ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Designing Decision-Support Technologies for Patient-Generated Data in Type 1 Diabetes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data.Journal of healthcare informatics research · 2024Article
- Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.Diabetes technology & therapeutics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
People living with type 1 diabetes generate data as a byproduct of diabetes management. The development of decision support technologies can be enabled by harnessing these patient-generated data, but a major challenge is for these technologies to provide meaningful and highly personalized guidance to support individual patients' decision-making processes. In this paper, results from a year-long qualitative study were reported. Twenty-six people with type 1 diabetes were interviewed regarding the types of self-generated data they use for decision-making, their decision-making processes using self-generated data, and the difficulties they experience when attempting to use this data for decision-making. These patients' behaviors and difficulties point to new approaches to designing decision support technologies for personal use, including patient-centered and automated data entry, automated and individualized data analysis, and humanized output.
Indexed as
Identifiers
29854235PMC5977683What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.