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The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT.

In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table ("Table 1") of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to [...]

To demonstrate the feasibility of pragmatic clinical trials comparing the effectiveness of treatments using the electronic medical record (EMR) and an adaptive assignment design.

Venous thromboembolism (VTE) prophylaxis is an important consideration for hospitalized older adults, and the Padua Prediction Score (PPS) is a risk prediction tool used to prioritize patient selection. We developed an automated PPS (APPS) algorithm using electronic health record (EHR) data. This study examines the accuracy of APPS and its individual components versus manual data extraction.

Outpatient providers often do not receive discharge summaries from acute care providers prior to follow-up visits. These outpatient providers may use the after-visit summaries (AVS) that are given to patients to obtain clinical information. It is unclear how effectively AVS support care coordination between clinicians.

The assessment of user preferences for performance characteristics of patient-oriented clinical prediction models is lacking. It is unknown if complex statistical aspects of prediction models are readily understandable by a general audience.

We can now quantify and characterize the harm patients suffer in the dental chair by mining data from electronic health records (EHRs). Most dental institutions currently deploy a random audit of charts using locally developed definitions to identify such patient safety incidents. Instead, selection of patient charts using triggers and assessment through calibrated reviewers may more efficiently identify dental adverse events (AEs).

U.S. poison control centers pose a special case for patient identity matching because they collect only minimal patient identifying information.