Ritesh Patil

Ritesh Patil


FHIR Healthcare: Refining Interoperability Standards for Patient Data

As digital health technologies are becoming more prominent, the terminology of FHIR healthcare is also gaining importance. Fast Healthcare Interoperability Resources, also commonly known as FHIR healthcare is one of the standard frameworks created with HL7 interoperability. FHIR healthcare developed through HL7 allows seamless data exchange across healthcare applications swiftly. 

FHIR healthcare being a cloud-based platform has some of the best HL7 features compared to other standards. This includes CDA, V2, and V3 lines of product. FHIR interoperability is a well-considered health data interoperability standard that promotes the Electronic Health Records (EHRs) exchange. The focus of FHIR interoperability is on addressing the emerging technological digitization in the healthcare sector and making medical data more accessible to patients. 

Organizations with FHIR healthcare aims to implement consistent mechanism and standards that help administer integrated remote patient monitoring and care delivery services. The combination of best features regarding previous data exchange standards into common specifications is what the FHIR data model offers to the use cases in the healthcare sector. 

FHIR Healthcare: Understanding the Relevance of What is Interoperability in Healthcare

Having relevant access to healthcare data, irrespective of where this data was being stored has enormous benefits for healthcare providers, patients, stakeholders, and society at various lengths.

But there is one tough concern that has to be tackled. The extensive healthcare IT systems have evolved across the world, but could not still implement seamless data exchange standards in healthcare. Even till a few years ago, various critical patient information was being stored in data silos and legacy systems. 

These systems used emails and faxes to exchange or transmit results, files, and patient records. Providers and clinicians had to treat patients without having the access to their complete medical history and researchers couldn’t identify the de-identified information they needed. 

The relevance of FHIR healthcare as one of the modern interoperability frameworks has been recognized by governments, software developers, providers, and has become a fast-growing sensation in the online implementation community. 

Healthcare data interoperability is now a beneficial solution requiring smooth patient data exchange and integrated healthcare automation. The FHIR standard allows healthcare IT developers to simplify the process of building applications for EHRs and enable exchanging and retrieving data from other apps more swiftly. 

Interoperability healthcare with FHIR was initially designed as an experimental project for HL7 healthcare software development. But, it gained extreme support from competitive EHR stakeholders. Therefore, HL7 FHIR standards in healthcare became one of the standardized portals for integrated app development, health IT systems, health informatics management, and a contributor to practicing healthcare and management. 

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FHIR Healthcare: Refining Interoperability Standards for Patient Data
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

 iOS App Dev

iOS App Dev


What is Big Data in Healthcare and How is it Used?

The pandemic is having an enormous impact on the healthcare sector. Between overwhelming hospitalization rates, intensifying cybersecurity threats, and an aggravating number of mental illnesses due to strict lockdown measures, hospitals are desperately searching for help. Big data in healthcare seems like a viable solution. It can proactively provide meaningful, up-to-date information enabling clinics to address pressing issues and prepare for what’s coming.

Hospitals are increasingly turning to big data development service providers to make sense of their operational data. According to Healthcare Weekly, the global big data market in the healthcare industry is expected to reach $34.3 billion by 2022, growing at a CAGR of 22.1%.

So, what is the role of big data analytics in healthcare? Which challenges to expect? And how to set yourself up for success?

How Big Data Can Help Solve Healthcare Problems

Big data has several accepted definitions. Here are two popular ones:

Douglas Laney’s definition. Laney is a former Chief Data Officer at Gartner. He states that big data is characterized by 3 Vs: volume, velocity, and variety. The volume stands for large amounts of data. Velocity refers to the speed of collecting data and making it accessible, while variety indicates the different types of data, such as text, video, logs, audio, etc.McKinsey’s definition. The renowned consulting firm defines big data as datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

According to an IDC report, the volume of big data is expected to reach 175 Zettabytes by 2025. To put it in perspective, it will take 1.8 billion years to download this amount of data with the average internet speed available nowadays.

#big-data #big-data-analytics #healthcare-and-big-data #healthcare-tech #medical-software-development #healthcare-software #big-data-processing #healthcare-software-solution

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data