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This case report describes the innovative design and build of an algorithm that integrates available data from separate hospital-based informatics systems, which perform different daily functions to augment the contact-tracing process of COVID-19 patients by identifying exposed neighboring patients and healthcare workers and assessing their risk. Prior to the establishment of the algorithm, contact-tracing teams comprising 6 members would spend up to 10 hours each to complete contact tracing for [...]

India reported its first coronavirus disease 2019 (COVID-19) case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, these unstructured data limit upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets.

Large health systems responding to the coronavirus disease 2019 (COVID-19) pandemic face a broad range of challenges; we describe 14 examples of innovative and effective informatics interventions.

We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care.

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present [...]

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects [...]

Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call "latent biases." Just as latent errors are generally described as errors "waiting to happen" in complex systems, latent biases are biases waiting to happen. Here we describe 3 [...]

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).

Family caregiving is an important public health issue, particularly with the aging population. 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.