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Michalowski M, Wilk S, Michalowski W, Rao M, Carrier M. Provision and evaluation of explanations within an automated planning-based approach to solving the multimorbidity problem. J Biomed Inform 2024; 156:104681. [PMID: 38960273 DOI: 10.1016/j.jbi.2024.104681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/28/2024] [Accepted: 06/22/2024] [Indexed: 07/05/2024]
Abstract
The multimorbidity problem involves the identification and mitigation of adverse interactions that occur when multiple computer interpretable guidelines are applied concurrently to develop a treatment plan for a patient diagnosed with multiple diseases. Solving this problem requires decision support approaches which are difficult to comprehend for physicians. As such, the rationale for treatment plans generated by these approaches needs to be provided. OBJECTIVE To develop an explainability component for an automated planning-based approach to the multimorbidity problem, and to assess the fidelity and interpretability of generated explanations using a clinical case study. METHODS The explainability component leverages the task-network model for representing computer interpretable guidelines. It generates post-hoc explanations composed of three aspects that answer why specific clinical actions are in a treatment plan, why specific revisions were applied, and how factors like medication cost, patient's adherence, etc. influence the selection of specific actions. The explainability component is implemented as part of MitPlan, where we revised our planning-based approach to support explainability. We developed an evaluation instrument based on the system causability scale and other vetted surveys to evaluate the fidelity and interpretability of its explanations using a two dimensional comparison study design. RESULTS The explainability component was implemented for MitPlan and tested in the context of a clinical case study. The fidelity and interpretability of the generated explanations were assessed using a physician-focused evaluation study involving 21 participants from two different specialties and two levels of experience. Results show that explanations provided by the explainability component in MitPlan are of acceptable fidelity and interpretability, and that the clinical justification of the actions in a treatment plan is important to physicians. CONCLUSION We created an explainability component that enriches an automated planning-based approach to solving the multimorbidity problem with meaningful explanations for actions in a treatment plan. This component relies on the task-network model to represent computer interpretable guidelines and as such can be ported to other approaches that also use the task-network model representation. Our evaluation study demonstrated that explanations that support a physician's understanding of the clinical reasons for the actions in a treatment plan are useful and important.
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Affiliation(s)
| | - Szymon Wilk
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Wojtek Michalowski
- Telfer School of Management, University of Ottawa, 55 Laurier Ave East, Ottawa, ON K1N 6N5, Canada
| | - Malvika Rao
- Telfer School of Management, University of Ottawa, 55 Laurier Ave East, Ottawa, ON K1N 6N5, Canada
| | - Marc Carrier
- The Ottawa Hospital, 725 Parkdale Ave, Ottawa, ON K1Y 4E9, Canada
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Soares A, Schilling LM, Richardson J, Kommadi B, Subbian V, Dehnbostel J, Shahin K, Robinson KA, Afzal M, Lehmann HP, Kunnamo I, Alper BS. Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project. J Med Internet Res 2024; 26:e54265. [PMID: 38916936 PMCID: PMC11234056 DOI: 10.2196/54265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy. OBJECTIVE This study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge. METHODS As an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources. RESULTS The EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers. CONCLUSIONS EBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence.
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Affiliation(s)
- Andrey Soares
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Lisa M Schilling
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Joshua Richardson
- Center for Informatics, Research Triangle Institute International, Berkeley, CA, United States
| | - Bhagvan Kommadi
- Quantica Computacao, Hyderabad, India
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
| | - Vignesh Subbian
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- College of Public Health, Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, United States
| | - Joanne Dehnbostel
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Computable Publishing LLC, Franklin, NC, United States
| | - Khalid Shahin
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Computable Publishing LLC, Franklin, NC, United States
| | - Karen A Robinson
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Muhammad Afzal
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Department of Computing and Data Science, Birmingham City University, England, United Kingdom
| | - Harold P Lehmann
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Ilkka Kunnamo
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Duodecim Publishing Company Ltd, Helsinki, Finland
| | - Brian S Alper
- Scientific Knowledge Accelerator Foundation, Franklin, NC, United States
- Computable Publishing LLC, Franklin, NC, United States
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Beckmann CL, Lodde G, Swoboda J, Livingstone E, Böckmann B. Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma. J Clin Med 2024; 13:3353. [PMID: 38893064 PMCID: PMC11172530 DOI: 10.3390/jcm13113353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.
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Affiliation(s)
- Catharina Lena Beckmann
- Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), 44227 Dortmund, Germany
| | - Georg Lodde
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, 45147 Essen, Germany
| | - Jessica Swoboda
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany;
| | - Elisabeth Livingstone
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, 45147 Essen, Germany
| | - Britta Böckmann
- Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), 44227 Dortmund, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany;
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Domínguez J, Prociuk D, Marović B, Čyras K, Cocarascu O, Ruiz F, Mi E, Mi E, Ramtale C, Rago A, Darzi A, Toni F, Curcin V, Delaney B. ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines. Learn Health Syst 2024; 8:e10391. [PMID: 38633019 PMCID: PMC11019374 DOI: 10.1002/lrh2.10391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/29/2023] [Accepted: 08/07/2023] [Indexed: 04/19/2024] Open
Abstract
Introduction Clinical 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. Methods We 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. Results Pulmonologists 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. Conclusion An 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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Affiliation(s)
- Jesús Domínguez
- Department of Population Health SciencesKing's College LondonLondonUK
| | | | | | | | | | - Francis Ruiz
- London School of Hygiene and Tropical MedicineLondonUK
| | - Ella Mi
- University of OxfordOxfordUK
| | - Emma Mi
- University of OxfordOxfordUK
| | | | | | | | | | - Vasa Curcin
- Department of Population Health SciencesKing's College LondonLondonUK
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García-García JA, Carrero M, Escalona MJ, Lizcano D. Evaluation of clinical practice guideline-derived clinical decision support systems using a novel quality model. J Biomed Inform 2024; 149:104573. [PMID: 38081565 DOI: 10.1016/j.jbi.2023.104573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 11/26/2023] [Accepted: 12/08/2023] [Indexed: 01/22/2024]
Abstract
Over the last decade, clinical practice guidelines (CPGs) have become an important asset for daily life in healthcare organizations. Efficient management and digitization of CPGs help achieve organizational objectives and improve patient care and healthcare quality by reducing variability. However, digitizing CPGs is a difficult, complex task because they are usually expressed as text, and this often leads to the development of partial software solutions. At present, different research proposals and CPG-derived CDSS (clinical decision support system) do exist for managing CPG digitalization lifecycles (from modeling to deployment and execution), but they do not all provide full lifecycle support, making it more difficult to choose solutions or proposals that fully meet the needs of a healthcare organization. This paper proposes a method based on quality models to uniformly compare and evaluate technological tools, providing a rigorous method that uses qualitative and quantitative analysis of technological aspects. In addition, this paper also presents how this method has been instantiated to evaluate and compare CPG-derived CDSS by highlighting each phase of the CPG digitization lifecycle. Finally, discussion and analysis of currently available tools are presented, identifying gaps and limitations.
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Affiliation(s)
- Julián Alberto García-García
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - Manuel Carrero
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - María José Escalona
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - David Lizcano
- School of Computer Science, Madrid Open University, UDIMA., Campus Collado Villalba, Madrid, Spain.
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Li XH, Liao JP, Chen MK, Gao K, Wang YB, Yan SY, Huang Q, Wang YY, Shi YX, Hu WB, Jin YH. The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review. J Med Syst 2023; 48:6. [PMID: 38148352 DOI: 10.1007/s10916-023-02007-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 10/13/2023] [Indexed: 12/28/2023]
Abstract
Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.
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Affiliation(s)
- Xu-Hui Li
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Jian-Peng Liao
- School of Public Health, Wuhan University, Wuhan, 430071, China
| | - Mu-Kun Chen
- School of Computer Science, Wuhan University, Wuhan, 430071, China
| | - Kuang Gao
- School of Computer Science, Wuhan University, Wuhan, 430071, China
| | - Yong-Bo Wang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Si-Yu Yan
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Qiao Huang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yun-Yun Wang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yue-Xian Shi
- School of Nursing, Peking University, Beijing, 100191, China
| | - Wen-Bin Hu
- School of Computer Science, Wuhan University, Wuhan, 430071, China.
| | - Ying-Hui Jin
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
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Ebben KC, de Kroon CD, Schmeink CE, van der Hel OL, van Vegchel T, Moncada‐Torres A, de Hingh IH, van der Werf J. A novel method for continuous measurements of clinical practice guideline adherence. Learn Health Syst 2023; 7:e10384. [PMID: 37860062 PMCID: PMC10582230 DOI: 10.1002/lrh2.10384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 10/21/2023] Open
Abstract
Introduction Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses. Method The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates. Results CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data. Conclusion The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.
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Affiliation(s)
- Kees C.W.J. Ebben
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
| | - Cornelis D. de Kroon
- Department of Obstetrics and GynecologyLeiden University Medical CenterLeidenThe Netherlands
| | | | - Olga L. van der Hel
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
| | - Thijs van Vegchel
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
| | - Arturo Moncada‐Torres
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
| | - Ignace H.J.T. de Hingh
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
- Department of Surgical OncologyCatharina HospitalEindhovenThe Netherlands
| | - Jurrian van der Werf
- Department of Research and DevelopmentNetherlands Comprehensive Cancer Organization (IKNL)UtrechtThe Netherlands
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Scott P, Heigl M, McCay C, Shepperdson P, Lima‐Walton E, Andrikopoulou E, Brunnhuber K, Cornelius G, Faulding S, McAlister B, Rowark S, South M, Thomas MR, Whatling J, Williams J, Wyatt JC, Greaves F. Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project. Learn Health Syst 2023; 7:e10394. [PMID: 37860056 PMCID: PMC10582221 DOI: 10.1002/lrh2.10394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 10/21/2023] Open
Abstract
Introduction Translating narrative clinical guidelines to computable knowledge is a long-standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed. Objectives The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content. Methods Following an initial 'collaborathon' in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon-scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete. Results While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology-agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision-support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems. Conclusions The project has shown that the WHO DAK, with some modification, is a promising approach to build technology-neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership.
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Affiliation(s)
- Philip Scott
- Institute of Management & HealthUniversity of Wales Trinity Saint DavidCarmarthenWalesUK
| | | | - Charles McCay
- Ramsey SystemsShrewsburyUK
- Professional Record Standards BodyLondonUK
| | | | | | | | | | - Gary Cornelius
- Computer & Information SciencesUniversity of StrathclydeGlasgowScotlandUK
| | | | | | | | - Matthew South
- Open Clinical CICLoughtonUK
- Institute of Applied Health ResearchUniversity of BirminghamBirminghamUK
| | - Mark R. Thomas
- Institute of Applied Health ResearchUniversity of BirminghamBirminghamUK
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Parra-Calderón CL, Román-Villarán E, Alvarez-Romero C, Escobar-Rodríguez GA, Martínez-Brocca MA, Martínez-García A, García-García JA, Escalona-Cuaresma MJ. A prospective observational concordance study to evaluate computational model-driven clinical practice guidelines for Type 2 diabetes mellitus. Int J Med Inform 2023; 178:105208. [PMID: 37703798 DOI: 10.1016/j.ijmedinf.2023.105208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Clinical Practice Guidelines (CPGs) provide healthcare professionals with performance and decision-making support during the treatment of patients. Sometimes, however, they are poorly implemented. The IDE4ICDS platform was developed and validated with CPGs for type 2 diabetes mellitus (T2DM). OBJECTIVE The main objective of this paper is to present the results of the clinical validation of the IDE4ICDS platform in a real clinical environment at two health clinics in the Andalusian Public Health System (SSPA) in the southern Spanish region of Andalusia. METHODS National and international knowledge sources on T2DM were selected and reviewed and used to define a diabetes CPG model on the IDE4ICDS platform. Once the diabetes CPG was configured and deployed, it was validated. A total of 506 patients were identified as meeting the inclusion criteria, of whom 130 could be recruited and 89 attended the appointment. RESULTS A concordance analysis was performed with the kappa value. Overall agreement between the recommendations provided by the system and those recorded in each patient's EHR was good (0.61 - 0.80) with a total kappa index of 0.701, leading to the conclusion that the system provided appropriate recommendations for each patient and was therefore well-functioning. CONCLUSIONS A series of possible improvements were identified based on the limitations for the recovery of variables related to the quality of these recolected variables, the detection of duplicate recommendations based on different input variables for the same patient, and clinical usability, such as the capacity to generate reports based on the recommendations generated. Nevertheless, the project resulted in the IDE4ICDS platform: a Clinical Decision Support System (CDSS) capable of providing appropriate recommendations for improving the management and quality of patient care and optimizing health outcomes. The result of this validation is a safe and effective pathway for developing and adopting digital transformation at the regional scale of the use of biomedical knowledge in real healthcare.
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Affiliation(s)
- Carlos Luis Parra-Calderón
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Esther Román-Villarán
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Celia Alvarez-Romero
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Germán Antonio Escobar-Rodríguez
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Maria Asunción Martínez-Brocca
- Virgen Macarena" University Hospital, Seville, Spain; Comprehensive Plan for Diabetes in Andalusia, Andalusian Health Service, Calle Doctor Fedriani, 3, 41009 Seville, Spain.
| | - Alicia Martínez-García
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Julián Alberto García-García
- Computer Languages and Systems Department, Escuela Técnica Superior de Ingeniería Informática, Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - María José Escalona-Cuaresma
- Computer Languages and Systems Department, Escuela Técnica Superior de Ingeniería Informática, Avda. Reina Mercedes s/n. 41012 Seville, Spain.
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Tailor A, Robinson SJ, Matson-Koffman DM, Michaels M, Burton MM, Lubin IM. An Evaluation Framework for a Novel Process to Codevelop Written and Computable Guidelines. Am J Med Qual 2023; 38:S35-S45. [PMID: 37668272 PMCID: PMC10476596 DOI: 10.1097/jmq.0000000000000140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Clinical practice guidelines (CPGs) support individual and population health by translating new, evidence-based knowledge into recommendations for health practice. CPGs can be provided as computable, machine-readable guidelines that support the translation of recommendations into shareable, interoperable clinical decision support and other digital tools (eg, quality measures, case reports, care plans). Interdisciplinary collaboration among guideline developers and health information technology experts can facilitate the translation of written guidelines into computable ones. The benefits of interdisciplinary work include a focus on the needs of end-users who apply guidelines in practice through clinic decision support systems as part of the Centers for Disease Control and Prevention's (CDC's) Adapting Clinical Guidelines for the Digital Age (ACG) initiative, a group of interdisciplinary experts proposed a process to facilitate the codevelopment of written and computable CPGs, referred to as the "integrated process (IP)."1 This paper presents a framework for evaluating the IP based on a combination of vetted evaluation models and expert opinions. This framework combines 3 types of evaluations: process, product, and outcomes. These evaluations assess the value of interdisciplinary expert collaboration in carrying out the IP, the quality, usefulness, timeliness, and acceptance of the guideline, and the guideline's health impact, respectively. A case study is presented that illustrates application of the framework.
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Affiliation(s)
| | | | | | | | | | - Ira M Lubin
- Centers for Disease Control and Prevention (CDC)
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11
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Matson-Koffman DM, Robinson SJ, Jakhmola P, Fochtmann LJ, Willett D, Lubin IM, Burton MM, Tailor A, Pitts DL, Casey DE, Opelka FG, Mullins R, Elder R, Michaels M. An Integrated Process for Co-Developing and Implementing Written and Computable Clinical Practice Guidelines. Am J Med Qual 2023; 38:S12-S34. [PMID: 37668271 PMCID: PMC10476601 DOI: 10.1097/jmq.0000000000000137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
The goal of this article is to describe an integrated parallel process for the co-development of written and computable clinical practice guidelines (CPGs) to accelerate adoption and increase the impact of guideline recommendations in clinical practice. From February 2018 through December 2021, interdisciplinary work groups were formed after an initial Kaizen event and using expert consensus and available literature, produced a 12-phase integrated process (IP). The IP includes activities, resources, and iterative feedback loops for developing, implementing, disseminating, communicating, and evaluating CPGs. The IP incorporates guideline standards and informatics practices and clarifies how informaticians, implementers, health communicators, evaluators, and clinicians can help guideline developers throughout the development and implementation cycle to effectively co-develop written and computable guidelines. More efficient processes are essential to create actionable CPGs, disseminate and communicate recommendations to clinical end users, and evaluate CPG performance. Pilot testing is underway to determine how this IP expedites the implementation of CPGs into clinical practice and improves guideline uptake and health outcomes.
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Affiliation(s)
| | | | | | - Laura J. Fochtmann
- Department of Psychiatry, Pharmacological Sciences and Biomedical Informatics, Stony Brook University
| | - DuWayne Willett
- Department of Internal Medicine’s Division of Cardiology at University of Texas, Southwestern Medical Center
| | - Ira M. Lubin
- Centers for Disease Control and Prevention (CDC)
| | | | | | | | - Donald E. Casey
- Department of Internal Medicine, Rush Medical College, Jefferson College of Population Health, Institute for Healthcare Informatics, University of Minnesota
| | | | | | - Randy Elder
- Centers for Disease Control and Prevention (CDC)
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12
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Zerbato F, Pufahl L, Teije AT. Preface: Special Issue on Knowledge Representation and Reasoning for Healthcare Processes. Artif Intell Med 2023; 143:102631. [PMID: 37673588 DOI: 10.1016/j.artmed.2023.102631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/07/2023] [Indexed: 09/08/2023]
Affiliation(s)
- Francesca Zerbato
- Institute of Computer Science, University of St. Gallen, St. Gallen, Switzerland.
| | - Luise Pufahl
- Information Systems, Technical University of Munich, Heilbronn, Germany
| | - Annette Ten Teije
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Garber JR, Patkar V. Computer-interpretable guidelines: electronic tools to enhance the utility of thyroid nodule clinical practice guidelines and risk stratification tools. Front Endocrinol (Lausanne) 2023; 14:1228834. [PMID: 37654563 PMCID: PMC10465787 DOI: 10.3389/fendo.2023.1228834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/11/2023] [Indexed: 09/02/2023] Open
Abstract
Clinicians seeking guidance for evaluating and managing thyroid nodules currently have several resources. The principal ones are narrative clinical guidelines and clinical risk calculators. This paper will review the strengths and weaknesses of both. The paper will introduce a concept of computer interpretable guideline, a novel way of transforming narrative guidelines in to a clinical decision support tool that can provide patient specific recommendations at the point of care. The paper then describes an experience of developing an interactive web based computer interpretable guideline for thyroid nodule management, called Thyroid Nodule Management App (TNAPP). The advantages of this approach and the potential barriers for widespread adaptation are discussed.
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Affiliation(s)
- Jeffrey R. Garber
- Atrius Health, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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14
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Michalowski M, Rao M, Wilk S, Michalowski W, Carrier M. Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines. Artif Intell Med 2023; 140:102550. [PMID: 37210156 DOI: 10.1016/j.artmed.2023.102550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 05/22/2023]
Abstract
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.
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Affiliation(s)
| | | | - Szymon Wilk
- Poznan University of Technology, Poznan, Poland
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15
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Walraven JEW, Verhoeven RHA, van der Hoeven JJM, van der Meulen R, Lemmens VEPP, Hesselink G, Desar IME. Pros and cons of streamlining and use of computerised clinical decision support systems to future-proof oncological multidisciplinary team meetings. Front Oncol 2023; 13:1178165. [PMID: 37274246 PMCID: PMC10233094 DOI: 10.3389/fonc.2023.1178165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/27/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Nowadays nearly every patient with cancer is discussed in a multidisciplinary team meeting (MDTM) to determine an optimal treatment plan. The growth in the number of patients to be discussed is unsustainable. Streamlining and use of computerised clinical decision support systems (CCDSSs) are two major ways to restructure MDTMs. Streamlining is the process of selecting the patients who need to be discussed and in which type of MDTM. Using CCDSSs, patient data is automatically loaded into the minutes and a guideline-based treatment proposal is generated. We aimed to identify the pros and cons of streamlining and CCDSSs. Methods Semi-structured interviews were conducted with Dutch MDTM participants. With purposive sampling we maximised variation in participants' characteristics. Interview data were thematically analysed. Results Thirty-five interviews were analysed. All interviewees agreed on the need to change the current MDTM workflow. Streamlining suggestions were thematised based on standard and complex cases and the location of the MDTM (i.e. local, regional or nationwide). Interviewees suggested easing the pressure on MDTMs by discussing standard cases briefly, not at all, or outside the MDTM with only two to three specialists. Complex cases should be discussed in tumour-type-specific regional MDTMs and highly complex cases by regional/nationwide expert teams. Categorizing patients as standard or complex was found to be the greatest challenge of streamlining. CCDSSs were recognised as promising, although none of the interviewees had made use of them. The assumed advantage was their capacity to generate protocolised treatment proposals based on automatically uploaded patient data, to unify treatment proposals and to facilitate research. However, they were thought to limit the freedom to deviate from the treatment advice. Conclusion To make oncological MDTMs sustainable, methods of streamlining should be developed and introduced. Physicians still have doubts about the value of CCDSSs.
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Affiliation(s)
- Janneke E. W. Walraven
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Rob H. A. Verhoeven
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
- Department of Medical Oncology, Amsterdam University Medical Center location University of Amsterdam, Amsterdam, Netherlands
| | | | - Renske van der Meulen
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Valery E. P. P. Lemmens
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Gijs Hesselink
- Department of Intensive Care, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Ingrid M. E. Desar
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
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16
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Savino M, Chiloiro G, Masciocchi C, Capocchiano ND, Lenkowicz J, Gottardelli B, Gambacorta MA, Valentini V, Damiani A. A process mining approach for clinical guidelines compliance: real-world application in rectal cancer. Front Oncol 2023; 13:1090076. [PMID: 37265796 PMCID: PMC10231435 DOI: 10.3389/fonc.2023.1090076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 06/03/2023] Open
Abstract
In the era of evidence-based medicine, several clinical guidelines were developed, supporting cancer management from diagnosis to treatment and aiming to optimize patient care and hospital resources. Nevertheless, individual patient characteristics and organizational factors may lead to deviations from these standard recommendations during clinical practice. In this context, process mining in healthcare constitutes a valid tool to evaluate conformance of real treatment pathways, extracted from hospital data warehouses as event log, to standard clinical guidelines, translated into computer-interpretable formats. In this study we translate the European Society of Medical Oncology guidelines for rectal cancer treatment into a computer-interpretable format using Pseudo-Workflow formalism (PWF), a language already employed in pMineR software library for Process Mining in Healthcare. We investigate the adherence of a real-world cohort of rectal cancer patients treated at Fondazione Policlinico Universitario A. Gemelli IRCCS, data associated with cancer diagnosis and treatment are extracted from hospital databases in 453 patients diagnosed with rectal cancer. PWF enables the easy implementation of guidelines in a computer-interpretable format and visualizations that can improve understandability and interpretability of physicians. Results of the conformance checking analysis on our cohort identify a subgroup of patients receiving a long course treatment that deviates from guidelines due to a moderate increase in radiotherapy dose and an addition of oxaliplatin during chemotherapy treatment. This study demonstrates the importance of PWF to evaluate clinical guidelines adherence and to identify reasons of deviations during a treatment process in a real-world and multidisciplinary setting.
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Affiliation(s)
- Mariachiara Savino
- Diagnostica per immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuditta Chiloiro
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Carlotta Masciocchi
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nikola Dino Capocchiano
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gottardelli
- Diagnostica per immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maria Antonietta Gambacorta
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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17
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Van Woensel W, Tu SW, Michalowski W, Sibte Raza Abidi S, Abidi S, Alonso JR, Bottrighi A, Carrier M, Edry R, Hochberg I, Rao M, Kingwell S, Kogan A, Marcos M, Martínez Salvador B, Michalowski M, Piovesan L, Riaño D, Terenziani P, Wilk S, Peleg M. A Community-of-Practice-based Evaluation Methodology for Knowledge Intensive Computational Methods and its Application to Multimorbidity Decision Support. J Biomed Inform 2023; 142:104395. [PMID: 37201618 DOI: 10.1016/j.jbi.2023.104395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 04/25/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023]
Abstract
OBJECTIVE The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. MATERIALS AND METHODS Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. RESULTS Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. DISCUSSION The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS. CONCLUSION We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).
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Affiliation(s)
| | - Samson W Tu
- Center for BioMedical Informatics Research, Stanford University, Stanford, CA, 94305, USA
| | | | | | - Samina Abidi
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
| | | | | | | | - Ruth Edry
- Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel; Rambam Medical Center, Haifa, Israel
| | - Irit Hochberg
- Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel; Rambam Medical Center, Haifa, Israel
| | - Malvika Rao
- Telfer School of Management, University of Ottawa, Ottawa, ON, Canada
| | | | - Alexandra Kogan
- Department of Information Systems, University of Haifa, Haifa, Israel, 3498838
| | - Mar Marcos
- Universitat Jaume I, Castelló de la Plana, Spain
| | | | | | - Luca Piovesan
- DISIT, Università del Piemonte Orientale, Alessandria, Italy
| | - David Riaño
- Universitat Rovira i Virgili, Tarragona, Spain; Institut d'Investigació Sanitària Pere Virgili, Tarragona, Spain
| | | | - Szymon Wilk
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Mor Peleg
- Department of Information Systems, University of Haifa, Haifa, Israel, 3498838
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18
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Lichtner G, Alper BS, Jurth C, Spies C, Boeker M, Meerpohl JJ, von Dincklage F. Representation of evidence-based clinical practice guideline recommendations on FHIR. J Biomed Inform 2023; 139:104305. [PMID: 36738871 DOI: 10.1016/j.jbi.2023.104305] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 01/11/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Various formalisms have been developed to represent clinical practice guideline recommendations in a computer-interpretable way. However, none of the existing formalisms leverage the structured and computable information that emerge from the evidence-based guideline development process. Thus, we here propose a FHIR-based format that uses computer-interpretable representations of the knowledge artifacts that emerge during the process of evidence-based guideline development to directly serve as the basis of evidence-based recommendations. METHODS We identified the information required to represent evidence-based clinical practice guideline recommendations and reviewed the knowledge artifacts emerging during the evidence-based guideline development process. We then conducted a consensus-based design process with domain experts to develop an information model for guideline recommendation representation that is structurally aligned to the evidence-based guideline recommendation development process and a corresponding representation based on FHIR resources developed for evidence-based medicine (EBMonFHIR). The resulting recommendations were modelled and represented in conformance with the FHIR Clinical Guidelines (CPG-on-FHIR) implementation guide. RESULTS The information model of evidence-based clinical guideline recommendations and its EBMonFHIR-/CPG-on-FHIR-based representation contain the clinical contents of individual guideline recommendations, a set of metadata for the recommendations, the ratings for the recommendations (e.g., strength of recommendation, certainty of overall evidence), the ratings of certainty of evidence for individual outcomes (e.g., risk of bias) and links to the underlying evidence (systematic reviews based on primary studies). We created profiles and an implementation guide for all FHIR resources required to represent the knowledge artifacts generated during evidence-based guideline development and their re-use as the basis for recommendations and used the profiles to implement an exemplary clinical guideline recommendation. CONCLUSIONS The FHIR implementation guide presented here can be used to directly link the evidence assessment process of evidence-based guideline recommendation development, i.e. systematic reviews and evidence grading, and the underlying evidence from primary studies to the resulting guideline recommendations. This not only allows the evidence on which recommendations are based on to be evaluated transparently and critically, but also enables guideline developers to leverage computable evidence in a more direct way to facilitate the generation of computer-interpretable guideline recommendations.
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Affiliation(s)
- Gregor Lichtner
- Universitätsmedizin Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
| | - Brian S Alper
- Computable Publishing LLC, Ipswich, MA, USA; Scientific Knowledge Accelerator Foundation, Ipswich, MA, USA
| | - Carlo Jurth
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
| | - Claudia Spies
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany; Einstein Center Digital Future, Berlin, Germany
| | - Martin Boeker
- Institute for Artificial Intelligence and Informatics in Medicine, Chair of Medical Informatics, Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joerg J Meerpohl
- Institute for Evidence in Medicine, Medical Center & Faculty of Medicine, University of Freiburg, Freiburg, Germany; Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
| | - Falk von Dincklage
- Universitätsmedizin Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany.
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19
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Martínez-Salvador B, Marcos M, Palau P, Domínguez Mafé E. A model-driven transformation approach for the modelling of processes in clinical practice guidelines. Artif Intell Med 2023; 137:102495. [PMID: 36868689 DOI: 10.1016/j.artmed.2023.102495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 12/13/2022] [Accepted: 01/18/2023] [Indexed: 01/24/2023]
Abstract
Clinical Practice Guidelines (CPGs) include recommendations aimed at optimising patient care, informed by a review of the available clinical evidence. To achieve their potential benefits, CPG should be readily available at the point of care. This can be done by translating CPG recommendations into one of the languages for Computer-Interpretable Guidelines (CIGs). This is a difficult task for which the collaboration of clinical and technical staff is crucial. However, in general CIG languages are not accessible to non-technical staff. We propose to support the modelling of CPG processes (and hence the authoring of CIGs) based on a transformation, from a preliminary specification in a more accessible language into an implementation in a CIG language. In this paper, we approach this transformation following the Model-Driven Development (MDD) paradigm, in which models and transformations are key elements for software development. To demonstrate the approach, we implemented and tested an algorithm for the transformation from the BPMN language for business processes to the PROforma CIG language. This implementation uses transformations defined in the ATLAS Transformation Language. Additionally, we conducted a small experiment to assess the hypothesis that a language such as BPMN can facilitate the modelling of CPG processes by clinical and technical staff.
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Affiliation(s)
| | - Mar Marcos
- Department of Computer Engineering and Science, Univ. Jaume I, Spain
| | - Patricia Palau
- Cardiology Department, Hospital Clínico Universitario de Valencia, Univ. de València, Spain; INCLIVA Instituto de Investigación Sanitaria, Univ. de València, Spain
| | - Eloy Domínguez Mafé
- Cardiology Department, Hospital Clínico Universitario de Valencia, Univ. de València, Spain; Predepartmental Unit of Medicine, Univ. Jaume I, Spain
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20
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Alman A, Maggi FM, Montali M, Patrizi F, Rivkin A. Monitoring hybrid process specifications with conflict management: An automata-theoretic approach. Artif Intell Med 2023; 139:102512. [PMID: 37100514 DOI: 10.1016/j.artmed.2023.102512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 01/07/2023] [Accepted: 01/31/2023] [Indexed: 03/07/2023]
Abstract
Complexity of medical treatments can vary from prescribing medicine for a specific ailment to managing a complex set of simultaneous medical issues. In the latter case, doctors are assisted by clinical guidelines which outline standard medical procedures, tests, treatments, etc. To facilitate the use of such guidelines, they can be digitized as processes and adopted in complex process engines offering additional help to health providers such as decision support while monitoring active treatments so as to detect flaws in treatment procedures and suggest possible reactions on them. For example, a patient may present symptoms of multiple diseases simultaneously (requiring multiple clinical guidelines to be followed), while also being allergic to some often-used drugs (requiring additional constraints to be respected). This can easily lead to treating a patient based on a set of process specifications which are not fully compatible with each other. While a scenario like that commonly occurs in practice, research in that direction has thus far given little consideration to how to specify multiple clinical guidelines and how to automatically combine their specifications in the context of the monitoring task. In our previous work (Alman et al., 20222), we presented a conceptual framework for handling the above cases in the context of monitoring. In this paper, we present the algorithms necessary for implementing key components of this conceptual framework. More specifically, we provide formal languages for representing clinical guideline specifications and formalize a solution for monitoring the interplay of such specifications expressed as a combination of (data-aware) Petri nets and temporal logic rules. The proposed solution seamlessly handles combination of the input process specifications and provides both early conflict detection and decision support during process execution. We also discuss a proof-of-concept implementation of our approach and present the results of extensive scalability experiments.
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21
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An B. Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6776-6799. [PMID: 37161128 DOI: 10.3934/mbe.2023292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The knowledge graph is a critical resource for medical intelligence. The general medical knowledge graph tries to include all diseases and contains much medical knowledge. However, it is challenging to review all the triples manually. Therefore the quality of the knowledge graph can not support intelligence medical applications. Breast cancer is one of the highest incidences of cancer at present. It is urgent to improve the efficiency of breast cancer diagnosis and treatment through artificial intelligence technology and improve the postoperative health status of breast cancer patients. This paper proposes a framework to construct a breast cancer knowledge graph from heterogeneous data resources in response to this demand. Specifically, this paper extracts knowledge triple from clinical guidelines, medical encyclopedias and electronic medical records. Furthermore, the triples from different data resources are fused to build a breast cancer knowledge graph (BCKG). Experimental results demonstrate that BCKG can support knowledge-based question answering, breast cancer postoperative follow-up and healthcare, and improve the quality and efficiency of breast cancer diagnosis, treatment and management.
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Affiliation(s)
- Bo An
- Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing 100732, China
- Beijing Academy of Artificial Intelligence, Beijing 100084, China
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López B, Raya O, Baykova E, Saez M, Rigau D, Cunill R, Mayoral S, Carrion C, Serrano D, Castells X. APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology. Heliyon 2023; 9:e13074. [PMID: 36798764 PMCID: PMC9925880 DOI: 10.1016/j.heliyon.2023.e13074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
Purpose Clinical practice guidelines (CPGs) have become fundamental tools for evidence-based medicine (EBM). However, CPG suffer from several limitations, including obsolescence, lack of applicability to many patients, and limited patient participation. This paper presents APPRAISE-RS, which is a methodology that we developed to overcome these limitations by automating, extending, and iterating the methodology that is most commonly used for building CPGs: the GRADE methodology. Method APPRAISE-RS relies on updated information from clinical studies and adapts and automates the GRADE methodology to generate treatment recommendations. APPRAISE-RS provides personalized recommendations because they are based on the patient's individual characteristics. Moreover, both patients and clinicians express their personal preferences for treatment outcomes which are considered when making the recommendation (participatory). Rule-based system approaches are used to manage heuristic knowledge. Results APPRAISE-RS has been implemented for attention deficit hyperactivity disorder (ADHD) and tested experimentally on 28 simulated patients. The resulting recommender system (APPRAISE-RS/TDApp) shows a higher degree of treatment personalization and patient participation than CPGs, while recommending the most frequent interventions in the largest body of evidence in the literature (EBM). Moreover, a comparison of the results with four blinded psychiatrist prescriptions supports the validation of the proposal. Conclusions APPRAISE-RS is a valid methodology to build recommender systems that manage updated, personalized and participatory recommendations, which, in the case of ADHD includes at least one intervention that is identical or very similar to other drugs prescribed by psychiatrists.
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Affiliation(s)
- Beatriz López
- Control Engineering and Intelligent Systems (eXiT), University of Girona, Spain,Corresponding author.
| | - Oscar Raya
- Control Engineering and Intelligent Systems (eXiT), University of Girona, Spain
| | | | - Marc Saez
- Research Group on Statistics, Econometrics and Health, University of Girona, Spain,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | | | - Ruth Cunill
- Sant Joan de Deu-Numancia Health Park, Barcelona, Spain
| | | | - Carme Carrion
- Health Lab Research Group, Universitat Oberta de Catalunya, Spain
| | | | - Xavier Castells
- TransLab Research Group, Dept. of Medical Sciences, University of Girona, Spain
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Bottrighi A, Piovesan L, Terenziani P. Supporting physicians in the coordination of distributed execution of CIGs to treat comorbid patients. Artif Intell Med 2023; 135:102472. [PMID: 36628779 DOI: 10.1016/j.artmed.2022.102472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Clinical Practice Guidelines (CPGs) encode the "best" medical practices to treat patients affected by a specific disease and are widely used in the medical practice. Starting from the '90s', several Computer-Interpretable Guideline (CIG) systems have been devised to provide physicians with CPG-based decision support. CPGs (and CIGs) are devoted to provide evidence-based recommendations for one specific disease. In order to support the treatment of patients affected by multiple diseases (i.e., comorbid patients), challenging additional tasks have to be addressed, such as (i) the detection of the interactions between CIG actions, (ii) their management, and, finally, (iii) the "merge" or conciliation of the CIGs. Several CIG approaches have been recently extended in order to face (at least one of) such challenging problems, and one of them is GLARE. However, besides the solutions to tasks (i)-(iii) above, the "run-time" support to physicians treating a comorbid patient requires additional capabilities, to support the distribution of the management of interactions and of the execution of CIGs among different physicians. In this paper, we propose a general framework, based on GLARE and GLARE-SSCPM, to provide such additional capabilities.
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Affiliation(s)
- Alessio Bottrighi
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
| | - Luca Piovesan
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
| | - Paolo Terenziani
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
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Iglesias N, Juarez JM, Campos M. Business Process Model and Notation and openEHR Task Planning for Clinical Pathway Standards in Infections: Critical Analysis. J Med Internet Res 2022; 24:e29927. [PMID: 36107480 PMCID: PMC9523526 DOI: 10.2196/29927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/23/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Clinical pathways (CPs) are usually expressed by means of workflow formalisms, providing health care personnel with an easy-to-understand, high-level conceptual model of medical steps in specific patient conditions, thereby improving overall health care process quality in clinical practice. From a standardized perspective, the business process model and notation (BPMN), a widely spread general-purpose process formalism, has been used for conceptual modeling in clinical domains, mainly because of its easy-to-use graphical notation, facilitating the common understanding and communication of the parties involved in health care. However, BPMN is not particularly oriented toward the peculiarities of complex clinical processes such as infection diagnosis and treatment, in which time plays a critical role, which is why much of the BPMN clinical-oriented research has revolved around how to extend the standard to address these special needs. The shift from an agnostic, general-purpose BPMN notation to a natively clinical-oriented notation such as openEHR Task Planning (TP) could constitute a major step toward clinical process improvement, enhancing the representation of CPs for infection treatment and other complex scenarios. Objective Our work aimed to analyze the suitability of a clinical-oriented formalism (TP) to successfully represent typical process patterns in infection treatment, identifying domain-specific improvements to the standard that could help enhance its modeling capabilities, thereby promoting the widespread adoption of CPs to improve medical practice and overall health care quality. Methods Our methodology consisted of 4 major steps: identification of key features of infection CPs through literature review, clinical guideline analysis, and BPMN extensions; analysis of the presence of key features in TP; modeling of relevant process patterns of catheter-related bloodstream infection as a case study; and analysis and proposal of extensions in view of the results. Results We were able to easily represent the same logic applied in the extended BPMN-based process models in our case study using out-of-the-box standard TP primitives. However, we identified possible improvements to the current version of TP to allow for simpler conceptual models of infection CPs and possibly of other complex clinical scenarios. Conclusions Our study showed that the clinical-oriented TP specification is able to successfully represent the most complex catheter-related bloodstream infection process patterns depicted in our case study and identified possible extensions that can help increase its adequacy for modeling infection CPs and possibly other complex clinical conditions.
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Affiliation(s)
- Natalia Iglesias
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
| | - Jose M Juarez
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
| | - Manuel Campos
- Instituto de Investigación de Tecnologías de la Información y las Comunicaciones Orientadas, University of Murcia, Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria - Arrixaca, Murcia, Spain
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25
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Fortmann J, Lutz M, Spreckelsen C. System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation. JMIR Form Res 2022; 6:e28013. [PMID: 35731571 PMCID: PMC9260532 DOI: 10.2196/28013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/14/2021] [Accepted: 03/17/2022] [Indexed: 12/04/2022] Open
Abstract
Background Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model. Objective Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context. Methods We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine. Results We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians. Conclusions The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.
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Affiliation(s)
- Jonas Fortmann
- Institute of Medical Informatics, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
- Smart Medical Technology for Healthcare Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Marlene Lutz
- Institute of Medical Informatics, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
| | - Cord Spreckelsen
- Smart Medical Technology for Healthcare Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
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26
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Litchfield I, Turner AM, Ferreira Filho JB, Lee M, Weber P. Automated conflict resolution for patients with multiple morbidity being treated using more than one set of single condition clinical guidance: A case study. Comput Biol Med 2022; 144:105381. [DOI: 10.1016/j.compbiomed.2022.105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/14/2022] [Accepted: 03/02/2022] [Indexed: 11/15/2022]
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Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.
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Kogan A, Peleg M, Tu SW, Allon R, Khaitov N, Hochberg I. A Goal-Oriented Methodology for Treatment of Patients with Multimorbidity - Goal Comorbidities (GoCom) Proof-of-Concept Demonstration. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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30
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Van Woensel W, Abidi S, Tennankore K, Worthen G, Abidi SSR. Clinical Guidelines as Executable and Interactive Workflows with FHIR-Compliant Health Data Input Using GLEAN. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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31
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Van Woensel W, Abidi S, Tennankore K, Worthen G, Abidi SSR. Explainable Decision Support Using Task Network Models in Notation3: Computerizing Lipid Management Clinical Guidelines as Interactive Task Networks. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Process Mining for Healthcare: Characteristics and Challenges. J Biomed Inform 2022; 127:103994. [DOI: 10.1016/j.jbi.2022.103994] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/16/2021] [Accepted: 01/10/2022] [Indexed: 12/23/2022]
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33
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Woensel WV, Scioscia F, Loseto G, Seneviratne O, Patton E, Abidi S, Kagal L. Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-Term Behavior Change. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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34
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Nair SS, Li C, Doijad R, Nagy P, Lehmann H, Kharrazi H. A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems. JAMIA Open 2021; 4:ooab106. [PMID: 34927003 PMCID: PMC8677433 DOI: 10.1093/jamiaopen/ooab106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/30/2021] [Indexed: 11/20/2022] Open
Abstract
Objective Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. Materials and Methods We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. Results Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. Conclusion Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.
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Affiliation(s)
- Sujith Surendran Nair
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Informatics, American College of Radiology, Virginia, USA
| | - Chenyu Li
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Ritu Doijad
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Paul Nagy
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Harold Lehmann
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Hadi Kharrazi
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Department of Health Policy and Management, Johns Hopkins School of Public Health, Baltimore, Maryland, USA
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35
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Van Woensel W, Abidi SSR, Abidi SR. Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning. Artif Intell Med 2021; 118:102127. [PMID: 34412844 DOI: 10.1016/j.artmed.2021.102127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/04/2021] [Accepted: 06/22/2021] [Indexed: 11/25/2022]
Abstract
In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.
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Affiliation(s)
- William Van Woensel
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Syed Sibte Raza Abidi
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Samina Raza Abidi
- Faculty of Medicine, Dalhousie University, 1459 Oxford Street, Halifax, NS B3H 4R2, Canada.
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36
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Keikes L, Kos M, Verbeek XAAM, Van Vegchel T, Nagtegaal ID, Lahaye MJ, Méndez Romero A, De Bruijn S, Verheul HMW, Rütten H, Punt CJA, Tanis PJ, Van Oijen MGH. Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity. Int J Qual Health Care 2021; 33:6184988. [PMID: 33760073 PMCID: PMC8023581 DOI: 10.1093/intqhc/mzab051] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/11/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Objective The interpretation and clinical application of guidelines can be challenging and time-consuming, which may result in noncompliance to guidelines. The aim of this study was to convert the Dutch guideline for colorectal cancer (CRC) into decision trees and subsequently implement decision trees in an online decision support environment to facilitate guideline application. Methods The recommendations of the Dutch CRC guidelines (published in 2014) were translated into decision trees consisting of decision nodes, branches and leaves that represent data items, data item values and recommendations, respectively. Decision trees were discussed with experts in the field and published as interactive open access decision support software (available at www.oncoguide.nl). Decision tree validation and a concordance analysis were performed using consecutive reports (January 2016–January 2017) from CRC multidisciplinary tumour boards (MTBs) at Amsterdam University Medical Centers, location AMC. Results In total, we developed 34 decision trees driven by 101 decision nodes based on the guideline recommendations. Decision trees represented recommendations for diagnostics (n = 1), staging (n = 10), primary treatment (colon: n = 1, rectum: n = 5, colorectal: n = 9), pathology (n = 4) and follow-up (n = 3) and included one overview decision tree for optimal navigation. We identified several guideline information gaps and areas of inconclusive evidence. A total of 158 patients’ MTB reports were eligible for decision tree validation and resulted in treatment recommendations in 80% of cases. The concordance rate between decision tree treatment recommendations and MTB advices was 81%. Decision trees reported in 22 out of 24 non-concordant cases (92%) that no guideline recommendation was available. Conclusions We successfully converted the Dutch CRC guideline into decision trees and identified several information gaps and areas of inconclusive evidence, the latter being the main cause of the observed disagreement between decision tree recommendations and MTB advices. Decision trees may contribute to future strategies to optimize quality of care for CRC patients.
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Affiliation(s)
- Lotte Keikes
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Noord-Holland 1105 AZ, Netherlands.,Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, Netherlands
| | - Milan Kos
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Noord-Holland 1105 AZ, Netherlands.,Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, Netherlands
| | - Xander A A M Verbeek
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, Netherlands
| | - Thijs Van Vegchel
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, Netherlands
| | - Iris D Nagtegaal
- Department of Pathology, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, Gelderland 6525 GA, Netherlands
| | - Max J Lahaye
- Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, Amsterdam, Noord-Holland 1066 CX, Netherlands
| | - Alejandra Méndez Romero
- Department of Radiation Oncology, Erasmus University Medical Center, Doctor Molewaterplein 40, Rotterdam, Zuid-Holland 3015 GD, Netherlands
| | - Sandra De Bruijn
- Department of Surgery, Reinier de Graaf Hospital, Reinier de Graafweg 5, Delft, Zuid-Holland, 2625 AD, Netherlands
| | - Henk M W Verheul
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Gelderland 6525 GA, Netherlands
| | - Heidi Rütten
- Department of Radiation Oncology, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, Gelderland 6525 GA, Netherlands
| | - Cornelis J A Punt
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, Netherlands
| | - Pieter J Tanis
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Noord-Holland 1105 AZ, Netherlands
| | - Martijn G H Van Oijen
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, Noord-Holland 1105 AZ, Netherlands.,Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, Netherlands
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Delory T, Jeanmougin P, Lariven S, Aubert JP, Peiffer-Smadja N, Boëlle PY, Bouvet E, Lescure FX, Le Bel J. A computerized decision support system (CDSS) for antibiotic prescription in primary care-Antibioclic: implementation, adoption and sustainable use in the era of extended antimicrobial resistance. J Antimicrob Chemother 2021; 75:2353-2362. [PMID: 32357226 DOI: 10.1093/jac/dkaa167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/03/2020] [Accepted: 03/31/2020] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES To describe the implementation and use of a computerized decision support system (CDSS) for antibiotic prescription in primary care in France (Antibioclic). The CDSS targets 37 infectious diseases and has been freely available on a website since 2011. METHODS Description and implementation of the architecture of a CDSS for antibiotic prescription in general practice. Analysis of the queries made between 2012 and 2018 on the CDSS by GPs. Analysis of two cross-sectional studies of users in 2014 and 2019. RESULTS The number of queries increased from a median of 796/day [IQR, 578-989] in 2012 to 11 125/day [5592-12 505] in 2018. Unique users increased from 414/day [245-494] in 2012 to 5365/day [2891-5769] in 2018. Time taken to make a query was 2 min [1.9-2.1]. Among 3 542 347 queries in 2018, 78% were for adults. Six situations accounted for ≥50% of queries: cystitis; acute otitis media; acute sinusitis; community-acquired pneumonia; sore throat; and pyelonephritis. Queries concerned pathologies for which antibiotic prescription was necessary (64%), was conditional on additional clinical steps (34%) or was not recommended (2%). Most users (81%) were GPs, with median age of 38 years [31-52] and 58% were female. Among the 4016 GPs who responded to the surveys, the vast majority (96%) reported using the CDSS during the consultation, with 24% systematically using Antibioclic to initiate an antibiotic course and 93% having followed the CDSS recommendation for the latest prescription. Most GPs were comfortable using the CDSS in front of a patient. CONCLUSIONS Antibioclic has been adopted and is widely used in primary care in France. Its interoperability could allow its adaptation and implementation in other countries.
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Affiliation(s)
- Tristan Delory
- Antibioclic steering committee, Paris, France.,Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, IPLESP, F75012 Paris, France.,AP-HP, Bichat hospital, Department of Infectious and Tropical Diseases, Paris, France.,Hôpital Annecy-Genevois (CHANGE), Délégation à la Recherche Clinique et l'Innovation, 1 avenue de l'hôpital, 74370 Epagny-Metz-Tessy, France
| | - Pauline Jeanmougin
- Antibioclic steering committee, Paris, France.,Department of General Practice, Faculty of Medicine, University of Nantes, Nantes, France
| | - Sylvie Lariven
- Antibioclic steering committee, Paris, France.,AP-HP, Bichat hospital, Department of Infectious and Tropical Diseases, Paris, France
| | | | - Nathan Peiffer-Smadja
- AP-HP, Bichat hospital, Department of Infectious and Tropical Diseases, Paris, France.,UMR 1137, INSERM, IAME, Paris, France.,Paris Diderot University Paris 7, Sorbonne Paris Cité, Paris, France
| | - Pierre-Yves Boëlle
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, IPLESP, F75012 Paris, France.,AP-HP, Saint Antoine hospital, Public Health unit, Paris, France
| | - Elisabeth Bouvet
- Antibioclic steering committee, Paris, France.,AP-HP, Bichat hospital, Department of Infectious and Tropical Diseases, Paris, France.,Paris Diderot University Paris 7, Sorbonne Paris Cité, Paris, France.,French National Authority for Health (HAS), Paris, France
| | - François-Xavier Lescure
- Antibioclic steering committee, Paris, France.,AP-HP, Bichat hospital, Department of Infectious and Tropical Diseases, Paris, France.,UMR 1137, INSERM, IAME, Paris, France.,Paris Diderot University Paris 7, Sorbonne Paris Cité, Paris, France
| | - Josselin Le Bel
- Antibioclic steering committee, Paris, France.,UMR 1137, INSERM, IAME, Paris, France.,Department of General Practice, Université Paris Diderot, Université de Paris, Sorbonne Paris Cité, 75018 Paris, France
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Čyras K, Oliveira T, Karamlou A, Toni F. Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines. ARGUMENT & COMPUTATION 2021. [DOI: 10.3233/aac-200523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.
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Affiliation(s)
- Kristijonas Čyras
- Department of Computing, Imperial College London, United Kingdom. E-mail:
| | | | - Amin Karamlou
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
| | - Francesca Toni
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
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Garber JR, Papini E, Frasoldati A, Lupo MA, Harrell RM, Parangi S, Patkar V, Baloch ZW, Pessah-Pollack R, Hegedus L, Crescenzi A, Lubitz CC, Paschke R, Randolph GW, Guglielmi R, Lombardi CP, Gharib H. American Association of Clinical Endocrinology And Associazione Medici Endocrinologi Thyroid Nodule Algorithmic Tool. Endocr Pract 2021; 27:649-660. [PMID: 34090820 DOI: 10.1016/j.eprac.2021.04.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVE The first edition of the American Association of Clinical Endocrinology/American College of Endocrinology/Associazione Medici Endocrinologi Guidelines for the Diagnosis and Management of Thyroid Nodules was published in 2006 and updated in 2010 and 2016. The American Association of Clinical Endocrinology/American College of Endocrinology/Associazione Medici Endocrinologi multidisciplinary thyroid nodules task force was charged with developing a novel interactive electronic algorithmic tool to evaluate thyroid nodules. METHODS The Thyroid Nodule App (termed TNAPP) was based on the updated 2016 clinical practice guideline recommendations while incorporating recent scientific evidence and avoiding unnecessary diagnostic procedures and surgical overtreatment. This manuscript describes the algorithmic tool development, its data requirements, and its basis for decision making. It provides links to the web-based algorithmic tool and a tutorial. RESULTS TNAPP and TI-RADS were cross-checked on 95 thyroid nodules with histology-proven diagnoses. CONCLUSION TNAPP is a novel interactive web-based tool that uses clinical, imaging, cytologic, and molecular marker data to guide clinical decision making to evaluate and manage thyroid nodules. It may be used as a heuristic tool for evaluating and managing patients with thyroid nodules. It can be adapted to create registries for solo practices, large multispecialty delivery systems, regional and national databases, and research consortiums. Prospective studies are underway to validate TNAPP to determine how it compares with other ultrasound-based classification systems and whether it can improve the care of patients with clinically significant thyroid nodules while reducing the substantial burden incurred by those who do not benefit from further evaluation and treatment.
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Affiliation(s)
- Jeffrey R Garber
- Endocrine Division, Harvard Vanguard Medical Associates, Boston, Massachusetts; Division of Endocrinology, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
| | - Enrico Papini
- Endocrinology and Metabolism Department, Regina Apostolorum Hospital, Albano, Rome, Italy
| | - Andrea Frasoldati
- Metabolism and Nutrition Department, Santa Maria Nuova Hospital Scientific Institute for Research, Hospitalization and Healthcare, Reggio Emilia, Italy
| | - Mark A Lupo
- Thyroid & Endocrine Center of Florida, Sarasota, Florida; Florida State University College of Medicine, Sarasota, Florida
| | - R Mack Harrell
- Memorial Center for Integrative Endocrine Surgery, Hollywood, Florida
| | - Sareh Parangi
- Harvard Medical School, Boston, Massachusetts; Newton-Wellesley Hospital, Newton, Massachusetts; Department of Endocrine Surgery, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Zubair W Baloch
- Hospital of the University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Rachel Pessah-Pollack
- Division of Endocrinology, Diabetes and Metabolism, NYU Langone Health, New York, New York
| | - Laszlo Hegedus
- University of Southern Denmark, Odense, Denmark; Department of Endocrinology and Metabolism, Odense University Hospital, Odense, Denmark
| | - Anna Crescenzi
- Pathology Unit, University Hospital Campus Bio-Medico, Rome, Italy
| | - Carrie C Lubitz
- Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts
| | - Ralf Paschke
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Gregory W Randolph
- Harvard Medical School, Boston, Massachusetts; Thyroid/Parathyroid Endocrine Surgical Division, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts; Endocrine Surgical Service, Massachusetts General Hospital, Boston, Massachusetts
| | - Rinaldo Guglielmi
- Endocrinology and Metabolism Department, Regina Apostolorum Hospital, Albano, Rome, Italy
| | - Celestino P Lombardi
- Endocrine Surgery Department, Policlinico Agostino Gemelli, Catholic University, Rome, Italy
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Parimbelli E, Wilk S, Cornet R, Sniatala P, Sniatala K, Glaser SLC, Fraterman I, Boekhout AH, Ottaviano M, Peleg M. A review of AI and Data Science support for cancer management. Artif Intell Med 2021; 117:102111. [PMID: 34127240 DOI: 10.1016/j.artmed.2021.102111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/23/2020] [Accepted: 05/11/2021] [Indexed: 02/09/2023]
Abstract
INTRODUCTION Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.
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Affiliation(s)
| | - S Wilk
- Poznan University of Technology, Poland
| | - R Cornet
- Amsterdam University Medical Centre, the Netherlands
| | | | | | - S L C Glaser
- Amsterdam University Medical Centre, the Netherlands
| | - I Fraterman
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - A H Boekhout
- Netherlands Cancer Institute, Amsterdam, the Netherlands
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Michel JJ, Schwartz SR, Dawson DE, Denneny JC, Erinoff E, Dhepyasuwan N, Rosenfeld RM. Quality Improvement in Otolaryngology-Head and Neck Surgery: Developing Registry-Enabled Quality Measures From Guidelines for Cerumen Impaction and Allergic Rhinitis Through a Transparent and Systematic Process. Otolaryngol Head Neck Surg 2021; 166:13-22. [PMID: 34000906 DOI: 10.1177/01945998211011987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND SIGNIFICANCE Quality measurement can drive improvement in clinical care and allow for easy reporting of quality care by clinicians, but creating quality measures is a time-consuming and costly process. ECRI (formerly Emergency Care Research Institute) has pioneered a process to support systematic translation of clinical practice guidelines into electronic quality measures using a transparent and reproducible pathway. This process could be used to augment or support the development of electronic quality measures of the American Academy of Otolaryngology-Head and Neck Surgery Foundation (AAO-HNSF) and others as the Centers for Medicare and Medicaid Services transitions from the Merit-Based Incentive Payment System (MIPS) to the MIPS Value Pathways for quality reporting. METHODS We used a transparent and reproducible process to create electronic quality measures based on recommendations from 2 AAO-HNSF clinical practice guidelines (cerumen impaction and allergic rhinitis). Steps of this process include source material review, electronic content extraction, logic development, implementation barrier analysis, content encoding and structuring, and measure formalization. Proposed measures then go through the standard publication process for AAO-HNSF measures. RESULTS The 2 guidelines contained 29 recommendation statements, of which 7 were translated into electronic quality measures and published. Intermediate products of the guideline conversion process facilitated development and were retained to support review, updating, and transparency. Of the 7 initially published quality measures, 6 were approved as 2018 MIPS measures, and 2 continued to demonstrate a gap in care after a year of data collection. CONCLUSION Developing high-quality, registry-enabled measures from guidelines via a rigorous reproducible process is feasible. The streamlined process was effective in producing quality measures for publication in a timely fashion. Efforts to better identify gaps in care and more quickly recognize recommendations that would not translate well into quality measures could further streamline this process.
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Affiliation(s)
| | | | | | - James C Denneny
- American Academy of Otolaryngology-Head and Neck Surgery, Alexandria, Virginia, USA
| | | | - Nui Dhepyasuwan
- American Academy of Otolaryngology-Head and Neck Surgery, Alexandria, Virginia, USA
| | - Richard M Rosenfeld
- State University of New York Downstate Medical Center, Brooklyn, New York, USA
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Lichtner V, Baysari M. Electronic display of a patient treatment over time: a perspective on clinicians' burn-out. BMJ Health Care Inform 2021; 28:bmjhci-2020-100281. [PMID: 33853861 PMCID: PMC8054091 DOI: 10.1136/bmjhci-2020-100281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 02/03/2021] [Indexed: 01/08/2023] Open
Affiliation(s)
- Valentina Lichtner
- UCL School of Pharmacy, University College London, London, UK
- Leeds University Business School, University of Leeds, Leeds, UK
| | - Melissa Baysari
- Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
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Fox J, South M, Khan O, Kennedy C, Ashby P, Bechtel J. OpenClinical.net: Artificial intelligence and knowledge engineering at the point of care. BMJ Health Care Inform 2021; 27:bmjhci-2020-100141. [PMID: 32723855 PMCID: PMC7388886 DOI: 10.1136/bmjhci-2020-100141] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/11/2020] [Accepted: 05/22/2020] [Indexed: 12/20/2022] Open
Abstract
Objective OpenClinical.net is a way of disseminating clinical guidelines to improve quality of care whose distinctive feature is to combine the benefits of clinical guidelines and other human-readable material with the power of artificial intelligence to give patient-specific recommendations. A key objective is to empower healthcare professionals to author, share, critique, trial and revise these ‘executable’ models of best practice. Design OpenClinical.net Alpha (www.openclinical.net) is an operational publishing platform that uses a class of artificial intelligence techniques called knowledge engineering to capture human expertise in decision-making, care planning and other cognitive skills in an intuitive but formal language called PROforma.3 PROforma models can be executed by a computer to yield patient-specific recommendations, explain the reasons and provide supporting evidence on demand. Results PROforma has been validated in a wide range of applications in diverse clinical settings and specialties, with trials published in high impact peer-reviewed journals. Trials have included patient workup and risk assessment; decision support (eg, diagnosis, test and treatment selection, prescribing); adaptive care pathways and care planning. The OpenClinical software platform presently supports authoring, testing, sharing and maintenance. OpenClinical’s open-access, open-source repository Repertoire currently carries approximately 50+ diverse examples (https://openclinical.net/index.php?id=69). Conclusion OpenClinical.net is a showcase for a PROforma-based approach to improving care quality, safety, efficiency and better patient experience in many kinds of routine clinical practice. This human-centred approach to artificial intelligence will help to ensure that it is developed and used responsibly and in ways that are consistent with professional priorities and public expectations.
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Affiliation(s)
| | | | - Omar Khan
- Institute for Digital Healthcare, Warwick University, Coventry, UK
| | | | - Peter Ashby
- OpenClinical CIC and Ashby Projects Ltd, Oxford, UK
| | - John Bechtel
- OpenClinical CIC and Badgerpass Ltd, Cambridge, UK
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Michalowski M, Rao M, Wilk S, Michalowski W, Carrier M. MitPlan 2.0: Enhanced Support for Multi-morbid Patient Management Using Planning. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-77211-6_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Michalowski M, Wilk S, Michalowski W, Carrier M. MitPlan: A planning approach to mitigating concurrently applied clinical practice guidelines. Artif Intell Med 2020; 112:102002. [PMID: 33581823 DOI: 10.1016/j.artmed.2020.102002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 11/01/2020] [Accepted: 12/06/2020] [Indexed: 10/22/2022]
Abstract
As the population ages, patients' complexity and the scope of their care is increasing. Over 60% of the population is 65 years of age or older and suffers from multi-morbidity, which is associated with two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, previously we developed a framework for mitigation, i.e., identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. That framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management plans. In the work presented here, we leverage our earlier research and simplify the mitigation process by representing it as a planning problem using the Planning Domain Definition Language (PDDL). This new framework, called MitPlan, identifies and addresses adverse interactions using durative planning actions that embody clinical actions (including medication administration and patient testing), supports a physician-defined length of planning horizons, and optimizes plans based on patient preferences and action costs. It supports a variety of criteria when developing management plans, including the total cost of prescribed treatment and the cost of the revisions to be introduced. The solution to MitPlan's planning problem is a sequence of timed actions that are easy to interpret when creating a management plan. We demonstrate MitPlan's capabilities using illustrative and clinical case studies.
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Affiliation(s)
| | - Szymon Wilk
- Poznan University of Technology, Poznan Poland
| | | | - Marc Carrier
- The Ottawa Hospital Research Institute, Ottawa Canada
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Hussain M, Afzal M, Malik KM, Ali T, Ali Khan W, Irfan M, Jamshed A, Lee S. Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105701. [PMID: 32882592 DOI: 10.1016/j.cmpb.2020.105701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. METHODS This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. RESULTS ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. CONCLUSION ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.
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Affiliation(s)
- Maqbool Hussain
- Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea; Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
| | - Muhammad Afzal
- Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea.
| | - Khalid M Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
| | - Taqdir Ali
- Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea.
| | - Wajahat Ali Khan
- College of Engineering and Technology, University of Derby, Markeaton Street, Derby DE223AW, United Kingdom.
| | - Muhammad Irfan
- Department of Radiation Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A.Johar Town, Lahore 54782, Pakistan; Department of Radiation Oncology, National Guard-Health Affairs, King Abdulaziz Medical City Riyadh, Kingdom of Saudi Arabia.
| | - Arif Jamshed
- Department of Radiation Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A.Johar Town, Lahore 54782, Pakistan.
| | - Sungyoung Lee
- Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea.
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Banjar HR, Alkhatabi H, Alganmi N, Almouhana GI. Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8066. [PMID: 33147715 PMCID: PMC7662618 DOI: 10.3390/ijerph17218066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 12/17/2022]
Abstract
The increasing number of COVID-19 patients has increased health care professionals' workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients.
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Affiliation(s)
- Haneen Reda Banjar
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia; (N.A.); (G.I.A.)
| | - Heba Alkhatabi
- Department of Medical Laboratory Technology, Faculty of Applied Medical Science, King Abdulaziz University, Jeddah 80200, Saudi Arabia;
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 80200, Saudi Arabia
| | - Nofe Alganmi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia; (N.A.); (G.I.A.)
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 80200, Saudi Arabia
| | - Ghaidaa Ibraheem Almouhana
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia; (N.A.); (G.I.A.)
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Torres Silva EA, Uribe S, Smith J, Luna Gomez IF, Florez-Arango JF. XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study. JMIR Form Res 2020; 4:e17512. [PMID: 33064087 PMCID: PMC7600017 DOI: 10.2196/17512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/24/2020] [Accepted: 08/16/2020] [Indexed: 01/30/2023] Open
Abstract
Background Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. Objective This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. Methods We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. Results In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. Conclusions Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.
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Affiliation(s)
| | - Sebastian Uribe
- Bioengineering Research Group, Universidad Pontificia Bolivariana, Medellin, Colombia
| | - Jack Smith
- Department of Microbial Pathogenesis and Immunology, Texas A&M Unversity, College Station, TX, United States
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Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary evaluation. J Biomed Inform 2020; 112:103587. [PMID: 33035704 DOI: 10.1016/j.jbi.2020.103587] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/03/2020] [Accepted: 09/06/2020] [Indexed: 11/23/2022]
Abstract
Patients with chronic multimorbidity are becoming more common as life expectancy increases, making it necessary for physicians to develop complex management plans. We are looking at the patient management process as a goal-attainment problem. Hence, our aim is to develop a goal-oriented methodology for providing decision support for managing patients with multimorbidity continuously, as the patient's health state is progressing and new goals arise (e.g., treat ulcer, prevent osteoporosis). Our methodology allows us to detect and mitigate inconsistencies among guideline recommendations stemming from multiple clinical guidelines, while consulting medical ontologies and terminologies and relying on patient information standards. This methodology and its implementation as a decision-support system, called GoCom, starts with computer-interpretable clinical guidelines (CIGs) for single problems that are formalized using the PROforma CIG language. We previously published the architecture of the system as well as a CIG elicitation guide for enriching PROforma tasks with properties referring to vocabulary codes of goals and physiological effects of management plans. In this paper, we provide a formalization of the conceptual model of GoCom that generates, for each morbidity of the patient, a patient-specific goal tree that results from the PROforma engine's enactment of the CIG with the patient's data. We also present the "Controller" algorithm that drives the GoCom system. Given a new problem that a patient develops, the Controller detects inconsistencies among goals pertaining to different comorbid problems and consults the CIGs to generate alternative non-conflicted and goal-oriented management plans that address the multiple goals simultaneously. In this stage of our research, the inconsistencies that can be detected are of two types - starting vs. stopping medications that belong to the same medication class hierarchy, and detecting opposing physiological effect goals that are specified in concurrent CIGs (e.g., decreased blood pressure vs. increased blood pressure). However, the design of GoCom is modular and generic and allows the future introduction of additional interaction detection and mitigation strategies. Moreover, GoCom generates explanations of the alternative non-conflicted management plans, based on recommendations stemming from the clinical guidelines and reasoning patterns. GoCom's functionality was evaluated using three cases of multimorbidity interactions that were checked by our three clinicians. Usefulness was evaluated with two studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was done with 27 6th medical students and interns. The participants solved complex realistic cases of multimorbidity patients: with and without decision-support, two cases in the first evaluation and 6 cases in the second evaluation. Use of GoCom increased completeness of the patient management plans produced by the medical students from 0.44 to 0.71 (P-value of 0.0005) in the first evaluation, and from 0.31 to 0.78 (P-value < 0.0001) in the second evaluation. Correctness in the first evaluation was very high with (0.98) or without the system (0.91), with non-significant difference (P-value ≥ 0.17). In the second evaluation, use of GoCom increased correctness from 0.68 to 0.83 (P-value of 0.001). In addition, GoCom's explanation and visualization were perceived as useful by the vast majority of participants. While GoCom's detection of goal interactions is currently limited to detection of starting vs. stopping the same medication or medication subclasses and detecting conflicting physiological effects of concurrent medications, the evaluation demonstrated potential of the system for improving clinical decision-making for multimorbidity patients.
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Souza-Pereira L, Pombo N, Ouhbi S, Felizardo V, Garcia N. Clinical decision support systems for chronic diseases: A Systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105565. [PMID: 32480191 DOI: 10.1016/j.cmpb.2020.105565] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/24/2020] [Accepted: 05/21/2020] [Indexed: 06/11/2023]
Abstract
UNLABELLED A Clinical Decision Support System (CDSS) aims to assist physicians, nurses and other professionals in decision-making related to the patient's clinical condition. CDSSs deal with pertinent and critical data, and special care should be taken in their design to ensure the development of usable, secure and reliable tools. OBJECTIVE This paper aims to investigate existing literature dealing with the development process of CDSSs for monitoring chronic diseases, analysing their functionalities and characteristics, and the software engineering representation in their design. METHODS A systematic literature review (SLR) is conducted to analyse the literature on CDSSs for monitoring chronic diseases and the application of software engineering techniques in their design. RESULTS Fourteen included studies revealed that the most addressed disease was diabetes (42.8%) and the most commonly proposed approach was diagnostic (85.7%). Regarding data sources, the studies show a predominance on the use of databases (85.7%), with other data sources such as sensors (42.8%) and self-report (28.6%) also being considered. Analysing the representation for engineering techniques, we found Behaviour diagrams (42.8%) to be the most frequent, closely followed by Structural diagrams (35.7%) and others (78.6%) being largely mentioned. Some studies also approached the requirement specification (21.4%). The most common target evaluation was the performance of the system (64.2%) and the most common metric was accuracy (57.1%). CONCLUSION We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.
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Affiliation(s)
- Leonice Souza-Pereira
- Instituto Federal do Triângulo Mineiro - Campus Uberlândia Centro, Brasil; Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal.
| | - Nuno Pombo
- Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal
| | - Sofia Ouhbi
- Computer Science and Software Engineering Department, CIT, UAE University, UAE
| | - Virginie Felizardo
- Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal
| | - Nuno Garcia
- Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal
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