On Sept. 8, EHR vendor Azalea Health announced its acquisition of dashboardMD, a provider of BI reporting and healthcare analytics.

This union of cloud-based software solutions gives healthcare providers the ability to draw actionable insights straight from the electronic health records, leading to better patient outcomes through technology and data-augmented care.

dashboardMD’s President and CEO Jose Valero will assume a new role as Director of Analytics, and Azalea’s Co-Founder and CEO Baha Zeidan will remain CEO. The teams from both companies will be merged once the deal closes.

Zeidan said in a press release, “Incorporating dashboardMD’s unrivaled analytics capabilities in our clinician-friendly EHR and bringing Jose in to lead Azalea’s data analytics practice will empower providers to better manage clinical quality measures and have enhanced visibility into their revenue.” He goes on to say that the joint effort will “supercharge the healthcare analytics space and help the medical field take a giant step forward.”

Also read: Choosing An EHR: A Comparison Of The Best Electronic Health Records Software

Azalea delivers electronic health records, revenue cycle management, data insights, and telehealth solutions designed for community and rural practices. The tool also provides resources to help clinicians meet their Meaningful Use requirements. With the dashboardMD acquisition, the solution will also offer clinical and financial analytics along with BI reporting for provider organizations. This reporting includes daily interactive dashboards, scheduled reports via email, performance scorecards, predictive analytics, and ad-hoc analysis tools.

EHR systems improve alongside healthcare analytics market boom

This acquisition is likely the catalyst for a wave of analytics-driven EHR solutions. Right now, the healthcare analytics market is $21.2 billion. By 2026, that number is projected to reach $75.1 billion, with COVID only causing the importance of big data in the health industry to expand.

In the past, EHR and EMR tools weren’t designed for data mining or integration. Most systems weren’t able to integrate medical datasets from other sources, and if they were, employees with the skills and time to do so were very limited. To make matters worse, poorly designed, time-consuming user interfaces were a common complaint among doctors and nurses. No one wanted to spend more time dealing with an EHR than they had to.

And while some EHR and EMR tools still don’t support the ability to access the data mine stored in unstructured files, many of these tools are evolving to provide reporting and analysis capabilities, along with better user interfaces. Here’s a few uses a clinician can leverage with a data-centric EHR tool:

  • Medical data history makes diagnosing and treating more accurate and efficient.
  • Disparate data sets can be combined to strengthen financial planning.
  • Tracking the flow of patient visits reveals where improvements can occur.
  • Your organization’s performance is clear — meaning comparison to peers and national standards is possible.

The amount of health data will only continue to increase as organizations move to aggregate all of their information into one platform. As the healthcare sector is one of the most data-heavy industries, and the use of artificial intelligence (AI) and machine learning (ML) is on the rise, there’s a lot of room for opportunity and value. But with opportunity comes great responsibility.

Bias problems with advances in the healthcare industry

Chris Bergh, CEO of DataKitchen, Inc., a DataOps consultancy and platform provider that manages analytics creation and operations for healthcare organizations, warns of the dangers that AI and ML pose in the healthcare industry.

“One common problem is that biased data is used to train and develop ML algorithms. The AI model then goes on to perpetuate bias on a grand scale – you might call it institutionalized bias. In one well known example, UnitedHealth Group’s Optum division developed an algorithm that predicted severity of illness based on past spending data. However, spending on patients reflects existing institutionalized racial bias.”

Bergh goes on, “Since less money is spent historically on black patients than white patients, with the same level of need, the algorithm underestimated the level of need of black patients by 17 percent to 46 percent. If an algorithm like that were deployed across a healthcare provider’s customer base, it could have imposed unfair constraints on healthcare spending on black patients.”

This is not to say that AI and ML should be avoided, but rather used with caution while bearing in mind the consequences of AI bias.

Azalea and dashboardMD aren’t yet in the conversation about AI, but this step in data analytics is one that moves the healthcare industry another rung higher on the technology ladder.

These companies are making smart moves that not only benefit them from a business standpoint, but also benefit doctors and patients who wouldn’t have been able to afford a heavy-hitting, on-premise analytics solution.