Financial Impact of a Radiology Safety Net Program for Resolution of Clinically Necessary Follow-up Imaging Recommendations

OBJECTIVE

Health care safety net (SN) programs can potentially improve patient safety and decrease risk associated with missed or delayed follow-up care, although they require financial resources. This study aimed to assess whether the revenue generated from completion of clinically necessary recommendations for additional imaging (RAI) made possible by an IT-enabled SN program could fund the required additional labor resources.

METHODS

Clinically necessary RAI generated October 21, 2019, to September 24, 2021, were tracked to resolution as of April 13, 2023. A new radiology SN team worked with existing schedulers and care coordinators, performing chart review and patient and provider outreach to ensure RAI resolution. We applied relevant Current Procedural Terminology, version 4 codes of the completed imaging examinations to estimate total revenue. Coprimary outcomes included revenue generated by total performed examinations and estimated revenue attributed to SN involvement. We used Student's t test to compare the secondary outcome, RAI time interval, for higher versus lower revenue-generating modalities.

RESULTS

In all, 24% (3,243) of eligible follow-up recommendations (13,670) required SN involvement. Total estimated revenue generated by performed recommended examinations was $6,116,871, with $980,628 attributed to SN. Net SN-generated revenue per 1.0 full-time equivalent was an estimated $349,768. Greatest proportion of performed examinations were cross-sectional modalities (CT, MRI, PET/CT), which were higher revenue-generating than non-cross-sectional modalities (x-ray, ultrasound, mammography), and had shorter recommendation time frames (153 versus 180 days, P < .001).

DISCUSSION

The revenue generated from completion of RAI facilitated by an IT-enabled quality and safety program supplemented by an SN team can fund the required additional labor resources to improve patient safety. Realizing early revenue may require 5 to 6 months postimplementation.

Communication of Incidental Imaging Findings on Inpatient Discharge Summaries After Implementation of Electronic Health Record Notification System

OBJECTIVES

Inadequate follow-up of incidental imaging findings (IIFs) can result in poor patient outcomes, patient dissatisfaction, and provider malpractice. At our institution, radiologists flag IIFs during report dictation to trigger electronic health record (EHR) notifications to providers and patients. Nurse coordinators directly contact patients or their primary care physicians (PCPs) regarding IIFs if follow-up is not completed within the recommended time frame. Despite these interventions, many patients and their PCPs remain unaware of IIFs. In an effort to improve awareness of IIFs, we aim to investigate communication of IIFs on inpatient discharge summaries after implementation of our EHR notification system.

METHODS

Inpatient records with IIFs from 2018 to 2021 were retrospectively reviewed to determine type of IIFs, follow-up recommendations, and mention of IIFs on discharge summaries. Nurse coordinators spoke to patients and providers to determine their awareness of IIFs.

RESULTS

Incidental imaging findings were reported in 51% of discharge summaries (711/1383). When nurse coordinators called patients and PCPs regarding IIFs at the time follow-up was due, the patients and PCPs were aware of 79% of IIFs (1096/1383).

CONCLUSIONS

With implementation of EHR notifications to providers regarding IIFs, IIFs were included in 51% of discharge summaries. Lack of inclusion of IIFs on discharge summaries could be related to transitions of care within hospitalization, provider alert fatigue, and many diagnostic testing results to distill. These findings demonstrate the need to improve communication of IIFs, possibly via automating mention of IIFs on discharge summaries, and the need for care coordinators to follow up on IIFs.

Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.

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.

Revenue and Cost Analysis of a System Utilizing Natural Language Processing and a Nurse Coordinator for Radiology Follow-up Recommendations

Radiology reports often contain recommendations for follow-up imaging, Provider adherence to these radiology recommendations can be incomplete, which may result in patient harm, lost revenue, or litigation. This study sought to perform a revenue assessment of a hybrid natural language processing (NLP) and human follow-up system. Reports generated from January 2020 to April 2021 that were indexed as overdue from follow-up recommendations by mPower Follow-Up Recommendation Algorithm (Nuance Communications Inc., Burlington, MA), were assessed for follow up and revenue. Follow-up exams completed because of the hybrid system were tabulated and given revenue amounts based on Medicare national reimbursement rates. These rates were then summated. A total of n =3011 patients were flagged via the mPower algorithm as having not received a timely follow-up indicated for procedure. Of these, n = 427 required the quality nurse to contact their healthcare provider to place orders. The follow-up imaging of these patients accounted for $62,937.66 of revenue. This revenue was calculated as higher than personnel cost (based on national average quality and safety nurse salary and time allotted on follow-ups). Our results indicate that a hybrid human-artificial intelligence follow-up system can be profitable, while potentially adding to patient safety. Our revenue figure likely significantly underestimates the true revenue obtained at our institution. This was due to the use of Medicare national reimbursement rates to calculate revenue, for the purposes of generalizability.

Opportunistic CT Assessment of Parathyroid Glands: Utility of Radiologist-Recommended Biochemical Evaluation for Diagnosing Primary Hyperparathyroidism

BACKGROUND. Existing gaps in primary hyperparathyroidism (PHPT) diagnosis and treatment have prompted calls for systemic change in the approach to this disease. One proposed change is opportunistic assessment for enlarged parathyroid glands on routine CT examinations, to target biochemical testing to individuals most likely to have un-diagnosed PHPT. OBJECTIVE. The purpose of our study was to assess the utility of a radiologist recommendation for biochemical testing in patients with a suspected enlarged parathyroid gland on routine CT for identifying previously undiagnosed PHPT. METHODS. This retrospective study included patients without known or suspected PHPT who underwent routine CT (i.e., performed for reasons other than known or suspected parathyroid disease) between August 2019 and September 2021 in which the clinical CT report included a radiologist recommendation for biochemical testing to evaluate for possible PHPT because of a suspected enlarged parathyroid gland. Neuroradiologists at the study institution included this recommendation on the basis of individual judgment without formal criteria. The EHR was reviewed to identify patients who underwent subsequent laboratory evaluation for PHPT. An endocrine surgeon used available laboratory results and clinical data to classify patients as having PHPT, secondary hyper-parathyroidism, or no parathyroid disorder independent of the CT findings. RESULTS. The sample comprised 39 patients (median age, 68 years; 20 women, 19 men) who received the radiologist recommendation for biochemical evaluation. Of these patients, 13 (33.3%) received the recommended biochemical evaluation. Of the 13 tested patients, three (23.1%) were classified as having PHPT, four (30.8%) as having secondary hyperparathyroidism, and six (46.2%) as having no parathyroid disorder. Thus, the number of patients needing to receive a radiologist recommendation for biochemical testing per correct PHPT diagnosis was 13.0, and the number of patients needing to undergo laboratory testing per correct PHPT diagnosis was 4.3. One of the three patients classified as having PHPT underwent surgical resection of the lesion identified by CT, which was shown on histopathologic evaluation to represent hypercellular parathyroid tissue. CONCLUSION. Radiologist recommendations for biochemical testing in patients with suspected enlarged parathyroid glands on routine CT helped to identify individuals with undiagnosed PHPT. CLINICAL IMPACT. Opportunistic assessment for enlarged parathyroid glands on routine CT may facilitate PHPT diagnosis.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports

PURPOSE

To evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult.

METHODS

We randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt "Explain this radiology report to a patient in layman's terms in second person: <Report Text>". Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance.

RESULTS

Mean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5.

DISCUSSION

Our study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

Applying Implementation Science Principles to Systematize High-Quality Care for Potentially Significant Imaging Findings

OBJECTIVE

Use principles of implementation science to improve the diagnosis and management of potentially significant imaging findings.

METHODS

Multidisciplinary stakeholders codified the diagnosis and management of potentially significant imaging findings in eight organs and created a finding tracking management system that was embedded in radiologist workflows and IT systems. Radiologists were trained to use this system. An automated finding tracking management system was created to support consistent high-quality care through care pathway visualizations, increased awareness of specific findings in the electronic medical record, templated notifications, and creation of an electronic safety net. Primary outcome was the rate of quality reviews related to eight targeted imaging findings. Secondary outcome was radiologist use of the finding tracking management tool.

RESULTS

In the 4 years after implementation, the tool was used to track findings in 7,843 patients who received 10,015 ultrasound, CT, MRI, x-ray, and nuclear medicine examinations that were interpreted by all 34 radiologists. Use of the tool lead to a decrease in related quality reviews (from 8.0% to 0.0%, P < .007). Use of the system increased from 1.7% of examinations in the early implementation phase to 3.1% (+82%, P < .00001) in the postimplementation phase. Each radiologist used the tool on an average of 294.6 unique examinations (SD 404.8). Overall, radiologists currently use the tool approximately 4,000 times per year.

DISCUSSION

Radiologists frequently used a finding tracking management system to ensure effective communication and raise awareness of the importance of recommended future follow-up studies. Use of this system was associated with a decrease in the rate of quality review requests in this domain.

Patient-level factors influencing adherence to follow-up imaging recommendations

PURPOSE

To determine which, if any, patient-level factors were associated with differences in completion of follow-up imaging recommendations at a tertiary academic medical center.

METHODS

In this IRB-approved, retrospective cohort study, approximately one month of imaging recommendations were reviewed from 2017 at a single academic institution that contained key words recommending follow-up imaging. Age, gender, race/ethnicity, insurance, smoking history, primary language, BMI, and home address were recorded via chart extraction. Home addresses were geocoded to Census Block Groups and assigned to a quintile of neighborhood socioeconomic status. A multivariate logistic regression model was used to evaluate each predictor variable with significance set to p = 0.05.

RESULTS

A total of 13,421 imaging reports that included additional follow-up recommendations were identified. Of the 1013 included reports that recommended follow-up, 350 recommended additional imaging and were analyzed. Three hundred eight (88.00%) had corresponding follow-up imaging present and the insurance payor was known for 266 (86.36%) patients: 146 (47.40%) had commercial insurance, 35 (11.36%) had Medicaid, and 85 (27.60%) had Medicare. Patients with Medicaid had over four times lower odds of completing follow-up imaging compared to patients with commercial insurance (OR 0.24, 95% CI 0.06-0.88, p = 0.032). Age, gender, race/ethnicity, smoking history, primary language, BMI, and neighborhood socioeconomic status were not independently associated with differences in follow-up imaging completion.

CONCLUSION

Patients with Medicaid had decreased odds of completing follow-up imaging recommendations compared to patients with commercial insurance.

Novel Quality Measure Set: Closing the Completion Loop on Radiology Follow-up Recommendations for Noncritical Actionable Incidental Findings

BACKGROUND

Care gaps occur when radiology follow-up recommendations are poorly communicated or not completed, resulting in missed or delayed diagnosis potentially leading to worse patient outcomes. This ACR-led initiative assembled a technical expert panel (TEP) to advise development of quality measures intended to improve communication and drive increased completion rates for radiology follow-up recommendations.

MATERIALS AND METHODS

A multistakeholder TEP was assembled to advise the development of quality measures. The project scope, limited to noncritical actionable incidental findings (AIFs), encourages practices to develop and implement systems ensuring appropriate communication and follow-up to completion.

RESULTS

A suite of nine measures were developed: four outcome measures include closing the loop on completion of radiology follow-up recommendations for nonemergent AIFs (with pulmonary nodule and abdominal aortic aneurysm use cases) and overall cancer diagnoses. Five process measures address communication and tracking of AIFs: inclusion of available evidence or guidelines informing the recommendation, communication of AIFs to the practice managing ongoing care, identifying when AIFs have been communicated to the patient, and employing tracking and reminder systems for AIFs.

CONCLUSION

This ACR-led initiative developed a measure set intended to improve patient outcomes by ensuring that AIFs are appropriately communicated and followed up. The intent of these measures is to focus improvement on specific areas in which gaps in communication and AIF follow-up may occur, prompting systems to devote resources that will identify and implement solutions to improve patient care.

Neck Imaging Reporting and Data System Category 3 on Surveillance Computed Tomography: Incidence, Biopsy Rate, and Predictive Performance in Head and Neck Squamous Cell Carcinoma

OBJECTIVES

Neck Imaging Reporting and Data System (NI-RADS) is a radiology reporting system for head and neck cancer surveillance. Imaging findings of high suspicion for recurrence are assigned Category 3 and recommended for "Biopsy, if clinically indicated." After implementing NI-RADS for surveillance neck computed tomography (CT), our objectives are to determine the incidence of squamous cell carcinoma (SCC) Category 3 lesions in the year post-implementation, the associated biopsy rate, and the positive predictive value of NI-RADS 3 for SCC recurrence.

STUDY DESIGN

Retrospective cohort study.

METHODS

Neck CTs reported with NI-RADS between February 2020 and February 2021 were reviewed to identify patients undergoing surveillance for SCC assigned NI-RADS 3. Cancer recurrence, defined as positive biopsy result or treatment of clinically determined recurrence, was determined by electronic medical record review.

RESULTS

During the study period, 580 neck CTs were reported with NI-RADS, of which 39 (7%) CTs obtained in 37 unique patients (28 male, 9 female, mean age 66.6 years) formed the study cohort. Biopsies were obtained in 23 lesions (45%), of which 17 (74%) were positive for recurrent SCC. One nondiagnostic biopsy was clinically determined to represent recurrence. Of 28 (55%) lesions not biopsied, 18 (64%) were ultimately treated as clinically determined recurrence. Thus, among 51 individual NI-RADS 3 lesions (32 primary, 19 neck), 36 (71%) represented recurrence.

CONCLUSION

The incidence of NI-RADS 3 lesions in our cohort was 7%. The biopsy rate was 45%, and the overall positive predictive value of NI-RADS 3 for recurrent SCC was 71%. Category 3 lesions are associated with substantial SCC recurrence risk and should be managed accordingly.

LEVEL OF EVIDENCE

4 Laryngoscope, 2022.

Closing the Compliance Loop on Follow-Up Imaging Recommendations: Comparing Radiologists' and Administrators' Attitudes

PURPOSE

To compare non-physician healthcare professional and radiologists' survey responses regarding attitudes and current practices, policies, and procedures related to the follow-up of nonemergent actionable incidental findings (AIF).

MATERIALS AND METHODS

The American College of Radiology (ACR) developed a survey with input from a technical expert panel (TEP). Survey items were developed by TEP members, refined by an ACR market research expert, and were examined for face and construct validity. The survey was distributed among ACR membership and various medical professional organizations. Responses from non-physician responders and radiologists were analyzed and compared using descriptive statistics.

RESULTS

The analysis included 375 responses, 247 from radiologists and 128 from non-physicians. All respondent groups stated that radiology follow-up recommendations are evidence-based. Both respondent groups indicated that there is up to moderate risk associated with AIF follow-up. Both respondent groups similarly favored that the accountability for communicating AIF lies first with the ordering provider, followed by primary care providers, then the patient, and lastly an automated process that is managed by a staff member and/or the radiologist. All respondent groups indicated that tracking processes were more commonly funded by the healthcare system than through the radiology budget.

CONCLUSION

There is alignment between non-physicians and radiologists regarding the implementation of tracking systems that assure completion of radiology follow-up recommendations. Building tracking systems represents an opportunity for multi-disciplinary collaboration to address care transition communication and process gaps.

Determining Follow-Up Imaging Study Using Radiology Reports

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.

Notification System for Overdue Radiology Recommendations Improves Rates of Follow-Up and Diagnosis

OBJECTIVE. The purpose of this study was to quantify improved rates of follow-up and additional important diagnoses made after notification for overdue workups recommended by radiologists. MATERIALS AND METHODS. Standard reports from imaging studies performed at our institution from October through November 2016 were searched for the words "recommend" or "advised," yielding 9784 studies. Of these, 5245 were excluded, yielding 4539 studies; reports for 1599 of these 4539 consecutive studies were reviewed to identify firm or soft recommendations or findings requiring immediate management. If recommended follow-ups were incomplete within 1 month of the advised time, providers were notified. Compliance was calculated before and after notification and was compared using a one-sample test of proportion. RESULTS. Of 1599 patients, 92 were excluded because they had findings requiring immediate management, and 684 were excluded because of soft recommendations, yielding 823 patients. Of these patients, 125 were not yet overdue for follow-up and were excluded, and 18 were excluded because of death or transfer to another institution. Of the remaining 680 patients, follow-up was completed for 503 (74.0%). A total of 177 (26.0%) of the 680 patients were overdue for follow-up, and providers were notified. Of these 177 patients, 36 (20.3%) completed their follow-ups after notification, 34 (19.2%) had follow-up designated by the provider as nonindicated, and 107 (60.5%) were lost to follow-up, yielding four clinically important diagnoses: one biopsy-proven malignancy, one growing mass, and two thyroid nodules requiring biopsy. The rate of incomplete follow-ups after communication decreased from 26.0% (177/680) to 20.7% (141/680) (95% CI, 17.7-23.9%; p = .002), with a 20.4% reduction in relative risk of noncompliance, and 39.5% (70/177) of overdue cases were resolved when nonindicated studies were included. CONCLUSION. Notification of overdue imaging recommendations reduces incomplete follow-ups and yields clinically important diagnoses.

A practical system that enables physicians to respond expeditiously to significant unexpected findings (SUFs) in radiological reports

PURPOSE

To demonstrate effectiveness of our present radiological report check flowchart enabling physicians to respond to significant unexpected findings (SUFs), by comparing the response periods from the examination date to the action date on untreated SUFs between the previous and present versions of our flowchart.

METHODS

In the flowchart's previous version used February-October 2019, SUFs, which were notified by email, were audited every month. The physician received a phone call and was asked to act on the untreated SUF. In the flowchart's present version used from November 2019 to May 2020, SUFs were audited every 2 weeks. The physician and his/her chief were asked to return a written response to the untreated SUF. We evaluated the difference in the response periods between the previous and present versions of the flowchart.

RESULTS

With the previous flowchart's use, untreated SUFs were 43 of 229 SUFs (18.8%) with the present flowchart untreated SUFs were 22 of 130 SUFs (16.9%). All SUFs in both periods were eventually responded. The present flowchart (median/range, 25/11-70 days) significantly had shorter response periods than the previous flowchart (70/16-290 days) (p < 0.0001).

CONCLUSION

The present flowchart employing a shortened primary audit interval, a written response, and the department chief's intervention, helped reduce the response periods.

Patient-centered Radiology Reporting for Lung Cancer Screening

Medicine is slowly transitioning toward a more patient-centered approach, with patients taking a more central role in their own care. A key part of this movement has involved giving patients increased access to their medical record and imaging results via electronic health portals. However, most patients lack the knowledge to fully understand medical documents, which are generally written above their comprehension level. Radiology reports, in particular, utilize complex terminology due to radiologists' historic function as consultants to other physicians, with little direct communication to patients. As a result, typical radiology reports lack standardized formatting, and they are often inscrutable to patients. Numerous studies examining patient preference also point to a trend for more accessible radiology reports geared toward patients. Reports designed with an infographic format, combining simple pictures and standardized text, may be an ideal format that radiologists can pursue to provide patient-centered care. Our team, through feedback from patient advisory groups, developed a patient-friendly low-dose computed tomography lung cancer screening report with an infographic format that is both visually attractive and comprehensible to the average patient. The report is designed with sections including a description of low-dose computed tomography, a section on individualized patient results, the meaning of the results, and a list of the next steps in their care. We believe that this form of the report has the potential to serve as a bridge between radiologists and patients, allowing for a better patient understanding of their health and empowering patients to participate in their health and health care.