A clinical data warehouse-based process for refining medication orders alerts

Boussadi, Abdelali; Caruba, Thibaut; Zapletal, Eric; Sabatier, Brigitte; Durieux, Pierre; Degoulet, Patrice

doi:10.1136/amiajnl-2012-000850

Cited by 21 publications

(19 citation statements)

References 13 publications

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“…In addition, our approach could be used for the joint analysis of omics data (i.e., peptidomics, proteomics and genomics data). Consequently, we envision that improvements in personalized medecine will require data mining tools such as our algorithm that could be implemented in Clinical Data Warehouses [28], [29], [30]. …”

Section: Discussionmentioning

confidence: 99%

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Luck

Bertho

Bateson³

et al. 2016

PLoS ONE

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1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.

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Section: Discussionmentioning

confidence: 99%

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Luck

Bertho

Bateson³

et al. 2016

PLoS ONE

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“…15,16 Clearly, a comprehensive, health system-wide approach is warranted. 13,15 To date, published efforts describing ways to improve the effectiveness of medication warning systems have focused on either heightening the clinical significance of alerts 14,21,22,[32][33][34][35][36] or altering their presentation and how providers experience them. 21,[36][37][38][39][40][41][42][43] The single medication warnings our providers receive are all presented in an identical font, and presumably response to each would be different if they were better distinguished from each other.…”

Section: Discussionmentioning

confidence: 99%

Factors associated with medication warning acceptance for hospitalized adults

et al. 2015

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“…patient selection in the context of a more personalized practice of medicine) [44][45][46]. In the pharmaceutical domain at HEGP, the EHR/CDW integration was found invaluable in the evaluation of the frequency of both drug prescription and drug dispensing errors [47][48], the impact of alerts on drug dosage adjustment [49], the role of pharmacy drug order validation [50], but also to automatically detect drug-drug interactions [51] or to perform in-silico evaluations of decision rules for further integration into the operational environment of the CPOE [52].…”

Section: The Use-quality Circlementioning

confidence: 99%

The Virtuous Circles of Clinical Information Systems: a Modern Utopia

Degoulet

2016

Yearb Med Inform

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SummaryContext: Clinical information systems (CIS) are developed with the aim of improving both the efficiency and the quality of care. Objective: This position paper is based on the hypothesis that such vision is partly a utopian view of the emerging eSociety. Methods: Examples are drawn from 15 years of experience with the fully integrated Georges Pompidou University Hospital (HEGP) CIS and temporal data series extracted from the data warehouses of Assistance Publique -Hôpitaux de Paris (AP-HP) acute care hospitals which share the same administrative organization as HEGP. Three main virtuous circles are considered: user satisfaction vs. system use, system use vs. cost efficiency, and system use vs quality of care. Results: In structural equation models (SEM), the positive bidirectional relationship between user satisfaction and use was only observed in the early HEGP CIS deployment phase (first four years) but disappeared in late post-adoption (≥8 years). From 2009 to 2013, financial efficiency of 20 AP-HP hospitals evaluated with stochastic frontier analysis (SFA) models diminished by 0.5% per year. The lower decrease of efficiency observed between the three hospitals equipped with a more mature CIS and the 17 other hospitals was of the same order of magnitude than the difference observed between pediatric and non-pediatric hospitals. Outcome quality benefits that would bring evidence to the system use vs. quality loop are unlikely to be obtained in a near future since they require integration with population-based outcome measures including mortality, morbidity, and quality of life that may not be easily available. Conclusion: Barriers to making the transformation of the utopian part of the CIS virtuous circles happen should be overcome to actually benefit the emerging eSociety. KeywordsEvaluation of clinical information systems; return on investment; outcome research; acceptance models; information system success criteria Yearb Med Inform 2016:256-63 http://dx

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A clinical data warehouse-based process for refining medication orders alerts

Cited by 21 publications

References 13 publications

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Factors associated with medication warning acceptance for hospitalized adults

The Virtuous Circles of Clinical Information Systems: a Modern Utopia

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