The RADCAT-3 system for closing the loop on important non-urgent radiology findings: a multidisciplinary system-wide approach

Medical Malpractice and Diagnostic Radiology: Challenges and Opportunities

Medicolegal challenges in radiology are broad and impact both radiologists and patients. Radiologists may be affected directly by malpractice litigation or indirectly due to defensive imaging ordering practices. Patients also could be harmed physically, emotionally, or financially by unnecessary tests or procedures. As technology advances, the incorporation of artificial intelligence into medicine will bring with it new medicolegal challenges and opportunities. This article reviews the current and emerging direct and indirect effects of medical malpractice on radiologists and summarizes evidence-based solutions.

Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance

IMPORTANCE

Following up on recommendations from radiologic findings is important for patient care, but frequently there are failures to carry out these recommendations. The lack of reliable systems to characterize and track completion of actionable radiology report recommendations poses an important patient safety challenge.

OBJECTIVES

To characterize actionable radiology recommendations and, using this taxonomy, track and understand rates of loop closure for radiology recommendations in a primary care setting.

DESIGN, SETTING, AND PARTICIPANTS

Radiology reports in a primary care clinic at a large academic center were redesigned to include actionable recommendations in a separate dedicated field. Manual review of all reports generated from imaging tests ordered between January 1 and December 31, 2018, by primary care physicians that contained actionable recommendations was performed. For this quality improvement study, a taxonomy system that conceptualized recommendations was developed based on 3 domains: (1) what is recommended (eg, repeat a test or perform a different test, specialty referral), (2) specified time frame in which to perform the recommended action, and (3) contingency language qualifying the recommendation. Using this framework, a 2-stage process was used to review patients' records to classify recommendations and determine loop closure rates and factors associated with failure to complete recommended actions. Data analysis was conducted from April to July 2021.

MAIN OUTCOMES AND MEASURES

Radiology recommendations, time frames, and contingencies. Rates of carrying out vs not closing the loop on these recommendations in the recommended time frame were assessed.

RESULTS

A total of 598 radiology reports were identified with structured recommendations: 462 for additional or future radiologic studies and 196 for nonradiologic actions (119 specialty referrals, 47 invasive procedures, and 43 other actions). The overall rate of completed actions (loop closure) within the recommended time frame was 87.4%, with 31 open loop cases rated by quality expert reviewers to pose substantial clinical risks. Factors associated with successful loop closure included (1) absence of accompanying contingency language, (2) shorter recommended time frames, and (3) evidence of direct radiologist communication with the ordering primary care physicians. A clinically significant lack of loop closure was found in approximately 5% of cases.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that creating structured radiology reports featuring a dedicated recommendations field permits the development of taxonomy to classify such recommendations and determine whether they were carried out. The lack of loop closure suggests the need for more reliable systems.

Electronic Health Record Closed-Loop Communication Program for Unexpected Nonemergent Findings

Background Reliance on examination reporting of unexpected imaging findings does not ensure receipt of findings or appropriate follow-up. A closed-loop communication system should include provider and patient notifications and be auditable through the electronic health record (EHR). Purpose To report the initial design of and results from using an EHR-integrated unexpected findings navigator (UFN) program that ensures closed-loop communication of unexpected nonemergent findings. Materials and Methods An EHR-integrated UFN program was designed to enable identification and communication of unexpected findings and aid in next steps in findings management. Three navigators (with prior training as radiologic technologists and sonographers) facilitated communication and documentation of results to providers and patients. Twelve months (October 2019 to October 2020) of results were retrospectively reviewed to evaluate patient demographics and program metrics. Descriptive statistics and correlation analysis were performed by using commercially available software. Results A total of 3542 examinations were reported within 12 months, representing 0.5% of all examinations performed (total of 749 649); the median patient age was 62 years (range, 1 day to 98 years; interquartile range, 23 years). Most patients were female (2029 of 3542 [57%]). Almost half of the examinations submitted were from chest radiography and CT (1618 of 3542 [46%]), followed by MRI and CT of the abdomen and pelvis (1123 of 3542 [32%]). The most common unexpected findings were potential neoplasms (391 of 3542 [11%]). The median time between examination performance and patient notification was 12 days (range, 0-136 days; interquartile range, 13 days). A total of 2127 additional imaging studies were performed, and 1078 patients were referred to primary care providers and specialists. Most radiologists (89%, 63 of 71 respondents) and providers (65%, 28 of 43 respondents) found the system useful and used it most frequently during regular business hours. Conclusion An electronic health record-integrated, navigator-facilitated, closed-loop communication program for unexpected radiologic findings led to near-complete success in notification of providers and patients and facilitated the next steps in findings management. © RSNA, 2021 See also the editorial by Safdar in this issue.

Implementation and Impact of a Comprehensive Radiology Report Categorization System on Communication of Important Results

PURPOSE

Effective communication of important imaging results is critical to patient care but difficult to accomplish efficiently. To improve communication at their institution, the authors introduced a radiology report categorization system (RADCAT) that organizes diagnostic imaging reports and uses automated communication systems. The study objectives were to (1) describe RADCAT's design, (2) evaluate its implementation for appropriate imaging, and (3) evaluate the communication of important, nonurgent results with recommended follow-up.

METHODS

This retrospective study was performed in a multihospital adult and pediatric tertiary referral academic health system. The intervention, a radiology report categorization system with five levels of acuity and IT-supported communication workflows, was globally implemented in November 2017. The primary outcomes were the successful implementation of RADCAT to appropriate diagnostic imaging reports and the successful communication of important, nonurgent results with recommended follow-up to ordering providers and patients by the radiology quality assurance team.

RESULTS

Over 18 months after implementation, 740,625 radiology reports were categorized under the RADCAT system, with 42%, 28%, and 30% from the emergency department, inpatient, and outpatient settings, respectively. A random selection of 100 studies from the 23,718 total reports without RADCAT categorization identified 4 diagnostic radiology reports that erroneously lacked RADCAT grading. In 2019, of the 38,701 studies with nonurgent imaging follow-up recommendations, 38,692 (nearly 100.0%) were successfully communicated to providers or patients on the basis of quality assurance data.

CONCLUSIONS

A comprehensive radiology report categorization system was successfully implemented across a multihospital adult and pediatric health system, demonstrating reliable communication of imaging results with recommendations for nonacute imaging follow-up.

Survey of radiologists and emergency department providers after implementation of a global radiology report categorization system

PURPOSE

Breakdown in communication of important imaging results threatens patient safety and risks malpractice claims. To facilitate closed-loop communication, our institution developed a unique radiology report categorization (RADCAT) system employing automated alert notification systems. This study aimed to understand users' initial experiences with the RADCAT system and obtain feedback.

METHODS

Web-based surveys were distributed to radiologists and emergency department (ED) providers at our hospital system within 1 year of institution-wide RADCAT implementation. Survey designs differed based on clinical setting. Most prompts utilized declarative statements with 5-point agreement Likert scales. Closed-response data was analyzed with descriptive statistics.

RESULTS

Response rates among radiologists and ED providers were 59.4% (63/106) and 38.4% (69/211), respectively. 78.0% (46/59) of radiologists and 60.9% (42/69) of ED providers agreed that RADCAT improves patient care. Of radiologists, 84.1% (53/63) agreed that RADCAT design is intuitive, and 57.6% (34/59) agreed that RADCAT improves efficiency. Of ED providers, 69.6% (48/69) agreed that RADCAT appropriately differentiates urgent and non-urgent findings, and 65.2% (45/69) agreed that auto-population of discharge documents with imaging results containing follow-up recommendations protects them from liability. Only 35.6% (21/59) of radiologists and 21.7% (15/69) of ED providers agreed that RADCAT implementation decreased reading room visits by ordering providers. Open-response feedback showed that some ED providers find RADCAT too complex while some radiologists desire improved transparency regarding imaging study communication status.

CONCLUSION

Since its implementation, RADCAT has been well received among radiologists and ED providers with agreement that it improves patient care and effectively distinguishes and communicates important imaging findings.

Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression

OBJECTIVE

Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers.

MATERIALS AND METHODS

All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes.

RESULTS

Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days.

DISCUSSION

NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis.

CONCLUSION

For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.

The role of radiology in diagnostic error: a medical malpractice claims review

Just as radiologic studies allow us to see past the surface to the vulnerable and broken parts of the human body, medical malpractice claims help us see past the surface of medical errors to the deeper vulnerabilities and potentially broken aspects of our healthcare delivery system. And just as the insights we gain through radiologic studies provide focus for a treatment plan for healing, so too can the analysis of malpractice claims provide insights to improve the delivery of safe patient care. We review 1325 coded claims where Radiology was the primary service provider to better understand the problems leading to patient harm, and the opportunities most likely to improve diagnostic care in the future.