Assemble the Perfect Financial Analytics Team
By Joncé Smith
For The Record
Vol. 28 No. 6 P. 8
According to a survey conducted by Stoltenberg Consulting at this year's HIMSS conference, data analytics is the hottest topic in HIT circles. While 73% of respondents, comprising CIOs, chief medical information officers, IT directors, managers, and consultants, reported that their organizations had a data analytics program in place, 37% stated they lacked the resources necessary to complete the initiatives requested by their end users. Others (36%) said their programs were still young, and they sought assistance to develop them to the desired state of maturity.
This raises the question: Where do health care organizations go from here? The answer lies in placing the right people with the correct skill sets together at the right place and time through the formation of a collaborative and forward-thinking multidisciplinary team.
Top-Down, Multidepartment Support
Any data analytics project, whether clinical or financial, will not yield much benefit if it is managed under a siloed mentality. The culture of a health care facility must nurture and embrace each data analytics project by focusing on enterprisewide goals and benefits. That is part of the reason why these projects require C-suite–level support and a multidisciplinary team composed of members from across the organization's key departments.
A cross-section of department champions brings in-depth knowledge and experience from their own workflows and processes to offer best practices and ideas on how to accurately consolidate data from these differing sources into a single, unified record structured for analysis with financial improvement as a goal.
Each department team member must be invested and actively involved in the analytics project to achieve the desired results. At a minimum, the department list for a financial analytics project should include managers from clinical operations that govern patient access areas, as well as key leadership from HIM, laboratory, radiology, pharmacy, and the business office. This composite management team will have the right sets of skills and experience to offer valuable direction and guidance critical to a financial data analytics project.
This project management team must also set the correct tone by focusing on how optimizing internal workflows will deliver improvements on key financial performance indicators. Such improvements will be measured within and across departments through clearly defined quantitative objectives. Most notably, these objectives should include measurements that can be clearly associated with specific phases of the revenue cycle controlled and governed by the respective departments external to the business office.
Yet, despite this recipe, a third and final ingredient is still required: full end-user adoption. Organizations can create and deliver the best solution with the latest and greatest technology, but without buy-in and use from targeted end users, a project will likely fail. To ensure successful adoption, an organization's culture must encourage and support active learning. End users should receive periodic updates related to the project during its early testing stages, well before the dates of the first formal education classes.
Also, the solution's benefits should be tied as closely as possible to aspects of end users' daily work tasks to allow them to easily understand the effects. Improvements to financial performance or driving better patient outcomes can be incorporated later to achieve a deeper, more meaningful understanding of how the new solution benefits the organization.
Characteristics and Skills
Finding and training the members of your data analytics team can be a tall order, which is why many organizations create their own teams by cross-training existing clinicians and staff. In many cases, these individuals already have extensive health care experience and bring a strong working knowledge of the facility's applications such as clinical systems, revenue cycle, and the EHR. Depending on their department, they may possess an analytical background and already have the natural instinct to ask forward-thinking questions that focus on future efficiency and care. Capitalizing on their previous education and inherent characteristics can directly benefit the project's outcome.
Other organizations look to external firms for experienced consultants to provide the necessary resources. This assistance may offer a faster turnaround, which can help quickly secure a project's resources, as well as provide fresh perspectives and valuable deep analytical skills. Because there's typically not enough time to adequately train and prepare internal resources to this level of skill and experience, most organizations ultimately elect to enlist external firms.
Analytical and Smart Thinking
The ideal candidate for participation in a data analytics projects would possess the ability to look beyond undesirable data results to identify potential root causes of a specific issue. This requires years of experience and industry knowledge spanning several departments.
An in-depth perspective of how data flow throughout work processes is the foundation of analytical ability. It requires the analyst to move backward one step at a time to study data from various supporting systems to quickly and accurately troubleshoot and identify potential underlying causes.
For example, within the revenue cycle, if claim generation dollars are too low, a smart analytical approach would be to check the past seven days of inpatient census counts combined with the recent number of completed charts in the HIM department. An examination of the last seven days of claim error volume rates should quickly pinpoint an area for further analysis. Each of these three data sets that directly impact claim generation dollars is found in a different source system.
Another valuable trait is the ability to quickly visualize and formulate creative solutions. Most people can recite what they've memorized, but analytical thinkers can create multiple solutions for a given problem, each of which may have its own set of advantages and drawbacks. Parsing through those pluses and minuses helps identify the optimal solution. In-depth knowledge of how an organization operates, including its internal workflows, is necessary not only to select the correct solution but also to minimize risks.
Finally, financial analytics staff should be able to embrace the ideas of others while critically analyzing their own concepts to ascertain faults and determine reasonability. To do this well, they must understand and see the big picture, master collaboration skills, and carry no personal agendas.
An active questioner can do this without reservation, undeterred by someone saying, "It's always been done that way." They are willing to change, eager to learn, and view failure as a pathway, not an obstacle, to learning.
— Joncé Smith is vice president of revenue management at Stoltenberg Consulting.