Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework

Joseph Finkelstein; Irena Parvanova; Zhaopeng Xing; Tuyet Trinh Truong; Andrew Dunn

doi:10.3233/SHTI230212

Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework

Joseph Finkelstein
, Irena Parvanova
, Zhaopeng Xing
, Tuyet Trinh Truong
, Andrew Dunn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.

Original language	English
Title of host publication	Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023
Editors	Maria Hagglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindskold, Parisis Gallos
Publisher	IOS Press BV
Pages	596-600
Number of pages	5
ISBN (Electronic)	9781643683881
DOIs	https://doi.org/10.3233/SHTI230212
State	Published - 18 May 2023
Event	33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 - Gothenburg, Sweden Duration: 22 May 2023 → 25 May 2023

Publication series

Name	Studies in Health Technology and Informatics
Volume	302
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
Country/Territory	Sweden
City	Gothenburg
Period	22/05/23 → 25/05/23

Keywords

Clinical decision support
implementation science
machine learning

Access to Document

10.3233/SHTI230212

Cite this

Finkelstein, J., Parvanova, I., Xing, Z., Truong, T. T., & Dunn, A. (2023). Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework. In M. Hagglund, M. Blusi, S. Bonacina, L. Nilsson, I. C. Madsen, S. Pelayo, A. Moen, A. Benis, L. Lindskold, & P. Gallos (Eds.), Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023 (pp. 596-600). (Studies in Health Technology and Informatics; Vol. 302). IOS Press BV. https://doi.org/10.3233/SHTI230212

Finkelstein, Joseph ; Parvanova, Irena ; Xing, Zhaopeng et al. / Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework. Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. editor / Maria Hagglund ; Madeleine Blusi ; Stefano Bonacina ; Lina Nilsson ; Inge Cort Madsen ; Sylvia Pelayo ; Anne Moen ; Arriel Benis ; Lars Lindskold ; Parisis Gallos. IOS Press BV, 2023. pp. 596-600 (Studies in Health Technology and Informatics).

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title = "Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework",

abstract = "The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.",

keywords = "Clinical decision support, implementation science, machine learning",

author = "Joseph Finkelstein and Irena Parvanova and Zhaopeng Xing and Truong, \{Tuyet Trinh\} and Andrew Dunn",

note = "Publisher Copyright: {\textcopyright} 2023 European Federation for Medical Informatics (EFMI) and IOS Press.; 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 ; Conference date: 22-05-2023 Through 25-05-2023",

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doi = "10.3233/SHTI230212",

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series = "Studies in Health Technology and Informatics",

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Finkelstein, J, Parvanova, I, Xing, Z, Truong, TT & Dunn, A 2023, Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework. in M Hagglund, M Blusi, S Bonacina, L Nilsson, IC Madsen, S Pelayo, A Moen, A Benis, L Lindskold & P Gallos (eds), Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. Studies in Health Technology and Informatics, vol. 302, IOS Press BV, pp. 596-600, 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023, Gothenburg, Sweden, 22/05/23. https://doi.org/10.3233/SHTI230212

Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework. / Finkelstein, Joseph; Parvanova, Irena; Xing, Zhaopeng et al.
Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. ed. / Maria Hagglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindskold; Parisis Gallos. IOS Press BV, 2023. p. 596-600 (Studies in Health Technology and Informatics; Vol. 302).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xing, Zhaopeng

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AU - Dunn, Andrew

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N2 - The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.

AB - The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.

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A2 - Madsen, Inge Cort

A2 - Pelayo, Sylvia

A2 - Moen, Anne

A2 - Benis, Arriel

A2 - Lindskold, Lars

A2 - Gallos, Parisis

PB - IOS Press BV

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Y2 - 22 May 2023 through 25 May 2023

ER -

Finkelstein J, Parvanova I, Xing Z, Truong TT , Dunn A. Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework. In Hagglund M, Blusi M, Bonacina S, Nilsson L, Madsen IC, Pelayo S, Moen A, Benis A, Lindskold L, Gallos P, editors, Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. IOS Press BV. 2023. p. 596-600. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230212

Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework

Abstract

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