Historically the healthcare industry has generated large amounts of data, driven by record keeping, compliance and regulatory requirements and patient care. While most data is stored in hard copy form, the current trend is towards the rapid digitalization of these large amounts of data.
By definition, big data in healthcare does not refer to electronic health data sets so large and compiles that are difficult to manage with traditional software and / or hardware; nor can they be easily managed with common or traditional data management tools and methods. In healthcare big data is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed.
The totality of data related to patient healthcare in the healthcare industry includes clinical data from CPOE and clinical support systems, patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs of patients; social media posts, including Twitter feeds, blogs and other platforms and web pages; and less patient-scientific information, including emergency care data, news feeds, and articles in medical journals.
For the big data scientists, there is, amongst this vast amount and array of data, opportunity. By discovering associations and understanding patterns and trends within the data, big data analytics has the potential to improve healthcare, lower the costs, and save lives.
When big data is synthesized and analyzed-healthcare providers and other stakeholders in the healthcare delivery system can develop a more thorough and insightful diagnoses and treatments, resulting,one would expect, in higher quality care at lower costs and in better outcomes overall.
By digitalizing, combining and effectively using big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits. Potential benefits include detecting diseases at earlier stages when they can be treated more easily and effectively;
- Managing a specific individual and population health and detecting healthcare fraud more quickly and efficiently. Many questions can be addressed with big data analytics. Certain developments or outcomes may be predicted and / or estimated based on vast amounts of historical data, such as length of stay
- Patients who will choose elective surgery; patients who likely will not benefit from surgery; complications.
- Patients at risk of medical complications; patients at risk of sepsis; causal factors of illness; disease progression
- Patients at risk for advancement of disease. Big data analytics could advance the comparative effectiveness research to determine more clinically relevant and cost- effective ways to diagnose and treat patients.
- It can also predict modeling to lower attrition and produce a leaner, faster, more targeted R and D pipeline in drugs and devices, statistical tools and algorithms to improve clinical trial design and patient recruitment to match treatments to individual patients, thus reducing trial failures and speeding new treatments to market and analyzing clinical trials and patient records to identify follow-on indications and discover adverse effects before products reach the market.
In addition big data analytics in healthcare can contribute to;
- Evidence-based medicine: and analyze a variety do structured and unstructured data-EMRs,Financial and operation data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission and provide more effective care;
- Genomic analytics: Execute gene sequencing more effectively and efficiently and cost effectively and make genomic analysis a part of regular medical care decision process and growing patient medical record.
- Pre-adjudication fraud analysis: the rapid analysis of large numbers of claim requests to reduce fraud, waste and abuse as well.
- Device monitoring: capture and analyze in real-time large volumes of fast-moving data in hospitals for safety monitoring and adverse event prediction.