Digital health technology to support patient-centered shared decision making at point of care for juvenile idiopathic arthritis

被引:0
作者
Huang, Bin [1 ,2 ]
Kouril, Michal [2 ,3 ]
Chen, Chen [1 ]
Daraiseh, Nancy M. [1 ,2 ]
Ferraro, Kerry [4 ]
Mannion, Melissa L. [5 ]
Brunner, Hermine I. [2 ,6 ]
Lovell, Daniel J. [2 ,6 ]
Morgan, Esi M. [7 ,8 ]
机构
[1] Cincinnati Childrens Hosp Med Ctr, Div Biostat & Epidemiol, Cincinnati, OH USA
[2] Univ Cincinnati, Coll Med, Dept Pediat, Cincinnati, OH USA
[3] Cincinnati Childrens Hosp, Div Biomed Informat, Cincinnati, OH USA
[4] JIA Parent, Lower Gwynedd, PA USA
[5] Univ Alabama Birmingham, Dept Pediat, Birmingham, AL USA
[6] Cincinnati Childrens Hosp & Med Ctr, Div Rheumatol, Cincinnati, OH USA
[7] Seattle Childrens Hosp, Dept Pediat, Div Rheumatol, Seattle, WA 98105 USA
[8] Univ Washington, Sch Med, Dept Pediat, Seattle, WA 98195 USA
关键词
digital health technology; shared decision making; clinical decision support system; juvenile idiopathic arthritis; learning network model; registry analysis; personalized medicine; STRATEGIES;
D O I
10.3389/fped.2024.1457538
中图分类号
R72 [儿科学];
学科分类号
100202 ;
摘要
Despite availability of multiple FDA approved therapies, many children with juvenile idiopathic arthritis (JIA) suffer pain and disability due to uncontrolled disease. The term JIA includes a heterogeneous set of conditions unified by chronic inflammatory arthritis, collectively affecting 1:1,000 children. When reviewing treatment options with families the rheumatologist currently refers to the experience of the average patient in relatively small controlled clinical trials, to consensus-based treatment plans, or increasingly the choice is dictated by the formulary restrictions of insurance payers. The current paradigm for treatment selection does not incorporate real-world evidence of treatment effectiveness centered to the individual patients with whom decisions are to be made. Treatment decisions based on the evidence of the average patient are not optimized to reflect the unique clinical characteristics of an individual with JIA and their disease course, nor does it account for heterogeneous treatment effects. To guide treatment choices centered around each patient, we describe a novel concept of utilizing digital health technology to bring patient-centered information into shared decision-making discussions based on comparative effectiveness analysis of electronic health record or observational clinical registry data of patients with similar characteristics. The envisioned digital tool will organize and present data relevant to the individual patient and enable evidence-based individualized treatment decision making when used in a collaborative manner with the patient family and rheumatologist. Capabilities in digital health technology, data capturing, and analytical methodologies are ripe for this endeavor. This brings the concept of a learning health system directly to the point of care.
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页数:7
相关论文
共 25 条
[1]   Doubly robust estimation in missing data and causal inference models [J].
Bang, H .
BIOMETRICS, 2005, 61 (04) :962-972
[2]   Pediatric Rheumatology Care and Outcomes Improvement Network's Quality Measure Set to Improve Care of Children With Juvenile Idiopathic Arthritis [J].
Bingham, Catherine A. ;
Harris, Julia G. ;
Qiu, Tingting ;
Gilbert, Mileka ;
Vora, Sheetal S. ;
Yildirim-Toruner, Cagri ;
Ferraro, Kerry ;
Lovell, Daniel J. ;
Taylor, Janalee ;
Mannion, Melissa L. ;
Weiss, Jennifer E. ;
Laxer, Ronald M. ;
Shishov, Michael ;
Oberle, Edward J. ;
Gottlieb, Beth S. ;
Lee, Tzielan C. ;
Pan, Nancy ;
Burnham, Jon M. ;
Fair, Danielle C. ;
Batthish, Michelle ;
Hazen, Melissa M. ;
Spencer, Charles H. ;
Morgan, Esi M. .
ARTHRITIS CARE & RESEARCH, 2023, 75 (12) :2442-2452
[3]   Understanding the window of opportunity concept in early rheumatoid arthritis [J].
Boers, M .
ARTHRITIS AND RHEUMATISM, 2003, 48 (07) :1771-1774
[4]   Longitudinal assessment of racial disparities in juvenile idiopathic arthritis disease activity in a treat-to-target intervention [J].
Chang, Joyce C. ;
Xiao, Rui ;
Burnham, Jon M. ;
Weiss, Pamela F. .
PEDIATRIC RHEUMATOLOGY, 2020, 18 (01) :88
[5]   Phenotypic variability and disparities in treatment and outcomes of childhood arthritis throughout the world: an observational cohort study [J].
Consolaro, Alessandro ;
Giancane, Gabriella ;
Alongi, Alessandra ;
van Dijkhuizen, Evert Hendrik Pieter ;
Aggarwal, Amita ;
Al-Mayouf, Sulaiman M. ;
Bovis, Francesca ;
De Inocencio, Jaime ;
Demirkaya, Erkan ;
Flato, Berit ;
Foell, Dirk ;
Garay, Stella Maris ;
Lazar, Calin ;
Lovell, Daniel J. ;
Montobbio, Carolina ;
Miettunen, Paivi ;
Mihaylova, Dimitrina ;
Nielsen, Susan ;
Orban, Ilonka ;
Rumba-Rozenfelde, Ingrida ;
Magalhaes, Claudia Saad ;
Shafaie, Nahid ;
Susic, Gordana ;
Trachana, Maria ;
Wulffraat, Nico ;
Pistorio, Angela ;
Martini, Alberto ;
Ruperto, Nicolino ;
Ravelli, Prof Angelo ;
Abdwani, Reem ;
Aghighi, Yahya ;
Aiche, Maya-Feriel ;
Ailioaie, Constantin ;
Ayaz, Nuray Aktay ;
Al-Abrawi, Safiya ;
Alexeeva, Ekaterina ;
Anton, Jordi ;
Apostol, Adriana ;
Arguedas, Olga ;
Avcin, Tadej ;
Barone, Patrizia ;
Berntson, Lillemor ;
Lucica Boteanu, Alina ;
Boyko, Yaryna ;
Burgos-Vargas, Ruben ;
Calvo Penades, Inmaculada ;
Chedeville, Gaelle ;
Cimaz, Rolando ;
Civino, Adele ;
Consolini, Rita .
LANCET CHILD & ADOLESCENT HEALTH, 2019, 3 (04) :255-263
[6]   Semiparametric estimation of treatment effect in a pretest-posttest study with missing data [J].
Davidian, M ;
Tsiatis, AA ;
Leon, S .
STATISTICAL SCIENCE, 2005, 20 (03) :261-282
[7]  
Hatef E, 2024, Evidence- and Consensus-Based Digital Healthcare Equity Framework
[8]   Estimates of the prevalence of arthritis and other rheumatic conditions in the United States [J].
Helmick, Charles G. ;
Felson, David T. ;
Lawrence, Reva C. ;
Gabriel, Sherine ;
Hirsch, Rosemarie ;
Kwoh, C. Kent ;
Liang, Matthew H. ;
Kremers, Hilal Maradit ;
Mayes, Maureen D. ;
Merkel, Peter A. ;
Pillemer, Stanley R. ;
Reveille, John D. ;
Stone, John H. .
ARTHRITIS AND RHEUMATISM, 2008, 58 (01) :15-25
[9]   Bayesian Nonparametric Modeling for Causal Inference [J].
Hill, Jennifer L. .
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2011, 20 (01) :217-240
[10]  
Huang B., 2020, New Statistical Methods to Compare the Effectiveness of Adaptive Treatment Plans, DOI [10.25302/11.2020.ME.140819894, DOI 10.25302/11.2020.ME.140819894]