Big data analytics has the ability to change the way healthcare professionals use technologies to gain insight and make decisions considering clinical data. This will be seen in the future with rapid implementation and use of big data analytics across the healthcare companies and industry. However, privacy issues and security as well as the need for continuous improvement require the most attention. Although Big data analytics and applications in healthcare are still in the middle stage of development, steady development in platforms and tools can enhance the process. 
Big data in healthcare include internet sources such as electronic health records, clinical decision support systems, CPOE, and external sources (government sources, laboratories, pharmacies, insurance companies & HMOs, etc.), in many different forms as (flat files, .csv, relational tables....).
Big data analytics in healthcare
big data is defined as “large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of the information” .
In other words, applying analytical methods to unanalyzed patient-related health and medical data in order to achieve a deeper understanding of results. This can provide care to individuals. Population data would tell the physician and patient during the decision-making process as well as determine the most appropriate treatment for that patient.
Sources and data types include:
Web and social media data: Twitter, LinkedIn, blogs, and the like. It can also include health plan websites, smartphone apps, etc. .
Machine to machine data: readings from remote sensors, meters, and other vital sign devices .
Big transaction data: health care claims and other billing records .
Biometric data: fingerprints, genetics, handwriting, retinal scans, x-ray, and other medical images, blood pressure, pulse .
Human-generated data: unstructured and semi-structured data such as EMRs, physicians' notes, email, and paper documents .
big data help reduce the inefficiency in the following areas:
- Clinical operations: Provide cost-effective ways for diagnosis and treatment.
- Research and development
- Public health .
- Evidence-based medicine .
- Genomic analytics 
Many things should be considered such as:
- Big data analytics platform that has to be available, continuity, ease of use, scalability, ability to manipulate at different levels of granularity, privacy and security enablement, and quality assurance [2, 6, 7].
- The gap between data collection and processing.
- Algorithms, models, and methods.
- The important managerial issues of ownership, governance, and standards.
- Data acquisition and data cleansing.
- Care data standardization, often fragmented or generated in legacy IT systems with incompatible formats .
- Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2, 3. https://doi.org/10.1186/2047-2501-2-3
- IHTT . Transforming Health Care through Big Data Strategies for leveraging big data in the health care industry. 2013.
- Manyika J, Chui M, Brown B, Buhin J, Dobbs R, Roxburgh C, Byers AH. Big Data: The Next Frontier for Innovation, Competition, and Productivity. USA: McKinsey Global Institute; 2011.
- IBM . IBM big data platform for healthcare.” Solutions Brief. 2012
- IBM . Large Gene interaction Analytics at University at Buffalo, SUNY. 2012.
- Ohlhorst F. Big Data Analytics: Turning Big Data into Big Money. USA: John Wiley & Sons; 2012.
- Bollier D. The Promise and Peril of Big Data. Washington, DC: The Aspen Institute; 2010.