Deployment and validation of the CLL treatment infection model adjoined to an EHR system
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Deployment and validation of the CLL treatment infection model adjoined to an EHR system. / Agius, Rudi; Riis-Jensen, Anders C.; Wimmer, Bettina; da Cunha-Bang, Caspar; Murray, Daniel Dawson; Poulsen, Christian Bjorn; Bertelsen, Marianne B.; Schwartz, Berit; Lundgren, Jens Dilling; Langberg, Henning; Niemann, Carsten Utoft.
In: npj Digital Medicine, Vol. 7, No. 1, 147, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Deployment and validation of the CLL treatment infection model adjoined to an EHR system
AU - Agius, Rudi
AU - Riis-Jensen, Anders C.
AU - Wimmer, Bettina
AU - da Cunha-Bang, Caspar
AU - Murray, Daniel Dawson
AU - Poulsen, Christian Bjorn
AU - Bertelsen, Marianne B.
AU - Schwartz, Berit
AU - Lundgren, Jens Dilling
AU - Langberg, Henning
AU - Niemann, Carsten Utoft
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - Research algorithms are seldom externally validated or integrated into clinical practice, leaving unknown challenges in deployment. In such efforts, one needs to address challenges related to data harmonization, the performance of an algorithm in unforeseen missingness, automation and monitoring of predictions, and legal frameworks. We here describe the deployment of a high-dimensional data-driven decision support model into an EHR and derive practical guidelines informed by this deployment that includes the necessary processes, stakeholders and design requirements for a successful deployment. For this, we describe our deployment of the chronic lymphocytic leukemia (CLL) treatment infection model (CLL-TIM) as a stand-alone platform adjoined to an EPIC-based Danish Electronic Health Record (EHR), with the presentation of personalized predictions in a clinical context. CLL-TIM is an 84-variable data-driven prognostic model utilizing 7-year medical patient records and predicts the 2-year risk composite outcome of infection and/or treatment post-CLL diagnosis. As an independent validation cohort for this deployment, we used a retrospective population-based cohort of patients diagnosed with CLL from 2018 onwards (n = 1480). Unexpectedly high levels of missingness for key CLL-TIM variables were exhibited upon deployment. High dimensionality, with the handling of missingness, and predictive confidence were critical design elements that enabled trustworthy predictions and thus serves as a priority for prognostic models seeking deployment in new EHRs. Our setup for deployment, including automation and monitoring into EHR that meets Medical Device Regulations, may be used as step-by-step guidelines for others aiming at designing and deploying research algorithms into clinical practice.
AB - Research algorithms are seldom externally validated or integrated into clinical practice, leaving unknown challenges in deployment. In such efforts, one needs to address challenges related to data harmonization, the performance of an algorithm in unforeseen missingness, automation and monitoring of predictions, and legal frameworks. We here describe the deployment of a high-dimensional data-driven decision support model into an EHR and derive practical guidelines informed by this deployment that includes the necessary processes, stakeholders and design requirements for a successful deployment. For this, we describe our deployment of the chronic lymphocytic leukemia (CLL) treatment infection model (CLL-TIM) as a stand-alone platform adjoined to an EPIC-based Danish Electronic Health Record (EHR), with the presentation of personalized predictions in a clinical context. CLL-TIM is an 84-variable data-driven prognostic model utilizing 7-year medical patient records and predicts the 2-year risk composite outcome of infection and/or treatment post-CLL diagnosis. As an independent validation cohort for this deployment, we used a retrospective population-based cohort of patients diagnosed with CLL from 2018 onwards (n = 1480). Unexpectedly high levels of missingness for key CLL-TIM variables were exhibited upon deployment. High dimensionality, with the handling of missingness, and predictive confidence were critical design elements that enabled trustworthy predictions and thus serves as a priority for prognostic models seeking deployment in new EHRs. Our setup for deployment, including automation and monitoring into EHR that meets Medical Device Regulations, may be used as step-by-step guidelines for others aiming at designing and deploying research algorithms into clinical practice.
U2 - 10.1038/s41746-024-01132-6
DO - 10.1038/s41746-024-01132-6
M3 - Journal article
AN - SCOPUS:85195504620
VL - 7
JO - npj Digital Medicine
JF - npj Digital Medicine
SN - 2398-6352
IS - 1
M1 - 147
ER -
ID: 395073517