Measuring radiology operations does not mean accumulating data without clear criteria. It means identifying what happens at each stage of the workflow, detecting bottlenecks, anticipating delays, and making decisions based on reliable information.<\/p>\n\n\n\n
In a diagnostic imaging institution, indicators can help answer key questions: how many studies are performed, how long a report takes, which modalities account for the greatest demand, where pending work accumulates, how workload is distributed among professionals, and how accessible the radiology history is when needed.<\/p>\n\n\n\n
However, measuring more does not always mean managing better. A dashboard with too many metrics but no interpretation can create confusion. And a poorly used indicator can increase pressure on the team without addressing the actual cause of delays.<\/p>\n\n\n\n
Therefore, radiology indicators must be analyzed within the complete workflow: from study acquisition to availability in the PACS, report issuance, result delivery, access to historical studies, and medical image storage.<\/p>\n\n\n\n
Digital radiology generates a large amount of information. Each study produces data about modality, time, status, assigned professional, availability time, report issuance, result delivery, and storage.<\/p>\n\n\n\n
When this information is analyzed systematically, the institution can better understand its operation and act before problems become complaints, delays, or team overload.<\/p>\n\n\n\n
Radiology indicators help to:<\/p>\n\n\n\n
Measurement also makes it possible to move from reactive management to a more preventive approach. Instead of discovering a delay when a referring physician follows up on a report, the institution can monitor the backlog and act before critical pending work accumulates.<\/p>\n\n\n\n
This does not mean reducing radiology to numbers. Indicators should support management, not replace professional judgment or clinical evaluation.<\/p>\n\n\n\n
In many organizations, the terms data, metric, indicator, and KPI are used as though they mean the same thing. However, distinguishing between them helps create more useful measurement.<\/p>\n\n\n\n
A data point is a basic unit of information. For example:<\/p>\n\n\n\n
On its own, a data point does not necessarily support decision-making. It needs context.<\/p>\n\n\n\n
A metric is created when something is measured or calculated from data. For example:<\/p>\n\n\n\n
A metric makes it possible to observe a pattern, but it still requires interpretation.<\/p>\n\n\n\n
An indicator is a metric linked to an operational, clinical, or management objective.<\/p>\n\n\n\n
For example, measuring the percentage of studies reported within the defined deadline helps assess whether the institution is meeting its committed turnaround times.<\/p>\n\n\n\n
The indicator answers a specific question: are we operating as needed?<\/p>\n\n\n\n
A KPI, or key performance indicator, is an indicator that is especially important to the institution’s strategy.<\/p>\n\n\n\n
Not everything that is measured should be a KPI. If everything is a priority, nothing is. KPIs should focus on the points that truly reflect the performance of radiology operations. For example:<\/p>\n\n\n\n
The key is to select indicators that support action. A good KPI does not merely display a number; it guides a decision.<\/p>\n\n\n\n
Radiology operations cannot be measured from a single perspective. For a balanced view, operational, productivity, quality, security, traceability, and storage indicators should be combined.<\/p>\n\n\n\n
These show how the daily workflow is functioning. They may include:<\/p>\n\n\n\n
These indicators help determine whether operations are stable, saturated, or unbalanced.<\/p>\n\n\n\n
These measure the relationship between work performed, available resources, and time used. They can be analyzed by:<\/p>\n\n\n\n
It is important to interpret them in context. Not all studies have the same complexity, urgency, or interpretation burden. Comparing only the number of reports can lead to incorrect conclusions.<\/p>\n\n\n\n
These help monitor process consistency and identify opportunities for improvement. They may include:<\/p>\n\n\n\n
These indicators must be analyzed carefully. An increase in corrections, for example, does not always indicate lower professional quality. It may reflect changes in criteria, higher volume, unclear processes, or insufficient clinical information.<\/p>\n\n\n\n
These make it possible to monitor access, use, and availability of information. They may include:<\/p>\n\n\n\n
Traceability is especially important when the institution works with multiple sites, remote users, or distributed workflows.<\/p>\n\n\n\n
The growth of the radiology archive must also be measured. Relevant indicators include:<\/p>\n\n\n\n
These data help anticipate infrastructure requirements, cloud storage needs, or archiving policies.<\/p>\n\n\n\n
Some indicators relate to technical image quality, exposure settings, and patient safety. These may include dose, repeat acquisitions, image rejection, or technical reference values defined by institutional protocols.<\/p>\n\n\n\n
This article focuses on operational management of the radiology workflow. However, it is important to recognize that productivity, technical quality, and patient safety should not be evaluated as separate worlds.<\/p>\n\n\n\n
An efficient operation is not one that merely produces more studies or reports in less time. It is one that sustains appropriate turnaround times, information availability, process quality, and safe working conditions.<\/p>\n\n\n\n
The radiology workflow can be analyzed as a chain of stages. Each stage generates information that can become an indicator.<\/p>\n\n\n\n
Measures the number of studies performed during a given period. It can be analyzed by:<\/p>\n\n\n\n
This indicator shows actual demand and helps detect seasonality, workload peaks, or changes in the study mix.<\/p>\n\n\n\n
It should not be analyzed in isolation. An increase in volume may be positive from an operational standpoint, but it can also create a backlog if interpretation capacity, storage, and result delivery do not grow accordingly.<\/p>\n\n\n\n
Not all modalities require the same amount of time or generate the same volume of information.<\/p>\n\n\n\n
An institution may perform few studies in a given modality, yet those studies may require more interpretation time, greater storage capacity, or more complex processes.<\/p>\n\n\n\n
Analyzing distribution by modality helps explain:<\/p>\n\n\n\n
This indicator is especially useful when the institution adds new equipment, opens sites, or expands its service portfolio.<\/p>\n\n\n\n
This indicator measures how long it takes for a study to become available for viewing after it is performed. It can help identify problems in:<\/p>\n\n\n\n
If this time increases, the problem may not be with the radiologist or reporting platform, but at an earlier stage of the workflow.<\/p>\n\n\n\n
>> To better understand the role of PACS within operations, read <\/strong>What Is a PACS in Radiology and How Does It Transform Your Institution\u2019s Operations?<\/strong><\/a><\/strong><\/p>\n\n\n\n This indicator measures the period between a study becoming available for interpretation and the report being issued. It helps evaluate:<\/p>\n\n\n\n It is a very important indicator, but it should not be used as the sole measure of productivity. A fast report does not always represent a better workflow if it increases the need for corrections, re-reads, or addenda.<\/p>\n\n\n\n Total turnaround time measures the period from a defined starting point until the result becomes available.<\/p>\n\n\n\n The starting point may vary by institution. Some organizations calculate it from the time the study is performed; others begin when it is received in the PACS or assigned to a professional.<\/p>\n\n\n\n For the indicator to be useful, the institution must define precisely:<\/p>\n\n\n\n Without a clear definition, the same indicator can produce different interpretations across departments.<\/p>\n\n\n\n An SLA is a service-level agreement. In radiology, it can be used to define expected reporting or delivery deadlines according to study type, priority, site, or modality.<\/p>\n\n\n\n Measuring SLA compliance shows what percentage of studies are completed within the committed time. For example:<\/p>\n\n\n\n SLA compliance helps organize expectations, but it must be designed realistically. Overly aggressive deadlines can create unproductive pressure if they are not supported by adequate processes, technology, and capacity.<\/p>\n\n\n\n The backlog represents studies that have not yet been reported or completed within the workflow.<\/p>\n\n\n\n It is one of the most important indicators for anticipating saturation. It can be analyzed by:<\/p>\n\n\n\n A low backlog does not always mean everything is working well. It is also necessary to analyze whether urgent studies are completed on time, whether distribution is balanced, and whether process quality is maintained.<\/p>\n\n\n\n This indicator shows which studies exceeded the expected time.<\/p>\n\n\n\n It is useful because it moves beyond a general average to a more specific view. An institution may have an acceptable average turnaround time and still accumulate individual cases with excessive delays. It is helpful to analyze:<\/p>\n\n\n\n This indicator helps prioritize corrective actions.<\/p>\n\n\n\n Productivity can be measured by the number of studies reported, volume of work completed, average interpretation time, or share of total reports.<\/p>\n\n\n\n However, this indicator must be used with particular care. Not all studies require the same effort. A fair comparison should consider:<\/p>\n\n\n\n Productivity is useful for managing capacity, but it should not become an isolated measure of professional performance.<\/p>\n\n\n\n These indicators show situations in which a report requires subsequent review, correction, or additional information. They can help detect:<\/p>\n\n\n\n They should not automatically be interpreted as individual failures. They often reflect workflow problems or incomplete information.<\/p>\n\n\n\n Repeated or rejected studies may indicate technical problems, acquisition difficulties, protocol errors, or a need for training.<\/p>\n\n\n\n They can also affect turnaround times, the patient experience, resource use, and operational workload.<\/p>\n\n\n\n This indicator must be analyzed together with the technical and quality criteria defined by the institution.<\/p>\n\n\n\n Access to prior studies is essential for diagnostic continuity. Measuring this area makes it possible to understand:<\/p>\n\n\n\n This indicator is directly related to the PACS and the medical image storage strategy.<\/p>\n\n\n\n One of the most common mistakes in radiology is confusing productivity with quality.<\/p>\n\n\n\n Productivity answers questions such as:<\/p>\n\n\n\n Quality answers different questions:<\/p>\n\n\n\n Both dimensions are necessary. If the institution measures only productivity, it may increase volume at the cost of fatigue, errors, or lost context. If it measures only quality without observing turnaround times and capacity, it may fail to identify delays that affect the patient experience and continuity of care.<\/p>\n\n\n\n Radiology management requires balance. A good dashboard should make it possible to view volume, time, quality, workload, and information availability together.<\/p>\n\n\n\n Turnaround time is one of the most widely used indicators in radiology. It is also one of the most sensitive.<\/p>\n\n\n\n It can help improve operations, but poor interpretation can create pressure and lead to incorrect decisions.<\/p>\n\n\n\n An urgent study should not compete under the same conditions as a routine study.<\/p>\n\n\n\n Therefore, turnaround times should be separated by priority:<\/p>\n\n\n\n Measuring all studies within a single average can conceal important problems. An operation may appear stable while critical studies are being completed late.<\/p>\n\n\n\n The expected turnaround time for a simple X-ray should not necessarily be compared with that of an MRI, a complex CT scan, or a study with multiple series.<\/p>\n\n\n\n Separating indicators by modality makes it possible to set more reasonable goals and identify specific bottlenecks.<\/p>\n\n\n\n Turnaround time has greater value when interpreted alongside clinical impact.<\/p>\n\n\n\n A delay in an urgent study can have a different effect from a delay in an outpatient follow-up. Therefore, indicators must align with the institution’s care delivery logic.<\/p>\n\n\n\n Reducing turnaround times is important, but it should not be the only goal. The institution must also monitor:<\/p>\n\n\n\n A faster operation is not necessarily better if it increases rework or reduces process quality.<\/p>\n\n\n\n A Reporting Center makes it possible to organize the production, review, and delivery of radiology reports. To manage it properly, specific indicators for the reporting process must be monitored.<\/p>\n\n\n\n Shows how many studies were distributed to professionals or teams.<\/p>\n\n\n\n It can be analyzed by modality, site, priority, or specialty.<\/p>\n\n\n\n Shows which studies are already available but do not yet have an issued report.<\/p>\n\n\n\n This indicator helps anticipate backlog and prioritize critical cases.<\/p>\n\n\n\n Measures how long it takes to issue the report from a defined point in the workflow.<\/p>\n\n\n\n It should be interpreted by study type, modality, and priority, rather than as a single average for the entire operation.<\/p>\n\n\n\n Shows whether reports are issued within expected time frames.<\/p>\n\n\n\n It is especially useful when the institution works with internal agreements, outsourced services, remote specialists, or distributed sites.<\/p>\n\n\n\n Helps determine whether study assignment is balanced.<\/p>\n\n\n\n It should not be analyzed only by quantity. Complexity, modality, availability, subspecialty, and supplementary tasks must also be considered.<\/p>\n\n\n\n Identifies cases that required subsequent adjustments.<\/p>\n\n\n\n This indicator can help improve templates, review processes, availability of prior studies, or communication between teams.<\/p>\n\n\n\n Not all areas have the same work pace. Separating indicators by specialty or modality makes it possible to set more realistic expectations and improve planning.<\/p>\n\n\n\n >> To learn more about this topic, read <\/strong>What Is a Radiology Reporting Center and How Can It Increase Productivity Without Compromising Diagnostic Quality?<\/strong><\/a><\/strong><\/p>\n\n\n\n >> You can also read <\/strong>Teleradiology and Remote Reporting: How to Reduce Turnaround Times with a Reporting Center Integrated with the PACS<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Radiology productivity does not depend only on the reporting process. It also depends on studies being available, organized, and accessible when professionals need them.<\/p>\n\n\n\n Monitors whether studies generated by modalities reach the PACS without failures.<\/p>\n\n\n\n It can help detect connectivity, configuration, identification, or integration problems.<\/p>\n\n\n\n Measures how long it takes for a study to be opened and viewed after it is acquired or received.<\/p>\n\n\n\n If this time increases, it can directly affect interpretation and result delivery.<\/p>\n\n\n\n Evaluates whether the patient’s prior studies are available for comparison.<\/p>\n\n\n\n This indicator is important in institutions with high study volumes, multiple sites, or extensive historical archives.<\/p>\n\n\n\n Identifies problems when opening prior studies, retrieving archived images, or accessing stored content.<\/p>\n\n\n\n It may indicate a need to review infrastructure, connectivity, storage, or archiving policies.<\/p>\n\n\n\n The radiology archive tends to grow steadily.<\/p>\n\n\n\n Measuring its growth helps anticipate capacity needs and prevent storage from becoming an operational limitation.<\/p>\n\n\n\n Not all modalities generate the same volume of information.<\/p>\n\n\n\n Analyzing use by modality or period helps define storage, archiving, and retrieval policies.<\/p>\n\n\n\n >> To learn more about medical image storage, read <\/strong>What Is Aurora Drive and How Does It Support DICOM Image Storage in the Cloud?<\/strong><\/a><\/strong><\/p>\n\n\n\n >> You can also read <\/strong>DICOM Images in the Cloud: How to Scale Radiology Storage Without Expanding Infrastructure<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Many institutions have digital systems but do not necessarily have sufficient operational visibility.<\/p>\n\n\n\n Warning signs include:<\/p>\n\n\n\n These signs do not always indicate a lack of effort. They often show that the institution needs to better integrate its data and establish which indicators are truly important.<\/p>\n\n\n\n Volume is important, but it does not explain the entire operation.<\/p>\n\n\n\n A service can increase its study volume while simultaneously accumulating backlog, delaying reports, or overloading the team.<\/p>\n\n\n\n Comparing only the number of reports can be unfair if modality, complexity, urgency, and the workload associated with each case are not considered.<\/p>\n\n\n\n Turnaround time measures speed, not diagnostic quality on its own.<\/p>\n\n\n\n It must be analyzed together with re-reads, corrections, repeated studies, user satisfaction, and clinical criteria.<\/p>\n\n\n\n Radiology work is not limited to issuing reports.<\/p>\n\n\n\n It may also include reviewing prior studies, communicating with teams, validating data, second readings, clinical meetings, and administrative tasks.<\/p>\n\n\n\n Studies with different priorities should not be measured against the same target.<\/p>\n\n\n\n Separating categories allows for fairer and more useful management.<\/p>\n\n\n\n The backlog shows the accumulated pressure on operations.<\/p>\n\n\n\n Without this indicator, the institution may not see the problem until delays are already evident.<\/p>\n\n\n\n An indicator is only as reliable as the data that feeds it.<\/p>\n\n\n\n Registration errors, incorrectly configured statuses, or incomplete integrations can produce unreliable metrics.<\/p>\n\n\n\n Indicators should be used to improve processes, not only to demand greater speed.<\/p>\n\n\n\n If the numbers are used solely to pressure the team, they may increase fatigue without addressing the causes of delays.<\/p>\n\n\n\n Indicator-based management requires available information, connected processes, and tools that provide a clearer view of the workflow.<\/p>\n\n\n\n Pixeon’s radiology solutions for Latin America focus on Pixeon Aurora PACS, the Reporting Center, and cloud-based medical image storage.<\/p>\n\n\n\n Pixeon Aurora PACS helps manage the capture, visualization, interpretation, storage, and distribution of medical images.<\/p>\n\n\n\n Within an indicator-driven operation, the PACS plays a central role because it organizes studies, provides access to patient history, supports comparison with prior exams, and maintains image availability within the radiology workflow.<\/p>\n\n\n\n It can also help detect problems related to study receipt, image access, prior study retrieval, and work distribution.<\/p>\n\n\n\n Pixeon’s Reporting Center enables radiology reports to be created, managed, and delivered from a cloud-based platform integrated with Pixeon Aurora PACS.<\/p>\n\n\n\n Its contribution to operational management is related to organizing the reporting process, distributing studies, supporting remote work, using templates, enabling voice recognition, and managing worklists.<\/p>\n\n\n\n This allows the institution to better monitor turnaround times, pending studies, and workload distribution associated with report issuance.<\/p>\n\n\n\n Aurora Drive complements the radiology ecosystem through cloud-based DICOM image storage.<\/p>\n\n\n\n Its role within an indicator-driven operation is to support the growth of the radiology archive and facilitate a more scalable strategy for retaining and retrieving recent and historical studies.<\/p>\n\n\n\n Storage must also be measured. If the institution does not know how quickly its archive is growing, which studies are accessed most frequently, or how long prior study retrieval takes, it may face operational limitations as volume increases.<\/p>\n\n\n\n The value of measuring indicators increases when the stages of the workflow are connected.<\/p>\n\n\n\n An integrated operation makes it easier to monitor:<\/p>\n\n\n\n >> To explore this approach further, read <\/strong>How to Connect PACS, Reporting, and Storage to Improve the Radiology Workflow<\/strong><\/a>.<\/strong><\/p>\n\n\n\n >> You can also read <\/strong>Interoperability in Radiology: How to Connect PACS, Reports, Images, and External Systems Without Duplicating Tasks<\/strong><\/a>.<\/strong><\/p>\n\n\n\n >> <\/strong>Does your institution need greater visibility into radiology operations? Talk to a Pixeon specialist and assess how to better monitor images, reports, turnaround times, and storage.<\/strong><\/a><\/strong><\/p>\n\n\n\n Before implementing or reviewing an indicator dashboard, verify whether your institution can answer these questions.<\/p>\n\n\n\n If several answers are unclear, the institution probably needs to review how it captures, organizes, and interprets its operational data.<\/p>\n\n\n\n Radiology indicators make it possible to understand how operations function, identify bottlenecks, and make more precise decisions. However, their value does not depend on measuring everything, but on measuring what truly helps improve the workflow.<\/p>\n\n\n\n An institution can have a great deal of data and little visibility. It can also have comprehensive dashboards without clear criteria for interpreting what is happening.<\/p>\n\n\n\n For this reason, radiology management requires balanced indicators: productivity, turnaround times, quality, traceability, storage, and information availability.<\/p>\n\n\n\n Measuring faster does not always mean managing better. Measuring better means understanding what is happening, why it is happening, and which decisions can improve operations without compromising quality or overloading the team.<\/p>\n\n\n\n The ultimate objective should not be to accumulate metrics. It should be to build a more efficient, traceable, connected radiology operation that is prepared to grow.<\/p>\n\n\n\n Radiology indicators are metrics used to monitor the performance of the radiology workflow. They can measure study volume, turnaround times, productivity, backlog, operational quality, image availability, reports, and storage.<\/p>\n\n\n\n A radiology service can measure study volume, distribution by modality, time until PACS availability, report issuance time, turnaround time, SLA compliance, backlog, delayed studies, productivity, corrections, historical study retrieval, and storage growth.<\/p>\n\n\n\n Turnaround time is the response time for a radiology study. It can be measured from acquisition, from availability in the PACS, or from assignment to the professional, through report issuance or delivery. The institution must clearly define the starting and ending points.<\/p>\n\n\n\n Productivity measures volume, turnaround times, and completion capacity. Quality examines whether the process is performed appropriately, safely, and consistently. Both aspects must be evaluated together to prevent the pursuit of speed from affecting workflow quality.<\/p>\n\n\n\n Productivity should be measured by considering modality, complexity, priority, image volume, availability of prior studies, non-interpretive tasks, reviews, and corrections. It should not be analyzed solely by the number of reports issued.<\/p>\n\n\n\n Useful indicators include pending studies, backlog age, time from PACS availability to report issuance, SLA compliance, workload distribution by professional, studies past the deadline, and turnaround times by modality or priority.<\/p>\n\n\n\n A PACS can be evaluated by analyzing studies received correctly, time until viewer availability, access to prior studies, retrieval failures, system availability, study distribution, and the capacity to support operational growth.<\/p>\n\n\n\n Metrics may include total stored volume, monthly growth, use by modality, frequency of historical study access, prior study retrieval time, access failures, and projected future capacity.<\/p>\n\n\n\n A radiology dashboard brings together key indicators for diagnostic imaging operations. It can display volume, turnaround times, pending work, productivity, deadline compliance, storage, and traceability to support management decisions.<\/p>\n\n\n\n No. Improvement depends on selecting relevant indicators, interpreting them correctly, and acting on the causes of problems. Measuring too much data without clear criteria can create confusion or pressure without improving the workflow.<\/p>\n\n\n\n To learn more about PACS, reporting, storage, migration, interoperability, and radiology workflow integration, read the following content:<\/p>\n\n\n\nTime from study availability to report issuance<\/h3>\n\n\n\n
\n
Total turnaround time<\/h3>\n\n\n\n
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SLA compliance<\/h3>\n\n\n\n
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Backlog of pending studies<\/h3>\n\n\n\n
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Delayed studies or studies past the deadline<\/h3>\n\n\n\n
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Productivity by professional, team, or site<\/h3>\n\n\n\n
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Re-reads, corrections, and addenda<\/h3>\n\n\n\n
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Repeated or rejected studies<\/h3>\n\n\n\n
Access to and retrieval of historical studies<\/h3>\n\n\n\n
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Productivity indicators vs. quality indicators<\/h2>\n\n\n\n
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How to interpret turnaround times without compromising diagnostic quality<\/h2>\n\n\n\n
Not all studies have the same urgency<\/h3>\n\n\n\n
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Not all modalities have the same complexity<\/h3>\n\n\n\n
TAT must be linked to clinical priorities<\/h3>\n\n\n\n
Speed must not be evaluated separately from quality<\/h3>\n\n\n\n
\n
Which indicators help manage a Reporting Center?<\/h3>\n\n\n\n
Assigned studies<\/h3>\n\n\n\n
Studies pending reporting<\/h3>\n\n\n\n
Average report issuance time<\/h3>\n\n\n\n
Deadline compliance<\/h3>\n\n\n\n
Workload distribution by professional<\/h3>\n\n\n\n
Reviewed or corrected reports<\/h3>\n\n\n\n
Productivity by specialty or modality<\/h3>\n\n\n\n
Which indicators help evaluate the PACS and storage?<\/h2>\n\n\n\n
Studies received correctly<\/h3>\n\n\n\n
Time until availability in the viewer<\/h3>\n\n\n\n
Access to historical studies<\/h3>\n\n\n\n
Retrieval failures or delays<\/h3>\n\n\n\n
Growth of the radiology archive<\/h3>\n\n\n\n
Storage use by modality or period<\/h3>\n\n\n\n
Signs that your institution lacks sufficient operational visibility<\/h2>\n\n\n\n
\n
Common mistakes when measuring productivity in radiology<\/h2>\n\n\n\n
Measuring only the number of studies<\/h3>\n\n\n\n
Comparing professionals without considering complexity<\/h3>\n\n\n\n
Using TAT as the only quality indicator<\/h3>\n\n\n\n
Ignoring administrative or non-interpretive workload<\/h3>\n\n\n\n
Failing to separate urgent, routine, and outpatient studies<\/h3>\n\n\n\n
Failing to measure backlog<\/h3>\n\n\n\n
Failing to review source data<\/h3>\n\n\n\n
Turning indicators into pressure without improving the workflow<\/h3>\n\n\n\n
How Pixeon helps monitor and improve the radiology workflow<\/h2>\n\n\n\n
Pixeon Aurora PACS<\/h3>\n\n\n\n
Reporting Center<\/h3>\n\n\n\n
Aurora Drive and cloud-based DICOM storage<\/h3>\n\n\n\n
A more integrated view of operations<\/h3>\n\n\n\n
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Indicator checklist for a more efficient radiology operation<\/h2>\n\n\n\n
Productivity<\/h3>\n\n\n\n
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Turnaround times<\/h3>\n\n\n\n
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Quality<\/h3>\n\n\n\n
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Storage<\/h3>\n\n\n\n
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Traceability<\/h3>\n\n\n\n
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Management<\/h3>\n\n\n\n
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Conclusion<\/h2>\n\n\n\n
\n\n\n\nFrequently asked questions about radiology indicators<\/h2>\n\n\n\n
What are radiology indicators?<\/h3>\n\n\n\n
Which indicators should a radiology service measure?<\/h3>\n\n\n\n
What is turnaround time in radiology?<\/h3>\n\n\n\n
What is the difference between productivity and quality in radiology?<\/h3>\n\n\n\n
How can radiologist productivity be measured without affecting quality?<\/h3>\n\n\n\n
Which indicators help reduce reporting delays?<\/h3>\n\n\n\n
Which indicators are useful for evaluating a PACS?<\/h3>\n\n\n\n
Which indicators help manage medical image storage?<\/h3>\n\n\n\n
What is a radiology dashboard?<\/h3>\n\n\n\n
Does measuring more indicators automatically improve operations?<\/h3>\n\n\n\n
What other Pixeon content can help modernize radiology operations?<\/h1>\n\n\n\n
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\n\n\n\nAbout Pixeon <\/h2>\n\n\n\n