Big Data in Healthcare: Applications and Challenges
Big Data in Healthcare: Applications and Challenges
With the rise of telemedicine solutions in the healthcare industry, so did the amount of data. Numerous sources of information are available and ready to provide caregivers the opportunity to offer better treatments and enhanced clinical outcomes. However, we also need tools to help us process this vast ocean of information. That is why, in industries like healthcare, a focus has shifted towards interoperability and standardization, allowing caregivers to utilize big data analytics in healthcare.
What Is Big Data in Healthcare?
Big data is any pool of information that is too vast, complex, or difficult to manage using traditional analytical tools. In healthcare, this data can come from a wide range of sources which means that aside from processing it, it is necessary to standardize the format in which we keep and share our data. There are over 40 standards developing organizations (SDOs) in the US alone, the most notable being the FHIR standard.
We can define big data in the healthcare industry through the 5Vs:
- Volume: the size and the amount of data
- Value: the insights and pattern recognition that can arise from data analytics
- Variety: the diversity and different types of data
- Velocity: the speed at which the data is accumulated
- Veracity: the accuracy of data
Care providers can use this knowledge to improve patient outcomes and save lives. However, not every medical organization can manage all the different data sources.
Healthcare Data Sources
According to the National Library of Medicine, health information can come from medical records, claims data, vital records, etc. Once digitalized, however, the amount of sources of information drastically increases. Most protected health information (PHI) is stored in electronic health records. However, patients can now utilize remote patient monitoring services and don wearable medical devices to increase the data pool. In turn, caregivers can use this information for big data analytics in healthcare and develop better treatment models.
Due to the sensitive nature of medical data, government agencies created standards and mandates on how health data can be kept and shared without the patient’s consent. To that end, in 2009, the Department of Health and Human Services adopted the HITECH Act to incentivize the adoption of EHRs. The move drastically increased the amount of digital data available, and it is only reasonable that the amount of healthcare big data will only increase in the future.
Applications of Big Data in Healthcare
The insight generated from data is a valuable tool to fight disease and decrease health inequity. Telemedicine offers care to geographically distant patients and reduces service costs, allowing many to enjoy the care that should be standard. We will take a closer look at some data analytics applications and how they can improve patient outcomes and save lives.
Big data in healthcare allows caregivers to analyze larger pools of information and use it for pattern recognition. Instead of manually analyzing and coming up with treatment options, a doctor can feed patient information to an algorithm that can create optimal solutions. This will reduce costs and save time, as it highlights the most effective solutions and eliminates the need for unnecessary tests.
Reduction of Medication Errors
Healthcare big data analytics can assist doctors in another way. The number of medical errors is higher than you think. In the US, as many as 251 000 die annually from preventable medical errors, making it the third leading cause of death.
We can prevent this through the use of big data. Tools that analyze it can fill the gaps in patient history and scan individual medical records, highlighting past medications, dosages, allergies, and relevant patient information. Big data application in healthcare can thus save lives by pointing out and preventing medical mistakes.
Digital solutions in healthcare play an essential role in healthcare delivery. They allow caregivers to track their patients remotely, utilizing medical and wearable devices to collect data. Subsequently, doctors can use that data to devise strategies and improve clinical outcomes. Moreover, in tracking patient progress and having patients participate in their healthcare delivery, they become engaged patients, which has been linked with improved clinical outcomes.
Big data use in healthcare also spans the question of security. AI tools can quickly scan and analyze insurance claims or traffic changes that might indicate a potential cyber attack. Given the growing number of attacks and rising costs, the healthcare industry must utilize all the tools available to combat online attackers.
CRM and Hospital Management
Having data means knowing when you might need staff in a particular department or when you might need to stock up on supplies. Healthcare and big data are intricately connected: through analytics, you can manage your supplies, staff, and patients and develop long-term strategies through analytics.
Big Data Analytics Challenges
As it became apparent above, many healthcare data sources exist.
Shifting through all that information stored in disparate databases is a challenge in its own right. When we consider that digital data is often stored in incompatible systems, the problem reaches an entirely new dimension.
Many issues concerning data are related to finding ways to manage and utilize it moving forward. Below is a non-exhaustive list of challenges related to big data and healthcare.
Patient and financial data are often stored separately in different systems. It is necessary to find a way to store and manage the data moving forward. Knowing the required data format and which policies apply to handling said data is crucial to making sense of vast oceans of information in healthcare.
Lack of Clear Procedural Codes
Many data systems currently do not assign unique patient identifiers to each patient. Analyzing healthcare data means having the benefit of a unique identifier to be able to skip the process of data matching.
The advantage of a unique identifier is that the process of data matching can be skipped over altogether. Without it, you will have to use a data-matching mechanism to identify abnormalities within a data set and put together each patient’s claims. Physician activity can also be mismatched if the diagnosis codes are unclear.
Recognizing the potential of big data in healthcare means understanding that there will be changes to the core business operations. IT staff becomes essential: they run analytics along with data scientists who provide the information. Potential organizational changes may be necessary to implement and develop this approach fully, but the results are well worth the effort.
With decades of experience in the healthcare industry, Vicert can develop a solution for your exact needs. Book a call with us to learn more about the challenges of big data in healthcare and of software that can handle it!