Race/Ethnicity and Gender Representation in Hematology and Oncology Editorial Boards: What is the State of Diversity?

INTRODUCTION: Women and underrepresented groups in medicine hold few academic leadership positions in the field of hematology/oncology. In this study, we assessed gender and race/ethnicity representation in editorial board positions in hematology/oncology journals. MATERIALS AND METHODS: Editorial leadership board members from 60 major journals in hematology and oncology were reviewed; 54 journals were included in the final analysis. Gender and race/ethnicity were determined based on publicly available data for Editor-in-Chief (EiC) and Second-in-Command (SiC) (including deputy, senior, or associate editors). Descriptive statistics and chi-squared were estimated. In the second phase of the study, editors were emailed a 4-item survey to self-identify their demographics. RESULTS: Out of 793 editorial board members, 72.6% were men and 27.4% were women. Editorial leadership were non-Hispanic white (71.1%) with Asian editorial board members representing the second largest majority at 22.5%. Women comprised only 15.9% of the EiC positions (90% White and 10% Asian). Women were about half as likely to be in the EiC position compared with men [pOR 0.47 (95% CI, 0.23-0.95, P = .03)]. Women represented 28.3% of SiC editorial positions. Surgical oncology had the lowest female representation at 2.3%. CONCLUSION: Women and minorities are significantly underrepresented in leadership roles on Editorial Boards in hematology/oncology journals. Importantly, the representation of minority women physicians in EiC positions is at an inexorable zero.

Understanding Test Results Follow-Up in the Ambulatory Setting: Analysis of Multiple Perspectives.

BACKGROUND: Delayed or incomplete test result follow-up, which can lead to missed and/or delayed diagnosis, is an important issue in the ambulatory setting. Delayed test result follow-up has been linked to poorer patient outcomes and increased risk of mortality and accounts for a large portion of medical malpractice claims. Yet improvements are difficult, reflecting the complexity of the test result follow-up process. Test result follow-up safety culture was investigated using qualitative and quantitative patient safety and quality of care data at an academic medical center. METHODS: After an environmental scan, five sources of data were used to compass multiple perspectives on safety culture-two national surveys (AHRQ MO SOPS for safety culture and CG-CAHPS for patient satisfaction); patient and family complaints; safety reports; and provider response times to test message results in the electronic health record. RESULTS: The following metrics were inspected: how patients and providers estimated the frequency for providing timely test results; how patients' satisfaction with their provider correlated with their provider's response time to test result messages; and qualitative themes in patient complaints and safety reports filed by clinic. The institution was compared to national benchmarks using surveys. As test result response time decreased, patient satisfaction increased (p = 0.0073). CONCLUSION: Test result follow-up culture was investigated using tools typically used to examine patient satisfaction and experience and staff culture. Use of these five sources of data led to an examination of multiple perspectives in follow-up culture and identification of possible explanations for inappropriate follow-up. These data sources can be further explored to identify possible solutions.

Redefining Available Therapy in Oncology Accelerated Approval Decisions.

When weighing rapid approval for follow-on drugs, should @FDAOncology recognize the drugs that came before?

Precision Immuno-Oncology in NSCLC through Gender Equity Lenses.

Precision immuno-oncology involves the development of personalized cancer treatments that are influenced by the unique nature of an individual's DNA, immune cells, and their tumor's molecular characterization. Biological sex influences immunity; females typically mount stronger innate and adaptive immune responses than males. Though more research is warranted, we continue to observe an enhanced benefit for females with lung cancer when treated with combination chemoimmunotherapy in contrast to the preferred approach of utilizing immunotherapy alone in men. Despite the observed sex differences in response to treatments, women remain underrepresented in oncology clinical trials, largely as a result of gender-biased misconceptions. Such exclusion has resulted in the development of less efficacious treatment guidelines and clinical recommendations and has created a knowledge gap in regard to immunotherapy-related survivorship issues such as fertility. To develop a more precise approach to care and overcome the exclusion of women from clinical trials, flexible trial schedules, multilingual communication strategies, financial, and transportation assistance for participants should be adopted. The impact of intersectionality and other determinants of health that affect the diagnosis, treatment, and outcomes in women must also be considered in order to develop a comprehensive understanding of the unique impact of immunotherapy in all women with lung cancer.

Vertebral osteomyelitis secondary to Streptococcus cristatus infection.

A 66-year-old male with a history of low back pain was found to have discitis and osteomyelitis. Biopsy and PCR testing revealed Streptococcus cristatus infection. This bacteria does not typically cause disease, and only a few cases in the literature have reported it to cause infection in the bones or joints. This case illustrates that vertebral osteomyelitis with a rare causative agent, S. cristatus, is possible and can be identified with PCR. Treatment typically requires long-term antibiotics tailored to the causative agent for a minimum of 6 weeks and can sometimes include surgical management.

Inequity in care delivery in cardio-oncology: dissecting disparities in underrepresented populations.

It is well known that patients with cancer have a significantly higher cardiovascular mortality risk than the general population. Cardio-oncology has emerged to focus on these issues including risk reduction, detection, monitoring, and treatment of cardiovascular disease or complications in patients with cancer. The rapid advances in early detection and drug development in oncology, along with socioeconomic differences, racial inequities, lack of support, and barriers to accessing quality medical care, have created disparities in various marginalized populations. In this review, we will discuss the factors contributing to disparities in cardio-oncologic care in distinct populations, including Hispanic/Latinx, Black, Asian and Pacific Islander, indigenous populations, sex and gender minorities, and immigrants. Some factors that contribute to differences in outcomes in cardio-oncology include the prevalence of cancer screening rates, genetic cardiac/oncologic risk factors, cultural stressors, tobacco exposure rates, and physical inactivity. We will also discuss the barriers to cardio-oncologic care in these communities from the racial and socioeconomic context. Appropriate and timely cardiovascular and cancer care in minority groups is a critical component in addressing these disparities, and there need to be urgent efforts to address this widening gap.

A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.

OBJECTIVE: To improve problem list documentation and care quality. MATERIALS AND METHODS: We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures. RESULTS: There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures. DISCUSSION: The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research. CONCLUSION: An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures.

Interviewing For Residency as an LGBTQ+ Applicant: Compounded Problems With Virtual Interviewing.

Applying to and interviewing for residency positions can be challenging tasks for LGBTQ+ applicants. Resident and faculty surgeons have expressed challenges in disclosing their sexual orientation and/or gender identity throughout the recruitment process. Discrimination and lack of LGBTQ+ inclusion in the recruitment process may have been exacerbated by virtual interviews in response to the COVID-19 pandemic. The authors describe their experiences interviewing virtually for academic general surgery programs, and academic and university-affiliated/community residency programs as LGBTQ+ applicants. Several recommendations are offered to help reduce LGBTQ+ discrimination and to help make institutions more inclusive with the overall aim of making resident recruitment more inclusive for LGBTQ+ applicants.

Barriers and facilitators to standardization of ultrasound use and probe disinfection in the ambulatory setting.

To determine barriers and facilitators to standardization of ultrasound probe disinfection at ambulatory sites, we conducted observations and interviews of staff. Variability was noted in disinfection practices and in the use of protective equipment even for procedures with the potential for the probe to contact sterile tissues. Standardization is needed.

Medication Use for Childhood Pneumonia at a Children's Hospital in Shanghai, China: Analysis of Pattern Mining Algorithms.

BACKGROUND: Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation. OBJECTIVE: The aim of this study was to examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. METHODS: We applied 3 pattern mining algorithms to 680,138 medication administration records from 30,512 childhood inpatients with diagnosis of pneumonia during a 6-year period at a children's hospital in China. Patients' ages ranged from 0 to 17 years, where 37.53% (11,453/30,512) were 0 to 3 months old, 86.55% (26,408/30,512) were under 5 years, 60.37% (18,419/30,512) were male, and 60.10% (18,338/30,512) had a hospital stay of 9 to 15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first 2 are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these 3 methods identified the top 10 patterns from 6 age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (73.66%, 500,982/680,138) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize 5 medication use and 2 treatment patterns. RESULTS: We identified 5 medication use patterns: (1) antiasthmatics and expectorants and corticosteroids, (2) antibiotics and (antiasthmatics or expectorants or corticosteroids), (3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics, (4) antibiotics and (medications for enteritis or skin diseases), and (5) (antiasthmatics or expectorants or corticosteroids) and (medications for enteritis or skin diseases). We also identified 2 frequent treatment patterns: (1) 42.89% (291,701/680,138) of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements and (2) 11.53% (78,390/680,138) were of various combinations of inhalation of antiasthmatics, expectorants, or corticosteroids. Fleiss kappa for the subject experts' evaluation was 0.693, indicating moderate agreement. CONCLUSIONS: Utilizing a pattern mining approach, we summarized 5 medication use patterns and 2 treatment patterns. These warrant further investigation.

Structured override reasons for drug-drug interaction alerts in electronic health records.

OBJECTIVE: The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. MATERIALS AND METHODS: We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. RESULTS: Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: "will monitor or take precautions," "not clinically significant," and "benefit outweighs risk." DISCUSSION: We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved. CONCLUSIONS: Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.

Communication failure: analysis of prescribers' use of an internal free-text field on electronic prescriptions.

Importance: Electronic prescribing promises to improve the safety and clarity of prescriptions. However, it also can introduce miscommunication between prescribers and pharmacists. There are situations where information that is meant to be sent to pharmacists is not sent to them, which has the potential for dangerous errors. Objective: To examine how frequently prescribers or administrative personnel put information intended for pharmacists in a field not sent to pharmacists, classify the type of information included, and assess the potential harm associated with these missed messages. Design, Setting, Participants: Medication record data from our legacy electronic health record were requested for ambulatory care patients seen at an academic medical center from January 1, 2000, to May 31, 2015 (20 123 881 records). From this database, 6 060 272 medication orders met our inclusion criteria. We analyzed a random sample of 10 000 medication orders with internal comments. Main Outcomes and Measures: Reviewers classified internal comments for intent. Comments intended for pharmacists were also sorted into descriptive categories and analyzed for the potential for patient harm. Results: We found that 11.7% of the prescriptions in our sample contained comments that were intended to be sent to pharmacists. Many comments contained information about the dose, route, or duration of the prescription (38.0%). Approximately a third of the comments intended for pharmacists contained information that had the potential for significant or severe harm if not communicated. Conclusion: We found undelivered comments that were clearly intended for pharmacists and contained important information for either pharmacists or patients. This poses a legitimate safety concern, as a portion of comments contained information that could have prevented severe or significant harm.

Development and evaluation of a novel user interface for reviewing clinical microbiology results.

Background: Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review. Methods: We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design. Results: The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale. Conclusions: The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study.

OBJECTIVE: Developing effective and reliable rule-based clinical decision support (CDS) alerts and reminders is challenging. Using a previously developed taxonomy for alert malfunctions, we identified best practices for developing, testing, implementing, and maintaining alerts and avoiding malfunctions. MATERIALS AND METHODS: We identified 72 initial practices from the literature, interviews with subject matter experts, and prior research. To refine, enrich, and prioritize the list of practices, we used the Delphi method with two rounds of consensus-building and refinement. We used a larger than normal panel of experts to include a wide representation of CDS subject matter experts from various disciplines. RESULTS: 28 experts completed Round 1 and 25 completed Round 2. Round 1 narrowed the list to 47 best practices in 7 categories: knowledge management, designing and specifying, building, testing, deployment, monitoring and feedback, and people and governance. Round 2 developed consensus on the importance and feasibility of each best practice. DISCUSSION: The Delphi panel identified a range of best practices that may help to improve implementation of rule-based CDS and avert malfunctions. Due to limitations on resources and personnel, not everyone can implement all best practices. The most robust processes require investing in a data warehouse. Experts also pointed to the issue of shared responsibility between the healthcare organization and the electronic health record vendor. CONCLUSION: These 47 best practices represent an ideal situation. The research identifies the balance between importance and difficulty, highlights the challenges faced by organizations seeking to implement CDS, and describes several opportunities for future research to reduce alert malfunctions.

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Objective: To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods: We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results: We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion: Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion: CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

A Picture is Worth 1,000 Words. The Use of Clinical Images in Electronic Medical Records.

OBJECTIVE: To understand how clinicians utilize image uploading tools in a home grown electronic health records (EHR) system. METHODS: A content analysis of patient notes containing non-radiological images from the EHR was conducted. Images from 4,000 random notes from July 1, 2009 - June 30, 2010 were reviewed and manually coded. Codes were assigned to four properties of the image: (1) image type, (2) role of image uploader (e.g. MD, NP, PA, RN), (3) practice type (e.g. internal medicine, dermatology, ophthalmology), and (4) image subject. RESULTS: 3,815 images from image-containing notes stored in the EHR were reviewed and manually coded. Of those images, 32.8% were clinical and 66.2% were non-clinical. The most common types of the clinical images were photographs (38.0%), diagrams (19.1%), and scanned documents (14.4%). MDs uploaded 67.9% of clinical images, followed by RNs with 10.2%, and genetic counselors with 6.8%. Dermatology (34.9%), ophthalmology (16.1%), and general surgery (10.8%) uploaded the most clinical images. The content of clinical images referencing body parts varied, with 49.8% of those images focusing on the head and neck region, 15.3% focusing on the thorax, and 13.8% focusing on the lower extremities. CONCLUSION: The diversity of image types, content, and uploaders within a home grown EHR system reflected the versatility and importance of the image uploading tool. Understanding how users utilize image uploading tools in a clinical setting highlights important considerations for designing better EHR tools and the importance of interoperability between EHR systems and other health technology.

Methods for Detecting Malfunctions in Clinical Decision Support Systems.

Clinical decision support systems, when used effectively, can improve the quality of care. However, such systems can malfunction, and these malfunctions can be difficult to detect. In this poster, we describe four methods of detecting and resolving issues with clinical decision support: 1) statistical anomaly detection, 2) visual analytics and dashboards, 3) user feedback analysis, 4) taxonomization of failure modes/effects.

Variation in high-priority drug-drug interaction alerts across institutions and electronic health records.

Objective: The United States Office of the National Coordinator for Health Information Technology sponsored the development of a "high-priority" list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive). Materials and methods: We conducted evaluations of electronic health records (EHRs) at a convenience sample of health care organizations across the United States using a standardized testing protocol with simulated orders. Results: Evaluations of 19 systems were conducted at 13 sites using 14 different EHRs. Across systems, 69% of the high-priority DDI pairs produced alerts. Implementation and display of the DDI alerts tested varied between systems, even when the same EHR vendor was used. Across the drug pairs evaluated, implementation and display of DDI alerts differed, ranging from 27% (4/15) to 93% (14/15) implementation. Discussion: Currently, there is no standard of care covering which DDI alerts to implement or how to display them to providers. Opportunities to improve DDI alerting include using differential displays based on DDI severity, establishing improved lists of clinically significant DDIs, and thoroughly reviewing organizational implementation decisions regarding DDIs. Conclusion: DDI alerting is clinically important but not standardized. There is significant room for improvement and standardization around evidence-based DDIs.

Analysis of clinical decision support system malfunctions: a case series and survey.

OBJECTIVE: To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions. MATERIALS AND METHODS: We identified and investigated several CDSS malfunctions at Brigham and Women's Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions. RESULTS: We identified four CDSS malfunctions at Brigham and Women's Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually. DISCUSSION: CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate. CONCLUSION: CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.