Evaluating the Impact of a Call Triage Assistant on Resident Efficiency, Errors, and Stress

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.

Practical Strategies to Retain Radiologists

Since the great resignation associated with the coronavirus disease 2019 pandemic, radiology practices are now challenged with maintaining adequate radiology staffing requirements to cope with increasing clinical workload requirements. The authors describe practical strategies for radiology practice leaders to retain radiologists in the current challenging job market, while mitigating their burnout.

Impact of a Reading Room Coordinator on Efficiency of On-Call Radiology Residents

OBJECTIVES

Few level I trauma, tertiary care, academic centers have a paid, permanent reading room coordinator (RRC) to facilitate image management services during off-hour calls, to minimize interruptions to reading workflow. The purpose of this study is to investigate the effect of an RRC on the efficiency of radiology residents signing preliminary reports for emergency department (ED) and inpatient studies.

METHODS

A pre- and postintervention retrospective review was performed, using carestream PACS to retrieve imaging studies read on call during two time periods-July 1 to December 1, 2019 (pre-RRC), and July 1 to December 1, 2021 (post-RRC). Efficiency of residents signing preliminary reports was measured by turnaround time (TAT), defined as the time from when a study was marked complete by a technologist to when a preliminary report was signed by a resident, in PACS.

RESULTS

In the above time periods, residents interpreted a total of 64,406 studies on call. For ED studies, the mean TAT was 7.0 min shorter post-RRC, compared with pre-RRC (95% confidence interval [CI]: -7.8 to -6.1, (t = 15.50, degrees of freedom (df) = 31,866, P < .0001). The percentage of ED studies signed within 30 min increased from 57.7% to 65.8%, an increase of 8.1% (95% CI: 7.0% to 9.1%) after employing an RRC (χ2 = 228.11, df = 1, P < .0001). For inpatient studies, the mean TAT was 10.2 min shorter post-RRC (95% CI: -12.3 to -8.0, t = 9.22, df = 25,193, P < .0001).

CONCLUSIONS

An RRC increased radiology resident on-call workflow efficiency, facilitating care for patients in both the ED and inpatient setting.

Reading room assistants to reduce workload and interruptions of radiology residents during on-call hours: Initial evaluation

PURPOSE

To determine how much timesaving and reduction of interruptions reading room assistants can provide by taking over non-image interpretation tasks (NITs) from radiology residents during on-call hours.

METHODS

Reading room assistants are medical students who were trained to take over NITs from radiology residents (e.g. answering telephone calls, administrative tasks and logistics) to reduce residents' workload during on-call hours. Reading room assistants' and residents' activities were tracked during 6 weekend dayshifts in a tertiary care academic center (with more than 2.5 million inhabitants in its catchment area) between 10 a.m. and 5p.m. (7-hour shift, 420 min), and time spent on each activity was recorded.

RESULTS

Reading room assistants spent the most time on the following timesaving activities for residents: answering incoming (41 min, 19%) and outgoing telephone calls (35 min, 16%), ultrasound machine related activities (19 min, 9%) and paramedical assistance such as supporting residents during ultrasound guided procedures and with patients (17 min, 8%). Reading room assistants saved 132 min of residents' time by taking over NITs while also spending circa 31 min consulting the resident, resulting in a net timesaving of 101 min (24%) during a 7-hour shift. The reading room assistants also prevented residents from being interrupted, at a mean of 18 times during the 7-hour shift.

CONCLUSION

This study shows that the implementation of reading room assistants to radiology on-call hours could provide a timesaving for residents and also reduce the number of times residents are being interrupted during their work.

Implementation of Medical Students as Radiology Reading Room Coordinators

OBJECTIVE

Effort has been made to minimize the burden of non-interpretive tasks (NITs), in particular by hiring and training non-radiologist support staff as reading room coordinators (RRCs). Our medical center recruited and trained senior medical students from our affiliated school of medicine to work alongside on-call radiology residents as RRCs.

METHODS

A 12-month Malpractice Carrier monetary grant was acquired to fund medical students at with the aim to reduce malpractice risk. After the first year, residents were surveyed regarding the impact of the RRCs on perceived on-call efficiency and morale. Furthermore, report turnaround times (TAT) on call shifts that were and were not accompanied by a RRC were compared.

RESULTS

89 % of residents strongly agreed that the RRC improved workflow efficiency, decreased distractions, and felt less stressed during the call shift when the RRC was on duty. 78 % strongly agreed to be more likely to contact a referring clinician when the RRC was able to help coordinate. The mean TAT in the presence of a RRC was 36.8 min, and the mean TAT in the absence of a RRC was 36.9 min DISCUSSION: After hiring medical students to assist on-call radiology residents with noninterpretive tasks, residents reported subjective indicators of program success, but average report turnaround time was unaffected. Nevertheless, we predict that this type of program will continue to grow among academic radiology departments, though additional research is required to evaluate national trends and impacts on radiologist productivity and well-being.

Decreasing Workflow and Educational Interruptions in the Reading Room: Working Smarter, Not Harder

OBJECTIVE

Disruptions in image interpretation lead to interrupted education and inefficiency, and ultimately delay patient care. In the academic reading room, time can often be spent rerouting phone calls. The objective of this study was to evaluate resident perception of current workflow, decrease interruptions, and improve patient care and resident education by implementing a cost-effective automated centralized phone tree.

MATERIALS AND METHODS

Phone call records were obtained between January 25 and February 23, 2021 and May 3 and June 30, 2021 prior to implementation of an automated centralized phone tree within the Emergency Radiology reading room. Calls during weekday business hours were evaluated. Postimplementation phone records were obtained over 4 weeks (August 20-September 16, 2021). Residents on rotation were surveyed prior to and after phone tree implementation regarding the qualitative impact.

RESULTS

The total number of phone calls decreased after phone tree implementation to 8 calls over a 19-day period from 100-200 phone calls over a 20-22 day period. There is a statistically significant difference (p-value < 0.017) in the median number of phone calls postimplementation for all compared preimplementation time points. Resident surveys also demonstrate a statistically significant improvement (p-value < 0.05) in the evaluated metrics.

CONCLUSIONS

Data demonstrate a quantitative decrease in the number of calls arriving at the Emergency Radiology reading room as well as qualitative improvements in resident workplace satisfaction, feelings of burnout, and burden of interruptions. These data suggest that a self-directed triage system (eg, phone tree) could provide a cost-effective and simple means of decreasing interruptions.

Resident-attending discrepancy rates for two consecutive versus nonconsecutive weeks of overnight shifts

PURPOSE

Recent Accreditation Council for Graduate Medical Education policy changes no longer limit the number of consecutive night shifts allowed for trainees. Few studies have examined radiology resident overnight performance over time. This study aimed to compare significant resident-attending discrepancy rates for residents working 2 nonconsecutive versus consecutive weeks of overnight shifts. The authors hypothesized significantly increased week-two discrepancies in the consecutive group.

METHODS

For 2020, a retrospective analysis of significant overnight resident-attending discrepancy rates over a 24-week period using database searches was performed for residents self-selecting 2 nonconsecutive versus consecutive weeks. The nonconsecutive group typically had a 7-day mix of days off and day shifts between their night shift weeks. Paired and unpaired t tests were performed with p < 0.05 considered significant.

RESULTS

For the 24 sets of 2 weeks covered by two residents at a time, eight were nonconsecutive and 16 were consecutive. The nonconsecutive group had 75.0% R4 coverage compared to 37.5% for the consecutive group. There were no significant study volume differences between the groups. A total of 27,906 studies (35.3% cross-sectional [CT and MR], 54.9% radiograph plus fluoroscopy, 9.8% US) were performed with 223 discrepancies (0.80%). Overall discrepancies for the nonconsecutive versus consecutive groups were 39/4505 (0.87%) versus 59/9462 (0.62%; p = 0.32) for week one and 46/4732 (1.0%) versus 79/9207 (0.86%; p = 0.60) for week two with no significant differences between the groups by modality.

CONCLUSION

Residents self-selecting 2 consecutive weeks of overnight shifts do not have increased resident-attending discrepancy rates compared to 2 nonconsecutive weeks.

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

BACKGROUND

Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services.

METHODS

In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors.

RESULTS

4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results.

CONCLUSION

Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS-ID: DRKS00023593).

WITHDRAWN: Radiology Extenders: Impact on Throughput and Accuracy for Routine Chest Radiographs

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