The promise of inter-organizational networks, in which organizations competing with each other in the same industry share critical business intelligence, has attracted considerable attention since the advent of Big Data.
Because the quality of insights gleaned from Big Data depends on the completeness of the data set being analyzed, it stands to reason that the more sources of data, the better the insights. Nonetheless, sharing raw data among potential competitors is an easier sell than exchanging concrete details about a specific system or business practice. a
In tightening markets, despite facing enormous pressure to preserve their organizations’ sustainability by rooting out process deficiencies that result in lost revenue, hospital revenue cycle managers have tended to resist sharing details about their revenue cycle practices. Even though logic would suggest that the experience of peer organizations would aid them in their efforts, conventional thinking has been that each hospital’s finance and revenue cycle functions are either too idiosyncratic to be useful to other institutions or too mission-critical to expose to competitors.
In at least one instance, however, facilitated collaboration among competitors has yielded greater revenue capture across the board. A core group of hospital leaders have dispensed with conventional thinking to improve revenue capture following their electronic health record (EHR) implementations and are sharing specific fixes and mending information gaps to address the charge-capture missteps, reporting errors, and other protocol problems that have been shown to cost healthcare institutions up to 5 percent of gross revenue. This group currently consists of leaders from 15 unique health systems from more than 10 different states and covering more than 50 unique hospital sites.
Overview of an Inter-Organizational Charge Auditing Program
The capacity and potential of a next-generation EHR system are powerful value propositions for healthcare organizations, offering the promise of adaptability to increasingly complex payment scenarios. However, the distinct requirements of next-generation EHRs for charge reporting and coding also understandably introduce new financial risks. Moreover, the risk of financial losses is particularly acute early in the EHR post-implementation phase, while the EHR is still being optimized and staff are still acclimating to the new protocols.
Recognizing the potential for revenue loss, EHR vendors have created applications within EHRs that would address this problem by flagging charging errors and missed charges before they are submitted. For instance, nurses may document infusions differently in the new system from how they did previously, and if they do so incorrectly, the infusion charge may never actually be triggered.
Infusions are ubiquitous in health care, and this area therefore is an obvious candidate for a “charge edit,” or fix. However, the working of edits can vary among different organizations, so this solution needs to be tailored to the specific departments, typical charges, and particular weaknesses of each hospital. The timing of firing edits and the routing of the accounts to be reviewed should be optimized to conform to each organizations’ structure so the program not only prevents revenue losses, but also identifies the specific reason for the loss. The focus for solutions is on creating a feedback loop for educating staff about where and how the charge should have been entered originally.
Identifying the areas where charging errors are occurring, creating edits to address them, determining how many edits to make, and deciding which ones to prioritize all are decisions that have a major financial impact for hospital revenue. Such intelligence offers value that extends far beyond simply meeting the needs individual organizations. The collaborating hospitals in the group cited here acknowledge that cross-industry collaboration and application of such knowledge also can accelerate the entire industry’s ability to focus on quantifying care.
From Remediation to Prevention
The collaboration initially resembled traditional benchmarking: Each institution arrived at the most useful edits by conducting an initial assessment. The idea was that, together, the collected insights of all the collaborating hospitals could contribute to an understanding of optimal results, which, in effect, could be used as an indirect benchmark for other hospitals’ performance.
The institutions now are realizing stronger results from a more direct approach: sharing best practices in almost real-time. This type of collaboration involves no mediation from a consulting intermediary, which was initially required. It also allows for the aggregation of a repository of edits that can be used by future partner institutions, thereby contributing to a rising tide of performance across the industry.
To illustrate, the network now has identified the following specific areas of opportunity that tend to apply to organizations across the nation.
Infusions. As described above, some of the most common missed charge opportunities involve infusions. Most institutions understand this issue because charging for these procedures has been a common and persistent problem encountered after EHR conversions. Because of the requirements of start and stop times, the added complexity of “carve-out periods,” and the intricate logic of what can and can’t be charged throughout an infusion, logic-based edits that flag a missing stop time, for instance, are invaluable.
Administrative codes. Another example of a simplistic logic-based error is an incorrect or missing administration code. When a nurse is administering chemotherapy, for instance, the administration code is different from the code applied to an infusion that involves less risk. All too often, hospitals charge for the higher medication (the chemotherapy) without also using the higher code for the infusion.
Computed tomography (CT). Automatic review of charges is especially useful when two aspects of a procedure are bound up with one another but the charges must be entered separately. An example is radiology with a contrast agent, where the CT exam must be charged first but the user must return to the record to note that contrast also was given. (Tying these two charges together is not an option because the exams can be performed with and without the contrast.)
Anesthesia. Similar to the CT example is the broader category of anesthesia. Edits should be made to flag where a procedure that typically uses anesthesia has been charged without adding a charge for the anesthesia.
Emergency department (ED) levels. This example is particularly complex because it could affect not only the charge at hand but also the way performance-based payment is calculated. If a patient with a high-acuity condition is treated in the ED under a lower-level designation, the error has ramifications beyond direct payment: It can lower the overall acuity ascribed to the patient group treated in the ED, thereby raising expectations for outcomes, leading to significant unwarranted financial penalties in addition to lost revenue at the account level.
In all of these examples, the most crucial phase is to identify potential losses, flag them, and charge correctly for the actions taken. From there, the organization can educate the clinical staff responsible for the charges about the problem and how to fix it. As with the ED levels, such fixes are crucial to downstream ROI and the additional revenue that’s posted. True innovation, however, isn’t realized until organizations share this type of information with each other to improve performance industrywide.
Inter-organizational network theory and practice regarding healthcare revenue management has only recently begun to spread throughout the industry. This case study in collaboration provides a model for future success.
Erick McKesson, MBA, PMP, is a director, Navigant, Denver.
Footnotes
a. For an overview, see Popp, J., Milward, H.B., MacKean, G., Casebeer, A., and Lindstrom, R., Inter-Organizational Networks: A Review of the Literature to Inform Practice , IBM Center for the Business of Government, 2014; see also Swan, J., Newell, S., and Nicolini, D., Mobilizing Knowledge in Health Care: Challenges for Management and Organization , Oxford University Press, July 2016.
