Sepsis and septic shock are common and potentially fatal conditions that often occur in intensive care unit (ICU) patients. Early prediction of patients at risk for septic shock is therefore crucial to minimizing the effects of these complications. Potential indications for septic shock risk span a wide range of measurements, including physiological data gathered at different temporal resolutions and gene expression levels, leading to a nontrivial prediction problem. Previous works on septic shock prediction have used small, carefully curated datasets or clinical measurements that may not be available for many ICU patients. The recent availability of a large, rich ICU dataset called MIMIC-II has provided the opportunity for more extensive modeling of this problem. However, such a large clinical dataset inevitably contains a substantial amount of missing data. We investigate how different imputation selection criteria and methods can overcome the missing data problem. Our results show that imputation methods in conjunction with predictive modeling can lead to accurate septic shock prediction, even if the features are restricted primarily to noninvasive measurements. Our models provide a generalized approach for predicting septic shock in any ICU patient.
{"title":"Septic Shock Prediction for Patients with Missing Data","authors":"Joyce Ho, Cheng H. Lee, Joydeep Ghosh","doi":"10.1145/2591676","DOIUrl":"https://doi.org/10.1145/2591676","url":null,"abstract":"Sepsis and septic shock are common and potentially fatal conditions that often occur in intensive care unit (ICU) patients. Early prediction of patients at risk for septic shock is therefore crucial to minimizing the effects of these complications. Potential indications for septic shock risk span a wide range of measurements, including physiological data gathered at different temporal resolutions and gene expression levels, leading to a nontrivial prediction problem. Previous works on septic shock prediction have used small, carefully curated datasets or clinical measurements that may not be available for many ICU patients. The recent availability of a large, rich ICU dataset called MIMIC-II has provided the opportunity for more extensive modeling of this problem. However, such a large clinical dataset inevitably contains a substantial amount of missing data. We investigate how different imputation selection criteria and methods can overcome the missing data problem. Our results show that imputation methods in conjunction with predictive modeling can lead to accurate septic shock prediction, even if the features are restricted primarily to noninvasive measurements. Our models provide a generalized approach for predicting septic shock in any ICU patient.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126288360","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}
M. Yeo, E. Rolland, Jackie Rees Ulmer, Raymond A. Patterson
Enterprises must manage their information risk as part of their larger operational risk management program. Managers must choose how to control for such information risk. This article defines the flow risk reduction problem and presents a formal model using a workflow framework. Three different control placement methods are introduced to solve the problem, and a comparative analysis is presented using a robust test set of 162 simulations. One year of simulated attacks is used to validate the quality of the solutions. We find that the math programming control placement method yields substantial improvements in terms of risk reduction and risk reduction on investment when compared to heuristics that would typically be used by managers to solve the problem. The contribution of this research is to provide managers with methods to substantially reduce information and security risks, while obtaining significantly better returns on their security investments. By using a workflow approach to control placement, which guides the manager to examine the entire infrastructure in a holistic manner, this research is unique in that it enables information risk to be examined strategically.
{"title":"Risk Mitigation Decisions for IT Security","authors":"M. Yeo, E. Rolland, Jackie Rees Ulmer, Raymond A. Patterson","doi":"10.1145/2576757","DOIUrl":"https://doi.org/10.1145/2576757","url":null,"abstract":"Enterprises must manage their information risk as part of their larger operational risk management program. Managers must choose how to control for such information risk. This article defines the flow risk reduction problem and presents a formal model using a workflow framework. Three different control placement methods are introduced to solve the problem, and a comparative analysis is presented using a robust test set of 162 simulations. One year of simulated attacks is used to validate the quality of the solutions. We find that the math programming control placement method yields substantial improvements in terms of risk reduction and risk reduction on investment when compared to heuristics that would typically be used by managers to solve the problem. The contribution of this research is to provide managers with methods to substantially reduce information and security risks, while obtaining significantly better returns on their security investments. By using a workflow approach to control placement, which guides the manager to examine the entire infrastructure in a holistic manner, this research is unique in that it enables information risk to be examined strategically.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131050281","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}
Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.
{"title":"Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media","authors":"Christopher C. Yang, Haodong Yang, Ling Jiang","doi":"10.1145/2576233","DOIUrl":"https://doi.org/10.1145/2576233","url":null,"abstract":"Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411739","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}
He Zhang, S. Mehrotra, David M. Liebovitz, Carl A. Gunter, B. Malin
In electronic medical record (EMR) systems, administrators often provide EMR users with broad access privileges, which may leave the system vulnerable to misuse and abuse. Given that patient care is based on a coordinated workflow, we hypothesize that care pathways can be represented as the progression of a patient through a system and introduce a strategy to model the patient’s flow as a sequence of accesses defined over a graph. Elements in the sequence correspond to features associated with the access transaction (e.g., reason for access). Based on this motivation, we model patterns of patient record usage, which may indicate deviations from care workflows. We evaluate our approach using several months of data from a large academic medical center. Empirical results show that this framework finds a small portion of accesses constitute outliers from such flows. We also observe that the violation patterns deviate for different types of medical services. Analysis of our results suggests greater deviation from normal access patterns by nonclinical users. We simulate anomalies in the context of real accesses to illustrate the efficiency of the proposed method for different medical services. As an illustration of the capabilities of our method, it was observed that the area under the receiver operating characteristic (ROC) curve for the Pediatrics service was found to be 0.9166. The results suggest that our approach is competitive with, and often better than, the existing state-of-the-art in its outlier detection performance. At the same time, our method is more efficient, by orders of magnitude, than previous approaches, allowing for detection of thousands of accesses in seconds.
{"title":"Mining Deviations from Patient Care Pathways via Electronic Medical Record System Audits","authors":"He Zhang, S. Mehrotra, David M. Liebovitz, Carl A. Gunter, B. Malin","doi":"10.1145/2544102","DOIUrl":"https://doi.org/10.1145/2544102","url":null,"abstract":"In electronic medical record (EMR) systems, administrators often provide EMR users with broad access privileges, which may leave the system vulnerable to misuse and abuse. Given that patient care is based on a coordinated workflow, we hypothesize that care pathways can be represented as the progression of a patient through a system and introduce a strategy to model the patient’s flow as a sequence of accesses defined over a graph. Elements in the sequence correspond to features associated with the access transaction (e.g., reason for access). Based on this motivation, we model patterns of patient record usage, which may indicate deviations from care workflows. We evaluate our approach using several months of data from a large academic medical center. Empirical results show that this framework finds a small portion of accesses constitute outliers from such flows. We also observe that the violation patterns deviate for different types of medical services. Analysis of our results suggests greater deviation from normal access patterns by nonclinical users. We simulate anomalies in the context of real accesses to illustrate the efficiency of the proposed method for different medical services. As an illustration of the capabilities of our method, it was observed that the area under the receiver operating characteristic (ROC) curve for the Pediatrics service was found to be 0.9166. The results suggest that our approach is competitive with, and often better than, the existing state-of-the-art in its outlier detection performance. At the same time, our method is more efficient, by orders of magnitude, than previous approaches, allowing for detection of thousands of accesses in seconds.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425399","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}
This study investigates the influence of the Medicare EHR Incentive Program on EHR adoption at acute care hospitals and the impact of EHR adoption on operational and financial efficiency/effectiveness. It finds that even before joining the incentive program, adopter hospitals had more efficient and effective Medicare operations than those of non-adopters. Adopters were also financially more efficient. After joining the program, adopter hospitals treated significantly more Medicare patients by shortening their stay durations, relative to their own non-Medicare patients and also to patients at non-adopter hospitals, even as their overall capacity utilization remained relatively unchanged. The study concludes that many of these hospitals had implemented EHR even before the initiation of the incentive program. It further infers that they joined this program with opportunistic intentions of tapping into incentive payouts which they maximized by taking on more Medicare patients. These findings give credence to critics of the program who have questioned its utility and alleged that it serves only to reward existing users of EHR technologies.
{"title":"Business Benefits or Incentive Maximization? Impacts of the Medicare EHR Incentive Program at Acute Care Hospitals","authors":"Rajesh Mirani, Anju Harpalani","doi":"10.1145/2543900","DOIUrl":"https://doi.org/10.1145/2543900","url":null,"abstract":"This study investigates the influence of the Medicare EHR Incentive Program on EHR adoption at acute care hospitals and the impact of EHR adoption on operational and financial efficiency/effectiveness. It finds that even before joining the incentive program, adopter hospitals had more efficient and effective Medicare operations than those of non-adopters. Adopters were also financially more efficient. After joining the program, adopter hospitals treated significantly more Medicare patients by shortening their stay durations, relative to their own non-Medicare patients and also to patients at non-adopter hospitals, even as their overall capacity utilization remained relatively unchanged. The study concludes that many of these hospitals had implemented EHR even before the initiation of the incentive program. It further infers that they joined this program with opportunistic intentions of tapping into incentive payouts which they maximized by taking on more Medicare patients. These findings give credence to critics of the program who have questioned its utility and alleged that it serves only to reward existing users of EHR technologies.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088976","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}
We discuss our approach to developing a novel modality for the computer-delivery of Brief Motivational Interventions (BMIs) for behavior change in the form of a personalized On-Demand VIrtual Counselor (ODVIC), accessed over the internet. ODVIC is a multimodal Embodied Conversational Agent (ECA) that empathically delivers an evidence-based behavior change intervention by adapting, in real-time, its verbal and nonverbal communication messages to those of the user’s during their interaction. We currently focus our work on excessive alcohol consumption as a target behavior, and our approach is adaptable to other target behaviors (e.g., overeating, lack of exercise, narcotic drug use, non-adherence to treatment). We based our current approach on a successful existing patient-centered brief motivational intervention for behavior change---the Drinker’s Check-Up (DCU)---whose computer-delivery with a text-only interface has been found effective in reducing alcohol consumption in problem drinkers. We discuss the results of users’ evaluation of the computer-based DCU intervention delivered with a text-only interface compared to the same intervention delivered with two different ECAs (a neutral one and one with some empathic abilities). Users rate the three systems in terms of acceptance, perceived enjoyment, and intention to use the system, among other dimensions. We conclude with a discussion of how our positive results encourage our long-term goals of on-demand conversations, anytime, anywhere, with virtual agents as personal health and well-being helpers.
{"title":"I Can Help You Change! An Empathic Virtual Agent Delivers Behavior Change Health Interventions","authors":"C. Lisetti, R. Amini, Ugan Yasavur, N. Rishe","doi":"10.1145/2544103","DOIUrl":"https://doi.org/10.1145/2544103","url":null,"abstract":"We discuss our approach to developing a novel modality for the computer-delivery of Brief Motivational Interventions (BMIs) for behavior change in the form of a personalized On-Demand VIrtual Counselor (ODVIC), accessed over the internet. ODVIC is a multimodal Embodied Conversational Agent (ECA) that empathically delivers an evidence-based behavior change intervention by adapting, in real-time, its verbal and nonverbal communication messages to those of the user’s during their interaction. We currently focus our work on excessive alcohol consumption as a target behavior, and our approach is adaptable to other target behaviors (e.g., overeating, lack of exercise, narcotic drug use, non-adherence to treatment). We based our current approach on a successful existing patient-centered brief motivational intervention for behavior change---the Drinker’s Check-Up (DCU)---whose computer-delivery with a text-only interface has been found effective in reducing alcohol consumption in problem drinkers. We discuss the results of users’ evaluation of the computer-based DCU intervention delivered with a text-only interface compared to the same intervention delivered with two different ECAs (a neutral one and one with some empathic abilities). Users rate the three systems in terms of acceptance, perceived enjoyment, and intention to use the system, among other dimensions. We conclude with a discussion of how our positive results encourage our long-term goals of on-demand conversations, anytime, anywhere, with virtual agents as personal health and well-being helpers.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255556","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}
Healthcare informatics has drawn substantial attention in recent years. Current work on healthcare informatics is highly interdisciplinary involving methodologies from computing, engineering, information science, behavior science, management science, social science, as well as many different areas in medicine and public health. Three major tracks, (i) systems, (ii) analytics, and (iii) human factors, can be identified. The systems track focuses on healthcare system architecture, framework, design, engineering, and application; the analytics track emphasizes data/information processing, retrieval, mining, analytics, as well as knowledge discovery; the human factors track targets the understanding of users or context, interface design, and user studies of healthcare applications. In this article, we discuss some of the latest development and introduce several articles selected for this special issue. We envision that the development of computing-oriented healthcare informatics research will continue to grow rapidly. The integration of different disciplines to advance the healthcare and wellbeing of our society will also be accelerated.
{"title":"Smart Health and Wellbeing","authors":"Christopher C. Yang, G. Leroy, S. Ananiadou","doi":"10.1145/2555810.2555811","DOIUrl":"https://doi.org/10.1145/2555810.2555811","url":null,"abstract":"Healthcare informatics has drawn substantial attention in recent years. Current work on healthcare informatics is highly interdisciplinary involving methodologies from computing, engineering, information science, behavior science, management science, social science, as well as many different areas in medicine and public health. Three major tracks, (i) systems, (ii) analytics, and (iii) human factors, can be identified. The systems track focuses on healthcare system architecture, framework, design, engineering, and application; the analytics track emphasizes data/information processing, retrieval, mining, analytics, as well as knowledge discovery; the human factors track targets the understanding of users or context, interface design, and user studies of healthcare applications. In this article, we discuss some of the latest development and introduce several articles selected for this special issue. We envision that the development of computing-oriented healthcare informatics research will continue to grow rapidly. The integration of different disciplines to advance the healthcare and wellbeing of our society will also be accelerated.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470178","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}
Ageing population is at the center of the looming healthcare crisis in most parts of the developed and developing world. Australia, like most of the western world, is bracing up for the looming ageing population crisis, spiraling healthcare costs, and expected serious shortage of healthcare workers. Assistive service and companion (social) robots are being seen as one of the ways for supporting aged care facilities to meet this challenge and improve the quality of care of older people including mental and physical health outcomes, as well as to support healthcare workers in personalizing care. In this article, the authors report on the design and implementation of first-ever field trials of Matilda, a human-like assistive communication (service and companion) robot for improving the emotional well-being of older people in three residential care facilities in Australia involving 70 participants. The research makes several unique contributions including Matilda’s ability to break technology barriers, positively engage older people in group and one-to-one activities, making these older people productive and useful, helping them become resilient and cope better through personalization of care, and finally providing them sensory enrichment through Matilda’s multimodal communication capabilities.
{"title":"Embodying Care in Matilda: An Affective Communication Robot for Emotional Wellbeing of Older People in Australian Residential Care Facilities","authors":"R. Khosla, Mei-Tai Chu","doi":"10.1145/2544104","DOIUrl":"https://doi.org/10.1145/2544104","url":null,"abstract":"Ageing population is at the center of the looming healthcare crisis in most parts of the developed and developing world. Australia, like most of the western world, is bracing up for the looming ageing population crisis, spiraling healthcare costs, and expected serious shortage of healthcare workers. Assistive service and companion (social) robots are being seen as one of the ways for supporting aged care facilities to meet this challenge and improve the quality of care of older people including mental and physical health outcomes, as well as to support healthcare workers in personalizing care. In this article, the authors report on the design and implementation of first-ever field trials of Matilda, a human-like assistive communication (service and companion) robot for improving the emotional well-being of older people in three residential care facilities in Australia involving 70 participants. The research makes several unique contributions including Matilda’s ability to break technology barriers, positively engage older people in group and one-to-one activities, making these older people productive and useful, helping them become resilient and cope better through personalization of care, and finally providing them sensory enrichment through Matilda’s multimodal communication capabilities.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129979127","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}
Zidong Wang, J. Eatock, S. McClean, Dongmei Liu, Xiaohui Liu, T. Young
In this article, the expectation maximization (EM) algorithm is applied for modeling the throughput of emergency departments via available time-series data. The dynamics of emergency department throughput is developed and evaluated, for the first time, as a stochastic dynamic model that consists of the noisy measurement and first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, the model parameters, the actual throughput, as well as the noise intensity, can be identified simultaneously. Four real-world time series collected from an emergency department in West London are employed to demonstrate the effectiveness of the introduced algorithm. Several quantitative indices are proposed to evaluate the inferred models. The simulation shows that the identified model fits the data very well.
{"title":"Modeling Throughput of Emergency Departments via Time Series: An Expectation Maximization Algorithm","authors":"Zidong Wang, J. Eatock, S. McClean, Dongmei Liu, Xiaohui Liu, T. Young","doi":"10.1145/2544105","DOIUrl":"https://doi.org/10.1145/2544105","url":null,"abstract":"In this article, the expectation maximization (EM) algorithm is applied for modeling the throughput of emergency departments via available time-series data. The dynamics of emergency department throughput is developed and evaluated, for the first time, as a stochastic dynamic model that consists of the noisy measurement and first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, the model parameters, the actual throughput, as well as the noise intensity, can be identified simultaneously. Four real-world time series collected from an emergency department in West London are employed to demonstrate the effectiveness of the introduced algorithm. Several quantitative indices are proposed to evaluate the inferred models. The simulation shows that the identified model fits the data very well.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131714728","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}
We propose a unique combination of system dynamics and real options into a robust and innovative model for analyzing return on investments in IT. Real options modeling allows a cost benefit analysis to take into account managerial flexibilities when there is uncertainty in the investment, while system dynamics can build a predictive model, in which one can simulate different real-life and hypothetical scenarios in order to provide measurements that can be used in the real options model. Our return on the investment model combines these long-established quantitative techniques in a novel manner. This study applies this robust hybrid model to a challenging IT investment problem: adoption of RFID in retail. Item-level RFID is the next generation of identification technology in the retail sector. Our method can help managers to overcome the complexity and uncertainties in the investment timing of this technology. We analyze the RFID considerations in retail decision-making using real data compiled from a Delphi study. Our model demonstrates how the cost and benefits of such an investment change over time. The results highlight the variable cost of RFID tags as the key factor in the decision process concerning whether to immediately adopt or postpone the use of RFID in retail. Our exploratory work suggests that it is possible to combine merchandising and pricing issues in addition to the traditional supply chain management issues in studying any multifaceted problem in retail.
{"title":"Real Options and System Dynamics for Information Technology Investment Decisions: Application to RFID Adoption in Retail","authors":"Narges Kasiri, R. Sharda","doi":"10.1145/2517309","DOIUrl":"https://doi.org/10.1145/2517309","url":null,"abstract":"We propose a unique combination of system dynamics and real options into a robust and innovative model for analyzing return on investments in IT. Real options modeling allows a cost benefit analysis to take into account managerial flexibilities when there is uncertainty in the investment, while system dynamics can build a predictive model, in which one can simulate different real-life and hypothetical scenarios in order to provide measurements that can be used in the real options model. Our return on the investment model combines these long-established quantitative techniques in a novel manner. This study applies this robust hybrid model to a challenging IT investment problem: adoption of RFID in retail. Item-level RFID is the next generation of identification technology in the retail sector. Our method can help managers to overcome the complexity and uncertainties in the investment timing of this technology. We analyze the RFID considerations in retail decision-making using real data compiled from a Delphi study. Our model demonstrates how the cost and benefits of such an investment change over time. The results highlight the variable cost of RFID tags as the key factor in the decision process concerning whether to immediately adopt or postpone the use of RFID in retail. Our exploratory work suggests that it is possible to combine merchandising and pricing issues in addition to the traditional supply chain management issues in studying any multifaceted problem in retail.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728993","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}
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