210 research outputs found

    Utilizing artificial intelligence in perioperative patient flow:systematic literature review

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    Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

    Doctor of Philosophy

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    dissertationBariatric surgery has become a prevalent and effective method to reduce body weight and improve the health profiles of morbidly obese individuals. However, variability in the success of the procedure exists, yet few research studies have examined lifestyle changes that may enhance surgical outcomes. Therefore, the primary purpose of this study was to objectively monitor the physical activity patterns of bariatric patients, presurgery and postsurgery. The secondary purpose of this study was to build support for the validation of the Cross-Cultural Activity Participation (CAPS) weekly physical activity questionnaire, a questionnaire that may take the place of objective measurements. Twenty-four bariatric patients were recruited for this study (height: 165.6 ± 9.9 cm, weight: 121.8 ± 24.8 kg and BMI: 44.0 ± 6.5) and were asked to complete 2 office visits (1 presurgery and 1 postsurgery) for testing and wear an accelerometer physical activity monitor for 7 days presurgery and 7 days postsurgery. The office visits included body composition testing via Bod Pod, reporting of dietary intake, reporting of weekly exercise and completion of the CAPS questionnaire. Accelerometers were worn for 7 days presurgery and 7 days, 3 to 5 weeks postsurgery. Findings show that participants did not significantly change their physical activity patterns postsurgery (p ≥ 0.05). Physical activity appears to positively impact health and assist in the retention of weight loss. Therefore, the lack of change in physical activity postsurgery signifies a postsurgical lifestyle change that may be improved upon. An archived data set was used to determine the validity of the CAPS questionnaire. CAPS-derived reports of moderate to vigorous physical activity was not significantly correlated with steps per day. Further, regression analysis revealed that the CAPS questionnaire could explain only 5.2% of the variation in steps per day. Therefore, it does not appear that the CAPS questionnaire is a valid surrogate measure of physical activity

    Operational and strategic decision making in the perioperative setting: Meeting budgetary challenges and quality of care goals.

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    Efficient operating room (OR) management is a constant balancing act between optimal OR capacity, allocation of ORs to surgeons, assignment of staff, ordering of materials, and reliable scheduling, while according the highest priority to patient safety. We provide an overview of common concepts in OR management, specifically addressing the areas of strategic, tactical, and operational decision making (DM), and parameters to measure OR efficiency. For optimal OR productivity, a surgical suite needs to define its main stakeholders, identify and create strategies to meet their needs, and ensure staff and patient satisfaction. OR planning should be based on real-life data at every stage and should apply newly developed algorithms

    Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.

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    BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies

    Obesity-induced chronic inflammation in C57Bl6J mice, a novel risk factor in the progression of renal AA amyloidosis?

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    Background: Compelling evidence links obesity induced systemic inflammation to the development of chronic kidney disease (CKD). This systemic inflammation may result from exacerbated adipose inflammation. Besides the known detrimental effects of typical pro-inflammatory factors secreted by the adipose tissue (TNF-α, MCP-1 and IL-6) on the kidney, we hypothesize the enhanced obesity-induced secretion of serum amyloid A (SAA), an acute inflammatory protein, to play a key role in aggravating obesity-induced CKD. Methods: Groups of male C57Bl/6J mice (n = 99 in total) were fed a low (10% lard) or high (45% lard) fat diet for a maximum of 52 weeks. Mice were sacrificed after 24, 40 and 52 weeks. Whole blood samples, kidneys and adipose tissues were collected. The development of adipose and renal tissue inflammation was assessed on gene expression and protein level. Adipocytokine levels were measured in plasma samples. Results: A distinct inflammatory phenotype was observed in the adipose tissue of HFD mice prior to renal inflammation, which was associated with an early systemic elevation of TNF-α, leptin and SAA (1A-C). With aging, sclerotic lesions appeared in the kidney, the extent of which was severely aggravated by HFD feeding. Lesions exhibited typical amyloid characteristics (2A) and pathological severity positively correlated with bodyweight (2B). Interestingly, more SAA protein was detected in lesions of HFD mice. Conclusion: Our data suggest a causal link between obesity induced chronic inflammation and AA amyloidosis in C57Bl/6J mice. Though future studies are necessary to prove this causal link and to determine its relevance for the human situation, obesity may hence be considered a risk factor for the development and progression of renal AA amyloidosis in the course of CKD. (Figure Presented)

    Electronically assisted surveillance systems of healthcare-associated infections: A systematic review

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    Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37 1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

    Using Statistical Process Control to Monitor Anastomotic Leak

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    Background Surgery remains a cornerstone in the treatment of bowel diseases, such as those involving cancer or inflammation. In the majority of patients, a section of bowel is resected and the remaining bowel is re-joined surgically using sutures or staples (bowel anastomosis). However, in some cases this anastomosis can break down (Anastomotic Leak (AL)), causing significant complications for the patients including increased mortality, prolonged hospital stay and worse cancer outcomes. Despite the significance of this complication most hospitals do not prospectively measure their leak rate or engage in activities to reduce it. Another key postoperative outcome which can act as a surrogate marker of performance is Postoperative Length of Stay (PLoS) One way to address this is to promote the use quality improvement (QI) methodologies such as Statistical Process Control (SPC). This involves mapping the data points in time order and seeing if the process is stable between a set of upper and lower parameters (i.e. confidence intervals) and observing whether there has been a statistical change. Methods The aim of this study was to retrospectively map AL rates and PLoS using Statistical Process Control at Royal Devon and Exeter Foundation NHS Trust. This was to provide a baseline measurement as part of the first phase of a QI project as well as investigating the suitability for SPC chart analysis for monitoring postoperative outcomes. All patients undergoing colorectal resections with ileo-colonic, colo-colonic colorectal, colo-anal or ileo-anal anastomoses from 01//01/2010 to 30/04/2017 were included in this study. AL was defined as cases where there was subsequent return-to-theatre, radiological drainage or medical management of the AL. SPC charts were used to map AL rates to establish whether variation in the rate over time was due to “common-cause variation” or “special-cause variation.” The G-Chart, a type of SPC chart used to count the number of events between rare incidents was used to map AL. I-Charts were used to map median monthly Postoperative Length of Stay (PLoS). Results The AL rate is relatively low at this hospital with a return-to-theatre rate of 4.3% and an overall rate of 6.1% (once conservatively managed ALs and radiologically drained leaks were included). The overall median PLoS was 6 days. The SPC charts show that there is a reasonable chance of special cause variation for the Elective, Stapled and Right-sided AL charts, with some overlap with the former two categories. SPC charts for Sutured ALs and Left-sided ALs both only exhibited common cause variation. SPC charts for all six sub-groups monitoring PLoS indicated periods of special cause variation. Discussion In terms of the AL rate, 4.3% is a very acceptable return-to-theatre rate in line with other studies. The rate of 6.1% is difficult to interpret given that not all cases of medically managed ALs would have been identified. The overall median PLoS was also consistent with the literature. This is the first phase of a QI project to reduce rates of AL at Royal Devon and Exeter Foundation NHS Trust which can now take place prospectively and an intervention can be planned and implemented. Also, now that the methodology is in place, SPC charts can also be used to ensure patient safety over time, acting within a Quality Assurance context. Despite their ability to identify retrospective periods of SCV, the findings in SPC charts monitoring AL and PLoS will now need to be corroborated with the historial clinical context as SPC charts cannot identify which factors have caused the shift. In summary, this dissertation demonstrates that using SPC charts are a feasible methodology to retrospectively map AL and PLoS rates in a Colorectal Unit

    Electronically assisted surveillance systems of healthcare-associated infections:a systematic review

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    Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital- wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

    Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

    Get PDF
    BackgroundSurveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.ObjectivesTo give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.MethodsIn this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.ResultsA total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.ConclusionsElectronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency
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