Collaborative Development of a PACS-Integrated Quality Control Dashboard: a Single Institutional Experience.

Regular communication between technologists and radiologists is necessary for maintaining optimal diagnostic image quality throughout a radiology practice. In a large hospital system with multiple sites, this task becomes increasingly difficult without simultaneously causing significant disruptions in the clinical workflow and decreased throughput. Thus, establishing a system for quality control reporting that enables effective communication in a seamless and convenient manner is imperative. In this report, we describe the development of a new integrated system, in collaboration with our PACS vendor, with tools that allow for instant reporting of quality errors and dashboards providing real-time up-to-date quality data across our hospital system, directly accessible from PACS. To date, 8,167 quality reports have been logged in our new system with roughly 355 submissions per month. Early user engagement and consensus feedback among radiologists and technologists have been positive suggesting an overall improvement from prior systems. We hope this report can help inform other radiology enterprises seeking to improve quality control reporting within their clinical practice.

Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel Pneumothorax Detection Artificial Intelligence Algorithm.

Although interest in artificial intelligence (AI) has exploded in recent years and led to the development of numerous commercial and noncommercial algorithms, the process of implementing such tools into day-to-day clinical practice is rarely described in the burgeoning AI literature. In this report, we describe our experience with the successful integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm for detecting pneumothoraces into an end-to-end solution capable of extracting, delivering, and prioritizing positive studies within our thoracic radiology clinical workflow. We also detail several sample cases from our AI algorithm and associated PACS workflow in action to highlight key insights from our experience. We hope this report can help inform other radiology enterprises seeking to evaluate and implement AI-related workflow solutions into daily clinical practice.

Implementation and Benefits of a Vendor-Neutral Archive and Enterprise-Imaging Management System in an Integrated Delivery Network.

The use of digital imaging has substantially grown in recent decades, in traditional services, new specialties, and departments. The need to share these data among departments and caregivers necessitated central archiving systems that are able to communicate with various viewing applications and electronic medical records. This promoted the development of modern vendor neutral archive (VNA) systems. The need to aggregate and share imaging data from various departments promoted the development of enterprise-imaging (EI) solutions that replace departmental silos of data with central healthcare enterprise databases. To describe the implementation process of a VNA-EI solution in a large health system and its outcomes. We review the background of VNA and EI solutions development and describe the characteristics and advantages of such systems. We then describe our experience in implementation of these solutions in a large integrated healthcare delivery network in northeast Ohio. We then present the process, challenges, costs, advantages, and outcomes of such implementation. The VNA and EI solution was launched in December 2015 and is still ongoing. It currently includes 54 radiology and 26 cardiology sites affiliated with the University Hospitals health system. This process was associated with more than 10% cost savings, 30% reduction in storage costs, superior support for disaster recovery, and 80% decrease in unscheduled outages. All these were achieved despite a 120% increase in archive retrieval needs and a 40% growth in image production. Implementation of a VNA and EI solution was successful and resulted in numerous measurable and qualitative improvements in a large and growing health system.

Radiologists' Variation of Time to Read Across Different Procedure Types.

The workload of US radiologists has increased over the past two decades as measured through total annual relative value units (RVUs). This increase in RVUs generated suggests that radiologists' productivity has increased. However, true productivity (output unit per input unit; RVU per time) is at large unknown since actual time required to interpret and report a case is rarely recorded. In this study, we analyzed how the time to read a case varies between radiologists over a set of different procedure types by retrospectively extracting reading times from PACS usage logs. Specifically, we tested two hypotheses that; i) relative variation in time to read per procedure type increases as the median time to read a procedure type increases, and ii) relative rankings in terms of median reading speed for individual radiologists are consistent across different procedure types. The results that, i) a correlation of -0.25 between the coefficient of variation and median time to read and ii) that only 12 out of 46 radiologists had consistent rankings in terms of time to read across different procedure types, show both hypotheses to be without support. The results show that workload distribution will not follow any general rule for a radiologist across all procedures or a general rule for a specific procedure across many readers. Rather the findings suggest that improved overall practice efficiency can be achieved only by taking into account radiologists' individual productivity per procedure type when distributing unread cases.

Synchronized navigation of current and prior studies using image registration improves radiologist's efficiency.

PURPOSE: The purpose of this study was to investigate how the use of multi-modal rigid image registration integrated within a standard picture archiving and communication system affects the efficiency of a radiologist while performing routine interpretations of cases including prior examinations. METHODS: Six radiologists were recruited to read a set of cases (either 16 neuroradiology or 14 musculoskeletal cases) during two crossover reading sessions. Each radiologist read each case twice, one time with synchronized navigation, which enables spatial synchronization across examinations from different study dates, and one time without. Efficiency was evaluated based upon time to read a case and amount of scrolling while browsing a case using Wilcoxon signed rank test. RESULTS: Significant improvements in efficiency were found considering either all radiologists simultaneously, the two sections separately and the majority of individual radiologists for time to read and for amount of scrolling. The relative improvement for each individual radiologist ranged from 4 to 32% for time to read and from 14 to 38% for amount of scrolling. CONCLUSION: Image registration providing synchronized navigation across examinations from different study dates provides a tool that enables radiologists to work more efficiently while reading cases with one or more prior examinations.

Factors Affecting Radiologist's PACS Usage.

The purpose of this study was to determine if any of the factors radiologist, examination category, time of week, and week effect PACS usage, with PACS usage defined as the sequential order of computer commands issued by a radiologist in a PACS during interpretation and dictation. We initially hypothesized that only radiologist and examination category would have significant effects on PACS usage. Command logs covering 8 weeks of PACS usage were analyzed. For each command trace (describing performed activities of an attending radiologist interpreting a single examination), the PACS usage variables number of commands, number of command classes, bigram repetitiveness, and time to read were extracted. Generalized linear models were used to determine the significance of the factors on the PACS usage variables. The statistical results confirmed the initial hypothesis that radiologist and examination category affect PACS usage and that the factors week and time of week to a large extent have no significant effect. As such, this work provides direction for continued efforts to analyze system data to better understand PACS utilization, which in turn can provide input to enable optimal utilization and configuration of corresponding systems. These continued efforts were, in this work, exemplified by a more detailed analysis using PACS usage profiles, which revealed insights directly applicable to improve PACS utilization through modified system configuration.

State of Integration Between PACS and Other IT Systems: A National Survey of Academic Radiology Departments.

PURPOSE: The aim of this study was to investigate the state of integration between PACS and other IT systems relevant to radiologists' routine work across US academic radiology departments (ARDs). The results were intended to assess readiness for the ongoing transition to value-based health care by providing insights into currently challenging areas of integration but also areas associated with high levels of anticipated workflow efficiency improvements. METHODS: A cross-sectional survey was conducted using an online survey approved by the Society of Chairs of Academic Radiology Departments and sent to its members. Collected responses were analyzed with descriptive statistics and Fisher's exact tests. RESULTS: The response rate was 26% (34 of 132 members), and the respondents covered a large spectrum of ARDs considering location, size aspects, year of PACS introduction, and filmless production. Most notable findings included widespread high-level integration of PACS with dictation systems (>90%), low penetration of integration between PACS and critical notification systems (15%), and an overall better integration of PACS and radiology information systems (82%) than of PACS and electronic medical records (47%). CONCLUSIONS: Integration supporting radiologists' personal productivity is well spread among US ARDs, but as we transition into a value-based health care delivery model, there is a need to focus further integration efforts on systems with the greatest potential to document value in a patient-centric setting. Examples of such focus areas include integration of PACS and electronic medical records, adoption of vendor-neutral archives, and the use of workflow management systems.

Analyzing PACS Usage Patterns by Means of Process Mining: Steps Toward a More Detailed Workflow Analysis in Radiology.

In this paper, statistical analysis and techniques from process mining are employed to analyze interaction patterns originating from radiologists reading medical images in a picture archiving and communication system (PACS). Event logs from 1 week of data, corresponding to 567 cases of single-view chest radiographs read by 14 radiologists, were analyzed. Statistical analysis showed that the numbers of commands and command types used by the radiologists per case only have a slightly positive correlation with the time to read a case (0.31 and 0.55, respectively). Further, one way ANOVA showed that the factors time of day, radiologist and specialty were significant for the number of commands per case, whereas radiologist was also significant for the number of command types, but with no significance of any of the factors on time to read. Applying process mining to the event logs of all users showed that a seemingly "simple" examination (single-view chest radiographs) can be associated with a highly complex interaction process. However, repeating the process discovery on each individual radiologist revealed that the initially discovered complex interaction process consists of one group of radiologists with individually well-structured interaction processes and a second smaller group of users with progressively more complex usage patterns. Future research will focus on metrics to describe derived interaction processes in order to investigate if one set of interaction patterns can be considered as more efficient than another set when reading radiological images in a PACS.

Online Error Reporting for Managing Quality Control Within Radiology.

Information technology systems within health care, such as picture archiving and communication system (PACS) in radiology, can have a positive impact on production but can also risk compromising quality. The widespread use of PACS has removed the previous feedback loop between radiologists and technologists. Instead of direct communication of quality discrepancies found for an examination, the radiologist submitted a paper-based quality-control report. A web-based issue-reporting tool can help restore some of the feedback loop and also provide possibilities for more detailed analysis of submitted errors. The purpose of this study was to evaluate the hypothesis that data from use of an online error reporting software for quality control can focus our efforts within our department. For the 372,258 radiologic examinations conducted during the 6-month period study, 930 errors (390 exam protocol, 390 exam validation, and 150 exam technique) were submitted, corresponding to an error rate of 0.25 %. Within the category exam protocol, technologist documentation had the highest number of submitted errors in ultrasonography (77 errors [44 %]), while imaging protocol errors were the highest subtype error for computed tomography modality (35 errors [18 %]). Positioning and incorrect accession had the highest errors in the exam technique and exam validation error category, respectively, for nearly all of the modalities. An error rate less than 1 % could signify a system with a very high quality; however, a more likely explanation is that not all errors were detected or reported. Furthermore, staff reception of the error reporting system could also affect the reporting rate.

Design and implementation of disaster recovery and business continuity solution for radiology PACS.

In the digital era of radiology, picture archiving and communication system (PACS) has a pivotal role in retrieving and storing the images. Integration of PACS with all the health care information systems e.g., health information system, radiology information system, and electronic medical record has greatly improved access to patient data at anytime and anywhere throughout the entire enterprise. In such an integrated setting, seamless operation depends critically on maintaining data integrity and continuous access for all. Any failure in hardware or software could interrupt the workflow or data and consequently, would risk serious impact to patient care. Thus, any large-scale PACS now have an indispensable requirement to include deployment of a disaster recovery plan to ensure secure sources of data. This paper presents our experience with designing and implementing a disaster recovery and business continuity plan. The selected architecture with two servers in each site (local and disaster recovery (DR) site) provides four different scenarios to continue running and maintain end user service. The implemented DR at University Hospitals Health System now permits continuous access to the PACS application and its contained images for radiologists, other clinicians, and patients alike.