Big Data and Pediatric Acute Kidney Injury: The Promise of Electronic Health Record Systems

Abstract

Over the last decade, our understanding of acute kidney injury (AKI) has evolved considerably. The development of a consensus definition standardized the approach to identifying and investigating AKI in children. As a result, pediatric AKI epidemiology has been refined and the consequences of renal injury are better established. Similarly, "big data" methodologies experienced a dramatic evolution and maturation, leading the critical care community to explore potential AKI/big data synergies. One such concept with tremendous potential is electronic health record (EHR) enabled informatics. Much of the promise surrounding these approaches is due to the unique position of the EHR which sits at the intersection of data accumulation and care delivery. EHR data is generated simply via the provision of routine clinical care and should be considered "big" from the standpoint of volume, variety, and velocity as a myriad of diverse elements accumulate rapidly in real time, spontaneously generating an immense dataset. This massive dataset interfaces directly with providers which creates tremendous opportunity. AKI can be diagnosed more accurately, AKI-related care can be optimized, and subsequent outcomes can be improved. Although applying big data concepts to the EHR has proven more challenging than originally thought, we have seen much success and continue to explore its potential. In this review article, we will discuss the EHR in the context of big data concepts, describe approaches applied to date, examine the challenges surrounding optimal application, and explore future directions.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…With a greater amount of content, high-throughput strategies can be applied to such a group of data so as to help in identifying a form of pre-AKI signal, which can subsequently assist in discriminating between patients who are of high risk and low risk for the AKI. The capability to predict AKI risk in this manner might subsequently have some forms of dramatic impact, as presently there is no scientifically proven treatment for AKI once one develops such conditions [72]. As patients who are considered to be of high risk get identified, the extent of care can get modified, and further strategies for harm prevention implemented.…”

“…AI-based clinical decision support systems (CDSS) can be implemented employing the expert system strategy, data-driven approach, or an ensemble approach by coupling both. An expert system consolidates a knowledge base containing a set of rules for specific clinical scenarios, and the initial rule set may be acquired from domain experts or learned from data through machine learning algorithms [72,[78][79][80] AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

“…AI-based clinical decision support systems (CDSS) can be implemented employing the expert system strategy, data-driven approach, or an ensemble approach by coupling both. An expert system consolidates a knowledge base containing a set of rules for specific clinical scenarios, and the initial rule set may be acquired from domain experts or learned from data through machine learning algorithms [72,[78][79][80].…”

A Review on the Application and Limitations of Administrative Health Care Data for the Study of Acute Kidney Injury Epidemiology and Outcomes in Children

“…Hand-in-hand with a veritable explosion of research demonstrating the morbidity and mortality related to AKI in adults and children, there has been significant attention paid to “big data” ( 18 , 25 , 37 ). Registries containing administrative health care data are readily available, efficient, and large ( 14 , 19 ). Validation studies have been shown that administrative health care data are highly specific, despite having modest sensitivity, for the diagnosis of AKI.…”

“…Examples of data collected are summarized in Figure 1 and include physician billing codes, prescription claims, vital records, and hospitalization/discharge summaries ( 13 ). These data contained within the administrative health databases are often referred to as “big data,” which are distinguished by the large volume of information, speed at which it is generated, and the wide range of fields that it covers ( 14 ). Epidemiological studies, in particular, benefit greatly from the availability of administrative health care databases to evaluate and track the health of large populations over a period ( 12 ).…”