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4 strategies to follow for successful performance in diagnostic imaging
Healthcare reform has created a new imperative for radiology and its hospital partners: become more efficient, improve quality and gain as much insight as possible. With the transition to value-based care, analytics are no longer an option; measuring, benchmarking and comparing departmental performance will become the price of entry.
Radiology departments are not immune to these requirements. Practice leaders must rethink and reconsider how to measure themselves and their impact to the hospital ecosystem. When executed properly, radiology becomes an indispensable strategic partner to hospital administration. If executed poorly, radiology becomes a cost center to be managed.
Diagnostic imaging is often used early in a patient’s hospitalization. For this reason the radiology department serves as a reliable bellwether, foreshadowing what is to come during the remainder of the care cycle across multiple departments in the hospital. When the right imaging services are delivered in a timely manner, there is a cascade of positive impacts that include accurate diagnosis, effective treatment and greater cost-efficiency with patients becoming healthier, spending less time in the hospital and using fewer scarce hospital resources.
Hospital leaders preparing for accountable care should explore and consider how radiology impacts performance across departments-a challenge best accomplished using analytics,
Radiology-focused analytics can help drive more thoughtful and data-driven decisions about coverage and service-level commitments, help improve efficiency and create a common fact-based language that aligns facts as opposed to just opinions. All of these benefits are consistent with futurist Ian Morrison’s “second curve” of value-based healthcare.
Since 2011 the American Hospital Association has educated its membership on four priorities they need to accomplish to succeed during this “second curve.” The implementation of these four “must-do strategies” is continually reinforced in the literature, at national meetings and in other professional forums as essential to an organization’s viability under healthcare reform. Hospitals have in essence created a new scorecard to define organizational success (see chart, page 42).
Frost & Sullivan also notes that while a handful of medical imaging providers may already be leveraging basic analytics solutions, the majority are far from being able to confidently measure, analyze, benchmark, monitor and improve clinical and operational quality metrics either for themselves or for hospital clients and employers.
One key challenge is the need for normalized data. Data metrics, benchmarking and performance comparisons require speaking the same language. Trying to normalize data between hospitals, or even within the same group, is a manual, tedious and often seemingly impossible exercise due to local variability in study description and nomenclature. It is the bottleneck in being able to harness the intelligence and insight inherent in practice data.
The benefits of harnessing information for actionable insight are exciting. For example, most hospitals can identify variations in ordering patterns today by harvesting information from computerized order entry systems. The chief medical officer (CMO) probably knows who the highest utilizers of imaging in the emergency department (ED) are today, but what can they really do with that information? Is higher utilization better or worse? Can they really compare between physicians or hospitals given the plethora of information systems? To gain insight that can lead to impact, tools like natural language processing can be used to mine clinical reports for consistent metrics. This information can then be used to calculate what percentage of ordered imaging procedures has abnormal findings present in the radiology report. That means radiology can demonstrate which physicians have the highest or lowest yield of findings in ordered procedures; the CMO has something much more meaningful to work with in order to change behavior and impact clinical and operational performance.
In addition to examining imaging appropriateness and utilization, radiology analytics can also provide insight into cost drivers for imaging procedures. When a study is ordered with contrast media, there is an increase in the time necessary to complete the procedure, increased risk of harm to the patient and the added expense from the contrast material itself. Studying normalized data patterns allows hospitals to learn how contrast usage varies from one institution to another throughout the system. Standardizing guidelines for contrast ordering based on data-driven facts help rein in cost and improve safety.
The analysis of radiology workflow and report turnaround times can also reveal departmental bottlenecks that may be slowing ED throughout. Report turnaround time is currently one of the least understood areas of radiology performance because traditional methods of measurement do not capture the entire story. Radiology groups normally measure report turnaround time from the moment images become available for interpretation until the moment the final report is signed. In reality, this represents only a small portion of the entire imaging cycle, which actually begins when the order for imaging is placed. A classic example is the role of prep tasks (between radiology and the ED) and the impact on “treat to street.”
To improve performance and significantly accelerate the imaging cycle, it is a better practice to measure each segment of the process. The number of identifiable segments will vary by institution, but at a minimum, radiology should be able to separately analyze and report on the interval from order entry to scan completion, differentiating the front-end workflow from the actual time required to read and dictate the case. By looking at data that encompasses the complete imaging cycle start-to-finish, hospitals can identify and correct problems with equipment capacity or staffing levels that slow the availability of results to the ED.
This idea of reviewing data from the entire process was part of the impetus behind vRad’s development of the RPC, or the Radiology Patient Care Indices, the first findings-based national radiology benchmarking metrics available to all hospitals, radiology groups and researchers. The data sets can help providers identify potential areas of weakness and shed light on workflow bottlenecks in clinical, operational and business processes. This higher visibility into the quality of services and patient outcomes is more essential than ever because it can gradually allow providers to uncover best practices and refine processes across the various enterprise functions.
It is critical that radiology starts to use analytics to answer questions, such as how turnaround time differs by location of service or how to shorten inpatient length of stay. Today it is standard to look at report turnaround time for the various locations of service. In the future, this must be taken a step further, teasing out performance on the specific studies required for discharge. Only by drilling down to that level, can a definitive understanding be achieved on how radiology is supporting or hindering the larger efficiency goals in the hospital.
As the pressure to demonstrate value in healthcare becomes more intense, the need for robust data-rich analytics will only increase. Radiology has a unique opportunity to lead the conversation on analytics because so many aspects of its work can be easily measured and reduced to metrics. That’s just the nature of the highly-digital specialty.
It is also foreseeable that the greater availability of data will strengthen the hand of radiology, not weaken it. The rapid expansion of analytics is good for both hospitals and radiology. Both groups benefit from this because it means that future discussions on performance will not be based on opinions, but rather will be derived from hard evidence.
Analytics for practice comparisons are largely subjective, and in many cases unavailable because of the challenge of comparing disparate data between healthcare facilities. Using a normalized data set allows radiology groups and hospitals to objectively compare their own use of imaging. These comparisons present a potential opportunity for radiology to take control of the dialogue around quality as it moves from a fee-for-volume to a fee-for-value world.
Dr. Strong is Chief Medical Officer of vRad, a global telemedicine company and the nation’s largest radiology practice with over 450 physicians.