Skip to main content

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

  1. Retained surgical items (RSI) are preventable events that pose a significant risk to patient safety. Current strategies for preventing RSIs rely heavily on manual instrument counting methods, which are prone t...

    Authors: Ekamjit S. Deol, Grant Henning, Spyridon Basourakos, Ranveer M. S. Vasdev, Vidit Sharma, Nicholas L. Kavoussi, R. Jeffrey Karnes, Bradley C. Leibovich, Stephen A. Boorjian and Abhinav Khanna
    Citation: Patient Safety in Surgery 2024 18:24
  2. Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Samm...

    Authors: Hans-Christoph Pape, Adam J. Starr, Boyko Gueorguiev and Guido A. Wanner
    Citation: Patient Safety in Surgery 2024 18:22
  3. Cervical spondylotic myelopathy (CSM) is a prevalent degenerative condition resulting from spinal cord compression and injury. Laminectomy with posterior spinal fusion (LPSF) is a commonly employed treatment a...

    Authors: Ehsan Alimohammadi, Elnaz Fatahi, Alireza Abdi and Seyed Reza Bagheri
    Citation: Patient Safety in Surgery 2024 18:21
  4. A structured risk assessment of patients with validated and evidence-based tools can help to identify modifiable factors before major surgeries. The Protego Maxima trial investigated the value of a new digitiz...

    Authors: Svenja Sliwinski, Sara Fatima Faqar-Uz-Zaman, Jan Heil, Lisa Mohr, Charlotte Detemble, Julia Dreilich, Dora Zmuc, Wolf O. Bechstein, Sven Becker, Felix Chun, Wojciech Derwich, Waldemar Schreiner, Christine Solbach, Johannes Fleckenstein, Natalie Filmann and Andreas A. Schnitzbauer
    Citation: Patient Safety in Surgery 2024 18:13
  5. Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms...

    Authors: Fatemeh Arjmandnia and Ehsan Alimohammadi
    Citation: Patient Safety in Surgery 2024 18:11
  6. Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient’s clinical ...

    Authors: Philip F. Stahel, Kathy W. Belk, Samantha J. McInnis, Kathryn Holland, Roy Nanz, Joseph Beals, Jaclyn Gosnell, Olufunmilayo Ogundele and Katherine S. Mastriani
    Citation: Patient Safety in Surgery 2024 18:10
  7. The Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating pos...

    Authors: Maíra Suzuka Kudo, Vinicius Meneguette Gomes de Souza, Carmen Liane Neubarth Estivallet, Henrique Alves de Amorim, Fernando J. Kim, Katia Ramos Moreira Leite and Matheus Cardoso Moraes
    Citation: Patient Safety in Surgery 2022 16:36

Meet the Guest Editors

Back to top

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