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The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the "Model to Data" (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers' direct interaction with patient data by delivering containerized models to the EHR data.

We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.

The COVID-19 pandemic has led to the rapid expansion of telehealth services as healthcare organizations aim to mitigate community transmission while providing safe patient care. As technology adoption rapidly increases, operational telehealth teams must maintain awareness of critical information, such as patient volumes and wait times, patient and provider experience, and telehealth platform performance. Using a model of situation awareness as a conceptual foundation and a user-centered design approach we [...]

Informatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).

Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software.

The objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation.

The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital.

To describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma.

As participants in the California Medicaid 1115 waiver, the University of California San Diego Health (UCSDH) used population health informatics tools to address health disparities. This case study describes a modern application of health informatics to improve data capture, describe health disparities through demographic stratification, and drive reliable care through electronic medical record-based registries. We provide a details in our successful approach using (1) standardized collection of race, ethnicity, language [...]

There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key [...]