédric; Teije, Annette ten; Geleijn, Edwin; Leeden, M. van der; Stuiver, Martijn M.; Harmelen, Frank van

doi:10.1016/j.artmed.2017.03.012

Cited by 31 publications

(44 citation statements)

References 23 publications

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“…with different granularities. While some of the existing works 47 , 48 , 59 mainly focus on a particular issue, such as ontological representation of CPGs, merging multiple CPGs, mitigating contradictory and inconsistent activities, temporal constraint verification and more, some of these works 41 , 53 , 54 , 58 , 63 , 67 consider combinations of these issues. Nevertheless, coping with all these complexities together is still a challenging issue in the existing literature.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Some of the objectives of CDSS applications that supplement CIG-based recommendations, are to provide remote care 31 , 41 , 45 to improve the patients’ satisfaction and reduce their health risks, 42 or to support caregivers in managing patients with multiple health conditions. 33 , 39 , 40 , 47 – 63 The use of CIGs and their integrations into the care can be synthesized into three stages. To this day, only a limited number of works have covered all the specified stages, with some limitations (see ‘Approaches to managing multimorbidity care using a CIG-based CDSS’ below).…”

Section: The Role Of Cigs In Cdss-augmented Delivery Of Carementioning

confidence: 99%

“…OWL 122 and SPARQL 133 were used for the implementation. In their latter work, 63 they extended the interaction detection approach 62 by enhancing its reusability of FOL rules to detect interactions and involving systematic evaluation of the interaction types. Lack of temporal constraints (e.g.…”

Section: Approaches To Managing Multimorbidity Care Using a Cig-basedmentioning

confidence: 99%

“… 33 , 39 , 40 , 47 – 50 This helps to eliminate clinical task duplications and to provide health resource management. A number of works 51 – 65 propose automatic discovery and resolution methods for dealing with adverse guideline interactions and their associated clinical knowledge constructs and some 66 – 69 work on how to handle temporal constraints (e.g. start time, end time and duration of treatments) in multiple guidelines.…”

Section: Introductionmentioning

confidence: 99%

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The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

2018

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Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: The Role Of Cigs In Cdss-augmented Delivery Of Carementioning

confidence: 99%

Section: Approaches To Managing Multimorbidity Care Using a Cig-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

2018

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“…The paper by Zamborlini, et al [7], finally, addresses the topic of recommendations interaction in multimorbidity patients followed by guideline-based clinical decision support systems. The problem is addressed by defining a specific model for knowledge formalization, the TMR model, which, if used for recommendation modelling, allows for the automatic calculation of the interaction among the recommendations, independently of the guideline.…”

mentioning

confidence: 99%

Artificial Intelligence in Medicine AIME 2013

Peek¹,

Morales²,

Peleg³

2015

Artificial Intelligence in Medicine

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ROAD2H: Development and evaluation of an open‐source explainable artificial intelligence approach for managing co‐morbidity and clinical guidelines

Domínguez,

Prociuk,

Marović

et al. 2023

Learning Health Systems

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IntroductionClinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real‐world co‐morbidity. In patient‐specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co‐morbidity are a requirement. In this work, we develop and evaluate a non‐proprietary, standards‐based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle‐income country in West Balkans.MethodsWe used an ontological framework, the Transition‐based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR‐based implementation of the Assumptions‐Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed‐methods evaluation was conducted with 20 simulated cases and five pulmonologists.ResultsPulmonologists agreed 97% of the time with the GOLD‐based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co‐morbidities was suggested in the future along with customisation of the level of explanation with expertise.ConclusionAn ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long‐term condition. Extension to other guidelines and multiple co‐morbidities is needed to test the approach further.

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Analyzing interactions on combining multiple clinical guidelines

Cited by 31 publications

References 23 publications

The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

Artificial Intelligence in Medicine AIME 2013

ROAD2H: Development and evaluation of an open‐source explainable artificial intelligence approach for managing co‐morbidity and clinical guidelines

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