The marketing buzz words "big data" permeated every nook and cranny of industry in the past decade. In the healthcare industry, the use of big data works to increase operational efficiency and provide patient health insights, improve profits and reduce overhead. It's also helping to predict epidemics which then assists with disease prevention, find disease cures, improve patient quality of life, help prevent deaths and contribute to new health care delivery models. These all contribute to meeting healthcare's triple goals.
Healthcare's Triple Goals
Worldwide, healthcare shares three common goals, although policies and practices may vary. Those goals include improving:
- the patient experience, quality of care and satisfaction,
- overall population health,
- the per-capita cost of healthcare.
Big data helps in numerous ways. It eradicates the amount of time health care staff have to spend gathering data. It automates gathering of medical information such as blood pressure and heart rate, provides real-time health alerts and creates an electronic health record. It simplifies and nearly automates medical billing, cutting the work of medical billing services. Analytics coupled with big data provides further insights into individual patient health and into aggregate health groups.
Big data is defined by the five "Vs" and a "C" - Volume, Velocity, Variety, Variability and Veracity plus Complexity. Gartner's Doug Laney originated the definition with three "V's" – volume, velocity and variety but other analysts added the latter two and complexity. While roadblocks exist to the use of big data in healthcare, they're the surmountable ones of technical expertise and a need for robust security.
13 Ways Big Data Improves the Healthcare Industry
- Electronic Health Records (EHRs): The ubiquity of big data has enabled the creation of electronic health records that provide patient data via complete digital record. In many cases, the EHR gets stored on a blockchain. That allows healthcare providers to access it using any operating system and add to it regardless of their operating system. The EHR combines the patient's medical history, drug allergies, heart rate and other vital stats, current and previous medications, etc. The use of EHRs results in a single patient record with zero duplication that can be updated in real-time. It does not require a medical expert to update data but can be added to via the patient's smartphone or health tracking device.
- Tele-Medicine: The speed of big data combined with broadband Internet enables tele-medicine, the delivery of medical services via telephonics. This allows long distance treatment through tele-surgery in conjunction with robots and consultations with specialists via video conferences, plus data gathering through smartphones and texts. It enables physicians to conduct primary consultations and the initial diagnosis long distance. Health professionals can also use it for medical education. Tele-medicine allows for the remote monitoring of health conditions and an improved level of prevention. This results in costs savings, increased admittance of patients in need of treatment while reducing the number of patients admitted for primary care and re-admittance.
- Real-Time Alerts: The capture of real-time data of vital statistics and health measurement data enables predictive and prescriptive analytics which, in turn, allow the creation and sending of real-time health alerts. For example, if a patient shares their FitBit data feed with their doctor, the doctor can create alerts for when the patient's heart rate or blood pressure spikes to high, providing a warning to the patient that lets take preventative action. This results in less health incidents, as well as providing information that prepares the patient's health care provider with specific data if a situation does arise.
- Prevention: That ties into the prevention aspect of big data. In addition to its assistance to medical personnel, it provides direct assistance to the patient. They can use health, medical and dieting apps to track everything from steps walked to calories consumed. The data from devices like Fitbit, Jawbone and Samsung Gear Fit track fitness progress and let you upload your data. The data then gets aggregated with that of other users. These apps let you analyze long-term trends in your own fitness and health development.
- Improved Partnerships: Data professionals and medical professionals that data share can identify developing problems through their partnership. For example, the Pittsburgh Health Data Alliance combines data from a variety of sources including medical records, insurance records, wearable devices, genetic data and patient social media to craft a comprehensive patient portrait, then offer a customized healthcare package. Another partnership that has just been announced is between Apple and IBM. The two companies are collaborating on a big data health platform that will allow iPhone and Apple Watch users to share data to IBM's Watson Health cloud healthcare analytics service.
- Simplified Aggregate Analysis: The automated analytics paired with a person's data doesn't just get analyzed on its own or in aggregate with other app user data. It also gets used in large-scale medical models that predict medical and health trends. It also helps spot disease spread and uncover genetic relationships to disease. This isn't limited to Apple and IBM partnering with each other or to medical researchers, but includes both, as well as independent analysts.
- Fighting Epidemics: Medical personnel in Africa use mobile phone location data to track population movements. Feeding this data into predictive models allows healthcare professionals to model the spread of the Ebola virus. This enables the government to limit movements to stop disease spread and to set up mobile treatment centers where they're most needed. The same methods have been applied to delivering disaster relief following the 2010 Haiti earthquake.
- Clinical Trials: Clinical trials use applicant data sets to choose the best subjects. By combining their big data via partnerships, pharmaceutical firms have discovered alternative uses for established drugs. One example of this is the that the anti-depressant desipramine also effectively treats lung cancer.
- Personalized Medicine: Beyond the development of custom health plans, comes personalized medicine. That literally refers to using big data to tailor medications to a patient's genetics. The healthcare professional combines the genetic blueprint with lifestyle and personal environment data, then compares their profile to those similar to it to determine the best drug treatment and dosage.
- Treating Killer Diseases: Combining big data is spurring new advances in the treatment of deadly diseases like cancer. Realizing that 96 percent of the available cancer patient data had not been analyzed, Flatiron Health developed the OncologyCloud, a cloud service that automates data gathering related to diagnosis and treatment, collates it and provides it to clinicians for research.
- Genomic Sequencing: Big data lets clinicians analyze genomic data en masse. It requires simply storing the genetic sequence in a Hadoop cluster to ready it for analysis. This enables push button analysis to identify genes that contribute to disease or healing.
- Better Privacy and Security: The combination of big data and the blockchain enhances security of medical information. Blockchain technology can store data in an anonymous manner but be recalled by those with access permissions to view it for purposes of treatment and adding updates to the record.
- Improved Operational Efficiency: Healthcare companies use big data as a part of their business intelligence strategy to examine historical patient admission rates and to analyze staff efficiency. Healthcare companies can cut down on healthcare cost and provide better care with the help of predictive analytics. Big data also helps in reducing medication errors by improving financial and administrative performance and reduce readmissions.
Roadblocks to Use
Big data does require a specific, specialized skill set. IT staff trained in traditional relational databases and SQL programming languages do not have the training for big data analysis. The organization moving into the use of big data needs to hire a data scientist educated in the area. These experts generally have a Ph.D. and competition for them is fierce. While demand for them has risen, more people are turning to the career so the number of people available to conduct the work is growing.
One solution to the need for experts is the development of query tools that simplify the data analysis process. Examples of these include Microsoft Polybase and Impala. Polybase lets the user query SQL relational databases and Hadoop Distributed File System (HDFS) systems with extended SQL syntax. Impala lets the user use SQL over a Hadoop database. On the security front, blockchain technology helps organizations meet with HIPAA compliance while implementing big data projects. Opening patient data to a large group requires enhanced privacy controls and security. Carefully vetting and selecting big data vendors and analyzing their security and distribution protocols is the first step.
Healthcare already benefits from big data implementation. The number of ways it helps the industry keeps growing. Your organization can benefit from big data and leverage it by hiring a data scientist and implementing apps like Polybase and Impala. Contact us at MedXM for more information on how big data can help you design the journey for better outcomes.