In a time of increasing risks within the healthcare industry, internal auditors can benefit from technology tools and strategies that give them more confidence in their audits. Data analytics is included among these supports, as it can help auditors better pinpoint risks and identify potential challenges, ultimately bringing more value to their healthcare organizations.
Data analytics supports myriad areas of the audit process, from risk assessment, audit planning, and fieldwork to follow-up, risk analysis, and continuous monitoring. Using the vast amount of data already existing within most healthcare organizations from sources such as patient accounting systems (PAS) and electronic health records, analysts can play an important role in highlighting risk areas and solving challenges.
The following examples show how two healthcare organizations’ internal audit teams worked with data analysts to generate insightful reports that helped them accomplish more successful audits.
A hospital CFO had concerns about a case management nursing team that worked remotely and was not using its time effectively. The team had worked on-site prior to the COVID-19 pandemic, and, according to hospital leadership, the staff was having a difficult time adjusting to remote work. The CFO brought the concern of time theft to the internal audit team, which conducted an audit to determine which nursing staff members were or were not using their time effectively.
Healthcare internal auditors established two main questions for the audit:
To start, data analysts worked with the remote nursing team’s manager to define productivity and what it meant for the hospital and nursing team. Then, they began gathering data to assist healthcare internal auditors with auditing productivity within the unit and determining potential solutions to leadership’s concerns. Among the types of data collected were activity logs from two different software platforms (a medical record review system and case management documentation system) and data from the hospital’s payroll system.
Working with healthcare internal auditors and hospital leadership, the analysts then developed an action plan to highlight various areas of concern about worker productivity. They created a duration field that allowed them to view how many hours per day the nurses were logged into the case management documentation system until their final task for the day was complete. After establishing how many hours each employee was working, the analysts performed counts of the various activities the nurses completed. This helped the team understand where the nurses were spending their time. The data analysts then cross-referenced payroll data to compare paid time off (PTO) hours the nurses had taken with the amount of time they were logged into the remote work system.
To filter the data further and gain more insights, analysts addressed some challenges they had encountered with the data. For example, analysts detected variations of employee names throughout the data, such as name changes, inconsistent use of nicknames, and hyphen usage. Analysts worked with hospital leadership to make sure each employee’s name was used consistently throughout all data files to avoid confusion. In addition, because leadership’s objective with this audit was to identify employees who might be stealing time, it was important that only tasks considered actual work by hospital managers were included. Any actions not corresponding to the managers’ definition of actual work were removed from the data files.
Working with healthcare internal auditors, the data analysts then created productivity reports highlighting information about the nursing team members’ work, including:
Using the analysts’ reports, the healthcare internal auditors were able to identify the employee who was stealing time and report that information to the CFO. Following the results of this audit, the CFO and other members of the hospital leadership team implemented necessary changes to staff training and management policies to prevent similar misuse of time from occurring in the future. The hospital’s labor relations department worked closely with the auditors to set new productivity goals for employees working remotely.
Errors in their charge capture processes can expose hospitals to numerous financial and compliance risks. Revenue loss can occur when inaccurate ICD-10 coding results in incorrect billing. Coding mistakes also can lead to breaches in compliance with hospital billing regulations. Healthcare internal auditors, boosted by data analysts’ work, can help hospitals identify and avoid charge capture errors.
For example, internal auditors can conduct charge capture testing in areas that are at high risk for errors, such as the cardiac catheterization lab, the emergency department, interventional radiology, oncology, and surgery. Data analysts can mine available hospital data, including 837 claims, PAS data, and health information management code specifics, to analyze charge capture and assist auditors in identifying potential issues.
As an example, one hospital’s internal audit team was asked to conduct an audit to determine a cause for the high rate of claims denials seen in patient accounts with infusion and injection treatments. Using 837 claims data, analysts at this hospital found anomalies in infusion and injection charges. Multiple initial infusions were appearing on patient accounts rather than one initial infusion and multiple additional infusions.
Following an audit of infusion and injection charges, healthcare internal auditors shared these findings with the hospital’s finance leaders. The leaders were able to improve billing based on this discovery, as having multiple initial infusions on an account rather than additional occurring infusions can lead to denials.
Today’s hospitals and healthcare systems are awash in data, which can be a powerful tool in solving their biggest challenges. Using data strategically with the help of data analysts can help healthcare internal auditors better serve their organizations by pinpointing issues and mitigating potential risks.
