Evidence map›Paper›PMID 42194479›Full record

ArticleHealthcare (Basel, Switzerland)2026

Clinical and Operational Outcomes Associated with the Adoption of a Digital Wound Care Solution in Home Health Settings.

Heba Tallah Mohammed, Robert D J Fraser, Tameka McCabe, Amy Cassata

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Heba Tallah MohammedSwift Medical Inc., Toronto, ON M5H 1A1, Canada.ORCID 0000-0002-0848-8384
Robert D J FraserSwift Medical Inc., Toronto, ON M5H 1A1, Canada.ORCID 0000-0003-2279-4203
Tameka McCabeVNS Health, New York, NY 10017, USA.
Amy CassataSwift Medical Inc., Toronto, ON M5H 1A1, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWounds increase the risk of hospitalization in home health (HH) settings by up to 52%. They also consume a notable portion of HH budgets due to frequent nursing visits for wound assessment and care. To address these challenges, a U.S.-based HH enterprise adopted a Digital Wound Care Solution (DWCS) to enhance wound management and operational efficiency. This study examines the impact of integrating the DWCS into practice, focusing on clinical and operational indicators and potential cost savings.

methodsThis study employed a quasi-experimental pre-post design to evaluate the impact of the DWCS on clinical and operational outcomes. Data were extracted from the DWCS and EMR databases, encompassing pre-adoption (2022) and post-adoption (2023) periods. The analysis included wound data from 16,276 patients in 2023 and 19,252 patients in 2022, covering an 8-month period (March-October) across 11 branches. The key performance indicators included skilled nursing (SN) visits per episode (VPE), time to complete SN visits, hospitalization rates, and staff optimization.

resultsThe adoption of the DWCS was associated with clinical and operational improvements. SN VPE decreased by 7.5%, resulting in an estimated annual savings of $1.3 million. A directional change in wound-related hospitalization rates was observed at 30 days (20.7% to 20.3%) and 60 days (32.4% to 31.5%); however, these changes did not reach statistical significance and should be interpreted as trends. The projected prevention of 200 hospitalizations with estimated annual cost savings of $3.4 million to the health system represents modeled projections based on observed directional trends rather than realized savings. A 1.9% shift in staff roles increased the utilization of licensed practical nurses with no adverse indicators identified within the scope of this analysis, saving $112,748 annually.

conclusionsThe adoption of the DWCS was associated with fewer and shorter SN visits and a shift toward more LPN utilization, with anticipated reductions in costs Wound-related admissions showed downward trends but did not reach statistically significant levels. This pre-post design precludes causal attribution, and findings should be interpreted as associations rather than definitive effects of the intervention. These findings support further investigation of wound care models integrating AI within a value-based home health setting.

Indexed as

artificial intelligence (AI)digital wound care technologyhome healthhospitalizationskilled nursingvalue-based carewound carewound healing

Identifiers

PMID42194479
PMCPMC13205269

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.