JMIR Perioperative Medicine
Technologies for pre- and post-operative education, preventative interventions, and clinical care for surgery and anaesthesiology patients, as well as informatics applications in anesthesia, surgery, critical care, and pain medicine
Editor-in-Chief: John F Pearson, MD, University of Utah School of Medicine
John F Pearson, MD, University of Utah School of Medicine
JMIR Perioperative Medicine (JPOP, Editor-in-chief: John F. Pearson MD, University of Utah School of Medicine) is an open access journal focusing on technologies, medical devices, apps, engineering, informatics and patient education for perioperative medicine and nursing, including pre- and post-operative education, preventative interventions and clinical care for surgery and anaesthesiology patients, as well as informatics applications in anesthesia, surgery, critical care and pain medicine.
We are read by clinicians and patients alike and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
JMIR Perioperative Medicine features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.
The journal is indexed in PubMed and PubMed Central.
During a limited period of time, there are no fees to publish in this journal.
The Enhanced Recovery After Surgery (ERAS) protocol has been recently extended to hepatopancreatobiliary (HPB) surgery, with excellent outcomes reported. Early mobilization is an essential facet of the ERAS protocol, but compliance has been reported to be poor. We recently reported our success in a 6-month clinical practice improvement program (CPIP) for early postoperative mobilization. During the COVID-19 pandemic, we experienced reduced staffing and resource availability, which can make CPIP sustainability difficult.
Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a “smart alarm” that can alert clinicians to apneic events that are predicted to be prolonged.
The rapid spread of the novel coronavirus (COVID-19) has presented immeasurable challenges to health care workers who remain at the frontline of the pandemic. A rapidly evolving body of literature has quantitatively demonstrated significant psychological impacts of the pandemic on health care workers. However, little is known about the lived experience of the pandemic for frontline medical staff.
Many promising telemedicine innovations fail to be accepted and used over time, and there are longstanding questions about how to best evaluate telemedicine services and other health information technologies. In response to these challenges, there is a growing interest in how to take the sociotechnical complexity of health care into account during design, implementation, and evaluation. This paper discusses the methodological implications of this complexity and how the sociotechnical context holds the key to understanding the effects and outcomes of telemedicine. Examples from a work domain analysis of a surgical setting, where a telemedicine service for remote surgical consultation was to be introduced, are used to show how abstracted functional modeling can provide a structured and rigorous means to analyze and represent the implementation context in complex health care settings.
Surgical audit is an essential aspect of modern reflective surgical practice and is key to improving surgical outcomes. The surgical logbook is an important method of data collection for both personal and unit audits; however, current electronic data collection tools, especially mobile apps, lack the minimum recommended data fields.
Monitoring surgical recovery has traditionally been confined to metrics measurable within the hospital and clinic setting. However, commercially available mobile sensors are now capable of extending measurements into a patient’s home. As these sensors were developed for nonmedical applications, their clinical role has yet to be established. The aim of this systematic review is to evaluate the relationship between data generated by mobile sensors and postoperative outcomes.
The clinical benefits of enhanced recovery programs (ERPs) have been extensively researched, but few studies have evaluated their cost-effectiveness. Our ERP for open liver resection is based closely on the guidelines produced by the Enhanced Recovery After Surgery Society (2016). This study follows on from a previous randomized controlled trial. We also undertook a long-term follow-up of the patients enrolled in the original trial alongside an analysis of the associated health economics.
Hospital stays after major surgery are shorter than ever before. Although enhanced recovery and early discharge have many benefits, some complications will now first manifest themselves in home settings. Remote patient monitoring with wearable sensors in the first days after hospital discharge may capture clinical deterioration earlier but is largely uncharted territory.