Search AMIA.org

Deprescribing is the clinically supervised process of stopping or reducing medications that are no longer beneficial. MedSafer is an electronic decision support tool that guides healthcare providers (HCPs) through the deprescribing process. We recently developed a novel patient-facing version of the software, allowing patients and caregivers to generate a personalized deprescribing report to bring to their prescriber.

Medical examiners and coroners (ME/C) oversee medicolegal death investigations which determine causes of death and other contextual factors that may have influenced a death. We utilize open data releases from ME/C offices covering 6 different geographic areas to demonstrate the strengths and limitations of ME/C data for forensic epidemiology research.

Health informatics continues to be a continuously evolving discipline. As a result, faculty in health informatics training programs cover a broad range of topics and work in highly diverse academic contexts. This is a strength of the field, and also introduces challenges in understanding faculty salary ranges and assessing potential salary disparities across contexts. Although limited studies have been done on salary ranges in specific academic contexts, prior to this [...]

Rare B-rapidly accelerated fibrosarcoma gene (BRAF) mutations in advanced melanoma, and other malignancies, represent a significant clinical challenge due to sparse evidence on the efficiency of targeted therapy. Conventional genomic databases do not integrate detailed outcome data on treatments for patients with these mutations, requiring innovative informatics approaches.For the use case of patients with rare BRAF-mutated melanoma, we developed a "Treatment Outcome Tool" as a web-based database on rare cancers [...]

Gather insights regarding the state of third-party access cybersecurity in healthcare delivery organizations (HDOs).An online multinational survey was deployed to eligible respondents to assess HDO third-party access, cybersecurity, and challenges.Of 209 respondents, only 51.1% reported having a comprehensive inventory of all third parties accessing their network. Sixty percent stated third-party access to sensitive/confidential information was not routinely monitored, despite 19% having more than 40, and 31% having 21 to 40 [...]

Health information exchanges (HIE), tools that electronically share clinical data across healthcare organizations, provide the opportunity to improve patient care. While widely available, HIE is utilized in only 2%-10% of patient encounters. Few studies have explored current barriers to use. The goal of this study was to evaluate current clinician perspectives on HIE and barriers to use at the point of care.

This study evaluates the performance and deployment feasibility of a machine learning (ML) model to identify adult-onset type 1 diabetes (T1D) initially coded as type 2 on electronic medical records (EMRs) from a health information exchange (HIE). To our knowledge, this is the first evaluation of such a model on real-world HIE data.

Clinical decision support (CDS) systems have been widely adopted across clinical settings to promote evidence-based practice for clinicians. CDS malfunctions often affect the user experience and indirectly or directly interfere with patient care. To enhance optimal performance, it is critical to constantly monitor the performance of the tool and react promptly when malfunctions are identified.This study aimed to describe malfunctions identified in the development and implementation of a CDS alert [...]

There is limited literature on prefilled order sentences, a form of prescription prefilled with dosage, route, and frequency information, and none on their effect in a targeted setting for community-acquired pneumonia, for which reported compliance is poor.Prefilled orders incorporated within computerized provider order entry systems (CPOE) may facilitate compliance guidelines by acting as a form of clinical decision support (CDS), providing a default choice for prescribers. We aim to assess [...]

Maternal harm, especially for Black women, is a significant health care issue. Unstructured clinical notes in electronic health records (EHRs) may reveal unsafe maternal care. Prior studies using natural language processing (NLP) have shown that tone and sentiment in notes contribute to preventable safety events.This study aimed to examine whether negative patient descriptors in EHR clinical notes are associated with adverse maternal outcomes and how their use varies by patient [...]