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.

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.

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.

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.

Dissemination of Strategies for Reducing Excessive Documentation Burden: 25x5 Task Force Activities Relevant to Nursing.

Burnout and workforce shortages are having a negative impact on nurses globally, particularly after the COVID-19 pandemic. Within the United States, excessive documentation burden (DocBurden) has been linked to nurse burnout. The experience of a system or system-imposed process inhibiting patient care is a core focus area of nursing informatics research. The American Medical Informatics Association (AMIA) 25x5 Task Force to Reduce DocBurden was created in 2022 to decrease U.S. health professionals' excessive DocBurden to 25% of current state within five years through impactful solutions across health systems that decrease non-value-added documentation, and leverage public/private partnerships and advocacy. This case study will describe the work of the 25x5 Task Force that is relevant to nursing practice. Specifically, we will describe three projects: A) Toolkit for Reducing Excessive DocBurden, B) Development of Pulse Survey for Health Professionals Perceived DocBurden, and C) HIT Roadmap to Promote Interoperability.

Effects of a Patient Portal Intervention to Address Diabetes Care Gaps: Protocol for a Pragmatic Randomized Controlled Trial.

BACKGROUND: Despite the potential to significantly reduce complications, many patients do not consistently receive diabetes preventive care. Our research team recently applied user-centered design sprint methodology to develop a patient portal intervention empowering patients to address selected diabetes care gaps (eg, no diabetes eye examination in last 12 months). OBJECTIVE: This study aims to evaluate the effect of our novel diabetes care gap intervention on completion of selected evidence-based diabetes preventive care services and secondary outcomes. METHODS: We are conducting a pragmatic randomized controlled trial of the effect of the intervention on diabetes care gaps. Adult patients with diabetes mellitus (DM) are recruited from primary care clinics affiliated with Vanderbilt University Medical Center. Participants are eligible if they have type 1 or 2 DM, can read in English, are aged 18-75 years, have a current patient portal account, and have reliable access to a mobile device with internet access. We exclude patients with medical conditions that prevent them from using a mobile device, severe difficulty seeing, pregnant women or women who plan to become pregnant during the study period, and patients on dialysis. Participants will be randomly assigned to the intervention or usual care. The primary outcome measure will be the number of diabetes care gaps among 4 DM preventive care services (diabetes eye examination, pneumococcal vaccination, hemoglobin A1c, and urine microalbumin) at 12 months after randomization. Secondary outcomes will include diabetes self-efficacy, confidence managing diabetes in general, understanding of diabetes preventive care, diabetes distress, patient portal satisfaction, and patient-initiated orders at baseline, 3 months, 6 months, and 12 months after randomization. An ordinal logistic regression model will be used to quantify the effect of the intervention on the number of diabetes care gaps at the 12-month follow-up. For dichotomous secondary outcomes, a logistic regression model will be used with random effects for the clinic and provider variables as needed. For continuous secondary outcomes, a regression model will be used. RESULTS: This study is ongoing. Recruitment was closed in February 2022; a total of 433 patients were randomized. Of those randomized, most (n=288, 66.5%) were non-Hispanic White, 33.5% (n=145) were racial or ethnic minorities, 33.9% (n=147) were aged 65 years or older, and 30.7% (n=133) indicated limited health literacy. CONCLUSIONS: The study directly tests the hypothesis that a patient portal intervention-alerting patients about selected diabetes care gaps, fostering understanding of their significance, and allowing patients to initiate care-will reduce diabetes care gaps compared with usual care. The insights gained from this study may have broad implications for developing future interventions to address various care gaps, such as gaps in cancer screening, and contribute to the development of effective, scalable, and sustainable approaches to engage patients in chronic disease management and prevention. TRIAL REGISTRATION: ClinicalTrials.gov NCT04894903; https://classic.clinicaltrials.gov/ct2/show/NCT04894903. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56123.

Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts.

BACKGROUND: Studies have shown that documentation burden experienced by clinicians may lead to less direct patient care, increased errors, and job dissatisfaction. Implementing effective strategies within health care systems to mitigate documentation burden can result in improved clinician satisfaction and more time spent with patients. However, there is a gap in the literature regarding evidence-based interventions to reduce documentation burden. OBJECTIVES: The objective of this review was to identify and comprehensively summarize the state of the science related to documentation burden reduction efforts. METHODS: Following Joanna Briggs Institute Manual for Evidence Synthesis and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we conducted a comprehensive search of multiple databases, including PubMed, Medline, Embase, CINAHL Complete, Scopus, and Web of Science. Additionally, we searched gray literature and used Google Scholar to ensure a thorough review. Two reviewers independently screened titles and abstracts, followed by full-text review, with a third reviewer resolving any discrepancies. Data extraction was performed and a table of evidence was created. RESULTS: A total of 34 articles were included in the review, published between 2016 and 2022, with a majority focusing on the United States. The efforts described can be categorized into medical scribes, workflow improvements, educational interventions, user-driven approaches, technology-based solutions, combination approaches, and other strategies. The outcomes of these efforts often resulted in improvements in documentation time, workflow efficiency, provider satisfaction, and patient interactions. CONCLUSION: This scoping review provides a comprehensive summary of health system documentation burden reduction efforts. The positive outcomes reported in the literature emphasize the potential effectiveness of these efforts. However, more research is needed to identify universally applicable best practices, and considerations should be given to the transfer of burden among members of the health care team, quality of education, clinician involvement, and evaluation methods.

Enhanced Patient Portal Engagement Associated with Improved Weight Loss Outcomes in Post-Bariatric Surgery Patients.

Background: Bariatric surgery is an effective intervention for obesity, but it requires comprehensive postoperative self-management to achieve optimal outcomes. While patient portals are generally seen as beneficial in engaging patients in health management, the link between their use and post-bariatric surgery weight loss remains unclear. Objective: This study investigated the association between patient portal engagement and postoperative body mass index (BMI) reduction among bariatric surgery patients. Methods: This retrospective longitudinal study included patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) at Vanderbilt University Medical Center (VUMC) between January 2018 and March 2021. Using generalized estimating equations, we estimated the association between active days of postoperative patient portal use and the reduction of BMI percentage (%BMI) at 3, 6, and 12 months post-surgery. Covariates included duration since surgery, the patient's age at the time of surgery, gender, race and ethnicity, type of bariatric surgery, severity of comorbid conditions, and socioeconomic disadvantage. Results: The study included 1,415 patients, mostly female (80.9%), with diverse racial and ethnic backgrounds. 805 (56.9%) patients underwent RYGB and 610 (43.1%) underwent SG. By one-year post-surgery, the mean (SD) %BMI reduction was 31.1% (8.3%), and the mean (SD) number of patient portal active days was 61.0 (41.2). A significantly positive association was observed between patient portal engagement and %BMI reduction, with variations revealed over time. Each 10-day increment of active portal use was associated with a 0.57% ([95% CI: 0.42- 0.72], P < .001) and 0.35% ([95% CI: 0.22- 0.49], P < .001) %BMI reduction at 3 and 6 months postoperatively. The association was not statistically significant at 12 months postoperatively (ß=-0.07, [95% CI: -0.24- 0.09], P = .54). Various portal functions, including messaging, visits, my record, medical tools, billing, resources, and others, were positively associated with %BMI reduction at 3- and 6-months follow-ups. Conclusions: Greater patient portal engagement, which may represent stronger adherence to postoperative instructions, better self-management of health, and enhanced communication with care teams, was associated with improved postoperative weight loss. Future investigations are needed to identify important portal features that contribute to the long-term success of weight loss management.

Real-Time Electronic Patient Portal Use Among Emergency Department Patients.

Importance: Patients with inequitable access to patient portals frequently present to emergency departments (EDs) for care. Little is known about portal use patterns among ED patients. Objectives: To describe real-time patient portal usage trends among ED patients and compare demographic and clinical characteristics between portal users and nonusers. Design, Setting, and Participants: In this cross-sectional study of 12 teaching and 24 academic-affiliated EDs from 8 health systems in California, Connecticut, Massachusetts, Ohio, Tennessee, Texas, and Washington, patient portal access and usage data were evaluated for all ED patients 18 years or older between April 5, 2021, and April 4, 2022. Exposure: Use of the patient portal during ED visit. Main Outcomes and Measures: The primary outcomes were the weekly proportions of ED patients who logged into the portal, viewed test results, and viewed clinical notes in real time. Pooled random-effects models were used to evaluate temporal trends and demographic and clinical characteristics associated with real-time portal use. Results: The study included 1 280 924 unique patient encounters (53.5% female; 0.6% American Indian or Alaska Native, 3.7% Asian, 18.0% Black, 10.7% Hispanic, 0.4% Native Hawaiian or Pacific Islander, 66.5% White, 10.0% other race, and 4.0% with missing race or ethnicity; 91.2% English-speaking patients; mean [SD] age, 51.9 [19.2] years). During the study, 17.4% of patients logged into the portal while in the ED, whereas 14.1% viewed test results and 2.5% viewed clinical notes. The odds of accessing the portal (odds ratio [OR], 1.36; 95% CI, 1.19-1.56), viewing test results (OR, 1.63; 95% CI, 1.30-2.04), and viewing clinical notes (OR, 1.60; 95% CI, 1.19-2.15) were higher at the end of the study vs the beginning. Patients with active portal accounts at ED arrival had a higher odds of logging into the portal (OR, 17.73; 95% CI, 9.37-33.56), viewing test results (OR, 18.50; 95% CI, 9.62-35.57), and viewing clinical notes (OR, 18.40; 95% CI, 10.31-32.86). Patients who were male, Black, or without commercial insurance had lower odds of logging into the portal, viewing results, and viewing clinical notes. Conclusions and Relevance: These findings suggest that real-time patient portal use during ED encounters has increased over time, but disparities exist in portal access that mirror trends in portal usage more generally. Given emergency medicine's role in caring for medically underserved patients, there are opportunities for EDs to enroll and train patients in using patient portals to promote engagement during and after their visits.

The State-of-the-Art of Patient Portals: Adapting to External Factors, Addressing Barriers, and Innovating.

BACKGROUND AND OBJECTIVE: Recent external factors-the 21st Century Cures Act and the coronavirus disease 2019 (COVID-19) pandemic-have stimulated major changes in the patient portal landscape. The objective of this state-of-the-art review is to describe recent developments in the patient portal literature and to identify recommendations and future directions for the design, implementation, and evaluation of portals. METHODS: To focus this review on salient contemporary issues, we elected to center it on four topics: (1) 21st Century Cures Act's impact on patient portals (e.g., Open Notes); (2) COVID-19's pandemic impact on portals; (3) proxy access to portals; and (4) disparities in portal adoption and use. We conducted targeted PubMed searches to identify recent empirical studies addressing these topics, used a two-part screening process to determine relevance, and conducted thematic analyses. RESULTS: Our search identified 174 unique papers, 74 were relevant empirical studies and included in this review. Among these papers, we identified 10 themes within our four a priori topics, including preparing for and understanding the consequences of increased patient access to their electronic health information (Cures Act); developing, deploying, and evaluating new virtual care processes (COVID-19); understanding current barriers to formal proxy use (proxy access); and addressing disparities in portal adoption and use (disparities). CONCLUSION: Our results suggest that the recent trends toward understanding the implications of immediate access to most test results, exploring ways to close gaps in portal adoption and use among different sub-populations, and finding ways to leverage portals to improve health and health care are the next steps in the maturation of patient portals and are key areas that require more research. It is important that health care organizations share their innovative portal efforts, so that successful measures can be tested in other contexts, and progress can continue.

Unsolicited Patient Complaints Following the 21st Century Cures Act Information-Blocking Rule.

Importance: The 21st Century Cures Act includes an information-blocking rule (IBR) that requires health systems to provide patients with immediate access to their health information in the electronic medical record upon request. Patients accessing their health information before they receive an explanation from their health care team may experience confusion and may be more likely to share unsolicited patient complaints (UPCs) with their health care organization. Objective: To evaluate the quantity of UPCs about physicians before and after IBR implementation and to identify themes in UPCs that may identify patient confusion, fear, or anger related to the release of information. Design, Setting, and Participants: This retrospective cohort study was conducted with an interrupted time-series analysis of UPCs spanning January 1, 2020, to June 30, 2022. The data were obtained from a single academic medical center, Vanderbilt University Medical Center, at which the IBR was implemented on January 20, 2021. Data analysis was performed from January 11 to July 15, 2023. Exposure: Implementation of the IBR on January 20, 2021. Main Outcomes and Measures: The primary outcome was the monthly rate of UPCs before and after IBR implementation. A qualitative analysis was performed for UPCs received after IBR implementation. The Wilcoxon rank-sum test was used to compare monthly complaints between the pre- and post-IBR groups. The Pearson χ2 test was used to compare proportions of complaints by UPC category between time periods. Results: The medical center received 8495 UPCs during the study period: 3022 over 12 months before and 5473 over 18 months after institutional IBR implementation. There was no difference in the monthly proportions of UPCs per 1000 patient encounters before (median, 0.81 [IQR, 0.75-0.88]) and after (median, 0.83 [IQR, 0.77-0.89]) IBR implementation (difference in medians, -0.02 [95% CI, -0.12 to 0.07]; P =.86). Segmented regression analysis revealed no difference in monthly UPCs (ß [SE], 0.03 [0.09]; P =.72). Conclusions and Relevance: In this cohort study, implementation of the Cures Act IBR was not associated with an increase in monthly rates of UPCs. These findings suggest that review of UPCs identified as IBR-specific complaints may allow clinicians and organizations to prepare patients that their test and procedure results may be available before clinicians are able to review them and respond.

Impact of notification policy on patient-before-clinician review of immediately released test results.

The 21st Century Cures Act mandates immediate availability of test results upon request. The Cures Act does not require that patients be informed of results, but many organizations send notifications when results become available. Our medical center implemented 2 sequential policies: immediate notifications for all results, and notifications only to patients who opt in. We used over 2 years of data from Vanderbilt University Medical Center to measure the effect of these policies on rates of patient-before-clinician result review and patient-initiated messaging using interrupted time series analysis. When releasing test results with immediate notification, the proportion of patient-before-clinician review increased 4-fold and the proportion of patients who sent messages rose 3%. After transition to opt-in notifications, patient-before-clinician review decreased 2.4% and patient-initiated messaging decreased 0.4%. Replacing automated notifications with an opt-in policy provides patients flexibility to indicate their preferences but may not substantially alleviate clinicians' messaging workload.

Perspectives of Patients About Immediate Access to Test Results Through an Online Patient Portal.

Importance: The 21st Century Cures Act Final Rule mandates the immediate electronic availability of test results to patients, likely empowering them to better manage their health. Concerns remain about unintended effects of releasing abnormal test results to patients. Objective: To assess patient and caregiver attitudes and preferences related to receiving immediately released test results through an online patient portal. Design, Setting, and Participants: This large, multisite survey study was conducted at 4 geographically distributed academic medical centers in the US using an instrument adapted from validated surveys. The survey was delivered in May 2022 to adult patients and care partners who had accessed test results via an online patient portal account between April 5, 2021, and April 4, 2022. Exposures: Access to test results via a patient portal between April 5, 2021, and April 4, 2022. Main Outcomes and Measures: Responses to questions related to demographics, test type and result, reaction to result, notification experience and future preferences, and effect on health and well-being were aggregated. To evaluate characteristics associated with patient worry, logistic regression and pooled random-effects models were used to assess level of worry as a function of whether test results were perceived by patients as normal or not normal and whether patients were precounseled. Results: Of 43 380 surveys delivered, there were 8139 respondents (18.8%). Most respondents were female (5129 [63.0%]) and spoke English as their primary language (7690 [94.5%]). The median age was 64 years (IQR, 50-72 years). Most respondents (7520 of 7859 [95.7%]), including 2337 of 2453 individuals (95.3%) who received nonnormal results, preferred to immediately receive test results through the portal. Few respondents (411 of 5473 [7.5%]) reported that reviewing results before they were contacted by a health care practitioner increased worry, though increased worry was more common among respondents who received abnormal results (403 of 2442 [16.5%]) than those whose results were normal (294 of 5918 [5.0%]). The result of the pooled model for worry as a function of test result normality was statistically significant (odds ratio [OR], 2.71; 99% CI, 1.96-3.74), suggesting an association between worry and nonnormal results. The result of the pooled model evaluating the association between worry and precounseling was not significant (OR, 0.70; 99% CI, 0.31-1.59). Conclusions and Relevance: In this multisite survey study of patient attitudes and preferences toward receiving immediately released test results via a patient portal, most respondents preferred to receive test results via the patient portal despite viewing results prior to discussion with a health care professional. This preference persisted among patients with nonnormal results.

Empowering patients to address diabetes care gaps: formative usability testing of a novel patient portal intervention.

Objective: The aim of this study was to design and assess the formative usability of a novel patient portal intervention designed to empower patients with diabetes to initiate orders for diabetes-related monitoring and preventive services. Materials and Methods: We used a user-centered Design Sprint methodology to create our intervention prototype and assess its usability with 3 rounds of iterative testing. Participants (5/round) were presented with the prototype and asked to perform common, standardized tasks using think-aloud procedures. A facilitator rated task performance using a scale: (1) completed with ease, (2) completed with difficulty, and (3) failed. Participants completed the System Usability Scale (SUS) scored 0-worst to 100-best. All testing occurred remotely via Zoom. Results: We identified 3 main categories of usability issues: distrust about the automated system, content concerns, and layout difficulties. Changes included improving clarity about the ordering process and simplifying language; however, design constraints inherent to the electronic health record system limited our ability to respond to all usability issues (eg, could not modify fixed elements in layout). Percent of tasks completed with ease across each round were 67%, 60%, and 80%, respectively. Average SUS scores were 87, 74, and 93, respectively. Across rounds, participants found the intervention valuable and appreciated the concept of patient-initiated ordering. Conclusions: Through iterative user-centered design and testing, we improved the usability of the patient portal intervention. A tool that empowers patients to initiate orders for disease-specific services as part of their existing patient portal account has potential to enhance the completion of recommended health services and improve clinical outcomes.