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To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility.

Many research fields, including psychology and basic medical sciences, struggle with poor reproducibility of reported studies. Biomedical and health informatics is unlikely to be immune to these challenges. This paper explores replication in informatics and the unique challenges the discipline faces.

In contrast to efficacy, safety hypotheses of clinical trials are not always pre-specified, and therefore, the safety interpretation work of a trial tends to be more exploratory, often reactive, and the analysis more statistically and graphically challenging.

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.

To build effective applications, technology designers must understand consumer health needs. Pregnancy is a common health condition, and expectant families have unanswered questions. This study examined consumer health-related needs in pregnant women and caregivers and determined the types of needs that were not met.

The overuse of cranial computed tomography (CT) to diagnose potential traumatic brain injuries (TBIs) exposes children with minor blunt head trauma to unnecessary ionizing radiation. The Pediatric Emergency Care Applied Research Network and the Clinical Research on Emergency Services and Treatments Network implemented TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) to decrease use of CTs in children with minor blunt head trauma.

Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.

One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM).