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The study sought to describe key features of clinical concepts and data required to implement clinical practice recommendations as clinical decision support (CDS) tools in electronic health record systems and to identify recommendation features that predict feasibility of implementation.

To improve efficient goal attainment of patients by analyzing the unstructured text in care manager (CM) notes (CMNs). Our task is to determine whether the goal assigned by the CM can be achieved in a timely manner.

To characterize interactivity during resident and nurse handoffs by investigating listening and question-asking behaviors during conversations.

Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR [...]

We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records.

Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata. Five percent of clinical research studies (N = 130 629) received postpublication comments (N = 171 556), resulting in 178 882 comment-article pairings, with 90% published [...]

Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.

Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States.

As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in [...]

Memorial Sloan Kettering Cancer Center has more than a decade's experience creating online interfaces for obtaining data from patients as part of routine clinical care. We have developed a set of "golden rules" for design of these interfaces. Many relate to the knowledge imbalance between professional staff (whether medical or informatics) and patients, who are often old and sick and have limited knowledge of technology. Others relate to the clinical [...]