Supported by the incredible generosity and foresight of the L.K. Whittier Foundation since 1998, the Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit (VPICU) has brought the benefits of information science technology to the care of critically ill children across the country. In partnership with the L.K. Whittier Foundation, the VPICU has become the central resource for pediatric critical care quality, research and education in the United States. Information derived from the VPICU data resources continues to improve the quality of care at Children’s Hospital Los Angeles (CHLA) and throughout the nation—letting the actual evidence from the care of thousands of critically ill children inform the care of the next child.
The VPICU mission is to create a common information space for the providers of critical care. This vision preceded Facebook, the social web, Listservs and big data and pioneered the application of machine learning and artificial intelligence to this task. We have created this common information space through web sites, telemedicine, internet-based communication, collaborative research and critical-care quality improvement.
Our successes have decreased the time to discovery and helped improve the quality of care for critically ill children by identifying and sharing best practices and benchmarking excellence. The VPICU has grown to national prominence, expanding the information space using crowd sourcing, collaboration, knowledge dissemination, dynamic content, cloud computing, rich user experiences and, most critically, user-generated content and design. Applications not even imagined when the VPICU was founded are reaching the bedside in the nation’s PICUs.
The VPICU is the pre-eminent communication highway in pediatric critical care and has supported research and quality improvement. It has contributed to saving the lives of tens of thousands of children. However, there is much more that we need do to bring the full benefits of the VPICU to the bedside of critically ill children. In the modern information landscape, big data is changing health care practice. The VPICU was founded on the concept of big data and has been exploiting big data for over ten years with the help of the L.K. Whittier Foundation. The VPICU has created a vast data lake of critical care data from across the United States detailing how critical illness happens to children. The data we have collected is so massive and nearly beyond human comprehension, that it now needs to be managed and made relevant and available in the right place at the right time.
Fifteen years ago, the common information space seemed a strange concept. The space, as imagined then, represented a combination of communication technologies that would bring pediatric critical care together as one Virtual ICU. Since then, this virtual space has been used to care for children geographically distant from academic ICUs, educate caregivers, conduct research and support quality improvement.
Today, the common information space is readily understood as a space where knowledge is shared and is bounded only by the internet and defined by myriad communication technologies developed by the VPICU.
Nowhere is rapid decision making more urgent, or more complex, than in managing critically ill children. Important to that decision making is the ability to identify similar patients. Patient similarity has long been a challenging thorn to many researchers because of its subjective nature.
The Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit Data Science team works closely with physicians at Children’s Hospital Los Angeles to develop a framework to address this challenge.
The clinical status of an individual patient can be defined by multiple contexts. To understand a patient’s clinical status, the VPICU Patient Similarity Framework combines these contexts as needed by corresponding modules. A module generates a representation for each individual patient that enables the computation of a mathematical distance between any two patients within a given clinical context (e.g. mortality, diagnosis, volatility, etc). The summation of the distances from various contexts of interest enables the retrieval of similar patients.
The adoption of electronic medical record (EMR) systems has enabled researchers to apply a wide range of emergent machine and deep learning methodologies to the collected data with the goal of discovering and applying knowledge for better diagnosis, prognosis and patient treatment. At the recent AIMED conference in Laguna Nigel CA it was abundantly clear, as it has been to many investigators in health care big data research for years, that one of the major impediments to achieving the ‘big data’ promise of improved care for children, is the lack of sufficiently large datasets. This is particularly true in pediatric critical care. Although we capture and record large amounts of clinical data it is not widely available for researchers. This problem stems from: the lack of suitably large amounts of high quality data; and from the siloed nature of datasets used by various groups and studies, which make it difficult to make “apples-to-apples” comparisons. Furthermore the nature of pediatric critical care, wherein one unit may see a very limited number of certain diagnoses, makes this collaboration even more essential.To address the problem, we propose a Pediatric Data Collaborative where:
The PICU Data Collaborative will be comprised of institutions who contribute anonymized pediatric critical care EMR data to a shared data platform which resides in a private cloud-computing environment.
Each Collaborative member will be responsible for collecting and aggregating data from its various sources based upon an agreed data schema and element list. The anonymization algorithm will be shared across members to ensure compliance and usability. Standard EMR data (including demographics, diagnoses, bedside and laboratory measurements, medications and procedures for each patient) would be aggregated during the initial phase with the subsequent potential to add additional data including notes, waveform data, imaging data and even genomic data at a later stage as the Collaborative matures. To facilitate new research and development, the data platform will enable automated or semi-automated methods converting the disparate data sets into a format that is easily digestible by data scientists. The platform will also provide artificial intelligence workflows and access to data science workspaces, empowering members to develop the science instead of wrangling with the technicalities of the data.
Benchmark data sets historically have led to rapid gains in their associated fields. The most noteworthy example of this is the ImageNet database which is freely available and easily accessible to all researchers. Since 2010, annual challenges using this standardized database have led to rapid advances in computer vision capabilities. Well-curated medical datasets that are openly available for benchmarks are scarce, and this dearth makes the tracking of real progress in EMR algorithm development nearly impossible. The MIMIC-III database, which includes vital measurements, laboratory results, notes, fluid balance, procedure codes, diagnoses, imaging reports, among other data, is the most comprehensive and only freely accessible dataset of its kind. It is primarily an adult (16 years or above) critical care database with more than 38,000 patients, but it also contains almost 8,000 neonates. This database has enabled significant progress, but its single-institution nature places uncertainty on the transferability of algorithm developments. Discoveries made from the MIMIC database are rarely validated using databases from other institutions.
Our proposed rich collection of curated and standardized data can be used for benchmarking algorithm development geared towards improving pediatric critical care. The Collaborative could define tasks or problems akin to the ImageNet challenges. Regardless of the task or challenge, a common test set would be set aside to compare algorithms and track progress. Access to the data set will be controlled by the collaborative.
An important activity of the Collaborative would be an annual symposium, where researchers and clinicians gather to:
Randall Wetzel, MBBS, MRCP, LRCS, MSB, FAAP, FCCM
Dr. Randall Wetzel is the founder and Director of The Laura P. and Leland K. Whittier VPICU at Children’s Hospital Los Angeles. He is a tenured Professor of Pediatrics and Anesthesiology at the University of Southern California and has served as an attending physician in pediatric critical care for over 40 years both at Johns Hopkins Hospital and at Children's Hospital Los Angeles. Dr. Wetzel completed his undergraduate and medical degrees in the UK. He then trained in pediatrics at Case Western’s Rainbow Babies and Children’s Hospital, Critical Care Medicine at Johns Hopkins Hospital and subsequently Anesthesiology at Hopkins. He is board certified in Pediatrics, Pediatric Critical Care, Anesthesiology and Pediatric Anesthesiology. Additionally he received an MBA with a focus on information technology from Hopkins. Recently his research has involved data science applied to critical care medicine. He has served as the Director of Critical Care Medicine and as the Chairman of Anesthesiology Critical Care Medicine at CHLA. He has trained hundreds of residents and over 100 critical care fellows. He also founded Virtual Pediatric Systems which collects information of all admissions to over 160 pediatric ICUs in the United States.
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