Identifying Inequities in Video and Audio Telehealth Services for Primary Care Encounters During COVID-19: Repeated Cross-Sectional, Observational Study.

BACKGROUND: The COVID-19 pandemic resulted in rapid changes in how patient care was provided, particularly through the expansion of telehealth and audio-only phone-based care. OBJECTIVE: The goal of this study was to evaluate inequities in video and audio-only care during various time points including the initial wave of the COVID-19 pandemic, later stages of the pandemic, and a historical control. We sought to understand the characteristics of care during this time for a variety of different groups of patients that may experience health care inequities. METHODS: We conducted a retrospective analysis of electronic health record (EHR) data from encounters from 34 family medicine and internal medicine primary care clinics in a large, Midwestern health system, using a repeated cross-sectional, observational study design. These data included patient demographic data, as well as encounter, diagnosis, and procedure records. Data were obtained for all in-person and telehealth encounters (including audio-only phone-based care) that occurred during 3 separate time periods: an initial COVID-19 period (T2: March 16, 2020, to May 3, 2020), a later COVID-19 period (T3: May 4, 2020, to September 30, 2020), and a historical control period from the previous year (T1: March 16, 2019, to September 30, 2019). Primary analysis focused on the status of each encounter in terms of whether it was completed as scheduled, it was canceled, or the patient missed the appointment. A secondary analysis was performed to evaluate the likelihood of an encounter being completed based on visit modality (phone, video, in-person). RESULTS: In total, there were 938,040 scheduled encounters during the 3 time periods, with 178,747 unique patients, that were included for analysis. Patients with completed encounters were more likely to be younger than 65 years old (71.8%-74.1%), be female (58.8%-61.8%), be White (75.6%-76.7%), and have no significant comorbidities (63.2%-66.8%) or disabilities (53.2%-61.1%) in all time periods than those who had only canceled or missed encounters. Effects on different subpopulations are discussed herein. CONCLUSIONS: Findings from this study demonstrate that primary care utilization across delivery modalities (in person, video, and phone) was not equivalent across all groups before and during the COVID-19 pandemic and different groups were differentially impacted at different points. Understanding how different groups of patients responded to these rapid changes and how health care inequities may have been affected is an important step in better understanding implementation strategies for digital solutions in the future.

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis.

BACKGROUND: Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. OBJECTIVE: While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. METHODS: We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event-related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. RESULTS: The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event-related discussions had 7 themes. Mental health-related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. CONCLUSIONS: We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions.

Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: State of the Art.

Embedded pragmatic clinical trials (ePCTs) are embedded in healthcare systems as well as their data environments. For people living with dementia (PLWD), settings of care can be different from the general population and involve additional people whose information is also important. The ePCT designs have the opportunity to leverage data that becomes available through the normal delivery of care. They may be particularly valuable in Alzheimer's disease and Alzheimer's disease-related dementia (AD/ADRD), given the complexity of case identification and the diversity of care settings. Grounded in the objectives of the Data and Technical Core of the newly established National Institute on Aging Imbedded Pragmatic Alzheimer's Disease and AD-Related Dementias Clinical Trials Collaboratory (IMPACT Collaboratory), this article summarizes the state of the art in using existing data sources (eg, Medicare claims, electronic health records) in AD/ADRD ePCTs and approaches to integrating them in real-world settings. J Am Geriatr Soc 68:S49-S54, 2020.

Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics.

OBJECTIVE: Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. MATERIALS AND METHODS: We analyzed the "healthiness" quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets. RESULTS: Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). DISCUSSION: Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment. CONCLUSIONS: Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.