How ElectrifAi Brings Machine Learning Solutions to Health Care

Machine learning is often touted as a means of corporate efficiency, but in some industries, it can mean the difference between life and death. Health care relies on technology to treat patients, reduce costs, and, most importantly, improve patient outcomes. Machine learning is more than a buzzword for this industry: Health care providers, pharmaceutical companies, and medical device manufacturers rely on it to quite literally save lives.

It’s something that artificial intelligence and machine learning company ElectrifAi knows firsthand. For years, its AI team has helped health care providers better serve patients through the power of machine learning technology.

“Every business needs to constantly listen to customers who will tell you the path forward or what needs to be changed and optimized,” ElectrifAi founder and CEO Edward Scott told Authority Magazine.

“Further, in today’s fast-changing world, leaders need to be curious and willing to try different approaches and adapt. Through it all, you have to be humble letting others take the credit. We saw this when we created ElectrifAi — now one of the U.S.’s leading machine learning software solutions providers.”

Why Machine Learning Is a Must for Health Care

Machine learning is a type of technology that gathers, organizes, and spots trends in data much faster than humans can. It’s a smart solution to the influx of patient data floating around, unused, in many health care organizations. It would typically take organizations years to make sense of their unstructured data, but machine learning solutions like ElectrifAi put this data to use so providers can improve the patient experience.

Says ElectrifAi: “Our global network of the most skilled and specialized talent in machine learning has helped clients solve complex business problems. We create, implement, and refine our solutions. After sifting through massive amounts of unruly and disparate data, we place a layer of intelligence atop the data to output the clear, crisp, and practical business knowledge you seek.”

This is very critical because today, providers face a precarious future. On a good day, the average has a 2% net margin. Sadly, most lose money. Much of it is due to rising labor costs and reduced reimbursement. But much of the financial duress can also be attributed to continued reliance on manual processes, inefficiency, and poor practices.

ElectrifAi’s Machine Learning Solutions for Health Care

Every health care organization is different, but ElectrifAi’s machine learning solutions are already making waves in this sector. Here’s how ElectrifAi’s machine learning innovations made a profound impact on some health care providers.

Remedying Billing Issues for a Small Health System

ElectrifAi worked with a small health care system that had a big billing problem. It had limited resources and high costs that led to millions of dollars in lost revenue every year.

In response, ElectrifAi created a model based on other hospitals’ data to check for missed charges and billing issues. It also created user-friendly dashboards and educated the client’s employees on how to spot potential billing errors. As a result, the client completely automated the pre-bill and post-bill process, increasing revenue by $14 million.

Reclassifying Suppliers and Spend for a Major Pharmaceutical Company

ElectrifAi served a leading pharmaceutical provider that had well over 50 data sources, but couldn’t look at companywide spend without a lot of manual work. Inconsistent spelling and multiple enterprise resource planning systems made it too labor-intensive to capture total spend just for one supplier. The client wanted the ability to look at metrics based on supplier, category, and other metrics, so ElectrifAi created a solution to streamline the client’s systems.

ElectrifAi’s platform used data validation, cleansing, and consolidation to simplify the pharmaceutical company’s systems. This reduced the total number of vendors in the system by 35% and successfully classified 99.5% of all spend in the business. Today, the pharmaceutical company enjoys a companywide view of its expenses across all business units.

Implementing Predictive Analytics to Spot High Costs

ElectrifAi also partnered with one of the largest health systems in the U.S., which earns over $4 billion in net revenue per year. Its revenue-tracking system used inefficient, rules-based revenue cycles, which meant the organization had lots of high costs and lost income. To add insult to injury, it also had issues with missed charges and coding errors.

ElectrifAi worked with the client to identify areas that would streamline the charge reconciliation process. Through predictive analytics, ElectrifAi found ways to simplify the organization’s complex charging patterns. It even used auditor feedback to help the business make more intelligent business predictions.

As a result, the client was able to analyze all of its outpatient accounts. It identified the departments with the biggest gaps in charge capture, which led to the discovery of $40 million in confirmed missed charges. ElectrifAi also helped the client automate the pre-bill and post-bill process to prevent this problem from happening again in the future.

Machine Learning Tech Is Saving More Lives

Machine learning might still be in its early days of implementation for health care providers, but it’s already making a life-changing difference for patients and providers. Machine learning has the potential to improve the patient experience, increase diagnostic accuracy, and reduce costs. It may not be an easy road ahead, but innovators like ElectrifAi are making it possible for providers to embrace the next phase of patient-first care in a digital environment.

“Every business has significant amounts of data,” explains Edward Scott, “and ElectrifAi unlocks the potential of that data with prebuilt machine learning software solutions that quickly help enterprise clients drive customer acquisition and retention as well as cut costs and risk through spend and contract analytics.”