Tracking and Resolving CT Dose Metric Outliers Using Root-Cause Analysis

Combining Failure Modes and Effects Analysis and Cause-Effect Analysis: A Novel Method of Risk Analysis to Reduce Anaphylaxis Due to Contrast Media

BACKGROUND

Contrast media agents are essential for computed tomography-based diagnoses. However, they can cause fatal adverse effects such as anaphylaxis in patients. Although it is rare, the chances of anaphylaxis increase with the number of examinations. Thus, we aimed to design a quality-improvement initiative to reduce patient risk to these agents.

METHODS

We analysed computed tomography processes using contrast iodine in a tertiary-care academic hospital that performs approximately 14,000 computed tomography scans per year in Japan. We applied a combination of failure modes and effects analysis and cause-effect analysis to reduce the risk of patients developing allergic reactions to iodine-based contrast agents during computed tomography imaging.

RESULTS

Our multidisciplinary team comprising seven professionals analysed the data and designed a 56-process flowchart of computed tomography imaging with iodine. We obtained 177 failure modes, of which 15 had a risk-probability number higher than 100. We identified the two riskiest processes and developed cause-and-effect diagrams for both: one was related to exchange of information between the radiation and hospital information system regarding the patient's allergy, the other was due to education and structural deficiencies in observation following the exam.

CONCLUSION

The combined method of failure mode effect analysis and cause-and-effect analysis reveals high-risk processes and suggests measures to reduce these risks. Failure modes and effects analysis is not well-known in healthcare but has significant potential for improving patient safety. Our findings emphasise the importance of adopting new techniques to reduce patient risk and carry out best practices in radiology.

Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression

BACKGROUND

Variability in radiation exposure from CT scans can be appropriate and driven by patient features such as body habitus. Quantitative analysis may be performed to discover instances of unwarranted radiation exposure and to reduce the probability of such occurrences in future patient visits. No universal process to perform identification of outliers is widely available, and access to expertise and resources is variable.

OBJECTIVE

The goal of this study is to develop an automated outlier detection procedure to identify all scans with an unanticipated high radiation exposure, given the characteristics of the patient and the type of the exam.

MATERIALS AND METHODS

This Institutional Review Board-approved retrospective cohort study was conducted from June 30, 2012 - December 31, 2013 in a quaternary academic medical center. The de-identified dataset contained 28 fields for 189,959 CT exams. We applied the variable selection method Least Absolute Shrinkage and Selection Operator (LASSO) to select important variables for predicting CT radiation dose. We then employed a regression approach that is robust to outliers, to learn from data a predictive model of CT radiation doses given important variables identified by LASSO. Patient visits whose predicted radiation dose was statistically different from the radiation dose actually received were identified as outliers.

RESULTS

Our methodology identified 1% of CT exams as outliers. The top-5 predictors discovered by LASSO and strongly correlated with radiation dose were Tube Current, kVp, Weight, Width of collimator, and Reference milliampere-seconds. A human expert validation of the outlier detection algorithm has yielded specificity of 0.85 [95% CI 0.78-0.92] and sensitivity of 0.91 [95% CI 0.85-0.97] (PPV = 0.84, NPV = 0.92). These values substantially outperform alternative methods we tested (F1 score 0.88 for our method against 0.51 for the alternatives).

CONCLUSION

The study developed and tested a novel, automated method for processing CT scanner meta-data to identify CT exams where patients received an unwarranted amount of radiation. Radiation safety and protocol review committees may use this technique to uncover systemic issues and reduce future incidents.

Optimization of CT protocols using cause-and-effect analysis of outliers

The aim of this study was to implement an outlier marking and analysis methodology to optimize CT examination protocols. CT Head examination data, including dose metrics along with technical parameters, were stored in an automatic dose registry system. Reference dose metrics distribution was obtained throughout a 1-year period. Outlier thresholds were calculated taking into account the specific shape of the distribution, by using a robust measure of the skewness; the medcouple parameter. Subsequently, outliers from a 4-month period were marked and Cause-and-Effect analysis was carried out by a multidisciplinary dose committee. Reference Dose metrics distributions were obtained from 3690 CT Head examinations. Both CTDIvol and DLP showed a certain degree of skewness, with a medcouple value of 0.05 and 0.11, respectively. All of the upper-outliers fell within 3 identifiable groups of causes, ordered by relative importance: i) inadequate protocol selection, ii) arms or objects in the field-of-view, and iii) abnormal scanning region diameter. Regarding the lower-outliers, 90% were attributable to the inclusion of additional series in the original head protocol and the remaining 10% to unknown causes. Also, a general Cause-and-Effect diagram for outliers was elaborated. While the Dose Reference Level method applies to the general performance of a CT protocol and allows comparison with other centers, the outlier method represents a step further in the optimization process. The proposed method focuses on detecting incorrect utilization of the CT, which mainly arises from inadequate knowledge of CT technology.

Tracking Patterns of Nonadherence to Prescribed CT Protocol Parameters

PURPOSE

Quantification of the frequency, understanding the motivation, and documentation of the changes made by CT technologists at scan time are important components of monitoring a quality CT workflow.

METHODS

CT scan acquisition data were collected from one CT scanner for a period of 1 year. The data included all relevant acquisition parameters needed to define the technical side of a CT protocol. An algorithm was created to sort these data in groups of irradiation events with the same combinations of scan acquisition parameters. For scans modified at scan time, it was hypothesized that these examinations would show up only once in the organized data. A classification scheme was developed to place each "one-off" examination into a category related to what motivated the scan-time change.

RESULTS

A total of 132,707 irradiation events were organized into 434 groups of unique scan acquisition parameters. One hundred forty-four irradiation events had acquisition parameters that showed up only once in the data. These "one-offs" were classified as follows: 25% represented rarely used protocols, 17% were due to service scans, 16% were changed for unknown and therefore undesired reasons, 15% were changed by technologists trying to adapt protocol to patient size, 12% were allowable scan-time changes, 8% of scans had tube current maxed out, and 6% of scans were changed to a higher dose mode as requested by radiologists.

CONCLUSIONS

The outcome of this study suggests many areas of needed technologist training and chances for optimizing this institution's CT protocols.