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.

Fig. 1. Healthcare organizational maturity in analytics over time using sepsis as a case study.

Improved patient outcomes can be realized for an organization as it progress through the various phases of analytics maturity, which are depicted here in grey boxes, with maturity level increasing from left to right. Below each phase of maturity, examples of relevant activities and data products for a sepsis case study are listed.

Fig. 2. The model for improvement adapted from Langley et al..

The crux of QI approaches is a rapid iterative approach to change implementation involving four phases: (1) Plan—state what you will do; (2) Do—put your stated plan into action; (3) Study—evaluate the results/impact of your implemented plan; (4) Act—adapt, adopt, or abandon your plan based on what you learn.