A visual analytics approach for pattern-recognition in patient-generated data.
J Am Med Inform Assoc
; 25(10): 1366-1374, 2018 10 01.
Article
en En
| MEDLINE
| ID: mdl-29905826
ABSTRACT
Objective:
To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload.Methods:
Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation.Results:
Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering.Conclusions:
Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Gráficos por Computador
/
Reconocimiento de Normas Patrones Automatizadas
/
Automonitorización de la Glucosa Sanguínea
/
Diabetes Mellitus Tipo 2
/
Datos de Salud Generados por el Paciente
/
Visualización de Datos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos