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Objectives: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2)., Materials and Methods: A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected., Results: Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians., Discussion: Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved., Conclusion: The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by 'informed non-experts' but at the same time attuned to existing, more sophisticated analysis tools., (© The Author 2016. Published by Oxford University Press.)

The in silico prediction of the functional consequences of mutations is an important goal of human pathogenetics. However, bioinformatic tools that classify mutations according to their functionality employ different algorithms so that predictions may vary markedly between tools. We therefore integrated nine popular prediction tools (PolyPhen-2, SNPs&GO, MutPred, SIFT, MutationTaster2, Mutation Assessor and FATHMM as well as conservation-based Grantham Score and PhyloP) into a single predictor. The optimal combination of these tools was selected by means of a wide range of statistical modeling techniques, drawing upon 10 029 disease-causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putatively ‘benign’ non-synonymous SNVs from UCSC. Predictive performance was found to be markedly improved by model-based integration, whilst maximum predictive capability was obtained with either random forest, decision tree or logistic regression analysis. A combination of PolyPhen-2, SNPs&GO, MutPred, MutationTaster2 and FATHMM was found to perform as well as all tools combined. Comparison of our approach with other integrative approaches such as Condel, CoVEC, CAROL, CADD, MetaSVM and MetaLR using an independent validation dataset, revealed the superiority of our newly proposed integrative approach. An online implementation of this approach, IMHOTEP (‘Integrating Molecular Heuristics and Other Tools for Effect Prediction’), is provided at http://www.uni-kiel.de/medinfo/cgi-bin/predictor/.