Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

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.

Case study of the integration of electronic patient-reported outcomes as standard of care in a head and neck oncology practice: Obstacles and opportunities.

BACKGROUND: Patient-reported outcomes (PROs) allow for the direct measurement of functional and psychosocial effects related to treatment. However, technological barriers, survey fatigue, and clinician adoption have hindered the meaningful integration of PROs into clinical care. The objective of the authors was to develop an electronic PROs (ePROs) program that meets a range of clinical needs across a head and neck multidisciplinary disease management team. METHODS: The authors developed the ePROs module using literature review and stakeholder input in collaboration with health informatics. They designed an ePROs platform that was integrated as the standard of care for personalized survey delivery by diagnosis across the disease management team. Tableau software was used to create dashboards for data visualization and monitoring at the clinical enterprise, disease subsite, and patient levels. All patients who were treated for head and neck cancer were eligible for ePROs assessment as part of the standard of care. A descriptive analysis of ePROs program implementation is presented herein. RESULTS: The Head and Neck Service at Memorial Sloan Kettering Cancer Center has integrated ePROs into clinical care. Surveys are delivered via the patient portal at the time of diagnosis and longitudinally through care. From August 1, 2018, to February 1, 2020, a total of 4154 patients completed ePROs surveys. The average patient participation rate was 69%, with a median time for completion of 5 minutes. CONCLUSIONS: Integration of the head and neck ePROs program as part of clinical care is feasible and could be used to assess value and counsel patients in the future. Continued qualitative assessments of stakeholders and workflow will refine content and enhance the health informatics platform. LAY SUMMARY: Patients with head and neck cancer experience significant changes in their quality of life after treatment. Measuring and integrating patient-reported outcomes as a part of clinical care have been challenging given the multimodal treatment options, vast subsites, and unique domains affected. The authors present a case study of the successful integration of electronic patient-reported outcomes into a high-volume head and neck cancer practice.

Rapid deployment of virtual mind-body interventions during the COVID-19 outbreak: feasibility, acceptability, and implications for future care.

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.

Low Rates of Screening for Celiac Disease Among Family Members.

BACKGROUND & AIMS: Given the increased morbidity and potential mortality of celiac disease, guidelines recommend screening high-risk individuals, including first-degree relatives of patients. We assessed how commonly celiac disease testing occurs in these individuals and identified factors that influence testing. METHODS: Relatives of 2081 patients with biopsy-diagnosed celiac disease and followed up at Columbia University Medical Center were identified using relationship inference from the electronic health record-a validated method that uses emergency contact information to identify familial relationships. We manually abstracted data from each record and performed univariate and multivariate analyses to identify factors associated with testing relatives for celiac disease. RESULTS: Of 539 relatives identified, 212 (39.3%) were tested for celiac disease, including 50.4% (193 of 383) of first-degree relatives and 71.5% (118 of 165) of symptomatic first-degree relatives. Of the 383 first-degree relatives, only 116 (30.3%) had a documented family history of celiac disease. On multivariate analysis, testing was more likely in adults (odds ratio [OR], for 18-39 y vs younger than 18 y, 2.27; 95% CI, 1.12-4.58); relatives being seen by a gastroenterologist (OR, 15.16; 95% CI, 7.72-29.80); relatives with symptoms (OR, 3.69; 95% CI, 2.11-6.47); first-degree relatives of a patient with celiac disease (OR, 4.90, 95% CI, 2.34-10.25); and relatives with a documented family history of celiac disease (OR, 11.9, 95% CI, 5.56-25.48). CONCLUSIONS: By using an algorithm to identify relatives of patients with celiac disease, we found that nearly 30% of symptomatic first-degree relatives of patients with celiac disease have not received the tests recommended by guidelines. Health care providers should implement strategies to identify and screen patients at increased risk for celiac disease, including methods to ensure adequate documentation of family medical history.

Reassessing risk models for atypical hyperplasia: age may not matter.

PURPOSE: The aim of this study was to investigate the influence of age at diagnosis of atypical hyperplasia ("atypia", ductal [ADH], lobular [ALH], or severe ADH) on the risk of developing subsequent invasive breast cancer or ductal carcinoma in situ (DCIS). METHODS: Using standard survival analysis methods, we retrospectively analyzed 1353 women not treated with chemoprevention among a cohort of 2370 women diagnosed with atypical hyperplasia to determine the risk relationship between age at diagnosis and subsequent breast cancer. RESULTS: For all atypia diagnoses combined, our cohort showed a 5-, 10-, and 15-year risk of invasive breast cancer or DCIS of 0.56, 1.25, and 1.30, respectively, with no significant difference in the (65,75] year age group. For women aged (35,75] years, we observed no significant difference in the 15-year risk of invasive breast cancer or DCIS after atypical hyperplasia, although the baseline risk for a 40-year-old woman is approximately 1/8 the risk of a 70-year-old woman. The risks associated with invasive breast cancer or DCIS for women in our cohort diagnosed with ADH, severe ADH, or ALH, regardless of age, were 7.6% (95% CI 5.9-9.3%) at 5 years, 25.1% (20.7-29.2%) at 10 years, and 40.1% (32.8-46.6%) at 15 years. CONCLUSION: In contrast to current risk prediction models (e.g., Gail, Tyrer-Cuzick) which assume that the risk of developing breast cancer increases in relation to age at diagnosis of atypia, we found the 15-year cancer risk in our cohort was not significantly different for women between the ages of 35 (excluded) and 75. This implies that the "hits" received by the breast tissue along the "high-risk pathway" to cancer might possibly supersede other factors such as age.

Using machine learning to parse breast pathology reports.

PURPOSE: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. METHODS: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. RESULTS: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. CONCLUSIONS: 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.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. RECENT FINDINGS: The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. SUMMARY: Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Procedure prediction from symbolic Electronic Health Records via time intervals analytics.

Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.

Breast Cancer Risk and Follow-up Recommendations for Young Women Diagnosed with Atypical Hyperplasia and Lobular Carcinoma In Situ (LCIS).

BACKGROUND: The risk of breast cancer in young women diagnosed with atypical hyperplasia and (LCIS) is not well defined. The objectives were to evaluate outcomes and to help determine guidelines for follow-up in this population. METHODS: A retrospective review of women under age 35 diagnosed with ADH, ALH, LCIS, and severe ADH from 1987 to 2010 was performed. Patient characteristics, pathology and follow-up were determined from chart review. RESULTS: We identified 58 young women with atypical breast lesions. Median age at diagnosis was 31 years (range 19-34). 34 patients had ADH, 11 had ALH, 8 had LCIS, and 5 had severe ADH. 7 (12%) patients developed breast cancer. The median follow-up was 86 months (range 1-298). Median time to cancer diagnosis was 90 months (range 37-231). 4 cancers were on the same side, 3 were contralateral. 4 were IDC, 1 was ILC, and 2 were DCIS. Cancer was detected by screening mammogram in 4 patients, 2 by clinical exam, and 1 unknown. In the entire cohort, 26 (45%) patients had screening mammograms as part of their follow up, 12 patients had only clinical follow up, and 20 had no additional follow up. 13 patients required subsequent biopsies. CONCLUSION: Young women with atypical breast lesions are at a markedly increased risk for developing breast cancer and should be followed closely. Based on our findings, we recommend close clinical follow-up, MRI starting at age 25 through age 29, and screening mammograms for those over 30 in this high-risk group of patients.

Opportunities and Challenges for Augmented Reality in Family Caregiving: Qualitative Video Elicitation Study.

BACKGROUND: Although family caregivers play a critical role in care delivery, research has shown that they face significant physical, emotional, and informational challenges. One promising avenue to address some of caregivers' unmet needs is via the design of digital technologies that support caregivers' complex portfolio of responsibilities. Augmented reality (AR) applications, specifically, offer new affordances to aid caregivers as they perform care tasks in the home. OBJECTIVE: This study explored how AR might assist family caregivers with the delivery of home-based cancer care. The specific objectives were to shed light on challenges caregivers face where AR might help, investigate opportunities for AR to support caregivers, and understand the risks of AR exacerbating caregiver burdens. METHODS: We conducted a qualitative video elicitation study with clinicians and caregivers. We created 3 video elicitations that offer ways in which AR might support caregivers as they perform often high-stakes, unfamiliar, and anxiety-inducing tasks in postsurgical cancer care: wound care, drain care, and rehabilitative exercise. The elicitations show functional AR applications built using Unity Technologies software and Microsoft Hololens2. Using elicitations enabled us to avoid rediscovering known usability issues with current AR technologies, allowing us to focus on high-level, substantive feedback on potential future roles for AR in caregiving. Moreover, it enabled nonintrusive exploration of the inherently sensitive in-home cancer care context. RESULTS: We recruited 22 participants for our study: 15 clinicians (eg, oncologists and nurses) and 7 family caregivers. Our findings shed light on clinicians' and caregivers' perceptions of current information and communication challenges caregivers face as they perform important physical care tasks as part of cancer treatment plans. Most significant was the need to provide better and ongoing support for execution of caregiving tasks in situ, when and where the tasks need to be performed. Such support needs to be tailored to the specific needs of the patient, to the stress-impaired capacities of the caregiver, and to the time-constrained communication availability of clinicians. We uncover opportunities for AR technologies to potentially increase caregiver confidence and reduce anxiety by supporting the capture and review of images and videos and by improving communication with clinicians. However, our findings also suggest ways in which, if not deployed carefully, AR technologies might exacerbate caregivers' already significant burdens. CONCLUSIONS: These findings can inform both the design of future AR devices, software, and applications and the design of caregiver support interventions based on already available technology and processes. Our study suggests that AR technologies and the affordances they provide (eg, tailored support, enhanced monitoring and task accuracy, and improved communications) should be considered as a part of an integrated care journey involving multiple stakeholders, changing information needs, and different communication channels that blend in-person and internet-based synchronous and asynchronous care, illness, and recovery.

Transforming patient-centered cancer care using telehealth: the MATCHES Center.

Modern cancer care is costly and logistically burdensome for patients and their families despite an expansion of technology and medical advances that create the opportunity for novel approaches to care. Therefore, there is a growing appreciation for the need to leverage these innovations to make cancer care more patient centered and convenient. The Memorial Sloan Kettering Making Telehealth Delivery of Cancer Care at Home Efficient and Safe Telehealth Research Center is a National Cancer Institute-designated and funded Telehealth Research Center of Excellence poised to generate the evidence necessary to inform the appropriate use of telehealth as a strategy to improve access to cancer services that are convenient for patients. The center will evaluate telehealth as a strategy to personalize cancer care delivery to ensure that it is not only safe and effective but also convenient and efficient. In this article, we outline this new center's research strategy, as well as highlight challenges that exist in further integrating telehealth into standard oncology practice based on early experiences.

Using electronic health record data to link families: an illustrative example using intergenerational patterns of obesity.

OBJECTIVE: Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. MATERIALS AND METHODS: We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. RESULTS: The P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37). CONCLUSION: P-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies.

The role of chemoprevention in modifying the risk of breast cancer in women with atypical breast lesions.

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.

Impact of Exercise on Susceptibility and Severity of COVID-19 in Patients with Cancer: A Retrospective Study.

BACKGROUND: Modifiable lifestyle-related factors heighten the risk and severity of coronavirus disease 2019 (COVID-19) in patients with cancer. Whether exercise lowers susceptibility or severity is not known. METHODS: We identified 944 cancer patients from Memorial Sloan Kettering Cancer Center (mean age: 64; 85% female; 78% White) completing an exercise survey before receiving a confirmed positive or negative SARS-CoV-2 test. Exercise was defined as reporting moderate-intensity ≥5 days per week, ≥30 minutes/session or strenuous-intensity ≥3 days per week, ≥20 minutes/session. Multivariable logistic regression was used to determine the relationship between exercise and COVID-19 susceptibility and severity (i.e., composite of hospital admission or death events) with adjustment for clinical-epidemiologic covariates. RESULTS: Twenty-four percent (230/944) of the overall cohort were diagnosed with COVID-19 and 35% (333/944) were exercisers. During a median follow-up of 10 months, 26% (156/611) of nonexercising patients were diagnosed with COVID-19 compared with 22% (74/333) of exercising patients. The adjusted OR for risk of COVID-19 was 0.65 [95% confidence interval (CI), 0.44-0.96, P = 0.03] for exercisers compared with nonexercisers. A total of 20% (47/230) of COVID-19 positive patients were hospitalized or died. No difference in the risk of severe COVID-19 as a function of exercise status was observed (P > 0.9). CONCLUSIONS: Exercise may reduce the risk of COVID-19 infection in patients with a history of cancer, but not its severity. IMPACT: This study provides the first data showing that exercise might lower the risk of COVID-19 in cancer patients, but further research is required.

Oncologist Perspectives on Telemedicine for Patients With Cancer: A National Comprehensive Cancer Network Survey.

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.

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

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.

Validation of Case Identification for Alopecia Areata Using International Classification of Diseases Coding.

BACKGROUND: Search algorithms used to identify patients with alopecia areata (AA) need to be validated prior to use in large databases. OBJECTIVES: The aim of the study is to assess whether patients with an International Statistical Classification of Diseases and Related Health Problems (ICD) 9 or 10 code for AA have a true diagnosis of AA. MATERIALS AND METHODS: A multicenter retrospective review was performed at Columbia University Irving Medical Center, Brigham and Women's Hospital, and Massachusetts General Hospital to determine whether patients with an ICD 9 codes (704.01 - AA) or ICD 10 codes (L63.0 -Alopecia Totalis, L63.1 - Alopecia Universalis, L63.2 - Ophiasis, L63.8 - other AA, and L63.9 - AA, unspecified) for AA met diagnostic criteria for the disease. RESULTS: Of 880 charts, 97.5% had physical examination findings consistent with AA, and 90% had an unequivocal diagnosis. AA was diagnosed by a dermatologist in 87% of the charts. The positive predictive value (PPV) of the ICD 9 code 704.01 was 97% (248/255). The PPV for the ICD 10 codes were 64% (75/118) for L63.0, 86% (130/151) for L63.1, 50% (1/2) for L63.2, 91% (81/89) for L63.8, and 93% (247/265) for L63.9. Overall, 89% (782/880) of patients with an ICD code for AA were deemed to have a true diagnosis of AA. CONCLUSIONS: Patients whose medical records contain an AA-associated ICD code have a high probability of having the condition.

Challenges with quality of race and ethnicity data in observational databases.

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.

Technology Access, Technical Assistance, and Disparities in Inpatient Portal Use.

BACKGROUND: Disadvantaged populations, including minorities and the elderly, use patient portals less often than relatively more advantaged populations. Limited access to and experience with technology contribute to these disparities. Free access to devices, the Internet, and technical assistance may eliminate disparities in portal use. OBJECTIVE: To examine predictors of frequent versus infrequent portal use among hospitalized patients who received free access to an iPad, the Internet, and technical assistance. MATERIALS AND METHODS: This subgroup analysis includes 146 intervention-arm participants from a pragmatic randomized controlled trial of an inpatient portal. The participants received free access to an iPad and inpatient portal while hospitalized on medical and surgical cardiac units, together with hands-on help using them. We used logistic regression to identify characteristics predictive of frequent use. RESULTS: More technology experience (adjusted odds ratio [OR] = 5.39, p = 0.049), less severe illness (adjusted OR = 2.07, p = 0.077), and private insurance (adjusted OR = 2.25, p = 0.043) predicted frequent use, with a predictive performance (area under the curve) of 65.6%. No significant differences in age, gender, race, ethnicity, level of education, employment status, or patient activation existed between the frequent and infrequent users in bivariate analyses. Significantly more frequent users noticed medical errors during their hospital stay. DISCUSSION AND CONCLUSION: Portal use was not associated with several sociodemographic characteristics previously found to limit use in the inpatient setting. However, limited technology experience and high illness severity were still barriers to frequent use. Future work should explore additional strategies, such as enrolling health care proxies and improving usability, to reduce potential disparities in portal use.

Engaging hospitalized patients with personalized health information: a randomized trial of an inpatient portal.

Objective: To determine the effects of an inpatient portal intervention on patient activation, patient satisfaction, patient engagement with health information, and 30-day hospital readmissions. Methods and Materials: From March 2014 to May 2017, we enrolled 426 English- or Spanish-speaking patients from 2 cardiac medical-surgical units at an urban academic medical center. Patients were randomized to 1 of 3 groups: 1) usual care, 2) tablet with general Internet access (tablet-only), and 3) tablet with an inpatient portal. The primary study outcome was patient activation (Patient Activation Measure-13). Secondary outcomes included all-cause readmission within 30 days, patient satisfaction, and patient engagement with health information. Results: There was no evidence of a difference in patient activation among patients assigned to the inpatient portal intervention compared to usual care or the tablet-only group. Patients in the inpatient portal group had lower 30-day hospital readmissions (5.5% vs. 12.9% tablet-only and 13.5% usual care; P = 0.044). There was evidence of a difference in patient engagement with health information between the inpatient portal and tablet-only group, including looking up health information online (89.6% vs. 51.8%; P < 0.001). Healthcare providers reported that patients found the portal useful and that the portal did not negatively impact healthcare delivery. Conclusions: Access to an inpatient portal did not significantly improve patient activation, but it was associated with looking up health information online and with a lower 30-day hospital readmission rate. These results illustrate benefit of providing hospitalized patients with real-time access to their electronic health record data while in the hospital. Trial Registration: ClinicalTrials.gov Identifier: NCT01970852.