The Top 7 Challenges of Implementing Big Data in Healthcare

Big Data is reshaping Healthcare in ways we never imagined. The potential benefits of Big Data in healthcare is huge, ranging from improving patient outcomes to transforming medical research. But let’s face it, implementing Big Data in healthcare isn’t simple. It’s like trying to build a super complicated machine from scratch. To make it work, you need the proper equipment, skills, and strategy. From data management to data security, there are plenty of challenges to overcome.

But don’t worry, we’re here to guide you through it all. In this blog, we’ll explore the 7 major challenges of implementing Big Data in healthcare.

Big Data in Healthcare

Big Data in Healthcare refers to the analysis of enormous volumes of data, both organized and unstructured, to find patterns, trends, and insights. Big Data is often defined by the three Vs: volume, velocity, and variety, which refers to the fact that it includes significant amounts of data produced quickly and of various sorts, such as text, photos, graphs, or videos.

Healthcare data specialists use this analytics to handle and analyze massive amounts of data, produce insights, and spot patterns and trends that may be used as a decision-making tool. The enormous amount of data produced by the healthcare sector offers a wealth of knowledge that may be applied to better patient outcomes and lower costs.

Healthcare organizations can acquire vital insights about patient care and disease management by evaluating this data using modern analytics technologies, which enables them to provide more personalized and efficient treatments. However, implementing Big Data in the healthcare industry comes with significant challenges that need to be overcome to fully harness its potential benefits.

7 Major Challenges of Implementing Big Data in Healthcare

• Data capture

The first step in putting Big Data solutions into practice in healthcare is data capture. Healthcare providers, however, encounter difficulties when trying to collect clean, accurate, and formatted data from numerous sources with various data governance practices. The challenge of data capture in healthcare arises due to the vast amount of data that needs to be collected from various sources such as electronic health records, wearables, medical devices, and more.

To support efficient analysis and decision-making, the data collected must be precise, consistent, and relevant. As there are several data formats, protocols, and systems utilized in healthcare, standardizing the data is a challenge. Another layer of complication is added by the sensitive nature of healthcare data, which need secure transmission and storage in order to protect patient privacy.

• Data Cleaning

Patient data must be gathered from various sources and organized into an accessible manner in order to use Big Data in healthcare efficiently. This requires collaboration between various organizations and the use of classification techniques to structure heterogeneous and unstructured data.  Moreover, data cleaning—which can be carried out manually or automatically using logic rules—is required to guarantee the consistency, accuracy, and correctness of the data collected.

Big Data for medical imaging requires extra care since improper management can create noise that can lead to errors.  The proper processing and storage of medical imaging data is therefore essential for accurate analysis and diagnosis because this can have a negative impact on patient care.

• Data Security

Data security is one of the top issues for most healthcare providers, with constant hacking and security violations that need to be handled on a continuous basis. The Health Insurance Portability and Accountability Act (HIPAA) has established guidelines for securing Protected Health Information (PHI) through its Security Rule. In order to keep PHI secure, healthcare providers are required by the Security Rule to put in place technical, administrative, and physical measures.

Technological safeguards are precautions created to prevent illegal access, use, or disclosure of electronic PHI. These protections include access control and transmission security, which make sure that PHI cannot be intercepted while being transmitted and limited access to PHI. Many organizations use security measures to protect their data, including up-to-date antivirus software, encryption of sensitive information, and multi-factor authentication. Yet, even the most secure data can be compromised.

• Data Storage 

Data storage for the healthcare industry is becoming a critical concern as healthcare data continues to rise quickly. Since it gives users control over security, access, and availability, on-premise storage is a popular choice. Unfortunately, it may not provide seamless communication across many departments and can be expensive to grow and difficult to manage.

Cloud storage is becoming a more widely used option as prices decline and dependability increases. A cloud-based health IT infrastructure is already used by about 90% of healthcare enterprises. Lower initial expenses, quick disaster recovery, and simpler scaling are all benefits of the cloud. Organizations must pick partners, though, who are aware of the significance of HIPAA and other security and compliance concerns unique to the healthcare industry.

Several service providers employ a hybrid approach to data storage to accommodate various data access and storage needs. However, providers should be careful when developing a hybrid infrastructure to ensure that disparate systems can communicate and share data when necessary.

• Data Visualization

Data visualization is a crucial part of Big Data implementation in healthcare. It helps to present complex information in an easy-to-understand format. Visual aids such as interactive charts and graphs with color coding can make data much more accessible for providers. Color-coding is a simple yet effective technique to convey information quickly, with red, yellow, and green widely understood as stop, caution, and go.

However, it’s important to follow good data presentation practices such as using appropriate proportions and correct labeling to avoid confusion. Complex flowcharts with low-quality graphics can lead to misinterpretation, which defeats the purpose of data visualization. Examples of effective data visualizations in healthcare include heat maps, bar charts, pie charts, scatterplots, and histograms, each with their own specific uses to communicate information and insights.

• Implementation costs

The implementation costs associated with Big Data analytics in healthcare are a significant challenge for medical clinics. To fully benefit from Big Data, clinics must purchase technology, acquire computational tools and software, and develop custom applications.

Moreover, the hiring of data scientists and training of existing staff to work with data are additional expenses that must be considered. While there is no way to avoid initial investment, hospitals can minimize unforeseen expenses by carefully considering all potential costs, including indirect ones, before implementing Big Data analytics.

• Real-Time Data

Healthcare data is not constant and requires frequent updates to stay relevant. Healthcare providers are faced with a difficulty since they need to make sure that their data assets are regularly checked for quality and integrity.

To address this, providers use a combination of manual and automated updates, prioritizing processes that can be completed without downtime and ensuring that updates do not compromise data quality or integrity. Providers also take care to avoid creating duplicate records, which can hinder access to critical patient information for healthcare decision-making.

How to Overcome these Challenges?

To overcome the 7 major challenges of implementing Big Data in healthcare, healthcare organizations must focus on the following solutions.

• Establishing data governance procedures and providing education to healthcare professionals can help improve data capture and data cleaning.
• Data security measures such as encryption and multi-factor authentication can be implemented to protect sensitive patient information.
• Cloud-based data storage can be used to ensure scalability and accessibility while maintaining data security.
• Effective data visualization techniques can help to present complex data in an easy-to-understand format.
• To reduce implementation costs, healthcare organizations can collaborate with technology vendors and focus on training existing staff to work with data.

By addressing these solutions, healthcare organizations can effectively implement Big Data in healthcare and fully harness its potential benefits.

Final Thoughts

In conclusion, the implementation of Big Data in healthcare presents significant opportunities for improving patient outcomes, reducing costs, and transforming medical research. However, it also comes with several challenges that must be addressed, such as data capture, data cleaning, data security, data storage, data visualization, implementation costs, and data privacy.

Overcoming these challenges requires a combination of technological expertise, effective strategies, and collaboration between different stakeholders in the healthcare industry. By doing so, healthcare providers can harness the potential benefits of Big Data analytics to provide more personalized and efficient treatments, improve disease management, and advance medical research.

Ksolves Expert BigData Service

At Ksolves, our team of experts in big data analytics has a wealth of experience in delivering innovative solutions to healthcare organizations looking to gain insights from their data. With our customized healthcare data analytics services, we can help you identify opportunities to improve patient outcomes, increase operational efficiency, and reduce costs.

Our team will work closely with you to understand your unique needs and develop a tailored solution that meets your specific goals and objectives. Ksolves can also help healthcare organizations manage compliance with regulations like HIPAA  (Health Insurance Portability and Accountability Act).  We prioritize compliance and data security in all our projects, giving our clients peace of mind and helping them stay ahead of evolving industry regulations.  When you choose Ksolves as your big data consulting partner, you can trust us to provide you with the highest level of expertise, professionalism, and quality service.