IISE Transactions on Healthcare Systems Engineering最新文献

{"title":"Radiotherapy toxicity prediction using knowledge-constrained generalized linear model.","authors":"Jiuyun Hu, Mirek Fatyga, Wei Liu, Steven E Schild, William W Wong, Sujay A Vora, Jing Li","doi":"10.1080/24725579.2023.2227199","DOIUrl":"10.1080/24725579.2023.2227199","url":null,"abstract":"<p><p>Radiation therapy (RT) is a frontline approach to treating cancer. While the target of radiation dose delivery is the tumor, there is an inevitable spill of dose to nearby normal organs causing complications. This phenomenon is known as radiotherapy toxicity. To predict the outcome of the toxicity, statistical models can be built based on dosimetric variables received by the normal organ at risk (OAR), known as Normal Tissue Complication Probability (NTCP) models. To tackle the challenge of the high dimensionality of dosimetric variables and limited clinical sample sizes, statistical models with variable selection techniques are viable choices. However, existing variable selection techniques are data-driven and do not integrate medical domain knowledge into the model formulation. We propose a knowledge-constrained generalized linear model (KC-GLM). KC-GLM includes a new mathematical formulation to translate three pieces of domain knowledge into non-negativity, monotonicity, and adjacent similarity constraints on the model coefficients. We further propose an equivalent transformation of the KC-GLM formulation, which makes it possible to solve the model coefficients using existing optimization solvers. Furthermore, we compare KC-GLM and several well-known variable selection techniques <i>via</i> a simulation study and on two real datasets of prostate cancer and lung cancer, respectively. These experiments show that KC-GLM selects variables with better interpretability, avoids producing counter-intuitive and misleading results, and has better prediction accuracy.</p>","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"1 1","pages":"130-140"},"PeriodicalIF":1.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41505159","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 framework for occupational health risk assessment of nursing personnel in Indian healthcare system","authors":"Rahul Mondal, Pradip Kumar Ray","doi":"10.1080/24725579.2023.2279759","DOIUrl":"https://doi.org/10.1080/24725579.2023.2279759","url":null,"abstract":"Abstract:Nursing personnel are exposed to different kinds of health hazards because of various job characteristics and complex work systems. The objective of this study is to identify and analyze the risk factors related to occupational health hazards of nurses in the Indian healthcare system. A three-phase methodology is used to assess occupational risk factors. Through literature review and opinions of experienced nursing personnel, the risk factors such as biological, chemical, ergonomic, physical, and psychosocial are identified and evaluated based on three parameters i.e., the effect of exposure, exposure period and exposure probability. The weights of the parameters are determined by Best-Worst Method (BWM) and the Fuzzy VIKOR method is considered to rank the risk factors in accordance with their level of severity. Biological and ergonomic risk factors are found to have higher levels of adverse effects in all the wards of Indian hospitals whereas, the impact of chemical, physical and psychosocial risk factors varied from ward to ward. The risk assessment framework as developed helps to categorize risk factors from high degree to moderate degree, which might help to prepare an action plan to mitigate the risks associated with nursing activities in order to improve their health and safety status at the hospital.Keywords: Occupational risk factorhealthcarenursing personnelfuzzy VIKORBWMDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.Additional informationNotes on contributorsRahul MondalRahul Mondal: Rahul Mondal is a research scholar in the Department of Industrial and Systems Engineering at Indian Institute of Technology Kharagpur. His area of research is Occupational health of Healthcare personnel.Pradip Kumar RayProfessor Pradip Kumar Ray: Professor Pradip Kumar Ray is currently Emeritus Professor at the Department of Industrial and Systems Engineering and advisor at Vinod Gupta School of Management at Indian Institute of Technology Kharagpur. He is a well-known researcher in the area of Healthcare Management Systems, Human Factors Engineering, Inventory Management, Productivity Management and Quality Management.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135340686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing Drone Delivery Networks for Vaccine Supply Chain: A Case Study of Niger","authors":"Sandra D. Eksigolu, Ruben A. Proano, Maximilian Kolter, Sarah Nurre Pinkley","doi":"10.1080/24725579.2023.2268113","DOIUrl":"https://doi.org/10.1080/24725579.2023.2268113","url":null,"abstract":"AbstractThis research aims to evaluate the use of drones to deliver pediatric vaccines in remote areas of low-income and low-middle-income countries. Delivering vaccines in these regions is challenging because of the inadequate road networks and long transportation distances that make it difficult to maintain the cold chain’s integrity during delivery. We propose a mixed-integer linear program to determine the location of drone hubs to facilitate vaccine delivery. The model considers the operational attributes of drones, vaccine wastage in the supply chain, cold storage, and transportation capacities. We develop a case study using data from Niger to determine the impact of drone deliveries in improving vaccine availability. Based on our numerical analysis, regional centers should be considered potential locations for drone hubs. We demonstrate that outreach sessions supported by drone deliveries of vaccines can improve vaccine availability. These improvements depend on the available budget to build drone hubs and purchase drones, available cold storage capacity, and the population density in the study region.Keywords: Drone delivery networkVaccine supply chain in low-income and low-middle-income countriesMixed-integer linear programNiger’s vaccine supply chainDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Access and Usage of mobile health (mHealth) for communication, health monitoring, and decision-making among patients with multiple chronic diseases (comorbidities)","authors":"Safa Elkefi","doi":"10.1080/24725579.2023.2267085","DOIUrl":"https://doi.org/10.1080/24725579.2023.2267085","url":null,"abstract":"AbstractMultiple co-existing chronic diseases impact patients' ability to manage their medical conditions. MHealth provides opportunities for continuous access to and better quality of care. This study explored access to mHealth and its Usage among people with comorbidities. Based on Social Cognitive Theory (SCT), this study also explores how environmental factors (quality of care and having a regular provider) and personal factors (self-efficacy and perception of health status) can impact behavioral factors (mHealth use for communication, health monitoring, and decision-making) of people with comorbidities. Multivariate logistic regression models use Health Information National Trends Survey data (2020-2021). The study included 9303 participants, and 3260 of them had comorbidities. The hypotheses are tested on people with comorbidities who used mHealth for health purposes.The use of mHealth to monitor health-related issues was significantly correlated with comorbidity. Having a regular provider impacts the decision to use mHealth for health monitoring, communication, and decision-making. Self-efficacy perception of patients with comorbidities impacts their use of mHealth for health monitoring and decision-making. Finally, a good perception of health status impacts the use of mHealth for health monitoring. Even though different factors impact different behaviors, the findings support the hypotheses of the social cognitive theory linking the person's behavior to their perceptions and environmental factors. These findings extend the literature supporting the validity of the social cognitive theory in healthcare applications and give insights into the importance of mHealth in supporting care for patients with comorbidities.Key Words: Chronic diseasesComorbiditymhealthCommunicationDecision makingAccess to careConsumer health informaticsQuality of CareDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136354190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Is It Worth It for Two Hospital Network Pharmacies to Operate as an Integrated Inventory System in the Presence of Supply Chain Disruptions?","authors":"Lauren L. Czerniak, Mark S. Daskin, Mariel S. Lavieri, Burgunda V. Sweet, Jennifer Leja, Matthew A. Tupps, Karl Renius","doi":"10.1080/24725579.2023.2264867","DOIUrl":"https://doi.org/10.1080/24725579.2023.2264867","url":null,"abstract":"AbstractHospital pharmacies stock perishable drugs that experience supply chain disruptions. A potential solution to alleviate the negative effects of shortages caused by these disruptions is lateral transshipments (i.e., sharing of inventory; integrated inventory system) between hospital network pharmacies with independent suppliers. However, it is unclear when it is beneficial to operate as an integrated inventory system. We create a modeling framework to solve for the integrated inventory policies in a two-hospital network pharmacy inventory system. We find that (i) to benefit from an integrated inventory system, the lateral transshipment cost must be sufficiently less than the shortage cost; sufficient largely influenced by the duration of and time between supply chain disruptions. We find (ii) hospital network pharmacies need to consider the duration of and time between supply chain disruptions when selecting a hospital network pharmacy with which to share inventory. The integrated inventory policies demonstrate that perishable inventory systems with supply chain disruptions may benefit from sharing inventory. In our hospital network pharmacy setting, this contradicts the strict regulations in current practice that generally prohibit hospital network pharmacies from sharing drugs or make it very difficult for hospital network pharmacies to stay compliant when sharing drugs outside of their network.KEYWORDS: supply chain managementinventory managementhospital pharmacyhealthcareDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135694397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"swModel-guided Concurrent Data Assimilation for Calibrating Cardiac Ion-channel Kinetics","authors":"Haedong Kim, H. Yang, A. Ednie, E. Bennett","doi":"10.1080/24725579.2023.2239271","DOIUrl":"https://doi.org/10.1080/24725579.2023.2239271","url":null,"abstract":"","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integer Programming Model for the Deployment of Mobile Health Clinics","authors":"Ang Li, Jiming Peng, Bilal Majeed, Sanghamitra M. Misra","doi":"10.1080/24725579.2023.2231981","DOIUrl":"https://doi.org/10.1080/24725579.2023.2231981","url":null,"abstract":"","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41988113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empirical Analysis of the Impact of Collaborative Care in Internal Medicine: Applications to Length of Stay, Readmissions, and Discharge Planning","authors":"P. Cronin, D. Morrice, J. Bard, Luci K. Leykum","doi":"10.1080/24725579.2023.2234935","DOIUrl":"https://doi.org/10.1080/24725579.2023.2234935","url":null,"abstract":"","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45168635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Active Learning for Defect Localization in 3D Systems","authors":"Jianxin Xie, B. Yao","doi":"10.1080/24725579.2023.2233992","DOIUrl":"https://doi.org/10.1080/24725579.2023.2233992","url":null,"abstract":"","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44763777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Unsupervised Embedding Harmonization System for Privacy-preserving Data Mining in Healthcare","authors":"Mai Li, Ying Lin, Hua Chen, R. Aparasu","doi":"10.1080/24725579.2023.2231011","DOIUrl":"https://doi.org/10.1080/24725579.2023.2231011","url":null,"abstract":"","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41545103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}