Big Data has changed the way we manage, analyze and leverage data in any industry. One of the most promising areas where Big Data can be applied to make a change is healthcare. Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general.
Average human lifespan is increasing together with the world population which poses new challenges to today’s treatment delivery methods. Healthcare professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers. Even if healthcare services are not your cup of tea, you are a potential patient, and just like everyone of us you should care about new healthcare analytics applications. Besides, it’s good to take a look around sometimes and see how other industries cope with Big Data. They can inspire you to adapt and adopt some good ideas.
One of the biggest hurdles that stands in the way to the data-driven healthcare is how medical data is spread across many sources governed by different states, hospitals and administrative departments. Integration of these data would require developing new infrastructure where all data providers collaborate with each other. Equally important is implementing new data analysis tools and strategies. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented like predictive analytics, machine learning and graph analytics. However, there are some glorious instances where healthcare doesn’t lag behind. Check out our list of Big Data examples in healthcare.
Electronic Health Records (EHRs)
It’s the most widespread application of Big Data in healthcare. Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results etc. Records are shared via secure information systems and are available for healthcare providers from both public and private sector. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication. EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders. Although EHR is a great idea, many countries still struggle to fully implement it. U.S. has made a major leap with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. Ambitious directive drafted by European Commission is supposed to change it. By 2020 centralized European health record system should become a reality.
Other Big Data in healthcare examples share one crucial functionality – real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions. However, doctors want patients to stay away from hospitals to avoid costly in-house treatments. Personal analytics devices, already trending as business intelligence buzzwords, have the potential to become part of a new healthcare delivery strategy. Wearables will collect patients’ health data continuously and send this data to the cloud. Patients will be able to share this data with their doctors to give them better insight into their well-being. Additionally, this information will be accessed to the database on the state of health of the general public, which will allow doctors to compare this data in socioeconomic context and modify healthcare delivery strategies accordingly. Healthcare institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing. For example, if patient’s blood pressure increases alarmingly, the system will send an alert in real time to the doctor who will then take action to reach the patient and administer measures to lower the pressure.
Predictive Analytics in Healthcare
We have already recognized predictive analytics as the biggest business intelligence trend for 2016 but the potential applications reach far beyond business and much further in the future. Optum Labs, an US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analytics tools that will improve the delivery of care. The goal is to help doctors make Big Data-informed decisions within seconds and improve patients’ treatment. This is particularly useful in case of patients with complex medical histories, suffering from multiple conditions. New tools would also be able to predict, for example who is at risk of diabetes and who is advised to make use of additional screenings or weight management.
Using Health Data For Informed Strategic Planning
The use of Big Data in healthcare allows for strategic planning thanks to better insights into people’s motivations. Healthcare professionals can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment.
University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such population growth and chronic diseases. Subsequently, academics compared this data with the availability of medical services in most heated areas. The insights allowed them to review their healthcare delivery strategy and add more care units to most problematic areas.
Telemedicine has been present on the healthcare services market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices and wearables, it has been able to come into full bloom. The term refers to delivery of remote clinical services using technology. It is used for primary consultations and initial diagnosis, remote patient monitoring and medical education for health professionals. Some more specific uses include telesurgery – doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location with a patient.
Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. It allows them to predict acute medical events in advance and prevent deterioration of patient’s conditions. By keeping patients away from hospitals, telemedicine helps to reduce costs of healthcare and improve the quality of service. Patients can avoid waiting lines and doctors don’t waste time for unnecessary consultations and paperwork. Telemedicine also improves the availability of healthcare as patients’ state can be monitored and consulted anywhere and anytime.
Examples of Big Data in healthcare prove that the development of medical applications of data should be the apple in the eye of data science, as they have the potential to safe people’s lives. Already today Big Data allows for early identification of illnesses of individual patients and socioeconomic groups and taking preventive actions because, as we all know, prevention is better than cure.
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