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Machine learning approach for improvement of patient safety in surgery

Guest Editors:
Kathryn Holland: Mission Health, United States
Roy Nanz: Mission Health, United States 

Submission Status: Closed   |   Submission Deadline: Closed

This collection is no longer accepting submissions


Patient Safety in Surgery is calling for submissions to our collection on machine learning approach for improvement of patient safety in surgery. The goal of the special issue in the journal is to provide novel insights into the understanding of machine learning and the impact on preventing patient safety events. We predict machine learning will allow health systems to improve patient safety surveillance programs by identifying patients “at risk” of sustaining preventable harm and thereby enabling the healthcare team to mitigate adverse events before they occur. 

Image credit: © [M] greenbutterfly / stock.adobe.com

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Meet the Guest Editors

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Kathryn Holland: Mission Health, United States

Kathryn Holland is a Chemical Engineer and has her MS in Biochemistry. She currently uses skills from these curricula on the Clinical Process Improvement and Data Science team at Mission Health in Western North Carolina. While Kathryn has more than 5 years’ experience in this role in healthcare, she has worked for the previous 10 years in pharmaceutical manufacturing and research. Her most honorable achievement during this time was working with the HCA Mission team to attain the ELSO Gold Excellence Award for ECMO Patient Selection and Reduction of IV Narcotics in ECMO Patients by Preventing Sequestration.
 

Roy Nanz: Mission Health, United States

Roy Nanz holds BS & MS degrees in Electrical Engineering with a focus in the Control Systems Theory. Roy is also a Certified Lean Six Sigma Master Black Belt with more than 25 years of experience in many industries including pharmaceutical new product development and manufacturing. Roy is currently using his analytical and problem solving skills on the Clinical Process Improvement and Data Science team at Mission Health in Western North Carolina. Roy has been deeply involved in the clinical process improvement work at Mission Health with particular focus areas: reducing septic shock mortalities, improving stroke patient outcomes and reducing surgical site infections. In addition, Roy has been instrumental in analyzing data and applying statistics on the clinical research grants and determinants of heath studies. Roy also utilizes his statistical skills to build predictive analytical models and his superpower is simplifying the complex.

About the collection

Patient Safety in Surgery is calling for submissions to our collection on machine learning approach for improvement of patient safety in surgery. Over the past several years, there has been a paradigm shift across healthcare systems to understand the causes leading up to patient safety events including, but not limited to: surgical site infections, hospital acquired infections, falls, medication errors, and hospital acquired pressure injuries. Healthcare systems managed these events by counting the occurrence after it was reported. This data was then used to drive continuous improvement efforts to support patient outcomes and operational efficiency. Going forward, healthcare systems are shifting focus from counting patient safety related events after they have occurred, to using machine-learning algorithms to predict the patient safety event and prevent harm. 

The goal of the special issue in the journal is to provide novel insights into the understanding of machine learning and the impact on preventing patient safety events. We predict machine learning will allow health systems to improve patient safety surveillance programs by identifying patients “at risk” of sustaining preventable harm and thereby enabling the healthcare team to mitigate adverse events before they occur. 

All submissions will undergo a formal peer review by at least two qualified referees.

The scope of the special issue of the journal includes, but is not limited to, the following article types and areas of research:

- Basic predictive algorithms to identify patient safety events

- Investigation of novel multi-regression predictive analytics to identify patient safety events

- Patient safety risk factors, indications, and complication elements that are used to identify safety events before the occurrence

- Case reports that describe unusual experiences and important lessons learned while preventing patient harm

- Review articles that cover pertinent areas of research described above