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Lesley Gotlib Conn on Muck Rack

Lesley Gotlib Conn

Savannah
As seen in: PLOS, BMJ Quality & Safety, Taylor & Francis Online, JAMA (The Journal of the American Medical Association), Savannah Morning News, CANNT Journal
writer, former reporter at Savannah Morning News
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Articles

Exploring challenges and opportunities related to the identification of mild traumatic brain injury and concussion in rehabilitation trauma inpatients: a qualitative study

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By Zoe Li, Yomna E. Ahmed, Lesley Gotlib Conn, Matthew Burke
| Taylor & Francis Online
Abstract Purpose To examine the perspectives of healthcare providers regarding missed mild traumatic brain injury and concussion (mTBI/C) diagnosis in trauma populations in rehabilitative and other post-acute settings. Materials and methods An inductive qualitative study design was undertaken to obtain insights from healthcare providers across care settings (e.g. acute care, rehabilitation) about mTBI/C. A total of 20 healthcare providers took part in semi-structured interviews.
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The effect of end-of-life decision-making tools on patient and family-related outcomes of care among ethnocultural minorities: A systematic review

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By Ayah Nayfeh, Lesley Gotlib Conn, Craig Dale, Sarah Kratina
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Abstract Background End-of-life decision-making tools are used to establish a shared understanding among patients, families and healthcare providers about medical treatment and goals of care.
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Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer | Emergency Medicine | JAMA Otolaryngology-Head & Neck Surgery | JAMA Network

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By Christopher Noel, Lesley Gotlib Conn, David Forner
| JAMA (The Journal of the American Medical Association) Verified
Key Points Question Can a machine learning model accurately predict emergency department use and unplanned hospitalization in patients with head and neck cancer? Findings Using data from more than 70 000 outpatient encounters in Ontario, Canada, this predictive modeling study developed a machine learning algorithm with high discrimination and calibration. Patient symptom scores were among the most predictive features in the model.
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