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Record W2187339890

Evaluating the Costs and Benefits of Innovations in Chronic Wound Care Products and Practices

2013· article· en· W2187339890 on OpenAlexaboutno aff
Theresa Hurd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingBest practiceHealth careBusinessWound careProduct (mathematics)PaceProcess (computing)MedicineProcess managementNursingMarketingComputer scienceManagementIntensive care medicine
DOInot available

Abstract

fetched live from OpenAlex

The management of innovation and change in healthcare can be a major challenge. It has been recognized that a key fac- tor in closing the gap between best practice and common practice is the ability of healthcare providers and organizations to rapidly disseminate innovations. 1 Today's healthcare environment offers a steady stream of innovations, often at a pace that seems much too fast for organizations to evaluate and integrate. Clinicians and administrators can feel overwhelmed and unable to decide which innovations are appropriate and how they might be utilized for optimal outcomes. They face constant pressure to innovate and accelerate the dissemination of innovation. Simultaneously, organizations must ensure the consistent delivery of proven patient care practices at the highest possible quality standards is not compromised in any way as innovations are adopted. This paper reviews the implementation of healthcare innovations in the field of chronic wound care. Two distinct types of innovation are profiled: • Process Innovation: A comprehensive program of clinical best practices focused on the prevention and care of chronic wounds is currently being implemented by a large community care organization providing in-home care services in Canada. The program incorporates a rigorous framework of measurement, monitoring, and benchmarking that tracks outcomes and resource requirements in order to generate continuous feedback on both cost and benefits. • Product Innovation: An innovative medical device—a portable, disposable negative pressure wound therapy (NPWT) system—has been introduced into clinical practice by wound care providers in acute care and community care orga- nizations. This product innovation has been adopted within the context of best practice wound care and prevention programs so tools are available to assess, evaluate, and monitor the utilization of the new technology. Results show 98% of patients reported they were pleased or satisfied with the NPWT device. Anecdotal data from patients described improvements ranging from increased social activities and improved self-esteem to a marked improvement in gen- eral overall wellness. Similarly, 99% of nurses were pleased or satisfied with the device. Only 2% of nurses reported any dif- ficulty with application of the product. Over the course of the evaluation, 68% of wounds treated with the portable negative pressure device were completely closed with a median time to healing of 9 weeks. This rate needs to be considered in the context of the wounds treated, many of which remained unhealed for a significant time before commencing treatment with portable NPWT (average wound duration before treatment was 9 weeks with a range from 1 to 68 weeks). A comparison of the cost of the single-use negative pressure system and traditional negative pressure systems shows that single-use NPWT can substantially reduce the cost per patient, as a result of fewer dressing changes and nurse visits per week. This paper provides qualitative and quantitative data related to the adoption of these innovations in a demanding, real- world clinical environment. The intent is to offer practical insights and describe results to date from innovations within a framework of managed adoption and evaluation that is designed to meet healthcare organizations priorities of high-quality care and improved efficiency.

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)0.057
metaresearch head score (Gemma)0.243
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 score0.057
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.243
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0080.007
Science and technology studies0.0010.003
Scholarly communication0.0070.008
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.121
GPT teacher head0.425
Teacher spread0.305 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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