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Telehealth programs have long held promise for addressing rural health disparities perpetuated by inadequate healthcare access. The COVID-19 (coronavirus disease 2019) pandemic and accompanying social distancing measures have hastened the implementation of telehealth programs in hospital systems around the globe. Here, we provide specific examples of telehealth efforts that have been implemented in a large rural healthcare system in response to the pandemic, and further describe how the massive shift [...]

People with long-term conditions require serial clinical assessments. Digital patient-reported symptoms collected between visits can inform these, especially if integrated into electronic health records (EHRs) and clinical workflows. This systematic review identified and summarized EHR-integrated systems to remotely collect patient-reported symptoms and examined their anticipated and realized benefits in long-term conditions.

The growing complexity of data systems in health care has precipitated increasing demand for clinical informatics subspecialists. The first board certification exam for the clinical informatics subspecialty was offered in 2013. Characterizing trends in this novel workforce is important to inform its development.

In the wake of COVID-19, clinicians took to telehealth to continue providing services to their patients, mostly via telephone or videoconferencing technology. Telehealth has many promised and proven benefits including convenience to the patient, potentially less distraction from the electronic health record (EHR), saves in travel time and expenses, and lowering patients' wait time in the clinic. However, there could be some unintended negative consequences including increased clinician burnout due [...]

Use of electronic health record data is expanding to support quality improvement and research; however, this requires standardization of the data and validation within and across organizations. Information models (IMs) are created to standardize data elements into a logical organization that includes data elements, definitions, data types, values, and relationships. To be generalizable, these models need to be validated across organizations. The purpose of this case report is to describe [...]

To develop a collection of concept-relationship-concept tuples to formally represent patients' care context data to inform electronic health record (EHR) development.

In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and [...]

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics [...]

Health and healthcare disparities continue despite clinical, research, and policy efforts. Large clinical datasets may not contain data relevant to healthcare disparities and leveraging these for research may be crucial to improve health equity. The Health Disparities Collaborative Research Group was commissioned by the Patient-Centered Outcomes Research Institute to examine the data science needs for quality and complete data and provide recommendations for improving data science around health disparities. The [...]