Objective We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy. Materials and Methods We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information. Results Among the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system’s EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR. Discussion Race and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health. Conclusions Patient self-recording may improve the completeness of race and ethnicity information.

Women with atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS), and severe ADH are at increased risk of breast cancer, but a systematic quantification of this risk and the efficacy of chemoprevention in the clinical setting is still lacking. The objective of this study is to evaluate a woman's risk of breast cancer based on atypia type and to determine the effect of chemoprevention in decreasing this risk. Review of 76,333 breast pathology reports from three institutions within Partners Healthcare System, Boston, from 1987 to 2010 using natural language processing was carried out. This approach identified 2,938 women diagnosed with atypical breast lesions. The main outcome of this study is breast cancer occurrence. Of the 2,938 patients with atypical breast lesions, 1,658 were documented to have received no chemoprevention, and 184/1,658 (11.1 %) developed breast cancer at a mean follow-up of 68 months. Estimated 10-year cancer risks were 17.3 % with ADH, 20.7 % with ALH, 23.7 % with LCIS, and 26.0 % with severe ADH. In a subset of patients treated from 1999 on (the chemoprevention era), those who received no chemoprevention had an estimated 10-year breast cancer risk of 21.3 %, whereas those treated with chemoprevention had a 10-year risk of 7.5 % (p < 0.001). Chemoprevention use significantly reduced breast cancer risk for all atypia types (p < 0.05). The risk of breast cancer with atypical breast lesions is substantial. Physicians should counsel patients with ADH, ALH, LCIS, and severe ADH about the benefit of chemoprevention in decreasing their breast cancer risk.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

PURPOSE: The use of telemedicine expanded dramatically in March 2020 following the COVID-19 pandemic. We sought to assess oncologist perspectives on telemedicine's present and future roles (both phone and video) for patients with cancer. METHODS: The National Comprehensive Cancer Network (NCCN) Electronic Health Record (EHR) Oncology Advisory Group formed a Workgroup to assess the state of oncology telemedicine and created a 20-question survey. NCCN EHR Oncology Advisory Group members e-mailed the survey to providers (surgical, hematology, gynecologic, medical, and radiation oncology physicians and clinicians) at their home institution. RESULTS: Providers (N = 1,038) from 26 institutions responded in Summer 2020. Telemedicine (phone and video) was compared with in-person visits across clinical scenarios (n = 766). For reviewing benign follow-up data, 88% reported video and 80% reported telephone were the same as or better than office visits. For establishing a personal connection with patients, 24% and 7% indicated video and telephone, respectively, were the same as or better than office visits. Ninety-three percent reported adverse outcomes attributable to telemedicine visits never or rarely occurred, whereas 6% indicated they occasionally occurred (n = 801). Respondents (n = 796) estimated 46% of postpandemic visits could be virtual, but challenges included (1) lack of patient access to technology, (2) inadequate clinical workflows to support telemedicine, and (3) insurance coverage uncertainty postpandemic. CONCLUSION: Telemedicine appears effective across a variety of clinical scenarios. Based on provider assessment, a substantial fraction of visits for patients with cancer could be effectively and safely conducted using telemedicine. These findings should influence regulatory and infrastructural decisions regarding telemedicine postpandemic for patients with cancer.

Introduction COVID-19 increased stress levels while reducing access to mind-body services in patients with cancer. We describe the rapid deployment of remotely delivered mind-body services to people with cancer during COVID-19, rates of participation, and acceptability from patients' perspectives. Methods Eligible participants were patients with cancer age ≥ 18 years enrolled in a single academic cancer center's online patient portal. Interventions included mind-body group therapy sessions in fitness, meditation, yoga, dance, tai chi, and music delivered using Zoom video conferencing. Sessions were 30-45 min and led by an integrative medicine clinician. Following each session, participants were asked to complete a three-item questionnaire assessing (1) satisfaction with the class session, (2) reduction in stress/anxiety, and (3) likelihood of recommending the class to others. Patients could also provide comments in real-time using the Zoom chat function. Results Among 5948 unique visits, the most frequently attended classes were fitness (n = 2513, 42.2%) followed by meditation (n = 1176, 19.8%) and yoga (n = 909, 15.3%). Of these visits, 3902 (65.6%) had an associated completed questionnaire. Across class types, a large majority of participants reported being extremely satisfied (n = 3733, 95.7%), experiencing extreme reductions in anxiety/stress (n = 3268, 83.8%), and being extremely likely to recommend the class to others (n = 3605, 92.4%). Fitness had the highest endorsement among class types (all p values < 0.001). Themes from the chat responses included gratitude, expressions of helpfulness, and feelings of connection. Conclusion High utilization of and satisfaction with these virtual mind-body services demonstrate the significant potential of remote delivery to facilitate patient access to services.

Objective:The opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports.Approach and Procedure:Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text.Results:There was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders.Conclusion:We have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.