ArticlePloS one2020
Keeping the power on to home medical devices.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- A dangerous combination: how co-occurring floods and blackouts increase cardiovascular hospitalizations.Journal of exposure science & environmental epidemiology · 2026Article
- The electricity cost burden of durable medical equipment in the United States.Scientific reports · 2024Article
- Article
- The impact of Hurricane Maria on individuals living with non-communicable disease in Puerto Rico: the experience of 10 communities.BMC public health · 2022Article
- Analyzing Healthcare Facility Resilience: Scientometric Review and Knowledge Map.Frontiers in public health · 2021Review
- Power Outages and Community Health: a Narrative Review.Current environmental health reports · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in digital health technologies have revolutionised home medical care. Yet many home medical devices (HMEDs, which includes devices referred to as 'life support equipment') rely upon a stable and resilient electricity supply. For users of HMEDs, interruptions to electricity supply can compromise treatment, well-being or survival. This paper addresses a challenge critical to the continued innovation in digital health technologies: the reliable supply of electricity. We bridge the current gap between electricity networks and digital health technologies through a novel method for the remote detection of the phase (that is, which part of the network that each house is connected to), in order to eliminate avoidable interruptions to supply for HMED users. We present an unsupervised phase identification algorithm capable of remote phase detection at scale, and without transformer data. This method translates data insights into actionable energy provision for HMED users and other vulnerable customers, enables more accurate management and planning, and improves electricity reliability which is critical for HMED users and the continued advances in digital health technologies.
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Identifiers
What 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.