Utilizing big data from electronic health records in pediatric clinical care

Abstract

Big data has the capacity to transform both pediatric healthcare delivery and research, but its potential has yet to be fully realized. Curation of large multi-institutional datasets of high-quality data has allowed for significant advances in the timeliness of quality improvement efforts. Improved access to large datasets and computational power have also paved the way for the development of high-performing, data-driven decision support tools and precision medicine approaches. However, implementation of these approaches and tools into pediatric practice has been hindered by challenges in our ability to adequately capture the heterogeneity of the pediatric population as well as the nuanced complexities of pediatric diseases such as sepsis. Moreover, there are large gaps in knowledge and definitive evidence demonstrating the utility, usability, and effectiveness of these types of tools in pediatric practice, which presents significant challenges to provider willingness to leverage these solutions. The next wave of transformation for pediatric healthcare delivery and research through big data and sophisticated analytics will require focusing efforts on strategies to overcome cultural barriers to adoption and acceptance. Impact Big data from EHRs can be used to drive improvement in pediatric clinical care. Clinical decision support, artificial intelligence, machine learning, and precision medicine can transform pediatric care using big data from the EHR. This article provides a review of barriers and enablers for the effective use of data analytics in pediatric clinical care using pediatric sepsis as a use case. The impact of this review is that it will inform influencers of pediatric care about the importance of current trends in data analytics and its use in improving outcomes of care through EHR-based strategies.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

“…Because patient outcomes depend on several physiological, functional, social, and environmental factors, generating accurate predictive models requires valid data and robust analytical approaches that can perform complex, iterative analyses to identify patterns and predictors within large, multiplex datasets that can cover several years of time. Therefore, the practical implementation of precision rehabilitation necessitates the development of automated, user-friendly data collection and analytical systems that can deliver actionable information to clinicians without the need for hours of manual or supervised data collection and analysis [292,[297][298][299][300].…”

Imperfection in adverse event detection: is this the opportunity to mature our focus on preventing harm in paediatrics?

“…Investigation of successful adaptation events through a Safety-II lens may increase our understanding of how things go right and AEs (ie, things going wrong) are prevented. With the burgeoning growth of healthcare analytics and the widespread sophistication of EHRs, healthcare institutions now have more data than ever to characterise, analyse and replicate improvement successes 15. By applying Safety-II concepts, large-scale or even national data could be used to develop predictive analytics that can identify opportunities to intervene early locally or suggest thresholds for tools such as trigger alerts for local populations.…”

Federated data health networks hold potential for accelerating emergency research

“…It is imperative to develop such a model as emergency care datasets become more complicated with the increasing availability of complex “omics” data (genome, metabolome, microbiome, transcriptome, etc) that may need to be integrated with continuously obtained physiologic data from health monitoring devices (eg, Fitbit, Apple Watch) into emergency care visits 13 . Federated machine learning involves the development of models locally and subsequently developing a global model by combining local model parameters in a Health Insurance Portability and Accountability Act secure, cloud‐based server 14 . Level II FDHNs will need to follow the guidelines suggested but sites will need additional technological support/investment and regulatory oversight.…”

“… 13 Federated machine learning involves the development of models locally and subsequently developing a global model by combining local model parameters in a Health Insurance Portability and Accountability Act secure, cloud‐based server. 14 Level II FDHNs will need to follow the guidelines suggested but sites will need additional technological support/investment and regulatory oversight.…”