Published on 07.01.15 in Vol 3, No 1 (2015): Jan-Mar
Works citing "Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review"
According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.3503):
(note that this is only a small subset of citations)
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Hsiao J, Chen R. Critical factors influencing physicians’ intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model. BMC Medical Informatics and Decision Making 2015;16(1)
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Konstantinidis ST, Bamidis PD. Why decision support systems are important for medical education. Healthcare Technology Letters 2016;3(1):56
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Piovesan L, Terenziani P, Theseider Dupré D. Conformance analysis for comorbid patients in Answer Set Programming. Journal of Biomedical Informatics 2020;103:103377
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Bowles J, Caminati M, Cha S, Mendoza J. A framework for automated conflict detection and resolution in medical guidelines. Science of Computer Programming 2019;182:42
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Excoffier S, Herzig L, N’Goran AA, Déruaz-Luyet A, Haller DM. Prevalence of multimorbidity in general practice: a cross-sectional study within the Swiss Sentinel Surveillance System (Sentinella). BMJ Open 2018;8(3):e019616
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MacLean RR, Sofuoglu M, Rosenheck R. Tobacco and alcohol use disorders: Evaluating multimorbidity. Addictive Behaviors 2018;78:59
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Palese A, Brusaferro S. A multidimensional vector model measuring clinical complexity may increase effectiveness in patient assessment. Internal and Emergency Medicine 2017;12(8):1287
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Anselma L, Piovesan L, Terenziani P. Temporal detection and analysis of guideline interactions. Artificial Intelligence in Medicine 2017;76:40
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Sinnott C, Byrne M, Bradley CP. Improving medication management for patients with multimorbidity in primary care: a qualitative feasibility study of the MY COMRADE implementation intervention. Pilot and Feasibility Studies 2017;3(1)
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Theis S, Rasche PWV, Bröhl C, Wille M, Mertens A. Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults. JMIR Medical Informatics 2018;6(3):e39
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Harry ML, Truitt AR, Saman DM, Henzler-Buckingham HA, Allen CI, Walton KM, Ekstrom HL, O’Connor PJ, Sperl-Hillen JM, Bianco JA, Elliott TE. Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study. BMC Health Services Research 2019;19(1)
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Bunn F, Goodman C, Russell B, Wilson P, Manthorpe J, Rait G, Hodkinson I, Durand M. Supporting shared decision-making for older people with multiple health and social care needs: a realist synthesis. Health Services and Delivery Research 2018;6(28):1
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Leiva-Fernández F, Prados-Torres JD, Prados-Torres A, del-Cura-González I, Castillo-Jimena M, López-Rodríguez JA, Rogero-Blanco ME, Lozano-Hernández CM, López-Verde F, Bujalance-Zafra MJ, Pico-Soler MV, Gimeno-Feliu LA, Poblador-Plou B, Martinez-Cañavate MT, Muth C. Training primary care professionals in multimorbidity management: Educational assessment of the eMULTIPAP course. Mechanisms of Ageing and Development 2020;192:111354
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Yeung P, Severinsen C, Good G, O’Donoghue K. Social environment and quality of life among older people with diabetes and multiple chronic illnesses in New Zealand: Intermediary effects of psychosocial support and constraints. Disability and Rehabilitation 2022;44(5):768
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Sadler E, Porat T, Marshall I, Hoang U, Curcin V, Wolfe CDA, McKevitt C, Valdes-Sosa PA. Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement. PLOS ONE 2017;12(5):e0177102
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Freedman S, Lin H, Prince J. Information Technology and Patient Health: An Expanded Analysis of Outcomes, Populations, and Mechanisms. SSRN Electronic Journal 2014;
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Brown B, Balatsoukas P, Williams R, Sperrin M, Buchan I. Multi-method laboratory user evaluation of an actionable clinical performance information system: Implications for usability and patient safety. Journal of Biomedical Informatics 2018;77:62
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Sinnige J, Korevaar JC, van Lieshout J, Westert GP, Schellevis FG, Braspenning JC. Medication management strategy for older people with polypharmacy in general practice: a qualitative study on prescribing behaviour in primary care. British Journal of General Practice 2016;66(649):e540
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Fraccaro P, Kontopantelis E, Sperrin M, Peek N, Mallen C, Urban P, Buchan IE, Mamas MA. Predicting mortality from change-over-time in the Charlson Comorbidity Index. Medicine 2016;95(43):e4973
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Bauer M, Monteith S, Geddes J, Gitlin MJ, Grof P, Whybrow PC, Glenn T. Automation to optimise physician treatment of individual patients: examples in psychiatry. The Lancet Psychiatry 2019;6(4):338
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Khalil H, Shamliyan TA, Middleton M. Interventions for Community-Dwelling Patients with Multiple Chronic Illnesses. The American Journal of Medicine 2017;130(2):148
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. Interactions: understanding people and process in prescribing in primary care. BMJ Quality & Safety 2018;27(3):176
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Freedman S, Lin H, Prince J. Information Technology and Patient Health: Analyzing Outcomes, Populations, and Mechanisms. American Journal of Health Economics 2018;4(1):51
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Keasberry J, Scott IA, Sullivan C, Staib A, Ashby R. Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice. Australian Health Review 2017;41(6):646
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Xu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Research Reviews 2017;37:53
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Riaño D, Ortega W. Computer technologies to integrate medical treatments to manage multimorbidity. Journal of Biomedical Informatics 2017;75:1
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Walton D, Oliveira T, Satoh K, Mebane W. Argumentation Analytics for Treatment Deliberations in Multimorbidity Cases: An Introduction to Two Artificial Intelligence Approaches. Topoi 2021;40(2):373
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Riahi S, Fischler I, Stuckey MI, Klassen PE, Chen J. The Value of Electronic Medical Record Implementation in Mental Health Care: A Case Study. JMIR Medical Informatics 2017;5(1):e1
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de Wit HAJM, Hurkens KPGM, Mestres Gonzalvo C, Smid M, Sipers W, Winkens B, Mulder WJ, Janknegt R, Verhey FR, van der Kuy PM, Schols JMGA. The support of medication reviews in hospitalised patients using a clinical decision support system. SpringerPlus 2016;5(1)
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Meguerditchian A, Tamblyn R, Meterissian S, Law S, Prchal J, Winslade N, Stern D. Adjuvant Endocrine Therapy in Breast Cancer: A Novel e-Health Approach in Optimizing Treatment for Seniors (OPTIMUM): A Two-Group Controlled Comparison Pilot Study. JMIR Research Protocols 2016;5(4):e199
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Prados-Torres A, del Cura-González I, Prados-Torres JD, Leiva-Fernández F, López-Rodríguez JA, Calderón-Larrañaga A, Muth C. Multimorbilidad en medicina de familia y los principios Ariadne. Un enfoque centrado en la persona. Atención Primaria 2017;49(5):300
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Bijlsma N, Cohen M. Environmental Chemical Assessment in Clinical Practice: Unveiling the Elephant in the Room. International Journal of Environmental Research and Public Health 2016;13(2):181
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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;12(2):149
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Kogan A, Peleg M, Tu SW, Allon R, Khaitov N, Hochberg I. Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary evaluation. Journal of Biomedical Informatics 2020;112:103587
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Sharma V, Piscoran O, Summers A, Woywodt A, van der Veer SN, Ainsworth J, Augustine T. The use of health information technology in renal transplantation: A systematic review. Transplantation Reviews 2021;35(2):100607
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Ji M, Yu G, Xi H, Xu T, Qin Y. Measures of success of computerized clinical decision support systems: An overview of systematic reviews. Health Policy and Technology 2021;10(1):196
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Karimi M, Tsiachristas A, Looman W, Stokes J, Galen MV, Rutten-van Mölken M. Bundled payments for chronic diseases increased health care expenditure in the Netherlands, especially for multimorbid patients.. Health Policy 2021;125(6):751
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Souza-Pereira L, Ouhbi S, Pombo N. Quality-in-use characteristics for clinical decision support system assessment. Computer Methods and Programs in Biomedicine 2021;207:106169
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Roth CB, Papassotiropoulos A, Brühl AB, Lang UE, Huber CG. Psychiatry in the Digital Age: A Blessing or a Curse?. International Journal of Environmental Research and Public Health 2021;18(16):8302
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Sinha A, Kerketta S, Ghosal S, Kanungo S, Lee JT, Pati S. Multimorbidity and Complex Multimorbidity in India: Findings from the 2017–2018 Longitudinal Ageing Study in India (LASI). International Journal of Environmental Research and Public Health 2022;19(15):9091
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Piovesan L, Terenziani P, Molino G. GLARE-SSCPM: An Intelligent System to Support the Treatment of Comorbid Patients. IEEE Intelligent Systems 2018;33(6):37
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Chen W, O’Bryan CM, Gorham G, Howard K, Balasubramanya B, Coffey P, Abeyaratne A, Cass A. Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. Implementation Science Communications 2022;3(1)
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Souza-Pereira L, Ouhbi S, Pombo N. A process model for quality in use evaluation of clinical decision support systems. Journal of Biomedical Informatics 2021;123:103917
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Guo H, Li J, Liu H, He J. Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study. BMC Medical Informatics and Decision Making 2022;22(1)
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Hahn W, Schütte K, Schultz K, Wolkenhauer O, Sedlmayr M, Schuler U, Eichler M, Bej S, Wolfien M. Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care. Journal of Personalized Medicine 2022;12(8):1278
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Chen W, Howard K, Gorham G, O’Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. Journal of the American Medical Informatics Association 2022;29(10):1757
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Mouazer A, Tsopra R, Sedki K, Letord C, Lamy J. Decision-support systems for managing polypharmacy in the elderly: A scoping review. Journal of Biomedical Informatics 2022;130:104074
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Ntalindwa T, Nduwingoma M, Karangwa E, Rashid Soron T, Uworwabayeho A, Uwineza A. Development of a Mobile App to Improve Numeracy Skills of Children With Autism Spectrum Disorder: Participatory Design and Usability Study. JMIR Pediatrics and Parenting 2021;4(3):e21471
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Samal L, Fu HN, Camara DS, Wang J, Bierman AS, Dorr DA. Health information technology to improve care for people with multiple chronic conditions. Health Services Research 2021;56(S1):1006
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Andolina A, Guazzone M, Piovesan L, Terenziani P. Temporal reasoning and query answering with preferences and probabilities for medical decision support. Expert Systems with Applications 2022;195:116565
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Zhou Y, Dai X, Ni Y, Zeng Q, Cheng Y, Carrillo-Larco RM, Yan LL, Xu X. Interventions and management on multimorbidity: An overview of systematic reviews. Ageing Research Reviews 2023;87:101901
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Marashi-Hosseini L, Jafarirad S, Hadianfard AM. A fuzzy based dietary clinical decision support system for patients with multiple chronic conditions (MCCs). Scientific Reports 2023;13(1)
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Jones JL, Simons K, Manski-Nankervis J, Lumsden NG, Fernando S, de Courten MP, Cox N, Hamblin PS, Janus ED, Nelson CL. Chronic disease IMPACT (chronic disease early detection and improved management in primary care project): An Australian stepped wedge cluster randomised trial. DIGITAL HEALTH 2023;9
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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. Learning Health Systems 2023;
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Wiwatkunupakarn N, Aramrat C, Pliannuom S, Buawangpong N, Pinyopornpanish K, Nantsupawat N, Mallinson PAC, Kinra S, Angkurawaranon C. The Integration of Clinical Decision Support Systems Into Telemedicine for Patients With Multimorbidity in Primary Care Settings: Scoping Review. Journal of Medical Internet Research 2023;25:e45944
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Yu T, Gao M, Sun G, Graffigna G, Liu S, Wang J. Cardiac rehabilitation engagement and associated factors among heart failure patients: a cross-sectional study. BMC Cardiovascular Disorders 2023;23(1)
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Hempel S, Bolshakova M, Hochman M, Jimenez E, Thompson G, Motala A, Ganz DA, Gabrielian S, Edwards S, Zenner J, Dennis B, Chang E. Caring for high-need patients. BMC Health Services Research 2023;23(1)
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Woodman RJ, Koczwara B, Mangoni AA. Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs. Frontiers in Medicine 2024;10
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According to Crossref, the following books are citing this article (DOI 10.2196/medinform.3503):
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Piovesan L, Terenziani P. Advances in Artificial Intelligence - IBERAMIA 2016. 2016. Chapter 7:77
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Oliveira T, Dauphin J, Satoh K, Tsumoto S, Novais P. Logic and Argumentation. 2020. Chapter 11:166
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Bottrighi A, Piovesan L, Terenziani P. Biomedical Engineering Systems and Technologies. 2019. Chapter 16:298
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Grace A, O'Donoghue J, Mahony C, Heffernan T, Molony D, Carroll T. Encyclopedia of E-Health and Telemedicine. 2016. chapter 38:486
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Ratner SP, DeWalt DA. Chronic Illness Care. 2018. Chapter 45:549
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. Developing and Utilizing Digital Technology in Healthcare for Assessment and Monitoring. 2020. Chapter 6:73
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Silva A, Silva A, Oliveira T, Novais P. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 2020. Chapter 30:338
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