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The danger within us : America's untested, unregulated medical device industry and one man's battle to survive it
A medical investigative journalist presents an unsettling exposâe of the underregulated medical device industry, revealing the corruption, greed, and deceit that have combined to render medical interventions a leading cause of death in America.
Book
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
This first article in a series describes the history of artificial intelligence in medicine; the use of AI in image analysis, identification of disease outbreaks, and diagnosis; and the use of chatbots.
Journal Article
AI and Medical Education — A 21st-Century Pandora’s Box
Artificial intelligence could have broad implications for medical education. Educators could lead the way when it comes to integrating this technology into clinical practice.
Journal Article
Artificial Intelligence in Medicine
The editors announce both a series of articles focusing on AI and machine learning in health care and the 2024 launch of a new journal,
NEJM AI
, a forum for evidence, resource sharing, and discussion of the possibilities and limitations of medical AI.
Journal Article
The Safety of Inpatient Health Care
Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted.
We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events.
In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%).
Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).
Journal Article
Key Issues as Wearable Digital Health Technologies Enter Clinical Care
The authors address the issues that must be confronted if we are to integrate the use of wearable digital health technologies into clinical care in a way that provides an enduring benefit to patients.
Journal Article
Where Medical Statistics Meets Artificial Intelligence
Challenges at the interface of medical statistics and AI are population inference vs. prediction, generalizability, reproducibility and interpretation of evidence, and stability and statistical guarantees.
Journal Article
Medical Artificial Intelligence and Human Values
Key PointsMedical Artificial Intelligence and Human ValuesAs large language models and other artificial intelligence models are used more in medicine, ethical dilemmas can arise depending on how the model was trained. A user must understand how human decisions and values can shape model outputs. Medical decision analysis offers lessons on measuring human values.A large language model will respond differently depending on the exact way a query is worded and how the model was directed by its makers and users. Caution is advised when considering the use of model output in decision making.
Journal Article
Artificial Intelligence in Molecular Medicine
Artificial Intelligence in Molecular MedicineMachine-learning methods for analyzing genomic, transcriptomic, epigenomic, proteomic, and metabolomic data sets have yielded clinically directive information, mostly for rare genetic diseases.
Journal Article