Evidence map›Paper›PMID 37016155›Full record

ArticleScientific reports2023

The role of INTERCheckWEB digital innovation in supporting polytherapy management.

Emanuela Foglia, Lucrezia Ferrario, Elisabetta Garagiola, Federica Asperti, Antonino Mazzone, Federico Gatti, Luca Varalli, Cristina Ponsiglione, Lorella Cannavacciuolo

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Emanuela FogliaLIUC Business School, LIUC- University Cattaneo, Healthcare Datascience LAB, Corso Matteotti 22, 21053, Castellanza, Varese, Italy.
Lucrezia FerrarioLIUC Business School, LIUC- University Cattaneo, Healthcare Datascience LAB, Corso Matteotti 22, 21053, Castellanza, Varese, Italy. lferrario@liuc.it.
Elisabetta GaragiolaLIUC Business School, LIUC- University Cattaneo, Healthcare Datascience LAB, Corso Matteotti 22, 21053, Castellanza, Varese, Italy.
Federica AspertiLIUC Business School, LIUC- University Cattaneo, Healthcare Datascience LAB, Corso Matteotti 22, 21053, Castellanza, Varese, Italy.
Antonino MazzoneASST Ovest Milanese Hospital, Legnano, Milano, Italy.
Federico GattiASST Ovest Milanese Hospital, Legnano, Milano, Italy.
Luca VaralliASST Ovest Milanese Hospital, Legnano, Milano, Italy.
Cristina PonsiglioneDepartment of Industrial Engineering, University of Naples Federico II, Naples, Italy.
Lorella CannavacciuoloDepartment of Industrial Engineering, University of Naples Federico II, Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aims at defining the factors affecting the clinicians' decision of changing or confirming the treatment options for frail patients in polytherapy, supporting prescribing patterns, thus also figuring out if the inclination of the clinicians towards digital solutions (INTERCheckWEB) and specific guidelines, could play a role in their decision. A literature review was performed, revealing the main individual, organizational and decisional factors, impacting on the clinicians' propensity to change the current patients' therapy: the clinician perceptions of support in case of clinical guidelines use or INTERCheckWEB use were studied. A qualitative approach was implemented, and thirty-five clinicians completed a questionnaire, aimed at evaluating fifteen different clinical cases, defining if they would change the patient's current therapy depending on the level of information received. Three methodological approaches were implemented. (1) Bivariate correlations to test the relationships between variables. (2) Hierarchical sequential linear regression model to define the predictors of the clinician propensity to change therapy. (3) Fuzzy Qualitative Comparative Analysis-fsQCA, to figure out the combination of variables leading to the outcome. Patient's age and autonomy (p value = 0.000), as well as clinician's perception regarding IT ease of use (p value = 0.043) and seniority (p value = 0.009), number of drugs assumed by the patients (p value = 0.000) and number of concomitant diseases (p value = 0.000) are factors influencing a potential change in the current therapy. The fsQCA-crisp confirms that the clinical conditions of the patients are the driving factors that prompt the clinicians towards a therapy change.

Identifiers

PMID37016155
PMCPMC10072813

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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.