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Record W3201421733 · doi:10.2196/22899

Telecare Service Use in Northern Ireland: Exploratory Retrospective Cohort Study

2021· article· en· W3201421733 on OpenAlexvenueno aff
Hala Al-Obaidi, Feras Jirjees, Sayer Al‐Azzam, Verity Faith, Mike Clarke, Evie Gardner, Ashley Agus, James C. McElnay

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersPublic Health AgencyHealth and Social Care Northern Ireland
KeywordsTelecareService (business)CohortMedicineTelemedicineCohort studyIntervention (counseling)Exploratory researchRetrospective cohort studyMedical emergencyHealth careNursingBusinessSurgeryInternal medicineMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Telecare is a health service that involves the home installation of a number of information technology support systems for individuals with complex needs, such as people with reduced mobility or disabilities and the elderly. It involves the use of sensors in patients' homes to detect events, such as smoke in the kitchen, a front door left open, or a patient fall. In Northern Ireland (NI), outputs from these sensors are monitored remotely by the telecare team, who can provide assistance as required by telephone or through the emergency services. The facilitation of such rapid responses has the aim of promoting early intervention and therefore maintaining patient well-being. OBJECTIVE: The aims of this study were to construct a descriptive summary of the telecare program in NI and evaluate hospital-based service use by telecare patients before and after the installation of telecare equipment. METHODS: An exploratory retrospective cohort study was conducted involving more than 2000 patients. Data analysis included the evaluation of health care use before and after the telecare service was initiated for individual participants. Individuals with data for a minimum of 6 months before and after the installation of the telecare service were included in this analysis. RESULTS: A total of 2387 patients were enrolled in the telecare service during the observation period (February 26, 2010-February 22, 2016). The mean age was 78 years (median 81 years). More women (1623/2387, 68%) were enrolled in the service. Falls detectors were the most commonly deployed detectors in the study cohort (824/1883, 43.8% of cases). The average number of communications (calls and/or alarms) between participants and the coordinating center was the highest for patients aged ≥85 years (mean 86 calls per year). These contacts were similarly distributed by gender. The mortality rate over the study period was higher in men than women (98/770, 14.4% in men compared to 107/1617, 6.6% in women). The number of nonelective hospital admissions, emergency room visits, and outpatient clinic visits and the length of hospital stays per year were significantly higher (P<.001) after the installation of the telecare equipment than during the period before installation. CONCLUSIONS: Despite the likely benefits of the telecare service in providing peace of mind for patients and their relatives, hospital-based health care use significantly increased after enrollment in the service. This likely reflects the increasing health care needs over time in an aging population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.446
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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