Notes

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Most studies were retrospective cohort studies/case series of hospitalized patients. Only four studies examined the effects of COPD on COVID-19 outcomes as their primary endpoint. In aggregate, COPD was associated with increased odds of hospitalization (OR 4.23, 95% confidence interval [CI] 3.65-4.90), ICU admission (OR 1.35, 95% CI 1.02-1.78), and mortality (OR 2.47, 95% CI 2.18-2.79).

Having a clinical diagnosis of COPD significantly increases the odds of poor clinical outcomes in patients with COVID-19. COPD patients should thus be considered a high-risk group, and targeted for preventative measures and aggressive treatment for COVID-19 including vaccination.
Having a clinical diagnosis of COPD significantly increases the odds of poor clinical outcomes in patients with COVID-19. COPD patients should thus be considered a high-risk group, and targeted for preventative measures and aggressive treatment for COVID-19 including vaccination.
Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.

This case-control study included patients aged 10-75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort).

The oss various populations and clinical settings.The objective of this study is to provide an overview of the Regenstrief Teaching Electronic Medical Record (tEMR), how the tEMR could be used, and how it is currently being used in health professions education. The tEMR is a derivative of a real-world electronic health record (EHR), a large, pseudonymized patient database, and a population health tool designed to support curricular goals. The tEMR has been successfully adopted at 12 health professional, public health, and health information technology (HIT) schools, with over 11 800 unique student users and more than 74 000 logins, for case presentation, to develop diagnostic and therapeutic plans, and to practice documentation skills. With the exponential growth of health-related data and the impact of HIT on work-life balance, it is critical for students to get early EHR skills practice and understand how EHR's work. The tEMR is a promising, scalable, flexible application to help health professional students learn about common HIT tools and issues.
Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review.

PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.
PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.
Family caregiving is an important public health issue, particularly with the aging population. selleck compound In recent years, mobile health (mHealth) technology has emerged as a potential low-cost, scalable platform to address caregiver support needs, and thereby alleviate the burden on caregivers. This study sought to examine the support needs of family caregivers in their lived experiences of outpatient care to inform the development of a future mHealth intervention.

We conducted 20 semi-structured interviews in 2 outpatient hematopoietic cell transplant (HCT) clinics at a large academic medical center in the Midwestern United States. A thematic analysis was performed to define emerging themes.

Qualitative data analysis identified 5 primary themes that HCT caregivers faced (I) lifestyle restrictions due to the patient's immunocompromised state; (II) Unmet needs due to limitations in the current resources, including unfamiliar medical tasks without necessary trainings; and (III) caregivers' adaptive strategies, including reformation of social relationships with family and friends. Based on these findings, we suggest 3 design considerations to guide the development of a future mHealth intervention.

The findings herein captured the family caregiver's lived experiences during outpatient care. There was broad agreement that caregiving was challenging and stressful. Thus, effective and scalable interventions to support caregivers are needed. This study provided data to guide the content and design of a future mHealth intervention in the outpatient setting.
The findings herein captured the family caregiver's lived experiences during outpatient care. There was broad agreement that caregiving was challenging and stressful. Thus, effective and scalable interventions to support caregivers are needed. This study provided data to guide the content and design of a future mHealth intervention in the outpatient setting.
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