10 Pitfalls to Watch Out for When Using Big Data in Healthcare

Big data in healthcare

Big data analytics is proving to be one of the most difficult projects in the healthcare business in recent years.

For healthcare organisations, the benefits of correctly integrating data-driven insights into clinical and operational operations might be tremendous. Turning data assets into data insights offers several benefits, including healthier patients, lower healthcare expenses, better performance visibility, and higher employee and customer satisfaction. The path to meaningful healthcare analytics, on the other hand, is winding and full of challenges to overcome. Because big data is inherently complex and unmanageable, provider organisations’ approaches to data collection, storage, analysis, and presentation to workers, business partners, and patients must be thoroughly examined.

Failure to effectively use storage

Front-line clinicians are typically concerned about where their data is stored, but IT is concerned about cost, security, and performance. As the volume of healthcare data grows, some providers are no longer able to control the costs and implications of on-premise data centres. While many organisations choose on-premise data storage because it allows them to control security, access, and uptime, an on-site server network may be expensive to build, maintain, and is prone to creating data silos among departments.

Not integrating security measures

Data security is a significant responsibility for healthcare organisations, especially in the wake of a slew of high-profile data breaches, hackings, and ransomware attacks. Healthcare data is exposed to a wide range of dangers, including phishing attacks, viruses, and computers left in taxis. The HIPAA Security Rule includes several technical safeguards for businesses that retain protected health information (PHI), including transmission security, authentication methods, and access, integrity, and auditing controls.

Failure to report important data

Providers must create a report that is clear, simple, and accessible to the target audience after they have mastered the query procedure. Once again, the data’s correctness and integrity have a significant influence on the report’s accuracy and dependability. Poor data at the start of the process will result in dubious reports at the conclusion, which can be harmful to physicians seeking to use the knowledge to treat patients.

Failure in sharing data

Only a small proportion of doctors work alone, and even fewer patients get all of their medical needs met in one place. As the industry evolves toward population health management and value-based care, sharing data with external partners is becoming increasingly important. Data interoperability is a persistent concern for businesses of all shapes and sizes, as well as those at various stages of data maturity.

Failure to query data 

Organizations can also query their data to obtain the desired outcomes. The capacity to query data is essential for reporting and analytics, but most healthcare organisations face many obstacles before they can successfully analyse their big data assets. They must first overcome data silos and interoperability issues that restrict query tools from accessing the whole company’s data repository.

Stewardship of data

Data in the healthcare field, particularly clinical data, has a long shelf life. Providers may want to use de-identified records for research, in addition to being obligated to maintain patient data available for at least six years, making continuous stewardship and curation a critical issue. Data can also be re-examined or repurposed for other reasons, such as quality assurance or performance benchmarking.

Failure in clearing data

Healthcare professionals may not realise how important it is to clean their data. Poor data quality may quickly derail a big data analytics endeavour when combining disparate data sources that may capture clinical or operational components in slightly different ways. Cleansing or scrubbing data ensures that databases are accurate, consistent, usable, and free of corruption. While the bulk of data cleaning tasks are still done by hand, some IT companies provide automated scrubbing solutions that use logic rules to compare, contrast, and fix large datasets.

Failure in data visualization

Clear and engaging data visualisation at the point of treatment can make it much simpler for a physician to assimilate information and use it effectively. Color-coding is a common data visualisation approach that elicits a quick response for example, red, yellow, and green are commonly recognised to represent stop, caution, and go, respectively. Good data presentation strategies, such as charts that employ the right proportions to demonstrate contrasting statistics and precise labelling of material.

Failure in updating data

Healthcare data isn’t static, and to stay current and relevant, most components will need to be updated regularly. For some datasets, such as patient vital signs, these updates may occur every few seconds. Other information, such as a person’s home location or marital status, may change just a few times over their lives. Understanding the volatility of big data, or how often and to what degree it changes, can be challenging for firms that do not regularly monitor their data assets.

Capturing data

Every piece of data comes from somewhere, but for many healthcare facilities, that somewhere may not have flawless data governance practises in place. Capturing data that is clean, complete, correct, and organised suitably for use in many systems is an ongoing issue for businesses, and many are failing. A recent study at an ophthalmology clinic found just 23.5% of EHR data matched patient-reported data.

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