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Über dieses Buch

This important new volume presents recent research in healthcare information technology and analytics. Individual chapters look at such issues as the impact of technology failure on electronic prescribing behavior in primary care; attitudes toward electronic health records; a latent growth modeling approach to understanding lifestyle decisions based on patient historical data; designing an integrated surgical care delivery system using axiomatic design and petri net modeling; and failure in a dynamic decision environment, particularly in treating patients with a chronic disease.

Other chapters look at such topics as the impact of information technology integration in integrated delivery systems; operations and supply chain control for inventory management in a health system pharmacy; decision-theoretic assistants based on contextual gesture recognition; evaluating emergency response medical information systems; clinical decision support in critical care; virtual worlds in healthcare; and natural language processing for understanding contraceptive use at the VA.



Chapter 1. The Impact of Technology Failure on Electronic Prescribing Behavior in Primary Care: A Case Study

Electronic Prescribing (e-Rx) has significant potential to improve quality of care and reduce medication errors. However, its adoption rate in primary care has been slow for a variety of reasons. We examine the adverse impact of an information technology (IT) failure on the prescribing process as a critical reliability barrier to adoption. Data from Allscripts TouchWorks® database containing prescriptions written by six physicians in two primary care settings were analyzed using a statistical change-point detection algorithm to identify the tipping point in actual usage and subsequent trends in usage behavior. Physicians overwhelmingly switched from electronic transmission of prescriptions to print option in the presence of such a failure. We propose an approach for a control system that will allow for early detection of system failures and rapid process improvement, and discuss implications for handling such failures in the rapidly evolving IT-enabled healthcare delivery context.
Yi-Chin Kato-Lin, Rema Padman, Keith T. Kanel, Toni Fera

Chapter 2. Individuals’ Attitudes Towards Electronic Health Records: A Privacy Calculus Perspective

National adoption of Electronic Health Records (EHRs) is considered an essential component of the health care system overhaul sought by policy makers and health care professionals, in both U.S. and Europe, to cut costs and increase benefits. And yet, along with the technological aspects, the human factor consistently proves to be a critical component to diffusion of any IT system, and is even more so regarding health care. The highly personal and sensitive nature of health care data and the associated concerns about privacy impede even the most efficient and technologically perfect system. Our objective is to investigate individuals’ attitudes towards EHR and what factors form these attitudes. If we understand individuals’ attitudes regarding EHR and the factors that influence them, we will be in a better position to take responsive measure to facilitate Privacy by Design for EHRs. A positivist research model is empirically tested using survey data from U.S. and Italy and structural equation modeling techniques. We find that perceived effectiveness of regulatory mechanisms positively impact trust; perceived effectiveness of technological mechanisms positively impacts perceived privacy control and trust; the latter two help reduce privacy concerns which, along with perceived benefits, convenience, and Internet experience, play the privacy calculus-type formation of attitudes towards EHR.
Tamara Dinev, Valentina Albano, Heng Xu, Alessandro D’Atri, Paul Hart

Chapter 3. Understanding Lifestyle Decisions Based on Patient Historical Data: A Latent Growth Modeling Approach

Healthcare issues related to chronic disease conditions and management does not have easy or immediate solutions. Evidence based decision making in such contexts requires long-term tracking and analysis of patient data in order to provide patient choices that produce extended quality of life. Using Latent Growth Modeling (LGM), we present a planning perspective to analyze underlying patterns of long-term chronic data related to the progression of Multiple Sclerosis (MS). Using the North American Research Committee on Multiple Sclerosis (NARCOMS) patient driven initiative that collects survey data on a biannual basis for the purpose of clinical trial recruitment and epidemiological research, this study analyzes three temporal data points spanning 3 years. Two LGM models are presented that identify patient traits correlating with disease progression. The traits analyzed are both patient and physician controlled.
Ronald Freeze, T. S. Raghu, Ajay Vinze

Chapter 4. Designing an Integrated Surgical Care Delivery System Using Axiomatic Design and Petri Net Modeling

Over the last decade, a wide range of philosophies and management strategies have been embraced by an increasing number of healthcare organizations, with the principal goal of improving productivity and the quality of care while reducing the associated costs. These have had a significant impact on various healthcare services, including the delivery of surgical care. These philosophies and strategies are composed of scientific techniques and methods, a majority of which can be classified under the Industrial and Systems Engineering (ISE) field of study. For the most part, these techniques have proved to be instrumental in engineering radical changes and improvements in the ailing healthcare industry. This research seeks to contribute to the growing efforts on the application of ISE tools in the healthcare industry. First, an attempt is made to illustrate how various techniques can be used to engineer tangible benefits into the processes for surgical care delivery. Axiomatic Design (AD) concepts are leveraged to synthesize redesign changes, which are then applied to a conceptual model of the surgical system. The model is developed using a Petri Net (PN) technique and subsequently validated, verified and analyzed using PN heuristics and Dependency Structure Matrix (DSM). The outcomes of this redesign depict process improvements with the potential to enhance the quality of surgical care while enabling close to 70 % productivity gains. As a result, this research proposes a framework for the systematic redesign of processes, especially when changes need to be tested and verified before implementation. The techniques identified within this framework are ideal for the successful completion of the ‘design’ and ‘plan’ phases when pursuing operations management philosophies like Six-Sigma (SS) and Total Quality Management (TQM).
Joshua Bosire, Shengyong Wang, Mohammad Khasawneh, Tejas Gandhi, Krishnaswami Srihari

Chapter 5. Examining Failure in a Dynamic Decision Environment: Strategies for Treating Patients with a Chronic Disease

In this paper we investigate the dynamic decision-making task of primary care physicians treating patients with type 2 diabetes to achieve a blood glucose goal. The focus of the study is on developing and testing an information processing theory that can explain why some physicians more often succeed and others more often fail to achieve desirable clinical goals. The developed theory is represented in the form of two types of computational models, one employing a feedback decision-making strategy and the other a feedforward strategy. The models were implemented in software and tested using data from a previously reported experiment where physicians treated simulated patients with type 2 diabetes. The physician data were scored for a defined set of treatment errors. Computational processes were systematically examined to identify and specify processes to perturb in order to generate the observed errors. Models were created for each physician by introducing perturbations in computational processes based on errors that each physician committed during the experiment. These models treated the same simulated patients that the physicians treated; results from each model treating the patients were compared with the represented physician’s results to test the sufficiency of the models to explain observed errors. Process perturbations which explained observed errors took two characteristic forms, both of which resulted in delayed treatment action: (1) elevated thresholds for triggering action and (2) overestimating delayed effects of medications. Physician models made predictions for types and timing of subjects’ treatment errors: physician models generated 79 % of the same types of treatment errors as committed by physicians. As demonstrated by this study, developing task specific information processing theories (expressed as computational models) are useful for investigating patterns of decision making that lead to errors of performance. Studies of this nature can support the design of decision support systems intended to reduce errors associated with dynamic tasks, such as treating a chronic disease.
Gregory W. Ramsey, Paul E. Johnson, Patrick J. O’Connor, JoAnn M. Sperl-Hillen, William A. Rush, George Biltz

Chapter 6. An Empirical Investigation of the Impact of Information Technology Integration in Healthcare Integrated Delivery Systems

Healthcare alliance networks, also known as integrated delivery systems (IDSs), have developed rapidly in the US. Yet, research has not kept pace. This study examines two interrelated needs of healthcare alliance network research—the value of information technology (IT) integration for the IDS and the value of IT integration for the participating hospitals. Hypotheses are developed that relate IT integration to quality and performance outcomes at both the IDS level and at the hospital level. In addition, a taxonomy of hospitals is developed based on size; and the value of IDS IT integration is examined for each hospital quartile. The results of the study suggest that IT integration does indeed have a significant impact on performance in the healthcare industry. In addition, the results suggest that the performance improvements realized at the IDS level of analysis do not extend equally to all hospitals in the IDS.
Evelyn Thrasher

Chapter 7. Beyond the Use of Robotics: Operations and Supply Chain Control for Effective Inventory Management in a Health System Pharmacy

This study describes a process improvement initiative conducted at Sanford Health Medical Center—Fargo an academic tertiary hospital that recently implemented an inventory management system. The objective of this project is to identify opportunities for improvement in inventory management and use of various drug dispensing technologies. Data was collected from wholesaler purchases, patient charge histories, as well as reporting from a robot, carousel system, and automated dispensing cabinets. Ultimately, the initiative uses supply chain management techniques to identify and implement appropriate inventory levels through utilization of a periodic inventory system. This reveals inventory cost history, cost upon initiation of automation, and forecasted costs with appropriate inventory levels upon implementation. The primary outcome upon implementation showed a 25.96 % decrease in cycle stock. Secondary outcomes included an increase frequency of drug being ordered (116.7 orders/week vs. 200 orders/week for top 100 drugs), supporting evidence showing 0.95 % of drugs have a weekend rate greater than one unit larger than the weekday rate and a decrease in whole orders sent to the wholesaler from 5/week to 4/week.
This study provides critical insight and practical guidelines to improve operational efficiency and cost effectiveness in a health system pharmacy. Such improvement efforts are becoming common as companies work to improve their operational efficiencies (Interfaces, 41(1):66–78, 2011).
Maari L. Loy, Rodney D. Traub, Limin Zhang, Pratap Kotala, Monte Roemmich, Jesse Breidenbach, Robert Nelson

Chapter 8. Decision-Theoretic Assistants Based on Contextual Gesture Recognition

This paper presents a novel approach that combines computer vision and decision theory for building intelligent assistants. It considers situations in which a person interacts with surrounding objects, where the system determines the most probable activity and based on it selects an action according to certain parameters. This framework is applicable to situations in which decisions are based on human activities and their interactions with objects in the environment. Examples of this type of situation include a caregiver that helps a handicapped person or an automatic video conference system that selects the best view according to the speaker’s actions. The system assumes that the human activity can be recognized based on hand gestures and their interaction with relevant objects present in the environment. The proposed approach combines contextual-based gesture recognition with a decision theoretic model for selecting the best action in uncertain conditions. Gesture recognition is based on hidden Markov models, combining motion and contextual information, where the context refers to the relative position of the hand to a nearby object. The posterior probability of each gesture is used in a Partially Observable Markov Decision Process (POMDP) to select the best action according to a utility function. The POMDP is implemented as a dynamic decision network (DDN). Experiments in two settings, videoconference and human care giving, show promising results in both gesture recognition and action selection. The experiments show that the proposed framework is robust to changes in the parameters (lookahead, probabilities and rewards), and shows that the performance is similar to that of a human assistant.
José Antonio Montero, Luis E. Sucar, Miriam Martínez

Chapter 9. Developing A Method to Evaluate Emergency Response Medical Information Systems

Emergency response medical information systems (ERMIS) are a specific type of medical information system used for communication and decision making during a crisis. Yet given the dependence on ERMIS during a crisis, these information systems are rarely evaluated to ascertain if the system is indeed successful. This research develops a method to evaluate the success of an ERMIS using a well-established research model as a guiding framework. We explain this method in the context of an ERMIS used in the diagnosis of pathogens in hospitals and state public health laboratories. We describe the insights obtained when using this method to evaluate emergency response medical information systems.
Ann Fruhling, Stacie Petter

Chapter 10. Effective Use of Clinical Decision Support in Critical Care: Using Risk Assessment Framework for Evaluation of a Computerized Weaning Protocol

Background: Clinical decision support aids such as computerized weaning protocols (CWPs) aim to reduce medical errors and improve patient safety. However, the dynamic nature of critical care environments demands context-specific and complexity -inclusive assessment of these support tools for optimal results.
Objective: To apply and validate the use of a risk assessment method called Functional Resonance Accident Method (FRAM), which is originally proposed for adverse event analysis in the aviation industry, to evaluate effective use of a CWP in a medical intensive care unit.
Study Design and Methods: Multiple data collection methods including (1) ethnographic observations, (2) semi-structured interviews, and (3) review of hospital documents related to workflow, procedures, and training were used to simulate a FRAM based model of the CWP and identify factors affecting its use. Subsequently, we validated our findings by shadowing clinicians during 65 weaning attempts (120 h of in vivo data).
Results: The factors posing risk to effective use of CWP included misinterpretation of CWP’s sedation assessment scale, communication and collaboration breakdowns, problems with on-time support delivery, and negative perception of the protocol among clinicians. During the in-situ validation, we found that 45 of the 65 attempts were favorable, 16 fell under near-miss category, while the remaining four were unfavorable.
Conclusions: Non-linear risk assessment method based on resilience engineering concepts is an effective approach for identification of factors for safe use of decision support aids in the real- world health care environment.
Sahiti Myneni, Debra McGinnis, Khalid Almoosa, Trevor Cohen, Bela Patel, Vimla L. Patel

Chapter 11. Virtual Worlds in Healthcare

A well-structured medical education system focusing on proper cognitive as well as psychomotor skills is the key to better health care. However, medical errors still remain one of the leading causes of death in the United States. The recent advancement of healthcare technologies along with various tools and techniques help with the information sharing and provide a better mechanism for learning. One such technology that is gaining popularity as a tool for delivering medical education and health care is virtual worlds. Virtual worlds (VWs) are based on web 2.0 technologies that enable users with internet connectivity to access the respective systems any time. This review paper presents various functions and applications of VWs in medical education and healthcare. This paper surveys various architectures of VWs that focus on medical education and identifies related training tasks that can be achieved using VWs.
Prabal Khanal, Ashish Gupta, Marshall Smith

Chapter 12. Natural Language Processing for Understanding Contraceptive Use at the VA

Objective: To evaluate the potential of Natural Language Processing (NLP) for understanding contraceptive use among female Veterans seeking care at Veterans Administration (VA) healthcare facilities.
Design: Retrospective chart review of a subset of female Veterans enrolled in the Women Veterans Cohort Study (WVCS) who sought care at the VA Connecticut Healthcare facility (in West Haven, CT) in 2009 and completed a survey that included self-reported contraceptive use. In addition, only notes that were annotated for contraceptive use from a prior study that included 227 patients WVCS participants were selected.
Methods: A biomedical ontology of contraceptive terms and concepts was created that included both permanent methods (e.g. hysterectomy) as well as non-permanent methods (e.g. oral contraceptives). The new ontology, along with a section of the VA’s National Drug File was used as the knowledge base for information extraction from the free-text medical records. Included were 208 annotated notes across 39 patients. The General Architecture for Text Engineering (GATE), an open-source application for development of NLP pipelines was used. The ontology was added to GATE along with a processing resource that was developed in order to create an ontology-aware information extraction plugin for the pipeline. In addition, prior resources developed for negation of concepts (e.g. The patient denies using a emergency contraceptive) were utilized.
The NLP pipeline extracted contraceptives currently used by the patient, ones not currently used (prior use or recommended use by the clinician), or whose use was negated. A Boolean matrix of concepts by each patient was produced for input into a decision tree classifier. Tenfold cross validation created iterations of training and testing sets to estimate active versus inactive contraceptive. Responses to self-reported contraceptive use on the prior survey were used as the gold standard.
Results: The use of manual annotation, development of a biomedical ontology, and creation of a natural language processing pipeline achieved high precision (0.83) and recall (0.84). The weighted F-measure was 0.83.
Conclusion: Our combined approach utilized annotation of concepts, a biomedical ontology of contraceptives, and a natural language processing pipeline for information extraction. Our results highlight the potential for biomedical informatics to support research of contraceptive use among female Veterans at the VA. Additional research needs to be done that evaluates the accuracy of contraceptive information in the VA’s Electronic Health Record (EHR) with the consideration of both free text and semi-structured data such as pharmacy records.
Matthew Scotch, Cynthia Brandt, Sylvia Leung, Julie Womack


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