Approximately 30 percent of a hospital or health system’s profit comes from imaging according, to Imaging Market Trends,1 published by Advisory Board. Even in the face of declining reimbursements, imaging departments continue to comprise a significant portion of the revenue stream in most healthcare organizations. Stewards of these departments are continuously looking for ways to optimize efficiency, increase patient and staff satisfaction, and lower costs without compromising the delivery of excellent patient care. Whether evaluating staffing levels, patient access, asset utilization or supply costs, all process improvement initiatives start with data gathering. A thorough, objective review and analysis of the current state is the cornerstone of improvement, which is then used to drive change. Seems simple enough — gather meaningful, accurate, actionable data that is clearly understood by multiple stakeholders, and use that to drive value throughout the imaging department. So why is it not that simple in the real world?
There are numerous ways a process improvement project can get derailed. Issues often overlooked are the lack of a common language across the IT and clinical team members and not using a collaborative or true team approach from the start. These two simple challenges can cause negative downstream effects to even the most well thought out projects.
Language as a Culprit
How could language be the culprit? Misunderstandings, lack of clarity and confusion caused by simply not speaking the same language can lead to an unintended outcome while failing to provide the information needed to make informed decisions.
Imagers have their own unique language, and even amongst imaging professionals, words and phrases can have multiple meanings. For example, volume can be interpreted as the number of billed CPT codes. It can also be interpreted as the number of patient procedures, irrespective of the billing component meaning strictly a matter of patient count. The term “turnaround time” can refer to procedures or exams (how long it takes to complete an exam from start to finish, or the time interval between the start of an exam and the start of the next exam), equipment utilization (how many minutes a scanner is idle between exams) or physician reports (how long it takes from the end of the exam to the final report). When you consider all the different terms that imaging managers, directors and staff use to describe the various metrics in a department, it’s easy to see how even the most straight-forward term can be misconstrued or misinterpreted to mean something different than intended. If the person working with the imaging leader on a project is non-clinical, such as a data analyst, they may not even know how to ask for clarity and the waters can get muddy very quickly.
Data teams also have their own unique language that is familiar and common to them. While slightly less nuanced within their profession, it’s not a language that non-data people tend to easily understand. A data file is only as good as the user’s understanding of exactly what it contains, how it was generated and the reliability of the data source. Something as seemingly routine as a count of patients or exams in a specified time frame can be a source of disconnect if the term “encounter” is not defined. An encounter can mean a patient’s visit to the hospital on any given day, regardless of how many clinical appointments or exams they have. Encounter can also be viewed as a single appointment — meaning a patient can have multiple encounters in a single day. Knowing which definition your data analyst is using can be crucial to getting a simple value like the number of patients seen in a specified time frame.
How and at what intervals specific data points are gathered for a project also has an extensive effect on what the data can tell you. How a file is obtained and how the parameters are defined can make a big difference in the actionability of the data itself, and the non-data expert needs to be aware of how those differences can affect the project. Say, for example, you want a report of all procedures completed within a certain time frame. Easy enough, right? Not so fast. The report you are working with includes the number of procedures performed but appears to be incomplete. Several validations, re-runs of the data and potential answers were investigated by both the imaging team and data team, but the cause was proving to be elusive. Almost by accident, you finally stumble upon the answers. First, an existing larger data report was used to extract the smaller data set you needed. However, that original data set had been filtered to accommodate a different project and that filtering eliminated data needed for your project. When an assumption is made that an existing report can be used because it contains all the necessary data fields without understanding all aspects of that report, the results can be adversely impact. The parameters requested made sense to the Imaging team and data team at the time of the request — completed procedures were requested from Monday through Sunday. However, the existing report qualified procedures based on the finalized report date. The problem? A misunderstanding of the overall project goal that led the data team to assume the definition of completed procedures meant a final report. Therefore, those procedures that were completed by the technologist but had not been finalized by the radiologist within the same timeframe were excluded from the data set. Unfortunately, things like this happen more often than we realize and can derail a project if they are not discovered immediately or at all.
Collaboration
This leads into the next issue that can adversely affect your project or reduce or eliminate common problems such as language incongruence: collaboration.
True collaboration requires complete understanding and alignment on the project end goal. It also requires that no assumptions are made and that everyone on the project team understands the meaning of terms being used as you lay out the project plan. Identifying up front that cross-functional team members have different backgrounds and expertise and defining a common language for the project will lead to an immediately congruent approach. This newly aligned team can then deliver an infallible project conclusion and information upon which leadership can make valuable, fact-based business decisions. In the above example, the data team was not even aware they were not aligned on the end goal of the project because there was no focus on defining a common language before the work began. Thus, the project team was left to make assumptions regarding the data set — without even realizing they were doing it. Qualifying the report on the finalized date and time seemed logical to the data team, and the imaging team never questioned or asked for definition around the data points available to use. Since that data set was the key piece used to evaluate the current state, identify outliers, and design a process for change, the entire project was useless. This caused unnecessary rework and a waste of the organizations most valuable asset — people. Resource time needed for a cross-functional team to successfully execute a project can cost thousands of dollars. Considering the number of projects in flight at any given time, not using a common data language and a collaborative approach can cause a very real, fiscal issue for the hospital or health system.
Collaboration is often seen as merely working together, and while that’s true, the most successful collaborations start with defining the project scope. Taking the time to share observations, define the data needs in detail, and clearly articulate the objectives, can reduce the opportunity for misinterpretation and assumptions. This is essential to accomplish your project goals.
Speaking the same language and working collaboratively from the beginning on any project is critical to its success. Ensuring that all team members have the same understanding of the scope, purpose and goal of the project paves the way for getting it right the first time, ultimately saving valuable time and resources while setting a solid foundation from which to drive change.
Stefanie Manack has over 15 years of experience in imaging as a technologist, lead technologist and manager. She holds a Master of Health Communications and a Bachelor of Science in Organizational Behavior degrees from Northwestern University, and an Associates in Applied Science in Medical Imaging. She is a Certified Radiology Administrator and Registered Radiology Technologist with advanced certifications in mammography and vascular-interventional radiography. She is currently an imaging operations manager with Accumen Inc.
Judy Zakutny has over 35 years of experience in the imaging and healthcare information technology industries. She held positions as a technologist, lead technologist, manager and system director. She has an Associate Degree in Applied Science Radiologic Technology from Lorain Community College. She is a retired radiologic technologist and held advanced certifications in computed tomography and magnetic resonance imaging. She is currently an imaging operations manager with Accumen, Inc.
Editor's note: You can attend Manack and Zakutny's presentation, The Many Languages of Data, at AHRA 2019 from 8:15-9:15 am on Tuesday, July 23. Click here for more information on the session.
Reference:
1. "Imaging Market Trends.” Advisory Board, May 4, 2018, www.advisory.com/research/service-line-strategy-advisor/ resources/2017/imaging-market-trends. Accessed July 18, 2019.
July 30, 2024 
