Improving Cancer Diagnosis and Care: Patient Access to Oncologic Imaging Expertise

Key Strategies to Promote Professional Wellness and Reduce Burnout in Oncology Clinicians

Burnout in oncologists has been increasing, especially after the COVID-19 pandemic. This is concerning because burnout can have both personal and professional repercussions, as well as a negative impact on patients and organizational financial health. Drawing on information and ideas discussed at an ASCO Town Hall session at the 2023 Annual Meeting developed by the State of Cancer Care in America Editorial Board, this study reviews key organizational strategies for improving professional well-being and argues for the importance of measuring and researching the well-being of the oncology workforce to ensure healthy work environments. Although both individual- and organizational-level interventions to mitigate burnout are valuable, organizational interventions have been shown to be the most effective. Thus, strategies to ensure professional wellness should focus on developing organizational policies, cultures, and best practices that create healthy work environments. Specific policies and best practices for organizations to prioritize include the following: (1) Eliminating low-value work, including processes related to electronic health record systems. (2) Restructuring teams to efficiently complete work. (3) Promoting clinician work-life integration. (4) Promoting psychological safety in the workplace to prevent workplace discrimination. (5) Identifying individual practice stressors. (6) Fostering community within the organization.

Screening log: Challenges in community patient recruitment for gynecologic oncology clinical trials

Background

Clinical trial participation can improve overall survival and mitigate healthcare disparities for gynecologic cancer patients in low-volume community centers. This study aimed to assess the effectiveness of a centrally regulated but administratively decentralized electronic screening log system to identify eligible patients across a large catchment area for a National Cancer Institute (NCI)-designated cancer center's open clinical trials.

Methods

Electronic screening log data collected between 2014 and 2021 from ten community partner sites in a single NCI-designated cancer center's catchment area were reviewed retrospectively. Clinical factors assessed included cancer site, primary versus recurrent disease status, and histology. Identification efficiency (the ratio of patients screened identified with an available trial) was calculated. Identification inefficiencies (failures to identify patients with a potentially relevant trial) were assessed, and etiologies were characterized.

Results

Across ten community partner sites, 492 gynecologic cancer patients were screened for seven open clinical trials during the study period. This included 170 (34.5 %) ovarian cancer patients, 156 (31.7 %) endometrial cancer patients, and 119 (24.2 %) cervical cancer patients. Over 40 % had advanced stage disease, and 10.6 % had recurrent disease. Only three patients were identified as having a relevant open trial; none ultimately enrolled due to not meeting trial eligibility criteria. An additional 2-52 patients were retrospectively found to have a relevant trial available despite not being identified as such within the electronic screening log system. Up to 14.4 % of patients had one or more missing minimum data elements that hindered full evaluation of clinical trial availability. Re-screening patients when new trials open may identify 12-15 additional patients per recurrent disease trial.

Conclusions

An electronic screening log system can increase awareness of gynecologic oncology clinical trials at a NCI-designated cancer center's community partner sites. However, it is inadequate as a single intervention to increase clinical trial enrollment. Providing adequate support staff, documenting clinical factors consistently, re-screening patients at relevant intervals, and coordinating with central study personnel may increase its utility.

Body oncologic imaging subspecialty training a curriculum based on the experience in a tertiary cancer center

PURPOSE

To describe the structure of a dedicated body oncologic imaging fellowship program. To summarize the numbers and types of cross-sectional imaging examinations reported by fellows.

METHODS

The curriculum, training methods, and assessment measures utilized in the program were reviewed and described. An educational retrospective analysis was conducted. Data on the number of examinations interpreted by fellows, breakdown of modalities, and examinations by disease management team (DMT) were collected.

RESULTS

A total of 38 fellows completed the fellowship program during the study period. The median number of examinations reported per fellow was 2296 [interquartile range: 2148 - 2534], encompassing all oncology-relevant imaging modalities: CT 721 [646-786], MRI 1158 [1016-1309], ultrasound 256 [209-320] and PET/CT 176 [130-202]. The breakdown of examinations by DMT revealed variations in imaging patterns, with MRIs most frequently interpreted for genitourinary, musculoskeletal, and hepatobiliary cancers, and CTs most commonly for general staging or assessment of nonspecific symptoms.

CONCLUSION

This descriptive analysis may serve as a foundation for the development of similar fellowship programs and the advancement of body oncologic imaging. The volume and diversity of examinations reported by fellows highlights the comprehensive nature of body oncologic imaging.

Implementing the European code of cancer practice in rural settings

Existing evidence often indicates higher cancer incidence and mortality rates, later diagnosis, lower screening uptake and poorer long-term survival for people living in rural compared to more urbanised areas. Despite wide inequities and variation in cancer care and outcomes across Europe, much of the scientific literature explicitly exploring the impact of rurality on cancer continues to come from Australia and North America. The European Code of Cancer Practice or "The Code" is a citizen and patient-centred statement of the most salient requirements for good clinical cancer practice and has been extensively co-produced by cancer patients, cancer professionals and patient advocates. It contains 10 key overarching Rights that a cancer patient should expect from their healthcare system, regardless of where they live and has been strongly endorsed by professional and patient cancer organisations as well as the European Commission. In this article, we use these 10 fundamental Rights as a framework to argue that (i) the issues and needs identified in The Code are generally more profound for rural people with cancer; (ii) addressing these issues is also more challenging in rural contexts; (iii) interventions and support must explicitly account for the unique needs of rural residents living with and affected by cancer and (iv) new innovative approaches are urgently required to successfully overcome the challenges faced by rural people with cancer and their caregivers. Despite equitable healthcare being a key European policy focus, the needs of rural people living with cancer have largely been neglected.

Building bridges between clinic and community: Supporting patients and caregivers living in rural and remote Canada

Advances in the detection, diagnosis, and treatment of cancer have paralleled significant developments in the understanding of tumour biology, pathophysiology, and genomics. In spite of this, cancer remains the leading cause of death in Canada, with an estimated two in five Canadians expected to be diagnosed with cancer and one in four Canadians expected to die of cancer in their lifetime. Although Canada has a publicly funded, universal healthcare system, profound inequities exist across the country. Such inequities are often due to a multitude of intersecting factors. The focus of this paper is to review the impact of rurality on cancer care. People residing in rural and remote regions are known to have reduced access to and availability of cancer care, from prevention through diagnosis, treatment, follow-up, and palliative care. Potential strategies to mitigate the challenges associated with rurality will be discussed, including an overview of the role that nurses can play in addressing the needs of patients in rural regions. Oncology nurses are well suited to help support patients, their loved ones, and healthcare colleagues in rural settings with a view to helping improve equity in access to care, quality of care, and outcomes of care for all Canadians.

The Impact of Commission on Cancer Accreditation Status, Hospital Rurality and Hospital Size on Quality Measure Performance Rates

BACKGROUND

Rural cancer patients receive lower-quality care and experience worse outcomes than urban patients. Commission on Cancer (CoC) accreditation requires hospitals to monitor performance on evidence-based quality measuresPlease confirm the list of authors is correc, but the impact of accreditation is not clear due to lack of data from non-accredited facilities and confounding between patient rurality and hospital accreditation, rurality, and size.

METHODS

This retrospective, observational study assessed associations between rurality, accreditation, size, and performance rates for four CoC quality measures (breast radiation, breast chemotherapy, colon chemotherapy, colon nodal yield). Iowa Cancer Registry data were queried to identify all eligible patients diagnosed between 2011 and 2017. Cases were assigned to the surgery hospital to calculate performance rates. Univariate and multivariate regression models were fitted to identify patient- and hospital-level predictors and assess trends.

RESULTS

The study cohort included 10,381 patients; 46% were rural. Compared with urban patients, rural patients more often received treatment at small, rural, and non-accredited facilities (p < 0.001 for all). Rural hospitals had fewer beds and were far less likely to be CoC-accredited than urban hospitals (p < 0.001 for all). On multivariate analysis, CoC accreditation was the strongest, independent predictor of higher hospital performance for all quality measures evaluated (p < 0.05 in each model). Performance rates significantly improved over time only for the colon nodal yield quality measure, and only in urban hospitals.

CONCLUSIONS

CoC accreditation requires monitoring and evaluating performance on quality measures, which likely contributes to better performance on these measures. Efforts to support rural hospital accreditation may improve existing disparities in rural cancer treatment and outcomes.

Radiology reporting in oncology-oncologists' perspective

BACKGROUND

Structured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists' opinions and expectations concerning the radiologist's report to identify general needs in daily practice and ways to improve interdisciplinary communication.

METHODS

A 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation.

RESULTS

A total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1-3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists' service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists' service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist.

CONCLUSION

Structured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.

Streamlining the Quantitative Metrics Workflow at a Comprehensive Cancer Center

INTRODUCTION

The objective of the project was to describe an efficient workflow for quantifying and disseminating tumor imaging metrics essential for assessing tumor response in clinical therapeutic trials. The clinical research utility of integration of the workflow into the electronic health record for radiology reporting was measured before and after the intervention.

MATERIALS AND METHODS

A search of institutional clinical trial databases was performed to identify trials with radiology department collaborators. Investigator initiated trials, or those which lacked a standardized or automated system of collaboration with the research team were selected for the study. A web based application integrated in the electronic health record platform, the Quantitative Imaging Analysis Core (QIAC) initiative was established as a divisional resource with institutional support to provide standardized and reproducible imaging metrics across the institution. The turnaround time for radiology reports before (phase 1) and after web based application workflow (phase 2) was measured. During our test period (November 2014 to June 2015), a total of 68 requests with 37 from phase 1 and 31 from phase 2 were analyzed for patients who were enrolled in prospective clinical therapeutic interventional trials.

RESULTS

The mean turnaround time for generation of quantitative tumor metric results after implementation of the web based QIAC workflow (phase 2) was significantly lower than prior (phase 1) (15.9 ± 21.3 vs 31.7 ± 35.4 hours, p= 0.0005). The mean time from the scan to the preliminary assessment was 19.6 ± 25.6 hours before and significantly reduced to 8.0 ± 9.9 hours with implementation of web based QIAC workflow.

CONCLUSION

Implementation of a web based QIAC workflow platform enabled significantly improved turnaround time for quantitative tumor metrics reports and enabled faster access to the reports.

Oncologic Errors in Diagnostic Radiology: A 10-Year Analysis Based on Medical Malpractice Claims

PURPOSE

To retrospectively analyze the nature and extent of oncology-related errors accounting for malpractice allegations in diagnostic radiology.

METHODS

The Comparative Benchmarking System of the Controlled Risk Insurance Company, a database containing roughly 30% of medical malpractice claims in the United States, was searched retrospectively for the period 2008 to 2017. Claims naming radiology as a primary service were identified and were stratified and compared by oncologic versus nononcologic status, allegation type (diagnostic versus nondiagnostic), and imaging modality.

RESULTS

Over the 10-year period, radiology was the primary responsible service for 3.9% of all malpractice claims (2,582 of 66,061) and 12.8% of claims with diagnostic allegations (1,756 of 13,695). Oncology (neoplasms) accounted for 44.0% of radiology cases with diagnostic allegations, a larger share than any other category of medical condition. Among radiology cases with diagnostic allegations, high-severity harm occurred in 79% of oncologic but just 42% of nononcologic cases. Of all oncologic radiology cases, 97.4% had diagnostic allegations, and just 55.0% of nononcologic radiology cases had diagnostic allegations. Imaging misinterpretation was a contributing factor for a large majority (80.7% [623 of 772]) of oncologic radiology cases with diagnostic allegations. The modalities most commonly used in oncologic radiology cases with diagnostic allegations involving misinterpretation were mammography and CT.

CONCLUSION

Oncology represents the largest source of radiology malpractice cases with diagnostic allegations. Oncologic radiology malpractice cases are more likely than nononcologic radiology cases to be due to diagnostic errors. Furthermore, compared with those that are nononcologic, oncologic radiology cases with diagnostic allegations are more likely to be associated with high-severity harm. Efforts are warranted to reduce misinterpretations of oncologic imaging.

Clinical Application of Computational Methods in Precision Oncology: A Review

Importance

There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems.

Objective

To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology.

Evidence Review

The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology.

Findings

Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics.

Conclusions and Relevance

Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

Building a precision oncology workforce by multidisciplinary and case-based learning

BACKGROUND

Participants in two recent National Academy of Medicine workshops identified a need for more multi-disciplinary professionals on teams to assist oncology clinicians in precision oncology.

METHODS

We developed a graduate school course to prepare biomedical students and pharmacy students to work within a multidisciplinary team of oncology clinicians, pathologists, radiologists, clinical pharmacists, and genetic counselors. Students learned precision oncology skills via case-based learning, hands-on data analyses, and presentations to peers. After the course, a focus group session was conducted to gain an in-depth student perspective on their interprofessional training experience, achievement of the course learning outcomes, ways to improve the course design in future offerings, and how the course could improve future career outcomes. A convenience sampling strategy was used for recruitment into the focus group session. A thematic content analysis was then conducted using the constant comparative method.

RESULTS

Major themes arising from student feedback were (1) appreciation of a customized patient case-based teaching approach, (2) more emphasis on using data analysis tools, (3) valuing interdisciplinary inclusion, and (4) request for more student discussion with advanced preparation materials.

CONCLUSIONS

Feedback was generally positive and supports the continuation and expansion of the precision oncology course to include more hands-on instruction on the use of clinical bioinformatic tools.

Body MRI Subspecialty Reinterpretations at a Tertiary Care Center: Discrepancy Rates and Error Types

OBJECTIVE. Radiology departments in tertiary care centers are frequently asked to perform secondary interpretations of imaging studies, particularly when a patient is transferred from a community hospital. Discrepancy rates in radiology vary widely, with low rates reported for preliminary resident reports that are overread by attending radiologists (2-6%) and higher rates (up to 56%) for secondary interpretations. Abdominal and pelvic imaging and cross-sectional imaging have the highest discrepancy rates. The purpose of our study was to determine the discrepancy rate and the most common reasons for discrepancies between abdominal and pelvic MRI reports obtained from outside institutions and secondary interpretations of these reports by a fellowship-trained radiologist at a tertiary care center. MATERIALS AND METHODS. We retrospectively identified 395 secondary MRI reports from January 2015 to December 2018 that were labeled as body MRI examinations at a tertiary care center. Thirty-eight cases were excluded for various reasons, including incorrect categorization or lack of outside report. We reviewed the outside reports, compared them with the secondary interpretations, and categorized the cases as discrepancy or no discrepancy. The discrepancies were subdivided into the most likely reason for the error using previously published categories; these categories were also divided into perceptive and cognitive errors. RESULTS. Of the 357 included cases, 246 (68.9%) had at least one discrepancy. The most common reason for error was faulty reasoning (34.3%), which is a cognitive error characterized by misidentifying an abnormality. Satisfaction of search, which is a perceptive error, was the most common reason for second discrepancies (15.0%). CONCLUSION. Secondary interpretations of body MR images at a tertiary care center identify a high rate of discrepancies, with cognitive error types predominating.

National Trends in Oncologic Diagnostic Imaging

OBJECTIVE

To characterize national trends in oncologic imaging (OI) utilization.

METHODS

This retrospective cross-sectional study used 2004 and 2016 CMS 5% Carrier Claims Research Identifiable Files. Radiologist-performed, primary noninvasive diagnostic imaging examinations were identified from billed Current Procedural Terminology codes; CT, MRI, and PET/CT examinations were categorized as "advanced" imaging. OI examinations were identified from imaging claims' primary International Classification of Diseases-9 and International Classification of Diseases-10 codes. Imaging services were stratified by academic practice status and place of service. State-level correlations of oncologic advanced imaging utilization (examinations per 1,000 beneficiaries) with cancer prevalence and radiologist supply were assessed by Spearman correlation coefficient.

RESULTS

The national Medicare sample included 5,051,095 diagnostic imaging examinations (1,220,224 of them advanced) in 2004 and 5,023,115 diagnostic imaging examinations (1,504,608 of them advanced) in 2016. In 2004 and 2016, OI represented 4.3% and 3.9%, respectively, of all imaging versus 10.8% and 9.5%, respectively, of advanced imaging. The percentage of advanced OI done in academic practices rose from 18.8% in 2004 to 34.1% in 2016, leaving 65.9% outside academia. In 2016, 58.0% of advanced OI was performed in the hospital outpatient setting and 23.9% in the physician office setting. In 2016, state-level oncologic advanced imaging utilization correlated with state-level radiologist supply (r = +0.489, P < .001) but not with state-level cancer prevalence (r = -0.139, P = .329).

DISCUSSION

OI usage varied between practice settings. Although the percentage of advanced OI done in academic settings nearly doubled from 2004 to 2016, the majority remained in nonacademic practices. State-level oncologic advanced imaging utilization correlated with radiologist supply but not cancer prevalence.

Closing the Rural Cancer Care Gap: Three Institutional Approaches

Patients in rural areas face limited access to medical and oncology providers, long travel times, and low recruitment to clinical trials, all of which affect quality of care and health outcomes. Rural counties also have high rates of cancer-related mortality and other negative treatment outcomes. On April 10, 2019, ASCO hosted Closing the Rural Cancer Care Gap, the second event in its State of Cancer Care in America series. The event focused on two aspects of rural cancer care: a review of the major issues and concerns in delivering rural cancer care and a discussion of creative solutions to address rural-nonrural disparities. This article draws from the event and supporting literature to summarize the challenges to delivering high-quality care in rural communities, update ASCO’s workforce data on the geographic distribution of oncologists, and highlight 3 institutional approaches to addressing these challenges in diverse rural settings. The experience of the 3 institutions featured in the article suggests that increasing rural patients’ access to care requires expanding services and decreasing travel distances, mitigating financial burdens when insurance coverage is limited, opening avenues to clinical trial participation, and creating partnerships between providers and community leaders to address local gaps in care. Because the characteristics of rural communities, health care resources, and patient populations are not homogeneous, rural health disparities require local solutions that are based on community needs, available resources, and trusting and collaborative partnerships.