Collaborating Authors

Women more likely to die of heart attack if doctor is male: Florida study

The Japan Times

MIAMI – Women suffering heart attacks in hospital emergency rooms in the United States are more likely to die if their doctor is a man than a woman, warned a study Monday. The study was based on more than 500,000 patients admitted to hospital emergency departments for acute myocardial infarction -- a medical term for heart attack -- in Florida between 1991 and 2010. Researchers at Harvard University found a "stark" difference in survival according to whether the patient's and doctor's gender matched. Namely, when women were treated by female doctors, "there was a significant and positive effect" on survival, said the study in the Proceedings of the National Academy of Sciences. Almost 12 percent of patients die when rushed for emergency treatment for a heart attack.

9 key thoughts on how machine learning and deep learning will affect healthcare


Artificial intelligence is becoming more important in the healthcare space. Data gathering for machine learning and deep learning capabilities have immense possibilities to improve diagnostics, care pathway creation and reproducibility in surgical procedures to ultimately achieve better clinical outcomes. The technology can also assist physicians with generating reports and administrative responsibilities, giving them more time to spend with patients. Here, nine clinical care and health IT company executives discuss how they expect machine learning and deep learning to improve healthcare in the future. "Deep learning can impact wearables focused on specific conditions, like remote cardiac monitoring, at an individual level by indicating how to personalize algorithms according to one's particular biometric and patient data.

Identifying Differences in Physician Communication Styles with a Log-Linear Transition Component Model

AAAI Conferences

We consider the task of grouping doctors with respect to communication patterns exhibited in outpatient visits. We propose a novel approach toward this end in which we model speech act transitions in conversations via a log-linear model incorporating physician specific components. We train this model over transcripts of outpatient visits annotated with speech act codes and then cluster physicians in (a transformation of) this parameter space. We find significant correlations between the induced groupings and patient survey response data comprising ratings of physician communication. Furthermore, the novel sequential component model we leverage to induce this clustering allows us to explore differences across these groups. This work demonstrates how statistical AI might be used to better understand (and ultimately improve) physician communication.

Machine Learning and Artificial Intelligence: Revolutionizing the Physician/Patient Relationship


Kali Durgampudi, VP of Innovation, Mobile Architecture, R&D, Healthcare Solutions, Nuance Communications, Kali currently serves as the vice president of Innovation, Mobile Architecture, R&D – Healthcare Solutions, Nuance Communications where he o... The abundance of Internet of Things (IoT) devices and connected technologies being introduced daily are pushing the limits of innovation and raising expectations with every passing day. Thanks to these developments, particularly in machine learning and artificial intelligence (AI), we have already seen some successes in autonomous cars, smart cities and manufacturing that we used to believe could only happen in our Jetsons fantasies. These innovations are also impacting the healthcare industry. While nurses and physicians will never be replaced by "Rosie" in the patient exam room, machine learning and AI are poised to transform the healthcare industry in ways that positively impact physicians and their patients.

2017 Survey of Physician Appointment Wait Times


The time it takes to schedule a new patient physician appointment in 15 major metropolitan areas has increased by 30 percent since 2014, according to a new survey conducted by Merritt Hawkins, a national physician search firm and a company of AMN Healthcare. The 2017 Survey of Physician Appointment Wait Times and Medicare and Medicaid Acceptance Rates indicates that it now takes an average of 24 days to schedule a new patient physician appointment in 15 of the largest cities in the U.S., up from 18.5 days in 2014, 20.5 days in 2009 and 21 days in 2004, previous years the survey was conducted. "Physician appointment wait times are the longest they have been since we began conducting the survey," said Mark Smith, president of Merritt Hawkins. "Growing physician appointment wait times are significant indicator that the nation is experiencing a shortage of physicians."