As they embrace digitization, healthcare organizations find themselves literally sitting on a treasure trove of data. The data contained in their electronic systems could help healthcare companies venture into new business areas, improve patients’ outcomes, and identify high-risk patients before they even experience symptoms. All in all, data analytics are capable of providing a holographic view of an entire organization.
In fact, as per recent surveys, most healthcare providers are already leveraging some kind of data analytics to extract valuable business insights. The study conducted by the Society of Actuaries reveals as much as 83% of health organizations are either already using data analytics solutions or plan to do so in the near future.
The laggards run the risk of losing a competitive edge, but having to deal with terabytes of highly variable data can be overwhelming. In this article, we will guide you through the important steps of becoming a data-driven health organization.
Types Of Big Data In Healthcare
Healthcare companies may endue one data type with significance, while completely overlooking data from other sources. All in all, the types of healthcare data are as follows:
– Electronic health records (EHR)
– Accounting systems
– HR systems
– Clinic management systems
– Pharmacy
– Social media
– Lab systems
– Health information exchange (HIE)
– Medical images
– Medical devices and equipment
– Inventory management systems
– Credentialing systems
– Claims, etc.
The findings that can be derived from analyzing these types of data fall into five categories, namely:
– Operational efficiency analytics
– Financial performance analytics
– Patients experience analytics
– Clinical analytics
– Population health analytics
As a healthcare company, you can leverage these analytics to reduce operational expenses, increase revenue, improve quality and safety, enhance patients satisfaction, and bring your services to a unified standard. No organization becomes data-driven overnight, so you might have to rethink your approaches and, most importantly, adopt a data-driven mindset.
What It Takes To Become A Data-Driven Organization
Apart from the support from clinical personnel and management, becoming data-driven requires investment. You will have to provide your organization with tools that help to capture, store, and analyze big data, and infrastructure with enough capacities to power these analytics systems.
Secondly, you need to know how to read the analytics data that you collect, and how you can transform it into business-relevant facts and insights. Most analytic systems today visualize data in comprehensive formats, but you still have to know how to interpret it correctly.
The personnel of your organization will have to adopt a habit of consulting data from analytic systems before making critical decisions. This can pose a challenge: most of the time, our thinking is trapped in familiar patterns, so learning to trust data can be tricky. However, we shouldn’t rely on data blindly – after all, it’s up to humans to make all key decisions.
Building A Data Infrastructure In Your Organization
You don’t usually start with buying expensive analytics software right away – there’s lots of preliminary work you have to do before you can finally implement complex analytics systems. Below are some of the important steps that you can take to prepare your organization for using data analytics.
1. Retrieve and categorizing existing data
Most healthcare organizations are now using digital records and systems for their various needs. The historic data residing in these systems will be your primary subject for analysis. Retrieving and categorizing these data should be the first step you take towards building data infrastructure. This process is also time-consuming and may take up to several months.
2. Identify the gaps in existing data
Once you’re done with the first step, think of the primary goals you want your healthcare organization to accomplish with data analytics. Identify gaps in your data, so that you know which metrics you have to collect.
3. Choose your data analytics toolset
As you choose the data analytics tools for your healthcare organization, there are some considerations you should take into account. First, find out if you can put the data you plan to use in the cloud. For security reasons, healthcare data requires on-premise analytic tools, so check if the software you plan to use can run on top of your local infrastructure. Next, choose vendors who specialize in healthcare analytics and are HIPAA compliant, which means that they handle patients’ data according to HIPAA rules and requirements.
Healthcare data is subject to regulations, and there are restrictions as to the software and tools you can use. This limits your choice to vendors who have expertise in building healthcare solutions, offer on-premise tools, and are HIPAA compliant. For example, you can’t use free tools for data-anonymization; you have to use tools like VARTEQ’s Data Dazzler instead.
4. Upgrade your IT infrastructure
Make sure that the apps you plan to use for data analytics have enough storage and computing capacities to run on. As a rule, big data analytics are highly resource-intensive, especially if you plan to run complex medical image analysis like deciphering CT and MRI data. You can use the cloud, though, for analyzing non-regulated data, such as social media data of client’s claims and testimonials.
Roadblocks and Challenges
Many roadblocks that healthcare companies encounter as they strive to become data-driven revolve around handling data and knowing how to make sense of it. Retrieving particular metrics may pose a challenge. Making sure these metrics are retrieved correctly may be equally difficult. Further, checking these metrics for the correctness and deciding on a report type may require careful consideration.
The quality of data is yet another challenge. Historic data you may want to analyze could be stored in formats that the analytics software can’t process. Also, the data may be kept in disparate databases which are hard to integrate. Obviously, handling these tasks on a daily basis requires knowledge and expertise, and hiring data engineers and analysts for big-data projects is a common practice.
Despite many difficulties, building a data-driven health organization is quite achievable, if you take a step-by-step approach and stay dedicated to constant improvement by test and experiment. In some cases, building custom analytics software tailored to the needs of your organization could be the best choice.
Read also: Big Data in Healthcare: Applications and Challenges
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