Issues related to data structure were addressed in the majority of the papers reviewed for this study. It is essential that the key functions of data processing are supported by the applications of big data [13]. Big data applications should be user-friendly, transparent, and menu-driven [13,14]. The majority of data in health care is unstructured, such as from natural language processing [12]. It is often fragmented, dispersed, and rarely standardized [12,13,15-21]. It is no secret that the EHRs do not share well across organizational lines, but with unstructured data, even within the same organization, unstructured data is difficult to aggregate and analyze. It is no wonder that 61% of the articles analyzed listed this as a concern; big data analytics will need to address this large challenge.

Research data within the health care sector is more heterogeneous than the research data produced within other research fields [3,5,12]. Data from both research and public health is often produced in large volumes [15,22,23]. Another structure-related issue results from the changing health care fee-for-service care model [4]. Finally, big data will need to address issues with the transparency of metadata [16,24].

There are considerable privacy concerns regarding the use of big data analytics, specifically in health care given the enactment of Health Insurance Portability and Accountability Act (HIPAA) legislation [15]. Data that is made available on open source is freely available and, hence, highly vulnerable [12,13,18,20]. Further, due to the sensitivity of health care data, there are significant concerns related to confidentiality [25,26]. Moreover, this information is centralized, and as such, it is highly vulnerable to attacks [25]. For these reasons, enabling privacy and security is very important, as illustrated by a frequency of mention in 50% of the literature reviewed.

Although the EHRs share data within the same organization, intra-organizational, EHR platforms are fragmented, at best. Data is stored in formats that are not compatible with all applications and technologies [13,22]. This lack of data standardization also causes problems in transfer of that data [5,25]. It complicates data acquisition and cleansing [5,25,26]. About 39% of the literature mentioned this challenge.

Limited interoperability poses a large challenge for big data, as data is rarely standardized [12,13,16,22]. This leaves big data to face issues related to the acquisition and cleansing of data into a standardized format to enable analysis and global sharing [13,17,23,25,27]. With globalization of data, big data will have to deal with a variety of standards, barriers of language, and different terminologies.

Data generation is inexpensive compared with the storage and transfer of the same. Once data is generated, the costs associated with securing and storing them remain high [25]. Costs are also incurred with transferring data from one place to another as well as analyzing it [14,21,22]. Some researchers have been able to combine the themes of Data structure and Storage and transfers when they illustrate how structured data can be easily stored, queried, analyzed, and so forth, but unstructured data is not as easily manipulated [13]. Cloud-based health information technology has the additional layer of security associated with the extraction, transformation, and loading of patient-related data [27]. The use of big data should address issues related to increased expenditures as well as the transmittance of secure or insecure information. About 28% of the literature mentioned this challenge.

Data governance will need to move up on the priority list of organizations, and it should be treated as a primary asset instead of a by-product of the business [15]. Data ownership and data stewardship should create new roles in business that consider big data analytics [15], and new partnerships will need to be brokered when sharing data [23,24,27]. About 14% of the literature mentioned this point.

It is important that health care workers are also kept up to date with the use of constantly changing technology, techniques, and a constantly moving standard of care [5,24]. Due to the constant evolution of technology, there exist populations of individuals lacking specific skills; as such this is also a significant continuing barrier to the implementation of big data [12]. About 11% of the literature expressed this challenge.

Self-reported data is extensively used in health care, and so it is crucial that the data collected in this manner be consistent [12]. Keeping information current as well as accurate is another challenge of data collection. Precision of data is also needed to provide accurate information [12]. Only 4% of the literature mentioned this challenge.

Health care organizations should be aware of the various legal issues that can surface in the process of managing high volume of sensitive information. Organizations implementing big data analytics as a part of their information systems will have to comply with a significant amount of standards and regulatory compliance issues specific to health care [28]. Only 4% of the literature mentioned this challenge.

One of the key requirements in health care is to be able to utilize big data in real time. Real time is defined by enabling the use of applications such as cloud computing to view said data in real time. The use of these technologies leads to issues of security and privacy within patient information [12]. Only 4% of the literature mentioned this challenge. Challenges most often mentioned or discussed were data structure (17/28, 61%), security (14/28, 50%), data standardization (11/28, 39%), and data storage and transfers (8/28, 29%). The other five challenges comprised less than 15% of the observations.

Big data has the potential and ability to improve the quality and efficiency of care [5,15,23,29-31]. Big data offers an ability to predict outcomes using the available primary or historical data and provide proof of benefit that could change established, industry-wide standards of care [25,28]. Leveraging technology at the patient end can also help with medication adherence [23,25]. This will most certainly play an important role in improving outcomes [2,13] and improve the health-related quality of life [20,26,32].

Quality of care will also be improved by reducing waste of information, which will reduce inefficiencies [13,26]. This will also assist in analyzing real-time resource utilization productivity [13]. Quality can also be improved by reducing the rates of readmissions, increasing operational efficiencies, and improving performance [5,12,13]. About 64% of the literature mentioned this opportunity.

The management of population health and the early detection of diseases were topics that the authors thought would have highly similar results after the analysis. Although there was a large overlap between the 2 themes, there was also specific variation between them. So, the researchers chose to keep them separate. The theme of managing population health focused on special populations rather than public health.

Big data analytics define populations at a finer level of granularity than has ever been previously achieved [5,14,15,33]. It can help in managing the overall health of a population as well as specific individual health [13,26,29]. Big data can enable population health management from a local or global perspective [31,34]. This capability becomes more salient from the global perspective when considering the aging of the population and age-related health issues shared by many populations and subpopulations, many of which are underserved [17,19,21,24,28,32]. About 61% of the literature mentioned this opportunity.

Big data allows for the early detection of diseases, which aids in clinical objectives related to achieving improved treatments and higher patient outcomes [12,13,15,22,25]. It is in this area that the authors found great promise in age-related illness and disease. Along with early detection, big data analytics can also help in the prevention of a wide range of deadly illnesses and personalized disease management and monitoring [5,19,21,22,29,34]. It enables providers to track healthy behaviors and helps patients in monitoring their respective conditions [25,32,33]. This capability holds great potential when faced with either age-related diseases, or worldwide health issues such as cardiology [16,22,28,31,34]. About 61% of the literature mentioned this opportunity.

Literature suggests that big data enables rapid capture of data and the conversion of primary, raw and unstructured data into meaningful information [15,17,31,34]. New knowledge can then be generated from high volumes of effective data, enabling reuse of the data [15,20,21,32,33]. Open-source technology increases accessibility to and transparency of the data [12,25,26,30,35]. Finally, data quality can be maintained using analytics to get rid of unnecessary information [27]. About 57% of the literature mentioned this opportunity.

Big data enables appropriate use of evidence-based medicine and helps health care providers make more informed decisions [12,13,15,22]. This, in turn, improves the quality of care provided to the patients [16,31,36]. Remote monitoring, patient profile analytics, and genomic analytics are examples of other applications that influence the decision-making process [13,25].

Decision-making process can be highly optimized by the availability of accurate and up-to-date information, as decision making is influenced by the generation of new practices and treatment guidelines within clinical research. Allowing big data to influence decision making will allow for a faster and simpler process. This is done by either supporting or replacing human decision making. About 39% of the literature mentioned this opportunity.

The literature suggests that the decrease in cost of the elements of computing, such as storage and processing, leads to a decrease in the cost of data-intensive tasks [2,13]. This pass-through of savings will be seen across the spectrum of medicine [24,36] and the health care workforce [25]. Savings will be realized through more cost-effective treatments and monitoring to improve medication adherence [25,31] and through the reduction of costly transportation costs, as is experienced in cardiology [12,17,22,34]. About 36% of the literature mentioned this opportunity.

Increasing the use of technology is slowly changing the direction of the health care sector from disease-centric care toward patient-centric care [5]. Big data will play a significant role in this transformation [37]. It will allow the information to be delivered to patients directly and empower them to play an active part in their care [5,15,27]. When patients are provided with the appropriate information, it will influence their decision making and allow them to make informed decisions [13,24]. Informed decisions will also be influenced by increased communication between patients, providers, as well as their communities [5,24,32,36]. About 29% of the literature mentioned this opportunity.

With the use of big data, the objectives of personalized medicine can be translated into clinical practice [5,25,30]. Access to and processing of large volumes of data should enable a personalized patient-specific record of risks of disease [25,29,32]. Big data applications aim to make this process more efficient [12]. About 24% of the literature mentioned this opportunity.

Big data will actively help in disseminating the knowledge acquired from the data collected [15,22,30]. Big data plays an active role in leveraging the practices and knowledge not only regionally but globally [12,15,29]. By globalizing data, it is made more widely accessible and providers may access new information from all regions [22,23,32]. About 24% of the literature mentioned this opportunity.

One of the most significant benefits offered by big data is that it is instrumental in detecting fraud in an efficient and effective manner [13,23]. For example, the unauthorized use of specific user accounts by third parties can be minimized [21]. Only about 11% of the literature mentioned this opportunity.

Big data offers opportunity for improving capabilities of threat detection quickly and more accurately. This can be especially beneficial for government use [22]. Big data augments the current acquisition of protection against the increasing threats of foreign countries, criminals, terrorists, and others. Only 3.6% of the literature mentioned this opportunity.

Opportunities most often mentioned or discussed were improve quality of care (18/28, 64%), managing population health (17/28, 61%), early detection of diseases (17/28, 60.7%), data quality structure and accessibility (16/28, 57%), improve decision making (11/28, 39.3%), cost reductions (10/28, 36%), patient-centric health care (8/28, 29%), enhancing personalized medicine (6/28, 24%), and globalization (6/28, 24%). The other two opportunities each comprised less than 15% of the observations.