The amount of data being digitally connected and stored is vast and is expanding rapidly. As a result, the science of data management and analysis is also advancing to enable medical and health organizations to convert this vast resource into information and knowledge that helps them achieve their objectives.
The development of data from refuse to riches has been key in the big data revolution of other industries. Progress in analytic techniques in the computer sciences, particularly in machine learning, have been a major incentive for dealing with these large knowledge sets.
These analytic techniques are in contrast to conventional statistical methods (derived from the social and physical sciences), which are mostly not useful for review of unstructured data such as text-based reports that do not fit into relational records.
In contrast to most consumer service industries, medicine adopted a practice of generating evidence from experimental (randomized trials) and quasi-experimental studies to inform patients and clinicians.
The evidence-based movement is founded on the belief that scientific inquiry is superior to expert opinion and testimonials. In this way, medicine was ahead of many other industries in terms of recognizing the value of data and information guiding rational decision making.
Usage of big data in the health sector:
- Big data may help with knowledge dissemination. Most physicians struggle to stay current with the latest evidence guiding clinical practice. The digitization of medical literature has greatly improved access; however, the sheer number of studies makes knowledge translation difficult. Even if a clinician had access to all the relevant evidence and guidelines, sorting through that information to develop a reasonable treatment approach for patients with multiple chronic illnesses is exceedingly complex.
- Big data also helps translate personalized medicine initiatives into clinical practice by extending the opening to use analytical abilities that can integrate practices biology (eg, genomics) with data. The Electronic Medical Records and Genomics Network does so by using natural language processing to phenotype patients, in an effort to streamline genomics analysis.
- Big data may allow for a transformation of health care by passing information immediately to patients, empowering them to perform a more active role. The current model saves patients’ records with health care specialists, putting the patient in a passive manner. In the future, medical records may remain with patients.
- Big data gives the opportunity to improve the medical record by linking traditional health-related data (eg, medication list and family history) to other personal data found on other sites (eg, income, education, neighborhood, military service,7 diet habits, exercise regimens, and forms of entertainment), all of which can be accessed without having to interview the patient with an exhaustive list of questions.
- By doing so, big data offers a chance to integrate the traditional medical model with the social determinants of health in a patient-directed fashion. Public health initiatives to reduce smoking and obesity could perhaps be delivered more efficiently in this way by targeting their messages to the most appropriate people based on their social media profiles
- big data may greatly expand the capacity to generate new knowledge. The cost of answering many clinical questions prospectively, and even retrospectively, by collecting structured data is prohibitive. Analyzing the unstructured data contained within EHRs using computational techniques (eg, natural language processing to extract medical concepts from free-text documents) permits finer data acquisition in an automated fashion. For instance, automated identification within EHRs using natural language processing was superior in detecting postoperative complications compared with patient safety indicators based on discharge coding.
- 4 Big data offers the potential to create an observational evidence base for clinical questions that would otherwise not be possible and may be especially helpful with issues of generalizability. The latter issue limits the application of conclusions derived from randomized trials performed on a narrow spectrum of participants to patients who exhibit very different characteristics.
- Big data helps in analyzing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response of the public
- This technology of Big data also offers faster development of more accurately targeted vaccines, like choosing the annual influenza strains
- It helps understand and turn large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, especially for the benefit of populations, Applying Big data analytics to patient profiles like segmentation and predictive modeling can help to recognize individuals who would profit from proactive concern or lifestyle changes, for example, the patients at risk of catching a specific disease (e.g., diabetes) who would benefit from preventive care.
