Conducting inclusive research in genetics for transgender, gender-diverse, and sex-diverse individuals: Case analyses and recommendations from a clinical genomics study.

A person's phenotypic sex (i.e., endogenous expression of primary, secondary, and endocrinological sex characteristics) can impact crucial aspects of genetic assessment and resulting clinical care recommendations. In studies with genetics components, it is critical to collect phenotypic sex, information about current organ/tissue inventory and hormonal milieu, and gender identity. If researchers do not carefully construct data models, transgender, gender diverse, and sex diverse (TGSD) individuals may be given inappropriate care recommendations and/or be subjected to misgendering, inflicting medical and psychosocial harms. The recognized need for an inclusive care experience should not be limited to clinical practice but should extend to the research setting, where researchers must build an inclusive experience for TGSD participants. Here, we review three TGSD participants in the Family History and Cancer Risk Study (FOREST) to critically evaluate sex- and gender-related survey measures and associated data models in a study seeking to identify patients at risk for hereditary cancer syndromes. Furthermore, we leverage these participants' responses to sex- and gender identity-related questions in FOREST to inform needed changes to the FOREST data model and to make recommendations for TGSD-inclusive genetics research design, data models, and processes.

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling.

BACKGROUND: Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants' perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden. OBJECTIVE: The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling. METHODS: Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus. RESULTS: We uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%). CONCLUSIONS: We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs.

Maintaining automated measurement of Choosing Wisely adherence across the ICD 9 to 10 transition.

BACKGROUND: It remains unclear how to incorporate terminology changes, such as the International Classification of Disease (ICD) transition from ICD-9 to ICD-10, into established automated healthcare quality metrics. OBJECTIVE: To evaluate whether general equivalence mapping (GEM) can apply ICD-9 based metrics to ICD-10 patient data. To develop and validate novel ICD-10 reference codesets. DESIGN: Retrospective analysis for eleven Choosing Wisely (CW) metrics was performed using three scripted algorithms on an institutional clinical data warehouse. ICD-10 data were compared against published ICD-9 based metric definitions using two equivalence mapping algorithms. A third algorithm implemented novel reference ICD-10 codes matching the original ICD-9 codes' intent for comparison with patient ICD-10 data. PARTICIPANTS: All adult patients seen at Vanderbilt University Medical Center, April - September 2016. MAIN MEASURES: The prevalence of eleven CW services during the six-month period. KEY RESULTS: The three algorithms found similar prevalence of avoidable CW services, with an unweighted-mean of 8.4% (range: 0.16-65%), or approximately 20,000 CW services out of 240,000 potential cases in 515,406 unique patients. The algorithms' median sensitivity was 0.80 (interquartile range: 0.75-0.95), median specificity was 0.88 (IQR: 0.77-0.94), and median Rand accuracy was 0.84 (IQR: 0.79-0.89). The attributed waste of these eleven services for the period ranged from $871,049 to $951,829 between methods. Accuracy assessment demonstrated that the GEM-based methods suffered recall losses for metrics requiring multistep mapping due to incompleteness, while novel ICD-10 metric definitions avoided these challenges. CONCLUSIONS: Comprehensive mapping enables use of legacy metrics across ICD generations, but requires computational complexity that can be avoided with novel ICD-10 based metric definitions. Variation in the dollars attributed to waste due to ICD mapping introduces ambiguity that may affect quality-based reimbursement.

A Scalable Framework to Detect Personal Health Mentions on Twitter.

BACKGROUND: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health. OBJECTIVE: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed. METHODS: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status. RESULTS: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author's status, while only 1% (1/100) disclosed another person's status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author's status, while 21% (21/100) disclosed another person's status. CONCLUSIONS: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

Comparison of new modeling methods for postnatal weight in ELBW infants using prenatal and postnatal data.

OBJECTIVES: Postnatal infant weight curves are used to assess fluid management and evaluate postnatal nutrition and growth. Traditionally, postnatal weight curves are based on birth weight and do not incorporate postnatal clinical information. The aim of the present study was to compare the accuracy of birth weight-based weight curves with weight curves created from individual patient records, including electronic records, using 2 predictive modeling methods, linear regression (LR) and an artificial neural network (NN), which apply mathematical relations between predictor and outcome variables. METHODS: Perinatal demographic and postnatal nutrition data were collected for extremely-low-birth-weight (ELBW; birth weight <1000 g) infants. Static weight curves were generated using published algorithms. The postnatal predictive models were created using the demographic and nutrition dataset. RESULTS: Birth weight (861 ± 83 g, mean ± 1 standard deviation [SD]), gestational age (26.2 ± 1.4 weeks), and the first month of nutrition data were collected from individual health records for 92 ELBW infants. The absolute residual (|measured-predicted|) for weight was 84.8 ± 74.4 g for the static weight curves, 60.9 ± 49.1 g for the LR model, and 12.9 ± 9.2 g for the NN model, analysis of variance: both LR and NN P<0.01 versus static curve. NPO (nothing by mouth) infants had greater weight curve discrepancies. CONCLUSIONS: Compared with birth weight-based and logistic regression-generated weight curves, NN-generated weight curves more closely approximated ELBW infant weight curves, and, using the present electronic health record systems, may produce weight curves better reflective of the patient's status.

The 21st Century Cures Act Information Blocking Rule in Post-Acute Long-Term Care.

Included as part of the 21st Century Cures Act, the information blocking rule entered the first compliance phase in April 2021. Under this rule, post-acute long-term care (PALTC) facilities must not engage in any activity that interferes with accessing, using, or exchanging electronic health information. In addition, facilities must respond to information requests in a timely fashion and allow records to be readily available to patients and their delegates. Although hospitals have been slow to adapt to these changes, skilled nursing and other PALTC centers have been even slower. With a Final Rule enacted in recent years, awareness of the information-blocking rules became more crucial. We believe this commentary will help our colleagues interpret the rule for the PALTC setting. In addition, we provide points of emphasis to help guide those providers and administrative staff workers toward compliance and avoid potential penalties.

Telehealth and Technology: New Directions in Cancer Care.

ABSTRACT: Telehealth is a broad concept that refers to any delivery of health care in real time using technologies to connect people or information that are not in the same physical location. Until fairly recently, telehealth was more aspiration than reality. This situation changed radically due in part to the COVID-19 pandemic, which led to a near-overnight inability for patients to be seen for routine management of chronic health conditions, including those with cancer. The purpose of this brief narrative review is to outline some areas where emerging and future technology may allow for innovations with specific implications for people with a current or past diagnosis of cancer, including underserved and/or historically excluded populations. Specific topics of telehealth are broadly covered in other areas of the special issue.

Patient-Reported Outcome Measures in Routine Clinical Practice: Practical Guidance for Institutional Review Boards.

The use of patient-reported outcome measures (PROMs) is increasingly common in routine clinical practice. As tools to quantify symptoms and health status, PROMs play an important role in focusing health care on outcomes that matter to patients. The uses of PROM data are myriad, ranging from clinical care to survey-based research and quality improvement. Discerning the boundaries between these use cases can be challenging for institutional review boards (IRBs). In this article, we provide a framework for classifying the three primary PROM use cases (clinical care, human subjects research, and quality improvement) and discuss the level of IRB oversight (if any) necessary for each. One of the most important considerations for IRB staff is whether PROMs are being used primarily for clinical care and thus do not constitute human subjects research. We discuss characteristics of PROMs implemented primarily for clinical care, focusing on: data platform; survey location; questionnaire length; patient interface; and clinician interface. We also discuss IRB oversight of projects involving the secondary use of PROM data that were collected during the course of clinical care, which span human subjects research and quality improvement. This framework provides practical guidance for IRB staff as well as clinicians who use PROMs as communication aids in routine clinical practice.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.