How Advanced Analytics are Driving Hospital Revenue Cycle, Patient Engagement Improvements

Q&A with Timothy Kinney

Tim Kinney, GuidehouseThis article appeared in the Second Quarter 2019 Issue of The Navigator. 
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Guidehouse Managing Director Timothy Kinney discusses ways hospitals and health systems are using advanced analytics to improve revenue cycle performance, as well as patient engagement and clinical outcome management.

Q1: From a revenue cycle management standpoint, what’s the current landscape of advanced analytics across hospitals and health systems? 
Kinney: While providers have been leveraging analytics for some time, we are seeing more and more health systems invest in a formal analytics department, and I expect that trend to continue. The goal is to harness clinical and financial data to drive better revenue cycle metrics, a top priority for many system CFOs and CIOs as they look to gain efficiencies and return on their electronic health record (EHR) investments. Some systems will build the capability internally while others may partner deliberately to build their analytics capabilities. For example, we work with a large health system in the Midwest that invested significantly in its analytics team, expanding it from a single full-time employee (FTE) to more than 10 FTE’s over the course of two years. Other health systems are purchasing bolt-on technology that can synthesize data from disparate clinical and financial systems.

Q2: What impact can advanced analytics have on patient engagement, including accounting and billing? 
Kinney: Analytics are helping with patient engagement on the front end. Historically, charity screening was a time-consuming and convoluted process, requiring providers to request and obtain hard copy documentation from the patient. That process can be cumbersome and is rarely completed with 100% accuracy. Now, systems can use specific demographic and financial data points to make quicker, more accurate decisions. Specifically, a patient can be qualified for charity at the time of admission, which is great for the patient and has the potential to decrease bad debt for health systems.

Q3: What role do EHRs play in the revenue cycle? 
Kinney: Not only do EHRs document clinical aspects of care, they can also help increase automation in the revenue cycle. EHRs contain all the touchpoints and data elements that ensure payment – registration, insurance eligibility, authorization, revenue capture, coding and claim scrubbing. With an effective EHR system, as a clinician documents care that’s delivered, charge capture takes place in parallel. This results in more rapid claim generation with minimal back-end review and even less clinician follow-up, as well as automated processes to reduce errors and denials.  

Moreover, when an EHR system is installed, the traditional separation between frontline clinicians and the back-office lessens. But an inability to effectively bridge these gaps can cause many EHR installations to fail. Ensuring collaboration and engagement among clinicians and the back-office from the outset of an EHR implementation is a must. Communication and transparency can help overcome traditional clinical-revenue cycle wedges and protect against cash flow interruptions.

Q4: In what other components of revenue cycle are providers leveraging advanced analytics to improve outcomes? 
Kinney: Revenue integrity - preventing issues that can cause revenue leakage or compliance risks through replicable processes and internal controls - is one example. Many health systems are using data and analytics to enhance charge capture processes as they’re able to quickly analyze data to determine if an account is missing a charge, based on the clinical procedure. In most cases, they can get the charge added prior to the bill going out the door, so it’s a significant efficiency gain.  

We are also seeing analytics being used to address denial management on the back end. Health systems can break down data much more quickly to determine root causes or if a specific payer is causing an issue. In addition, systems are mining data to determine underpayments at the account level, which can add millions of dollars to the bottom line if they can quantify and obtain additional payment from the payer.

This article appeared in the Second Quarter 2019 Issue of The Navigator.
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