Big data analytics

Big data analytics

Big data analytics is turning out to be one of the toughest undertakings in recent memory for the healthcare industry. Providers who have barely come to grips with putting data into their electronic health records (EHR) are now being asked to pull actionable insights out of them – and apply those learnings to complicated initiatives that directly impact their reimbursement rates. For healthcare organizations that successfully integrate data-driven insights into their clinical and operational processes, the rewards can be huge. Healthier patients, lower care costs, more visibility into performance, and higher staff and consumer satisfaction rates are among the many benefits of turning data assets into data insights. The road to meaningful healthcare analytics is a rocky one, however, filled with challenges and problems to solve

Capture

All data comes from somewhere, but unfortunately for many healthcare providers, it doesn’t always come from somewhere with impeccable data governance habits. Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict. In one recent study at an ophthalmology clinic, EHR data matched patient-reported data in just 23.5 per cent of records. When patients reported having three or more eye health symptoms, their EHR data did not agree at all. Poor EHR usability, convoluted workflows, and an incomplete understanding of why big data is important to capture well can all contribute to quality issues that will plague data throughout its lifecycle.

Cleaning

Dirty data can quickly derail a big data analytics project, especially when bringing together disparate data sources that may record clinical or operational elements in slightly different formats. Data cleaning – also known as cleansing or scrubbing – ensures that datasets are accurate, correct, consistent, relevant, and not corrupted in any way