MétaCan
Menu
Back to cohort
Record W2142525946 · doi:10.2196/resprot.3501

Care Models of eHealth Services: A Case Study on the Design of a Business Model for an Online Precare Service

2015· article· en· W2142525946 on OpenAlexvenueno aff
Dorine PD van Meeuwen, Quirine J van Walt Meijer, Lianne Simonse

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
Fundersnot available
KeywordseHealthBusiness modelService designKnowledge managementService (business)Health careIndustrialization of services business modelNew business developmentComputer scienceBusinessProcess managementService delivery frameworkElectronic businessMarketingBusiness relationship management

Abstract

fetched live from OpenAlex

BACKGROUND: With a growing population of health care clients in the future, the organization of high-quality and cost-effective service providing becomes an increasing challenge. New online eHealth services are proposed as innovative options for the future. Yet, a major barrier to these services appears to be the lack of new business model designs. Although design efforts generally result in visual models, no such artifacts have been found in the literature on business model design. This paper investigates business model design in eHealth service practices from a design perspective. It adopts a research by design approach and seeks to unravel what characteristics of business models determine an online service and what are important value exchanges between health professionals and clients. OBJECTIVE: The objective of the study was to analyze the construction of care models in-depth, framing the essential elements of a business model, and design a new care model that structures these elements for the particular context of an online pre-care service in practice. METHODS: This research employs a qualitative method of an in-depth case study in which different perspectives on constructing a care model are investigated. Data are collected by using the visual business modeling toolkit, designed to cocreate and visualize the business model. The cocreated models are transcribed and analyzed per actor perspective, transactions, and value attributes. RESULTS: We revealed eight new actors in the business model for providing the service. Essential actors are: the intermediary network coordinator connecting companies, the service dedicated information technology specialists, and the service dedicated health specialist. In the transactions for every service providing we found a certain type of contract, such as a license contract and service contracts for precare services and software products. In addition to the efficiency, quality, and convenience, important value attributes appeared to be: timelines, privacy and credibility, availability, pleasantness, and social interaction. Based on the in-depth insights from the actor perspectives, the business model for online precare services is modeled with a visual design. A new care model of the online precare service is designed and compiled of building blocks for the business model. CONCLUSIONS: For the construction of a care model, actors, transactions, and value attributes are essential elements. The design of a care model structures these elements in a visual way. Guided by the business modeling toolkit, the care model design artifact is visualized in the context of an online precare service. Important building blocks include: provision of an online flow of information with regular interactions to the client stimulates self-management of personal health and service-dedicated health expert ensure an increase of the perceived quality of the eHealth service.

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)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0070.006
Open science0.0030.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.782
GPT teacher head0.637
Teacher spread0.145 · 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 designQualitative
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

Citations32
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research ProtocolsSame topicInformation Systems Theories and ImplementationFrench-language works237,207