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Record W2790699578 · doi:10.2196/humanfactors.8319

Development of eHOME, a Mobile Instrument for Reporting, Monitoring, and Consulting Drug-Related Problems in Home Care: Human-Centered Design Study

2018· article· en· W2790699578 on OpenAlexvenueno aff
Nienke Elske Dijkstra, Carolina Geertruida Maria Sino, Eibert R. Heerdink, Marieke J. Schuurmans

Bibliographic record

VenueJMIR Human Factors · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityContext (archaeology)MedicineDelphi methodNursingMedical emergencyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Home care patients often use many medications and are prone to drug-related problems (DRPs). For the management of problems related to drug use, home care could add to the multidisciplinary expertise of general practitioners (GPs) and pharmacists. The home care observation of medication-related problems by home care employees (HOME)-instrument is paper-based and assists home care workers in reporting potential DRPs. To facilitate the multiprofessional consultation, a digital report of DRPs from the HOME-instrument and digital monitoring and consulting of DRPs between home care and general practices and pharmacies is desired. OBJECTIVE: The objective of this study was to develop an electronic HOME system (eHOME), a mobile version of the HOME-instrument that includes a monitoring and a consulting system for primary care. METHODS: The development phase of the Medical Research Council (MRC) framework was followed in which an iterative human-centered design (HCD) approach was applied. The approach involved a Delphi round for the context of use and user requirements analysis of the digital HOME-instrument and the monitoring and consulting system followed by 2 series of pilots for testing the usability and redesign. RESULTS: By using an iterative design approach and by involving home care workers, GPs, and pharmacists throughout the process as informants, design partners, and testers, important aspects that were crucial for system realization and user acceptance were revealed. Through the report webpage interface, which includes the adjusted content of the HOME-instrument and added home care practice-based problems, home care workers can digitally report observed DRPs. Furthermore, it was found that the monitoring and consulting webpage interfaces enable digital consultation between home care and general practices and pharmacies. The webpages were considered convenient, clear, easy, and usable. CONCLUSIONS: By employing an HCD approach, the eHOME-instrument was found to be an easy-to-use system. The systematic approach promises a valuable contribution for the future development of digital mobile systems of paper-based tools.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.250
GPT teacher head0.447
Teacher spread0.196 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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