IISE Transactions on Healthcare Systems Engineering最新文献

{"title":"Spatiotemporal modeling and optimization for personalized cardiac simulation","authors":"B. Yao","doi":"10.1080/24725579.2021.1879322","DOIUrl":"https://doi.org/10.1080/24725579.2021.1879322","url":null,"abstract":"Abstract Computational modeling of the heart has contributed tremendously in quantitatively understanding the cardiac functions, showing great potential to assist medical doctors in heart-disease diagnosis. However, cardiac simulation is generally subject to uncertainties and variabilities among different individuals. Traditional “one-size-fits-all” simulation is limited in providing individualized optimal diagnosis and treatment for patients with heart disease. Realizing the full potential of cardiac computational modeling in clinical practice requires effective and efficient model personalization. In this paper, we develop a spatiotemporal modeling and optimization framework for cardiac model calibration. The proposed calibration framework not only effectively quantifies the spatiotemporal discrepancy between the simulation model and the physical cardiac system, but also increases the computational efficiency in personalized modeling of cardiac electrophysiology. The model performance is validated and evaluated in the 3D cardiac simulation. Numerical experiments demonstrate that the proposed framework significantly outperforms traditional approaches in calibrating the cardiac simulation.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"145 - 160"},"PeriodicalIF":0.0,"publicationDate":"2021-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1879322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60128523","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":"Task, usability, and error analyses of ambulance-based telemedicine for stroke care","authors":"Hunter Rogers, Kapil Chalil Madathil, Anjali Joseph, Nathan J. Mcneese, C. Holmstedt, R. Holden, J. McElligott","doi":"10.1080/24725579.2021.1883775","DOIUrl":"https://doi.org/10.1080/24725579.2021.1883775","url":null,"abstract":"Abstract Past research has established that telemedicine improves stroke care through decreased time to treatment and more accurate diagnoses. The goals of this study were to 1) study how clinicians complete stroke assessment using a telemedicine system integrated in ambulances, 2) determine potential errors and usability issues when using the system, and 3) develop recommendations to mitigate these issues. This study investigated use of a telemedicine platform to evaluate a stroke patient in an ambulance with a geographically distributed caregiving team comprised of a paramedic, nurse, and neurologist. It first determined the tasks involved based on 13 observations of a simulated stroke using 39 care providers. Based on these observational studies, a Hierarchical Task Analysis (HTA) was developed, and subsequently, a heuristic evaluation was conducted to determine the usability issues in the interface of the telemedicine system. This was followed by a Systematic Human Error Reduction and Prediction Approach (SHERPA) to determine the possibility of human error while providing care using the telemedicine work system. The results from the HTA included 6 primary subgoals categorizing the 97 tasks to complete the stroke evaluation. The heuristic evaluation found 123 unique violations to heuristics, with an average severity of 2.38. One hundred and thirty-one potential human errors were found with SHERPA, the two most common being miscommunication and selecting an incorrect option. Several recommendations are proposed, including improvement of labeling, consistent formatting, rigid or suggested formatting for data input, automation of task structure and camera movement, and audio/visual improvements to support communication.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"192 - 208"},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1883775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47188841","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 integrated opioid prescription optimization framework for total joint replacement surgery patients","authors":"Sujee Lee, Philip A. Bain, Albert J. Musa, C. Baker, Jingshan Li","doi":"10.1080/24725579.2021.1873878","DOIUrl":"https://doi.org/10.1080/24725579.2021.1873878","url":null,"abstract":"Abstract Opioid overdose, addiction, and death have become a nationwide crisis in recent years. Opioid leftover due to over-prescription at hospitals to treat chronic or surgical pains is one of the main contributors to the epidemic. To reduce leftovers, opioid prescriptions should be adjusted and tailored to patients’ needs. However, insufficient prescription may result in frequent refills for patients with high opioid-use levels, which can lead to inefficiency to patients, physicians, and pharmacists. Therefore, developing an optimal opioid prescription model to provide the necessary and patient-specific amount of opioids with minimal refills has a significant importance. In this paper, we introduce an integrated analytical framework, which intends to optimize both opioid prescription and number of refills based on stratification of patients’ opioid usage levels and corresponding stochastic programming. A case study for total joint replacement surgery patients at a community hospital is then introduced to illustrate the applicability and benefits of the framework.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"209 - 223"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1873878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42317856","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":"Modeling the length-of-stay of patients with geriatric diseases or alcohol use disorder using phase-type distributions with covariates","authors":"Wanlu Gu, Neng Fan, H. Liao","doi":"10.1080/24725579.2020.1866715","DOIUrl":"https://doi.org/10.1080/24725579.2020.1866715","url":null,"abstract":"Abstract The hospital length-of-stay (LOS), as an important measure of the effectiveness of healthcare, represents the level of medical requirement and is highly related to the treatment costs. As the human life expectancy has being increased rapidly in the past few decades, there is a pressing need to improve health systems for geriatric patients. Similarly, the alcohol use disorder (AUD), as a chronic relapsing brain disease related to severe problem drinking, has caused negative impacts to society and put patients’ health and safety at risk. In both cases, more efficient hospital management is in demand due to increasing requirements for long-term hospital treatment and the continuously rising medical cost. In order to improve the healthcare efficiency, an accurate modeling of the LOS data and the further analysis of potential influencing factors are necessary. In this paper, we utilize the Coxian Phase-Type (PH) distribution and apply Maximum Likelihood Estimation (MLE) to fit the patient flow information of both geriatric patients and AUD patients collected in a hospital. The influences of the covariates of age, gender, admission type, admit source, and financial class on LOS are assessed and compared through Expectation-Maximization (EM) algorithms. The results show that the LOS data of both types of patients can be modeled well, and the differences with respect to covariates can be accurately identified by the proposed methods. Using the fitted Coxian PH distribution and the estimated coefficients of covariates will provide a guide for better decision-making in healthcare service and resource allocation.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"181 - 191"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1866715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48891972","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":"Health Care 4.0: A Vision for Smart and Connected Health Care.","authors":"Jingshan Li,&nbsp;Pascale Carayon","doi":"10.1080/24725579.2021.1884627","DOIUrl":"https://doi.org/10.1080/24725579.2021.1884627","url":null,"abstract":"<p><p>Industry 4.0 has transformed manufacturing industry into a new paradigm. In a manner similar to manufacturing, health care delivery is at the dawn of a foundational change into the new era of smart and connected health care, referred to as <i>Health Care 4.0</i>. In this paper, we discuss the historical evolution of Health Care 1.0 to 4.0, describe the characteristics of smart and connected care in Health Care 4.0, identify multiple research challenges and opportunities of Health Care 4.0 in terms of data, model, dynamics, and integration, and outline the implications of people, process, system and health outcomes. Finally, conclusions and recommendations are presented in the areas of (1) involvement of multiple disciplines and perspectives, (2) development of technologies and methodologies with combination of quantitative and qualitative approaches, (3) closed-loop integration of sociotechnical system, and (4) design of person-centered system with specific attention to human needs and health equity.</p>","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 3","pages":"171-180"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2021.1884627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39396946","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":"Forecasting the demand of mobile clinic services at vulnerable communities based on integrated multi-source data","authors":"Bilal Majeed, Jiming Peng, Ang Li, Ying Lin, R. Delgado","doi":"10.1080/24725579.2020.1859305","DOIUrl":"https://doi.org/10.1080/24725579.2020.1859305","url":null,"abstract":"Abstract Demand forecasting plays an important role in the deployment of mobile clinic services to vulnerable communities such as school zones and census tracts as it can help the service provider to maximize its coverage under limited resources. In this paper, we consider the issue of how to predict the vaccination delinquency in schools and census tracts. Such an issue is rather challenging as the delinquency is only observed in schools for which very limited information is available; while rich demographic and economic information is available for census tracts, no observations of delinquency have been made at the census tract level. To address the above challenge, we first develop a hierarchical approach to forecast the demand for vaccinations in schools and census tracts. In the first stage of the hierarchical approach, we solve a linear optimization model to compute an association matrix that can align some common features in both census tracts and school zones. Then we use the estimated association to develop a forecasting model to predict the vaccination delinquency in both schools and census tracts. A non-convex quadratic optimization (QO) model is also proposed to find the association matrix and the forecasting model simultaneously. We also introduce an alternative update scheme for the non-convex QO and establish the convergence of the algorithm. Moreover, the two association matrices generated from the proposed approaches can be used to impute the information in the school zone data, which further allows us to apply existing forecasting models to predict the demand in school zones based on the imputed data. A case study from the Houston Independent School District (HISD) and its associated communities is reported to demonstrate the efficacy of the new models and techniques.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"113 - 127"},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1859305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41987959","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":"Application of blockchain technique to reduce platelet wastage and shortage by forming hospital collaborative networks","authors":"S. Rajendran","doi":"10.1080/24725579.2020.1864522","DOIUrl":"https://doi.org/10.1080/24725579.2020.1864522","url":null,"abstract":"Abstract Given the short shelf life of blood products and a decrease in donor population, hospitals endeavor to achieve an equilibrium between shortage and outdating. Though collaborating and sharing blood among hospitals will result in a reasonable reduction in surplus and deficit, health regulations strongly discourage blood exchange among hospitals due to traceability issues. With the recent advancement in peer-to-peer networking, blockchains have been used for record transactions in businesses with the advantage of being timestamped. This research is one of the first to address the blood inventory management problem using the blockchain technique by forming a collaborative network of hospitals and exchanging blood in that system, enabling transparency and traceability. A novel clustering technique, called multi-criteria clustering (MCC), using a mixed-integer programming approach, is proposed to form the blockchain network that bases its clustering decisions on a number of conflicting criteria, such as collaboration preference, geographical distribution, hospital size, and operational heterogeneity. Subsequent to this, a mathematical model is developed to identify purchasing strategies for each hospital in a blockchain network, given the opportunity to share blood among other hospitals in that network. This two-phase approach is tested using hospital settings in a neighborhood of a metropolitan city. Sensitivity analysis is performed to evaluate the effectiveness of the blockchain system with respect to change in cost, shelf life, demand distribution, and coefficient of demand variation. Based on the results, it can be concluded that, for all settings, the proposed blockchain network significantly outperforms the current system with independently acting hospitals, in spite of the additional blockchain development and operating expenses that are incurred.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"128 - 144"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1864522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47577755","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":"A Multi-Stage predictive model for missed appointments at outpatient primary care settings serving rural areas","authors":"Laith Abu Lekham, Yong Wang, E. Hey, Sarah S. Lam, M. Khasawneh","doi":"10.1080/24725579.2020.1858210","DOIUrl":"https://doi.org/10.1080/24725579.2020.1858210","url":null,"abstract":"ABSTRAT Missed appointments are a significant cause of inefficiency in the healthcare industry. Many researchers have studied this problem in various healthcare settings. However, a few studies are concerned with predicting missed appointments at outpatient primary care settings serving rural areas. This study holistically investigates the factors behind two types of missed appointments - no shows and cancelations - at an outpatient primary care medical center serving rural areas and develops a predictive model to reduce their incidence. The study was carried out in three main phases. First, exploratory data analysis was conducted to discover the patterns related to missed appointments. Second, the association between some of the attributes and appointment status was analyzed. Third, three prediction models – binary, multi-class, multi-stage chain - were considered for missed appointments. The third model is a new proposed multi-stage chain model to predict missed appointments. Machine learning classifiers including logistic regression, decision tree, and tree-based ensemble classifiers were used in the three models. It was found that appointment lead time is a key driver for missed appointments. The multi-stage chain model produced the best results with 73.0% precision, 73.3% recall, 73.0% F1-score, and 73.3% accuracy. Based on this analysis, several interventions were proposed to reduce missed appointments.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"79 - 94"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1858210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49511650","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":"Understanding local health departments decision to pursue/defer accreditation: A mixed-method systems thinking approach","authors":"Mina Ostovari, Sandra S. Liu, Yuehwern Yih, Denny Yu","doi":"10.1080/24725579.2020.1854396","DOIUrl":"https://doi.org/10.1080/24725579.2020.1854396","url":null,"abstract":"Abstract This study explores factors impacting local health departments (LHDs) decision to pursue accreditation in states with low public health expenditures. With only three accredited LHDs so far and as a state with a low public health expenditure, Indiana serves as an example for exploring possible factors impacting the LHD’s decision to pursue accreditation. We used the systems thinking approach to understand LHDs’ organizational characteristics impacting their decision to pursue accreditation using a mixed-method of quantitative (descriptive statistics/random forests) and qualitative (interviews) approaches. We interviewed 12 local health departments across Indiana, six of which were pursuing accreditation while the others had deferred it. Using the descriptive analysis of the interviews and the random forests variable importance, we identified significant organizational factors that impacted the LHD decision. The variable importance method identified guiding-information measure as the most significant factor for pursuing accreditation. Guiding information shows whether the guidelines provided by the governing entities were clear and coordinated. Another distinguishing factor among the pursuing/deferring LHDs was the implementation measure. Similar to pursuing LHDs, the deferring LHDs had started the quality improvement process for accreditation, but the activities got halted during the implementation phase. The priorities and leadership of the governing entities were the main drivers for the successful implementation of accreditation. Moreover, aligning the tasks required in the accreditation process with the LHDs regular quality improvement activities may facilitate achieving accreditation.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"70 - 78"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1854396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47406717","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":"Factors affecting the choice of a dental care provider by older adults based on online consumer reviews","authors":"Amal Ponathil, A. Khasawneh, Kaileigh A. Byrne, Kapil Chalil Madathil","doi":"10.1080/24725579.2020.1854394","DOIUrl":"https://doi.org/10.1080/24725579.2020.1854394","url":null,"abstract":"Abstract Background: The percentage of patients relying on information provided by their peers in the form of consumer reviews rather than the information found in healthcare reports from federal agencies is expected to increase. To encourage the use of these healthcare reports, which contain data from a more representative sample, several researchers have suggested incorporating consumer reviews in them. Objective: This study aims to identify the factors in online consumer reviews that affect the decisions of healthcare consumers as they choose their providers. Method: We recruited 310 participants through Qualtrics Research Services to participate in a 3*2*2*2 within-subjects study. We used the dentistry domain to test the effect of the valence of the review, the staff rating, the facility cleanliness rating and the dentist’s bedside manner rating on patient's trust in the information, the decision to choose a dentist and the confidence in the decision. Results: The findings suggest that valence of the review, the dentist’s bedside manner rating and the cleanliness rating of the facility significantly influence the consumer’s decision to choose a dentist and their trust in the information. In addition, the staff rating was considered the least important element in the consumer reviews. Conclusion: We conclude that in addition to narratives of patient experience in the form of reviews, consumer ratings like bedside manner and cleanliness are important cues that aid the consumer’s decision-making process while ratings such as a staff rating, which do not consider the interactions with the actual healthcare provider, are not as critical. Highlights Factors were identified in online reviews that affect consumer decisions of healthcare providers. A 3*2*2*2 within-subjects study was conducted based on the dentistry domain. Narratives of patient experiences in the form of reviews and consumer ratings of bedside manner and cleanliness were found to be important cues in a consumer’s decision. Staff ratings which do not consider the interactions with the healthcare provider were not as critical.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"51 - 69"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24725579.2020.1854394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46323763","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}