Three disruptive technologies transforming healthcare today

While healthcare is experiencing its most rapid pace of innovation in recent history, not every organization has been able to keep up with this pace and provide staff with the most advanced, efficient technology available.

Due to the slow adoption of newer technologies, grandfathered processes such as paper-based documentation and HIPAA considerations around data sharing, many hospitals and physician practices still use archaic tools to support patient care and financial operations.

Cumbersome interfaces and subpar connections between clinical and financial data have become the norm for many healthcare organizations; as a result, these entities run the risk of being unprepared for the constant changes bombarding the industry. Innovators are working intently to provide solutions that help providers adapt to these rapid changes, which encompass everything from ever-increasing merger and acquisition activity to the growing demands of patient-driven consumerism.

As the industry continues to embrace value-based care and comply with quality-driven payment programs such as MIPS and MACRA, organizations can benefit from modern solutions that deliver meaningful information to the right staff—thus enabling them to prioritize resources and make optimal decisions in a proactive manner. These solutions will help organizations successfully navigate new payment models and other shifts in the healthcare space. Moreover, they will lay the groundwork for future innovation.

While there is a bevy of new technologies available in the healthcare market, these three rise to the surface as the most important to watch.

The cloud. One of cloud-based technology’s greatest benefits is its ability to streamline the deployment of new features and functionalities. Companies that offer cloud-based programs can deploy upgrades with unmatched speed, offering the latest advancements without a prolonged and potentially cumbersome implementation process. Streamlined deployment allows for nimbler adjustments, pivoting in response to changes in healthcare and getting new tools to customers swiftly and efficiently. Such solutions also allow rapid scalability, which is advantageous as mergers and acquisitions continue to increase. Growing organizations can promptly and painlessly add new users to cloud-based software systems without having troublesome start-up times that could negatively impact patient care and financial operations.

Predictive analytics. This technology helps organizations allocate staff and resources to the clinical and financial areas that have the greatest need or will provide the most benefit. For example, the denials management function is currently one of the most unwieldy aspects of healthcare finance, consuming significant time and resources. Some organizations wait until they receive a denial to respond to it, which makes for an inefficient and costly linear approach. Predictive analytics can help an organization focus on those denials that have the greatest likelihood of being overturned and provide the biggest return on the time invested. Another area where these tools can be useful is patient payments. The predictive nature of the technology allows the solution to determine an individual’s propensity to pay their bill; as a result, organizations know up front how to customize the financial conversation and plan for the income. Additionally, patients can be offered payment plans early in the process, allowing the organization to avoid incurring bad debt.

Artificial intelligence/machine learning. Not only are these technologies adaptive and newer to the healthcare industry, they also go hand-in-hand with predictive analytics. Due to their fast-learning capabilities, they are positioned to help organizations be more proactive in managing claims and entering patient information, which leads to ensuring better care and long-term performance. Revisiting the denials example, machine learning can analyze online documents and identify patterns in denials. By doing so, these features uncover root causes of those patterns and can bring these to the attention of monitoring staff and leaders. The organization can then take the proper steps to resolve the root causes of each pattern or prevent the denials from occurring in the first place. Healthcare organizations can also leverage artificial intelligence to address the many challenges inherent in rising patient financial responsibility; for instance, helping patients understand their financial responsibility prior to receiving care, thus improving their experience.

When hospitals, health systems and physician practices welcome cloud-based programs that incorporate predictive analytics and machine learning, they will be able to further transform care and provide a positive patient financial experience throughout the entire payment process. They will also become more proactive, data-driven and nimble—and as a result, will be more prepared for future changes in the healthcare industry.

Author bio:
Paul Bradley leads the research and development of predictive modeling technologies in revenue cycle solutions at Waystar. He is responsible for driving the predictive analytics platform to improve the business and process of healthcare. Waystar’s Charge Integrity technology helps our clients achieve ROI multiples of 4 to 10.

Paul brings 19 years of research experience to Waystar’s focus on classification and clustering algorithms, underlying mathematical problem formulations, issues related to scalability and their application to healthcare problems.

Before assuming his current role at Waystar, Paul was a co-founder and Chief Data Scientist at MethodCare, now known as Waystar, which utilized predictive modeling and analytics over large data sets in patient access, charge integrity and reimbursement solutions. Prior to MethodCare, Paul consulted on data mining algorithm integration with Microsoft Research and SQL Server, and led data analysis solution implementations for a number of Microsoft divisions.

Earlier, Paul was a researcher at Microsoft Research, where he developed new data mining algorithms and components that shipped with Microsoft’s flagship database SQL Server.

Paul earned his PhD and MS degrees in Computer Science and a BS in Mathematics from the University of Wisconsin.

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