JMIR perioperative medicine最新文献

{"title":"A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation.","authors":"Rodney Allanigue Gabriel,&nbsp;Sierra Simpson,&nbsp;William Zhong,&nbsp;Brittany Nicole Burton,&nbsp;Soraya Mehdipour,&nbsp;Engy Tadros Said","doi":"10.2196/40455","DOIUrl":"https://doi.org/10.2196/40455","url":null,"abstract":"<p><strong>Background: </strong>Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery.</p><p><strong>Objective: </strong>The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery.</p><p><strong>Methods: </strong>Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model.</p><p><strong>Results: </strong>There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption.</p><p><strong>Conclusions: </strong>Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e40455"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10760314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.","authors":"Eric Mlodzinski, Gabriel Wardi, Clare Viglione, Shamim Nemati, Laura Crotty Alexander, Atul Malhotra","doi":"10.2196/41056","DOIUrl":"10.2196/41056","url":null,"abstract":"<p><strong>Background: </strong>Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited.</p><p><strong>Objective: </strong>We sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general.</p><p><strong>Methods: </strong>We developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses.</p><p><strong>Results: </strong>Out of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers' perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks.</p><p><strong>Conclusions: </strong>These data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in gene","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e41056"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9367308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.","authors":"Rodney Allanigue Gabriel,&nbsp;Bhavya Harjai,&nbsp;Sierra Simpson,&nbsp;Austin Liu Du,&nbsp;Jeffrey Logan Tully,&nbsp;Olivier George,&nbsp;Ruth Waterman","doi":"10.2196/39650","DOIUrl":"https://doi.org/10.2196/39650","url":null,"abstract":"<p><strong>Background: </strong>Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration.</p><p><strong>Objective: </strong>The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration.</p><p><strong>Methods: </strong>We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R², root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance.</p><p><strong>Results: </strong>A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R²=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R² of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R² of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model.</p><p><strong>Conclusions: </strong>Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e39650"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10750721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study.","authors":"Fabrice Ferré,&nbsp;Rodolphe Laurent,&nbsp;Philippine Furelau,&nbsp;Emmanuel Doumard,&nbsp;Anne Ferrier,&nbsp;Laetitia Bosch,&nbsp;Cyndie Ba,&nbsp;Rémi Menut,&nbsp;Matt Kurrek,&nbsp;Thomas Geeraerts,&nbsp;Antoine Piau,&nbsp;Vincent Minville","doi":"10.2196/39044","DOIUrl":"https://doi.org/10.2196/39044","url":null,"abstract":"<p><strong>Background: </strong>The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context.</p><p><strong>Objective: </strong>Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications.</p><p><strong>Methods: </strong>This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed.</p><p><strong>Results: </strong>Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively.</p><p><strong>Conclusions: </strong>The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e39044"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10591888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Controlled Trial.","authors":"Carissa A Low,&nbsp;Michaela Danko,&nbsp;Krina C Durica,&nbsp;Julio Vega,&nbsp;Meng Li,&nbsp;Abhineeth Reddy Kunta,&nbsp;Raghu Mulukutla,&nbsp;Yiyi Ren,&nbsp;Susan M Sereika,&nbsp;David L Bartlett,&nbsp;Dana H Bovbjerg,&nbsp;Anind K Dey,&nbsp;John M Jakicic","doi":"10.2196/41425","DOIUrl":"https://doi.org/10.2196/41425","url":null,"abstract":"<p><strong>Background: </strong>Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and preliminary effects of a real-time mobile intervention that detects and disrupts prolonged SB before and after cancer surgery, relative to a monitoring-only control condition.</p><p><strong>Objective: </strong>Our aim was to evaluate the feasibility and preliminary effects of a perioperative SB intervention on objective activity behavior, patient-reported quality of life and symptoms, and 30-day readmissions.</p><p><strong>Methods: </strong>Patients scheduled for surgery for metastatic gastrointestinal cancer (n=26) were enrolled and randomized to receive either the SB intervention or activity monitoring only. Both groups used a Fitbit smartwatch and companion smartphone app to rate daily symptoms and collect continuous objective activity behavior data starting from at least 10 days before surgery through 30 days post discharge. Participants in the intervention group also received prompts to walk after any SB bout that exceeded a prespecified threshold, with less frequent prompts on days that patients reported more severe symptoms. Participants completed end-of-study ratings of acceptability, and we also examined adherence to assessments and to walking prompts. In addition, we examined effects of the intervention on objective SB and step counts, patient-reported quality of life and depressive and physical symptoms, as well as readmissions.</p><p><strong>Results: </strong>Accrual (74%), retention (88%), and acceptability ratings (mean overall satisfaction 88.5/100, SD 9.1) were relatively high. However, adherence to assessments and engagement with the SB intervention decreased significantly after surgery and did not recover to preoperative levels after postoperative discharge. All participants exhibited significant increases in SB and symptoms and decreases in steps and quality of life after surgery, and participants randomized to the SB intervention unexpectedly had longer maximum SB bouts relative to the control group. No significant benefits of the intervention with regard to activity, quality of life, symptoms, or readmission were observed.</p><p><strong>Conclusions: </strong>Perioperative patients with metastatic gastrointestinal cancer were interested in a real-time SB intervention and rated the intervention as highly acceptable, but engagement with the intervention and with daily symptom and activity monitoring decreased significantly after surgery. There were no significant effects of the intervention on step counts, patient-reported quality of life or symptoms, and postoperative readmissions, and there was an apparent adverse effect on maximum SB. Results highlight the need for additional work to modify the intervention to make reducing SB and engaging with mobile health technology after","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e41425"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9402252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Wearable Technology in Measuring and Supporting Patient Outcomes Following Total Joint Replacement: Review of the Literature.","authors":"Gregory Iovanel,&nbsp;David Ayers,&nbsp;Hua Zheng","doi":"10.2196/39396","DOIUrl":"https://doi.org/10.2196/39396","url":null,"abstract":"<p><strong>Background: </strong>The incidence rate of total joint replacement (TJR) continues to increase due to the aging population and the surgery that is very successful in providing pain relief to and improving function among patients with advanced knee or hip arthritis. Improving patient outcomes and patient satisfaction after TJR remain important goals. Wearable technologies provide a novel way to capture patient function and activity data and supplement clinical measures and patient-reported outcome measures in order to better understand patient outcomes after TJR.</p><p><strong>Objective: </strong>We examined the current literature to evaluate the potential role of wearable devices and compare them with existing methods for monitoring and improving patient rehabilitation and outcomes following TJR.</p><p><strong>Methods: </strong>We performed a literature search by using the research databases supported by the University of Massachusetts Chan Medical School's Lamar Soutter Library, including PubMed and Scopus, supplemented with the Google Scholar search engine. A specific search strategy was used to identify articles discussing the use of wearable devices in measuring and affecting postoperative outcomes of patients who have undergone TJR. Selected papers were organized into a spreadsheet and categorized for our qualitative literature review to assess how wearable data correlated with clinical measures and patient-reported outcome measures.</p><p><strong>Results: </strong>A total of 9 papers were selected. The literature showed the impact of wearable devices on evaluating and improving postoperative functional outcomes. Wearable-collected data could be used to predict postoperative clinical measures, such as range of motion and Timed Up and Go times. When predicting patient-reported outcomes, specifically Hip Disability and Osteoarthritis Outcome Scores/Knee Injury and Osteoarthritis Outcome Scores and Veterans RAND 12-Item Health Survey scores, strong associations were found between changes in sensor-collected data and changes in patient-reported outcomes over time. Further, the step counts of patients who received feedback from a wearable improved over time when compared to those of patients who did not receive feedback.</p><p><strong>Conclusions: </strong>These findings suggest that wearable technology has the potential to remotely measure and improve postoperative orthopedic patient outcomes. We anticipate that this review will facilitate further investigation into whether wearable devices are viable tools for guiding the clinical management of TJR rehabilitation.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"6 ","pages":"e39396"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10687650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personal Devices to Monitor Physical Activity and Nutritional Intake After Colorectal Cancer Surgery: Feasibility Study.","authors":"Manouk J W van der Linden,&nbsp;Lenny M W Nahar van Venrooij,&nbsp;Emiel G G Verdaasdonk","doi":"10.2196/40352","DOIUrl":"https://doi.org/10.2196/40352","url":null,"abstract":"<p><strong>Background: </strong>The use of self-monitoring devices is promising for improving perioperative physical activity and nutritional intake.</p><p><strong>Objective: </strong>This study aimed to assess the feasibility, usability, and acceptability of a physical activity tracker and digital food record in persons scheduled for colorectal cancer (CRC) surgery.</p><p><strong>Methods: </strong>This observational cohort study was conducted at a large training hospital between November 2019 and November 2020. The study population consisted of persons with CRC between 18- and 75 years of age who were able to use a smartphone or tablet and scheduled for elective surgery with curative intent. Excluded were persons not proficient in Dutch or following a protein-restricted diet. Participants used an activity tracker (Fitbit Charge 3) from 4 weeks before until 6 weeks after surgery. In the week before surgery (preoperative) and the fifth week after surgery (postoperative), participants also used a food record for 1 week. They shared their experience regarding usability (system usability scale, range 0-100) and acceptability (net promoter score, range -100 to +100).</p><p><strong>Results: </strong>In total, 28 persons were included (n=16, 57% male, mean age 61, SD 8 years), and 27 shared their experiences. Scores regarding the activity tracker were as follows: preoperative median system usability score, 85 (IQR 73-90); net promoter score, +65; postoperative median system usability score, 78 (IQR 68-85); net promotor score, +67. The net promoter scores regarding the food record were +37 (preoperative) and-7 (postoperative).</p><p><strong>Conclusions: </strong>The perioperative use of a physical activity tracker is considered feasible, usable, and acceptable by persons with CRC in this study. Preoperatively, the use of a digital food record was acceptable, and postoperatively, the acceptability decreased.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"5 1","pages":"e40352"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10450600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.","authors":"Priya Ramaswamy, Aalap Shah, Rishi Kothari, Nina Schloemerkemper, Emily Methangkool, Amalia Aleck, Anne Shapiro, Rakhi Dayal, Charlotte Young, Jon Spinner, Carly Deibler, Kaiyi Wang, David Robinowitz, Seema Gandhi","doi":"10.2196/40831","DOIUrl":"10.2196/40831","url":null,"abstract":"<p><strong>Background: </strong>Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit.</p><p><strong>Objective: </strong>In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network.</p><p><strong>Methods: </strong>We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes.</p><p><strong>Results: </strong>The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework.</p><p><strong>Conclusions: </strong>It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"5 1","pages":"e40831"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10419394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiences of Health Care Professionals Working Extra Weekends to Reduce COVID-19-Related Surgical Backlog: Cross-sectional Study.","authors":"Clyde Matava, Jeannette P So, Alomgir Hossain, Simon Kelley","doi":"10.2196/40209","DOIUrl":"10.2196/40209","url":null,"abstract":"<p><strong>Background: </strong>During the quiescent periods of the COVID-19 pandemic in 2020, we implemented a weekend-scheduled pediatric surgery program to reduce COVID-19-related backlogs. Over 100 staff members from anesthesiologists to nurses, surgeons, and administrative and supporting personnel signed up to work extra weekends as part of a novel weekend elective pediatric surgery program to reduce COVID-19-related backlog: Operating Room Ramp-Up After COVID-19 Lockdown Ends-Extra Lists (ORRACLE-Xtra).</p><p><strong>Objective: </strong>In this study, we sought to evaluate staff perceptions and their level of satisfaction and experiences with working extra scheduled weekend elective surgical cases at the end of the 3-month pilot phase of ORRACLE-Xtra and identify key factors for participation.</p><p><strong>Methods: </strong>Following the pilot of ORRACLE-Xtra, all perioperative staff who worked at least 1 weekend list were invited to complete an online survey that was developed and tested prior to distribution. The survey collected information on the impact of working weekends on well-being, overall satisfaction, and likelihood of and preferences for working future weekend lists. Logistic regression was used to estimate the association of well-being with satisfaction and willingness to work future weekend lists.</p><p><strong>Results: </strong>A total of 82 out of 118 eligible staff responded to the survey for a response rate of 69%. Staff worked a median of 2 weekend lists (IQR 1-9). Of 82 staff members, 65 (79%) were satisfied or very satisfied with working the extra weekend elective lists, with surgeons and surgical trainees reporting the highest levels of satisfaction. Most respondents (72/82, 88%) would continue working weekend lists. A sense of accomplishment was associated with satisfaction with working on the weekend (odds ratio [OR] 19.97, 95% CI 1.79-222.63; P=.02) and willingness to participate in future weekend lists (OR 17.74, 95% CI 1.50-200.70; P=.02). Many (56/82, 68%) were willing to work weekend lists that included longer, more complex cases, which was associated with a sense of community (OR 0.12, 95% CI 0.02-0.63; P=.01).</p><p><strong>Conclusions: </strong>Staff participating in the first 3 months of the ORRACLE-Xtra program reported satisfaction with working weekends and a willingness to continue with the program, including doing longer, more complex cases. Institutions planning on implementing COVID-19 surgical backlog work may benefit from gathering key information from their staff.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"5 1","pages":"e40209"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10344034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the Reliable Measurement Period for Preoperative Baseline Values With Telemonitoring Before Major Abdominal Surgery: Pilot Cohort Study.","authors":"Marjolein E Haveman,&nbsp;Rianne van Melzen,&nbsp;Mostafa El Moumni,&nbsp;Richte C L Schuurmann,&nbsp;Hermie J Hermens,&nbsp;Monique Tabak,&nbsp;Jean-Paul P M de Vries","doi":"10.2196/40815","DOIUrl":"https://doi.org/10.2196/40815","url":null,"abstract":"<p><strong>Background: </strong>Preoperative telemonitoring of vital signs, physical activity, and well-being might be able to optimize prehabilitation of the patient's physical and mental condition prior to surgery, support setting alarms during in-hospital monitoring, and allow personalization of the postoperative recovery process.</p><p><strong>Objective: </strong>The primary aim of this study was to evaluate when and how long patients awaiting major abdominal surgery should be monitored to get reliable preoperative individual baseline values of heart rate (HR), daily step count, and patient-reported outcome measures (PROMs). The secondary aim was to describe the perioperative course of these measurements at home.</p><p><strong>Methods: </strong>In this observational single-center cohort study, patients used a wearable sensor during waking hours and reported PROMs (pain, anxiety, fatigue, nausea) on a tablet twice a day. Intraclass correlation coefficients (ICCs) were used to evaluate the reliability of mean values on 2 specific preoperative days (the first day of telemonitoring and the day before hospital admission) and randomly selected preoperative periods compared to individual reference values. Mean values of HR, step count, and PROMs per day were visualized in a boxplot from 14 days before hospital admission until 30 days after surgery.</p><p><strong>Results: </strong>A total of 16 patients were included in the data analyses. The ICCs of mean values on the first day of telemonitoring were 0.91 for HR, 0.71 for steps, and at least 0.86 for PROMs. The day before hospital admission showed reliability coefficients of 0.76 for HR, 0.71 for steps, and 0.92-0.99 for PROMs. ICC values of randomly selected measurement periods increased over the continuous period of time from 0.68 to 0.99 for HR and daily step counts. A lower bound of the 95% CI of at least 0.75 was determined after 3 days of measurements. The ICCs of randomly selected PROM measurements were 0.89-0.94. Visualization of mean values per day mainly showed variable preoperative daily step counts (median 2409, IQR 1735-4661 steps/day) and lower postoperative daily step counts (median 884, IQR 474-1605 steps/day). In addition, pain was visually reduced until 30 days after surgery at home.</p><p><strong>Conclusions: </strong>In this prospective pilot study, for patients awaiting major abdominal surgery, baseline values for HR and daily step count could be measured reliably by a wearable sensor worn for at least 3 consecutive days and PROMs during any preoperative day. No clear conclusions were drawn from the description of the perioperative course by showing mean values of HR, daily step count, and PROM values over time in the home situation.</p>","PeriodicalId":73557,"journal":{"name":"JMIR perioperative medicine","volume":"5 1","pages":"e40815"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10336160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}