Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade – Are We Ready for Big Data in Routine Health Care?

Abstract

Recently, information availability has become more elaborate and widespread, and treatment decisions are based on a multitude of factors, including imaging, molecular or pathological markers, surgical results, and patient’s preference. In this context, the term “Big Data” evolved also in health care. The “hype” is heavily discussed in literature. In interdisciplinary medical specialties, such as radiation oncology, not only heterogeneous and voluminous amount of data must be evaluated but also spread in different styles across various information systems. Exactly this problem is also referred to in many ongoing discussions about Big Data – the “three V’s”: volume, velocity, and variety. We reviewed 895 articles extracted from the NCBI databases about current developments in electronic clinical data management systems and their further analysis or postprocessing procedures. Few articles show first ideas and ways to immediately make use of collected data, particularly imaging data. Many developments can be noticed in the field of clinical trial or analysis documentation, mobile devices for documentation, and genomics research. Using Big Data to advance medical research is definitely on the rise. Health care is perhaps the most comprehensive, important, and economically viable field of application.

Retrospective analysis of breast radiotherapy treatment plans: Curating the ‘non‐curated’

“…see Figs , and ). Provided that sufficient data storage is available, there is no need to pre‐plan the recording of dose‐volume information or spend time manually entering dose‐volume information into purpose‐built databases or spreadsheets. The need to consistently apply treatment planning and data collection protocols (ideally with minimal human involvement in data entry and minimal opportunity for user error ) in order to obtain usable results : Information automatically written into the DICOM RTPLAN and RTSTRUCT objects already permits the identification and filtering of treatments that do not conform to the desired protocol (e.g. in this study, 35.7% of plans were excluded as they did not contain the data needed for this analysis, also more than 8% of plans were identified as containing wedges instead of or in addition to the field‐in‐field beams required by the local planning protocol).…”

Use of Multicenter Data in a Large Cancer Registry for Evaluation of Outcome and Implementation of Novel Concepts

“…While, in the past, many centers build up single databases without network and communication, such effects as described in the present manuscript offer significant short- and long-term benefit for all participants, generate large and multicenter data, and provide a comprehensive platform for scientific work as well as quality-related evaluations ( 25 , 26 ).…”

Big Data in Environment and Human Health

“…There is also a difference in exposure data that are specific to the location (as seen with environmental data) compared to the exposure data specific to an individual (as with health data). In addition to the data quality issues already mentioned (Khoury & Ioanni dis, 2014;Normandeau, 2013), there is a lack of standardization and coordination of re search tools and practices concerning environment and health data (Kessel & Combs, 2016). New efforts by international groups such as the Open Geospatial Consortium (OGC) (see Health DWG for OGC human health data standardization, and ELIXIR for bio medical data) are cause for hope in the future.…”