Search AMIA.org

Nursing terminologies like the Omaha System are foundational in realizing the vision of formal representation of social determinants of health (SDOH) data and whole-person health across biological, behavioral, social, and environmental domains. This study objective was to examine standardized consumer-generated SDOH data and resilience (strengths) using the MyStrengths+MyHealth (MSMH) app built using Omaha System. Overall, 19 SDOH concepts were analyzed including 19 Strengths, 175 Challenges, and 76 Needs with additional [...]

This study aimed to identify phenotypes of nutritional needs of home-visited clients with low income, and compare overall changes in knowledge, behavior, and status of nutritional needs before and after home visits by identified phenotypes.

Critically ill patients are at greater risk of healthcare-associated infections (HAIs). The use of maintenance bundles helps to reduce this risk but also generates a rapid accumulation of complex data that is difficult to aggregate and subsequently act upon.

We sought to understand the objectives, targeted populations, therapeutic elements, and delivery characteristics of patient portal interventions.

Artificial intelligence (AI) has tremendous potential to improve the cognitive and work burden of clinicians across a range of clinical activities, which could lead to reduced burnout and better clinical care. The recent explosion of generative AI nicely illustrates this potential. Developers and organizations deploying AI have a responsibility to ensure AI is designed and implemented with end-user input, has mechanisms to identify and potentially reduce bias, and that the [...]

To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases.

The 21st Century Cures Act provides improved access to one's medical record but presents new challenges to adolescent confidentiality in the domain of health care information technology. Adolescent patients, who have the right to confidential care in certain areas as dictated by state law, face the prospect of parents and guardians erroneously accessing their confidential health information.

Tertiary and quaternary (TQ) care refers to complex cases requiring highly specialized health services. Our study aimed to compare the ability of a natural language processing (NLP) model to an existing human workflow in predictively identifying TQ cases for transfer requests to an academic health center.