Structured and unstructured healthcare data is important for monitoring disease and the health of populations, and facilitating decision-making in precision medicine. Researchers define specific characteristics of healthcare big data to effectively utilize it for multiple purposes. In this connection, big data frameworks allow the extraction of continuous data, data analysis, and personalizing data. As a result, high-performance systems can convert large data volumes into actionable insights for improving the health of populations and improving care quality at reduced costs.
Geography: Global; Focus Area: Healthcare big data for improving care and reducing costs
Structured and Unstructured Healthcare Big Data
Healthcare big data is recorded and stored as structured data (disease terminology, symptom and diagnostic information, patient admission history, laboratory results, services availed, drug and billing information), semi-structured data (data generated from sensors and devices for monitoring patient behavior), and unstructured data (clinical letters, prescriptions, discharge summary, biomedical literature). Healthcare data serves multiple purposes (Palanisamy & Thirunavukarasu, 2019):
- Analysis of healthcare leads to important information for use by stakeholders for efficient clinical decision-making, population health management, and disease surveillance.
- The development of healthcare frameworks using big data assists in personalized care and precision medicine.
- Understanding patterns in data leads to novel healthcare solutions such as the patient-centric model and disease-centric model.
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Utilizing Healthcare Big Data
To achieve a purpose, healthcare data needs to be curated using domain specific tools and techniques. Complex, interdependent medical data needs to be simplified, interconnections between health features identified, and target attributes need to be selected for healthcare analytics (Palanisamy & Thirunavukarasu, 2019). Data streamed from devices must be integrated to feed into predictive models, medical image formats must be constructed and compressed. Big data tools are also useful in data enrichment activities.
Redefining the characteristics of big data as “silo, security, and variety” in place of “volume, velocity, and variety” helps understand the context of big data in healthcare (Palanisamy & Thirunavukarasu, 2019). “Silos” refer to legacy databases containing public healthcare information in hospitals. “Security” refers to the care required to maintain healthcare data. “Variety” refers to the multiple formats of healthcare data including structured, unstructured, and semi-structured data.
Big Data Frameworks for Healthcare Data
Several big data frameworks have been proposed in research studies (Palanisamy & Thirunavukarasu, 2019):
- A framework consisting of data source layer, transformation layer, big data platform layer, and analytical layer takes internal and external data sources in multiple formats and extracts, transforms, and loads data using staging techniques (middleware and data warehousing). Further specific operations are performed by the Hadoop distributed file system (HDFS) using map-reduce. Finally, “querying, reporting, online analytical processing, and data mining” is performed in the analysis stage.
- Another patient-centric personalized healthcare framework used a collaborative filtering approach to using patient similarities to predict personalized disease risk. The system was designed to handle ICD codes.
- Another framework used vital signs extracted from accelerometers (as continuous time series data) to provide healthcare services. Motion data, respiration data, and ECG signals were used to feed into an interoperable system designed using Hadoop and Map-Reduce algorithm.
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In addition, several disease specific frameworks have been designed such as for AIDS, in policy enforcement, authentication and authorization (secure health systems), decision-support for practitioners, public health services, and drug design (Palanisamy & Thirunavukarasu, 2019).
In effect big data frameworks are designed to meet specific healthcare objectives through the use of standard guidelines. Given the continuous data growth and evolution, integration is a primary means of aggregating data from multiple sources for transforming data, tracking procedures and metrics, and using business rules to build prescriptive and predictive models. Powerful, high-performance systems can query large volumes of data and transform it into “actionable knowledge” and identifying trends and deviations to improve public health and clinical health delivery. Insights gained from the data analysis decrease costs and improve care delivery.
Keywords
healthcare data, security, population health, cost cutting, patient-centric, healthcare costing, improving care quality, variety, big data, big data frameworks, silo, personalized medicine
References
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