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Record W158855400

Telehealth success: evaluation framework development.

2001· article· en· W158855400 on OpenAlexaff
Marilynne Hebert

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSouth Health Campus
Fundersnot available
KeywordsTelehealthContext (archaeology)Process managementComputer scienceProcess (computing)Quality (philosophy)Knowledge managementInformation and Communications TechnologyHealth careConceptual frameworkManagement scienceTelemedicineRisk analysis (engineering)BusinessEngineering
DOInot available

Abstract

fetched live from OpenAlex

Implementing telehealth applications represents a substantial investment of resources, which is one reason why success is of great interest. Many research and evaluation studies have investigated measures of successful telehealth systems. However, the term "telehealth" represents a wide range of variables including clinical application, characteristics of the information being transmitted, temporal relationships of data transfer and the organizational context. These sources of variability pose many challenges for evaluation as well as for building a cumulative history of research. A conceptual framework is required that assists in categorizing results and drawing conclusions based on an accumulation of findings. One measure of "success" in health care is quality patient care and this reflects a primary reason for ICT investments. For this reason, Donabedian's work in evaluating quality provides the basis for the proposed framework. DeLone and McLean's definitions of IS success assist in conceptualizing Donabedian's structure-outcome-process variables in a telehealth context. Multiple evaluation approaches have been used to address different types of questions. Prior to the technologies being introduced to clinical care, there are usually many studies to demonstrate their effectiveness. Health Technology Assessment examines a broader context than the technology alone, including costs and comparing alternatives that would exist in the absence of telehealth. It considers performance measures; outcomes; summary measures, operational considerations, and other issues. Program Evaluation examines use of the technology to provide a service or deliver a program. Evaluation questions often address whether the program goals have been met and if it is operating as expected. Perhaps of greater concern than the evaluation approach taken is generalizability of findings. Recent studies have given inadequate attention to defining what is done (i.e. comparison of telehealth to most appropriate alternative), identifying the beneficiaries of telehealth (i.e. ensuring randomly selected participants take part) and what is measured (i.e. including benefits, drawbacks and side effects of telehealth). Evaluation efforts and frameworks have identified "success" factors such as technical acceptability of the system, cost/benefit/effectiveness, organizational support, satisfaction, recruitment and retention, client outcomes such as quality of life, acceptance by consumers and providers. Less is known about the relationship among these variables and whether the findings around one variable are generalizable to other settings or applications. For example, organizational support may be essential for successful provider-patient interactions via videoconference, which result in higher quality of life. A conceptual framework would assist in accumulating this type of evidence and supporting more advanced research efforts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.367
Teacher spread0.288 · 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 teacher head, 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

Citations52
Published2001
Admission routes1
Has abstractyes

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