Big Data in Healthcare

Healthcare is a multi-dimensional system established with the sole aim for the prevention, diagnosis, and treatment of health-related issues or impairments in human beings. The major components of a healthcare system are the health professionals (physicians or nurses), health facilities (clinics, hospitals for delivering medicines and other diagnosis or treatment technologies), and a financing institution supporting the former two. The health professionals belong to various health sectors like dentistry, medicine, midwifery, nursing, psychology, physiotherapy, and many others. Healthcare is required at several levels depending on the urgency of situation. Professionals serve it as the first point of consultation (for primary care), acute care requiring skilled professionals (secondary care), advanced medical investigation and treatment (tertiary care) and highly uncommon diagnostic or surgical procedures (quaternary care). At all these levels, the health professionals are responsible for different kinds of information such as patient's medical history (diagnosis and prescriptions related data), medical and clinical data (like data from imaging and laboratory examinations), and other private or personal medical data. Previously, the common practice to store such medical records for a patient was in the form of either handwritten notes or typed reports. Even the results from a medical examination were stored in a paper file system. In fact, this practice is really old, with the oldest case reports existing on a papyrus text from Egypt that dates back to 1600 BC. In Stanley Reiser's words, the clinical case records freeze the episode of illness as a story in which patient, family and the doctor are a part of the plot".

With the advent of computer systems and its potential, the digitization of all clinical exams and medical records in the healthcare systems has become a standard and widely adopted practice nowadays. In 2003, a division of the National Academies of Sciences, Engineering, and Medicine known as Institute of Medicine chose the term "electronic health records" to represent records maintained for improving the health care sector towards the benefit of patients and clinicians. Electronic health records (EHR) as defined by Murphy, Hanken and Waters are computerized medical records for patients any information relating to the past, present or future physical/mental health or condition of an individual which resides in electronic system(s) used to capture, transmit, receive, store, retrieve, link and manipulate multimedia data for the primary purpose of providing healthcare and health-related services".

Electronic health records

It is important to note that the National Institutes of Health (NIH) recently announced the "All of Us" initiative (https://allofus.nih.gov/) that aims to collect one million or more patients' data such as EHR, including medical imaging, socio-behavioral, and environmental data over the next few years. EHRs have introduced many advantages for handling modern healthcare related data. Below, we describe some of the characteristic advantages of using EHRs. The first advantage of EHRs is that healthcare professionals have an improved access to the entire medical history of a patient. The information includes medical diagnoses, prescriptions, data related to known allergies, demographics, clinical narratives, and the results obtained from various laboratory tests. The recognition and treatment of medical conditions thus is time efficient due to a reduction in the lag time of previous test results. With time we have observed a significant decrease in the redundant and additional examinations, lost orders and ambiguities caused by illegible handwriting, and an improved care coordination between multiple healthcare providers. Overcoming such logistical errors has led to reduction in the number of drug allergies by reducing errors in medication dose and frequency. Healthcare professionals have also found access over web based and electronic platforms to improve their medical practices significantly using automatic reminders and prompts regarding vaccinations, abnormal laboratory results, cancer screening, and other periodic checkups. There would be a greater continuity of care and timely interventions by facilitating communication among multiple healthcare providers and patients. They can be associated to electronic authorization and immediate insurance approvals due to less paperwork. EHRs enable faster data retrieval and facilitate reporting of key healthcare quality indicators to the organizations, and also improve public health surveillance by immediate reporting of disease outbreaks. EHRs also provide relevant data regarding the quality of care for the beneficiaries of employee health insurance programs and can help control the increasing costs of health insurance benefits. Finally, EHRs can reduce or absolutely eliminate delays and confusion in the billing and claims management area. The EHRs and internet together help provide access to millions of health-related medical information critical for patient life.

Digitization of healthcare and big data

Similar to EHR, an electronic medical record (EMR) stores the standard medical and clinical data gathered from the patients. EHRs, EMRs, personal health record (PHR), medical practice management software (MPM), and many other healthcare data components collectively have the potential to improve the quality, service efficiency, and costs of healthcare along with the reduction of medical errors. The big data in healthcare includes the healthcare payer-provider data (such as EMRs, pharmacy prescription, and insurance records) along with the genomics-driven experiments (such as genotyping, gene expression data) and other data acquired from the smart web of internet of things (IoT) (Fig. 1). The adoption of EHRs was slow at the beginning of the 21st century however it has grown substantially after 2009. The management and usage of such healthcare data has been increasingly dependent on information technology. The development and usage of wellness monitoring devices and related software that can generate alerts and share the health related data of a patient with the respective health care providers has gained momentum, especially in establishing a real-time biomedical and health monitoring system. These devices are generating a huge amount of data that can be analyzed to provide real-time clinical or medical care. The use of big data from healthcare shows promise for improving health outcomes and controlling costs.

Big data in biomedical research

A biological system, such as a human cell, exhibits molecular and physical events of complex interplay. In order to understand interdependencies of various components and events of such a complex system, a biomedical or biological experiment usually gathers data on a smaller and/or simpler component. Consequently, it requires multiple simplified experiments to generate a wide map of a given biological phenomenon of interest. This indicates that more the data we have, the better we understand the biological processes. With this idea, modern techniques have evolved at a great pace. For instance, one can imagine the amount of data generated since the integration of efficient technologies like next-generation sequencing (NGS) and Genome wide association studies (GWAS) to decode human genetics. NGS-based data provides information at depths that were previously inaccessible and takes the experimental scenario to a completely new dimension. It has increased the resolution at which we observe or record biological events associated with specific diseases in a real time manner. The idea that large amounts of data can provide us a good amount of information that often remains unidentified or hidden in smaller experimental methods has ushered-in the '-omics' era. The 'omics' discipline has witnessed significant progress as instead of studying a single 'gene' scientists can now study the whole 'genome' of an organism in 'genomics' studies within a given amount of time. Similarly, instead of studying the expression or 'transcription' of single gene, we can now study the expression of all the genes or the entire 'transcriptome' of an organism under 'transcriptomics' studies. Each of these individual experiments generate a large amount of data with more depth of information than ever before. Yet, this depth and resolution might be insufficient to provide all the details required to explain a particular mechanism or event. Therefore, one usually finds oneself analyzing a large amount of data obtained from multiple experiments to gain novel insights. This fact is supported by a continuous rise in the number of publications regarding big data in healthcare (Fig. 2). Analysis of such big data from medical and healthcare systems can be of immense help in providing novel strategies for healthcare. The latest technological developments in data generation, collection and analysis, have raised expectations towards a revolution in the field of personalized medicine in near future.