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When administering an infusion to a patient, it is necessary to verify that the infusion pump settings are in accordance with the injection orders provided by the physician. However, the infusion rate entered into the infusion pump by the health care provider cannot be automatically reconciled with the injection order information entered into the electronic medical records (EMRs). This is because of the difficulty in linking the infusion rate entered [...]

Patient-reported outcome (PRO) measures have become an essential component of quality measurement, quality improvement, and capturing the voice of the patient in clinical care. In 2004, the National Institutes of Health endorsed the importance of PROs by initiating the Patient-Reported Outcomes Measurement Information System (PROMIS), which leverages computer-adaptive tests (CATs) to reduce patient burden while maintaining measurement precision. Historically, PROMIS CATs have been used in a large number of research [...]

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

We developed a prototype patient decision aid, EyeChoose, to assist college-aged students in selecting a refractive surgery. EyeChoose can educate patients on refractive errors and surgeries, generate evidence-based recommendations based on a user's medical history and personal preferences, and refer patients to local refractive surgeons.

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

 Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring.

Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm.

Surveillance algorithms that predict patient decompensation are increasingly integrated with clinical workflows to help identify patients at risk of in-hospital deterioration. This scoping review aimed to identify the design features of the information displays, the types of algorithm that drive the display, and the effect of these displays on process and patient outcomes.