Big data in healthcare has the power to improve care, lower costs, and save lives. Healthcare providers are steadily digitizing their internal operations, resulting in mountains of new data being collected daily. This is augmented by the volumes of information being generated by people using Fitbits and other personal health and fitness devices.

At Billing Savi, data is a core part of our business: We provide our healthcare clients with insights that transform their practices by streamlining operations, improving patient care, and reducing errors. 

Analyzing all this information requires proper management and sophisticated technology. In this article, we’ll dig into some of the biggest opportunities that healthcare data presents today, the kinds of systems we need to draw valuable insights from it, and how data can improve healthcare and make the world a better place. 

Partnering with Healthcare Clients

The rise of healthcare data and digitization

Like many other industries, healthcare finds itself sitting on vast amounts of data. This data comes from various sources:

  • Hospital records
  • Patient medical records
  • Examination results 
  • Biomedical research
  • Insurance records

Providers are quickly digitizing their healthcare operations by incorporating electronic medical records that store medical and clinical data collected from patients in computers instead of on physical charts. Electronic health records allow providers to see a digitized version of their patient’s entire medical history. This includes their medical diagnoses, prescriptions, allergies, and test results. 

We built our Savi Sense analytics platform to help healthcare organizations better understand their data. The system empowers users to identify key drivers of revenue growth and identify problem areas in patient care and billing. In the right hands, a robust analytics platform like Savi Sense helps providers dramatically improve outcomes for their patients. 

Better results with AI

The enormous amounts of healthcare data being generated every day are healthcare’s greatest asset and biggest challenge. Sophisticated analytics tools are needed to extract valuable insights from these huge stores of data. Due to their immense volume and scale, it’s pretty much impossible for humans to effectively sort through it. 

This is where AI becomes critical: AI isn’t good at everything (yet), but one place modern intelligences shine is in sorting through large amounts of data and identifying patterns. Pairing human understanding with insights and patterns detected by AI systems, the healthcare industry has a huge opportunity to streamline operations and touch more lives with the same amount of human time and energy.

Automating routine detection tasks, such as identifying common ailments and even tumors in visual scans, for instance, frees up healthcare professionals for more challenging tasks that only a human can handle face-to-face. However, with the great power of AI pattern detection comes a responsibility to weed out unconscious bias and use it responsibly.

Reducing bias in training data

An AI system is only as reliable and useful as its training data. This is where combining datasets can result in a smarter, more helpful AI. For instance, adding data from your CRM systems can help remove some bias from your analyses. 

CRM systems help manage customer data. They support sales management, deliver actionable insights, integrate with social media, and facilitate team communication.

Unfortunately, human beings can bring inherent bias when evaluating information and can easily miss red flags within their datasets. For example, a laboratory might think a certain sales representative is highly valuable, based on the number of samples they bring in. However, AI data analysis could show that the rep was actually bringing in unpayable samples.

As in almost all cases, a single datapoint (volume of samples collected) doesn’t tell the whole story. A properly trained AI system can see a much bigger picture, more quickly, than a human, and could highlight the discrepancy between samples collected and samples paid to help the human team change behaviors, resulting in better care, streamlined operations, and increased revenue for the organization.

Improving treatment accuracy with data

Healthcare providers can improve treatment accuracy by presenting data-derived insights within user workflows that guide them to take appropriate actions. By understanding the effectiveness of different forms of care, practitioners can make better decisions and feel more confident about their chosen options. 

More and more healthcare organizations are turning their data into useful, actionable information. Doing so is consistently increasing the quality of patient care and helping medical professionals better understand the latest practices for treating diseases, injury, or illness.

Reducing prescription errors

Prescription errors afflict millions of people a year in the U.S. alone, with thousands losing their lives annually.

Innovative data companies such as MedAware are attempting to tackle this problem by recognizing mistakes before they affect the patient. These platforms identify and prevent medication-related errors by applying advanced machine learning algorithms and outlier-detection mechanisms. 

By analyzing and harnessing the practice patterns of thousands of physicians treating millions of patients around the globe, these programs can accurately flag medications that conflict with the profile of the patient, physician, or institution. The right data, analyzed by the right system, is saving lives right now, as you read this!

A healthier world, thanks to data

Healthcare data holds tremendous potential to improve patient care, reduce costs, and minimize errors. By using AI, CRM systems, and human analysis, providers can vastly improve their understanding of how effectively their healthcare operations are working. 

As the industry continues to embrace data technologies, we could see a tremendous transformation in the quality of treatments and effectiveness of our healthcare system. Whatever the future of healthcare holds, data will continue to be a huge part of it.


Sumit Mahendru is CEO of Savi Group, a nationwide healthcare revenue cycle, analytics, and medical records management company.

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