As we noted in the inaugural issue of the
International Journal of Healthcare Information Systems & Informatics (IJHISI), in spite of continuing efforts of well-meaning researchers and practitioners, the Healthcare Information Systems & Informatics (HISI) field is still in its infancy and much of the published work has been scattered in different disciplined-based outlets. Indeed, the evolving HISI discipline is in dire need of coherent directing frameworks to advance healthcare management and informatics as well as clinical informatics research and practices (Tan, 2006). Accordingly, this inaugural volume represents an attempt to serve as another focal point for pulling together the works that would further evolve the field of HISI from the perspective of building relevant theories, applying and adapting both traditional and more innovative methodologies to the field, as well as suggesting alternative strategies and models for improving HISI implementation, evaluation and best practices. The primary aim is to educate new researchers, practitioners and students on the significance of research and best practices in building the HISI discipline. A secondary aim is to showcase how research in the HISI discipline can contribute to HISI practice. From this understanding, it is hope that we would better manage the future of HISI development and how specific current HISI research and evolving HISI practice can be further explored to impact the life and activities of health professionals and patients alike in our ever-changing society.
The contributions presented in this volume may be sequenced into four major parts. Part I, comprising chapters one through three, provides an overview of HISI theoretical development and some of the identifiable knowledge gaps in need of further attention from HISI researchers and practitioners. These chapters focus on new concepts and constructs awaiting theoretical development to scope particular areas within the emerging HISI discipline, for example, mobile health (M-Health), information and communication technology (ICT) diffusion, and understanding how the Internet has impact on e-health knowledge diffusion among targeted populations. These chapters are grouped together in Part I to illustrate how new questions specific to key areas of HISI may be conjured and to highlight the challenges faced in building HISI theories when working in a rapidly emerging field.
Consider the question of how a discipline emerges to evolve a tradition of interrelated body of knowledge. In order to focus on this question seriously, we must ask ourselves a related, follow-up question: that is, “What makes an emerging discipline attractive and appealing enough to gain the attention of a whole new group of researchers and students from across other well-rooted and more traditionally recognized disciplines to want to inquire about phenomena in this evolving field?” Soon, we will realize that what defines a discipline is nothing more than its accumulation of relevant theory and practice that are applicable to the challenges of that discipline, not just any disciplines. Certainly, a critical mass of researchers and a substantial body of identifiable research literature for a growing discipline need to exist for anyone discipline to become recognized. While there are many theories that can be applied across disciplines, each new and evolving discipline will be characterized by its continuous drawing of theoretical concepts from other more established disciplines, and the growing number of researchers working on “gaps” encountered within the discipline. Over the years, then, if a new discipline is to be established, it has to germinate roots of its own in the form of accepted theories, frameworks and models, alongside a stream of related, but identifiable “gaps” in knowledge and practice specific to that discipline. This is why theory-based research in HISI is critical to advance the field and one of the aims of this volume is a call for researchers to come forward to contribute to building sound HISI theories.
Many young researchers neglect theory building when deciding on a topic to study and the majority of less well-trained researchers failed to appreciate the need to close the “gaps” that are currently challenging the growth of an emerging field. In relation to identifying gaps and HISI theoretical development, Part I of this volume illustrates how an important “gap” in the HISI area can be used to generate a new perspective in HISI research. The Olla-Tan chapter (Chapter 1), “Designing a M-Health Framework for Conceptualizing Mobile Health Systems,” for example, adopts a grounded theoretical approach to breaking down and compartmentalizing Mobile Health (M-Health) systems into principal dimensions based on content analysis of the extant literature. These dimensions include Communication infrastructure, Application Purpose, Device type, Data Display and Application Domain. Communication infrastructure characterizes the mobile telecommunication technologies and networks. Application purpose identifies the goal and objectives for the M-Health system to be applied, for example, e-prescription if the system is to aid prescription services, clinical data exchange or decision support or some other purposes. Device Type relates to the type of device being used such as tablet PCs, portable data assistants (PDAs), sensors and other devices. Data display describes how the data will be presented and transmitted to the user, for example, through a series of images, emails and/or textual data. Application domain categorizes the domain of mobile technology application and defines the specific area that the M-Health system will be implemented such as rural community health, wireless home healthcare or some other domains. Altogether, the M-Health Reference Model serves as a guide to healthcare stakeholders and m-health system implementers in identifying the technological infrastructure, understanding the business requirements and operational needs of the M-health systems. In addition, the chapter highlights the security model of a proposed m-health system implementation, incorporating a case that illustrates how the M-Health reference model can be used to encapsulate a better understanding of the Blood Donor Recruitment (BDR) system components, especially on issues related to security of blood donor data capture and transfer. The M-Health Reference Model may be considered a first attempt to building theory for an important HISI domain that can impact the business models and the security policy for the implementation of a mobile health system.
Although information and communications technology (ICT) is being used in the United Kingdoms National Health Services (NHS) to improve medical delivery, it has not been sufficiently tested and applied to improve our understanding of the factors motivating improvements and changes in terms of ICT acceptance among end-users in health organizations. More importantly, one often neglected question in HISI theoretical research is, “Which theory best modeled the HISI phenomenon being observed?” In Chapter 2, Osbourne and Clarke worked on such a question by proposing that ICT, as a basis for telemedicine, can reduce existing communication barriers and initiate new forms of information exchange between medical professionals and patients. They further showed how an integrated theoretical approach to relate and combine existing theories could best be used to explain ICT acceptance and adoption behavior. Specifically, they compared three published models, including Davis’ dominant and adapted technology acceptance model (TAM), the Rogers innovation and diffusion theory (IDT), and the less well-known Triandis theory of interpersonal behavior (TIB), and showed that by integrating key research constructs embedded in each of these models, a new model emerged to explain more variance in IT usage intention. Accordingly, they noted that the “TAM assumes that there are no barriers to preventing an individual from using a technology if he or she wished. The IDT and the TIB however feature variables, which can affect adoption by the individual, hence filling one of the gaps of the TAM.” For these reasons, the authors’ next step in theory testing will be a planned follow-up study on new medical technology diffusion within a Primary Care Trust as part of the NHS initiative to see if their proposed integrated model “lives up to (their) expectation.”
An interesting variation in HISI theory development from the strategies presented in the two previous chapters, Christopher Reddick concentrated on building theory from existing datasets in Chapter 3. In his contribution on “The Internet, Health Information, and Managing Health: An Examination of Boomers vs. Seniors,” he made use of secondary data analysis on a large empirical dataset compiled by the Kaiser Family Foundation to provide plausible explanation and valuable insights into an emerging and important HISI knowledge domain, that is, the trending of Internet use for e-health information retrieval among the elderly vs. the baby boomers. The Internet, according to Dr. Reddick, is now considered a major source of health information for baby boomers, “second only to their family doctor.” Yet, for the seniors, who probably have the greatest need for health information, the Internet has not been perceived as their “primary” source of information nearly as extensively as the baby boomers. The “central hypothesis” for this observation is that if people feel more at ease with and have a better attitude towards using the Internet for e-health information retrieval, they would more likely use it to manage their personal health or do so on someone’s else behalf. In view of this, one basis for sound HISI theory development is to search out an empirically validated data source that is relevant to the knowledge domain at hand. In this case, data on e-Health and the Elderly were separated by its online access from boomers versus seniors to show that boomers use online health information marginally greater than seniors for the management of their health and that those who were more aware and had positive feelings towards online health information (for both boomers and seniors) would use the Internet more to manage their health. This partly answers a few very important questions that have been frequently asked by many past e-health researchers: “Is there a trend of rising Internet use for referencing e-health information among selected populations, more specifically, the aging population?” “Has online health information actually helped some people more than others to manage their health more effectively?” and “Is the use of Internet-based information reliable and safe for individual health-related decision making?” Although Dr. Reddick did not addressed all these questions, his analytical approach at least touched on the deduction of a sound theoretical explanation for use of Internet-related information among seniors versus the baby boomers. A follow-up analysis could be applied to extend his answers to other related or more complex questions, provided that collected data on key and relevant variables of interest would also have been made available. This brings us to the next part of this volume, Part II.
Part II, comprising Chapters 4 through 12, concentrates on HISI methodological approaches and the applications of different analytical techniques, including qualitative and quantitative approaches as well as a variety of research designs to extracting knowledge from empirically collected data when attempting to address specific HISI research questions. The focus on HISI methodologies is to help the readers appreciate the fact that an evolving discipline such as HISI must necessarily borrow or draw its building blocks of knowledge from more established sciences. A wide range of HISI methodologies is covered in Part II, as well as throughout this volume, for example, innovative statistical modeling techniques, simulation, secondary data analysis, case studies, paper-based vs. web-based surveys, interviews and “triangulation.” The first two contributions in this section, Chapters 4 and 5, demonstrate how various analytic approaches, specifically neural networks, are gaining popularity as new and innovative methods for mining and understanding complex HISI datasets. In contrast, the next two chapters, Chapter 6 and 7, touch on the application of more qualitative methodologies, specifically, the use of “case study.” The next four subsequent chapters, Chapters 8 through 11, will then provide the readers with insights on the extend to which survey-based methodologies can be used to answer diverse complex, and often interesting questions, for example, questions relating to information technology (IT) usage among primary care physicians, IT usage in private group medical practices, the use and diffusion of computerized physician order entry systems in a particular country (in this case, China), and the perceived benefits and risks that might be anticipated by healthcare organizations on the use and adoption of Electronic Health Record (EHR) systems. Chapter 12, a study combining survey and interview methods much like a triangulation approach, concludes Part II. An additional illustration of the specific application of the triangulation approach used in HISI research and practice may also be found in the concluding chapter of this volume, Chapter 20.
Through the Trauma Audit and Research Network (TARN), Chesney et. al., in Chapter 4 on “Data Mining Medical Information: Should Artificial Neural Networks Be Used to Analyse Trauma Audit Data?” formally advocated the use of an innovative data mining approach in the creation of an artificial neural network (ANN) model for the analysis of accumulated trauma data as compared to traditional logistic regression analysis. The authors showed how their results obtained from the use of the ANN model with “the output set to be the probability that a patient will die” compared with those obtained from traditional logistic regression analysis of 10 years of TARN data. TARN is a network designed to provide effective feedback and accurate classification of care for injured patients. Essentially, the ANN approach entails a layered system of key inputs, the weighing of factors for classification probability calculations, and an adjusted outcome neural network analysis. In this sense, ANN modeling begins with the system recording injury details such as demographics, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the probable outcome of the treatment in order to accurately discriminate between those patients who are expected to live vs. those who are predicted to die. Results showed that both ANNs versus traditional analytic approaches achieve roughly the same predictive accuracy, although ANNs are found to be more complex to interpret than the logistic regression model. Therefore, the authors managed to show us how novel forms of statistical modeling can be applied as tools to analyze complex HISI datasets, although their findings further suggested the usefulness of applying both traditional and non-traditional analysis techniques together, as well as including as many factors in the analysis as possible.
In the next chapter, our attention is again focused on neural networks (NNs) methodology, but this time it is shifted to how the methodology could be applied to study an identified gap in the healthcare decision modeling space. Here, Walczak and colleagues in their chapter, Chapter 5, on “Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism,” demonstrated the robustness of automated intelligence in improving diagnostic capabilities when predicting the likelihood of pulmonary embolism (PE) in a surgical patient population. Accordingly, their research question entailed how well advanced decision-making tools such as nonparametric NNs improve the diagnostic capabilities in predicting PE, which may have mortality rates as high as 10 percent. Among a multitude of diseases, trauma, and related medical problems, PE is noted as one of the most difficult and costly to diagnose illness that patients are faced with today. Therefore, the authors argued for the need of applying intelligent tools in order to identify patients at risk for PE and their analyses revealed that using NN diagnostic models (in particular, the backpropagation train NN) “enables the leveraging of knowledge gained from standard clinical laboratory tests, the d-dimer assay and reactive glucose, significantly improving its overall positive predictive value compared to using either test in isolation.” In other words, the authors showed once again that superior positive prediction can be achieved with good use of HISI analytic capabilities, in this particular case, when the D-dimer result value is used in combination with the Glu-R result value owing to the additive nature of the NN modeling method, and not when each of these result values is used independently.
We now shift focus to one qualitative approach – the “case study.” In Chapter 6, Wiggins, Beachboard, Trimmer and Pumphrey contributed an interesting perspective on IT governance from a rural healthcare perspective. Here, they used a “single-site” case study in their piece, “Entrepreneurial IT Governance: Electronic Medical Records in Rural Healthcare,” to document the implementation of an EMR in a rural family practice residency program. The residency program, which trains primary care physicians and provides primary care services to rural communities aims initially at enhancing the practice’s clinical research capabilities. But this simple goal was soon transformed into a much larger goal of extending the system throughout rural clinics and providers in the region. The authors argued for an innovative, relationship-oriented approach for organizations aiming for a successful adoption of IT as a means of improving healthcare in the rural settings.
Expanding on this idea of a “single-site” case study approach, Schwieger, Melcher, Raganathan and Wen evaluated the impact of advanced IT and HISI adoption within a medical organizational setting in Chapter 7 with a study over a ten-month period interpreted under a modified Adaptive Structuration Theory (AST) model. AST, which is rapidly becoming an important theoretical paradigm for comprehending IT-related evaluations, was modified to illustrate the changing interrelationships among the variables affecting the adoption and application of the HISI technology studied. Specifically, the case illustrates the complex interactions between medical billing technology and organizational processes. As the organization attempted to install and implement the new system, the employees found that in order to maintain daily operations, they would have to modify and adapt several aspects of the organization, technology and operations. As the system was slowly integrated into operations and the organization’s needs evolved through the adaptation process, the study, in turn, found that different iterations of the model could emphasize different structures. The case further illustrates that the capacity to manage HISI technologies often requires the organization to prioritize its needs and focus its energies on a critical structure while temporarily disregarding others until the primary healthcare processes are under control.
Shifting to the use of survey-based methodologies, Andersen and Balas in Chapter 8 on “A Survey on Computerization of Primary Care in the United States” focused on IT usage among primary care physicians. As these physicians represent a major stakeholder group in the US healthcare system, insights into the adoption and diffusion patterns of IT among them is important to enhance our knowledge of how IT is being deployed in primary care delivery. With increased productivity and improved quality being the prime goals in the use and deployment of IT in healthcare delivery, many questions were asked by the authors, for example: “What types of IT are being used by physicians?” “What are their perceived benefits?” “What are the major barriers to IT acceptance?” These and other questions are among those investigated in the study. One interesting though puzzling result they found is that a high number of physicians did not indicate any interest at all in the types of IT applications being surveyed. Overall, their results revealed that perceived benefits and barriers are important predictors of IT implementation. Their study showed how survey method can be used to investigate the diffusion of IT applications in real-world healthcare settings.
Next, Sobol and Prater in their chapter entitled, “Differences in Computer Usage for U.S. Group Medical Practices: 1994 vs. 2003,” conducted a follow-up mailed survey of IT use in private group medical practices on issues such as in-house expertise versus outsourcing and the use of e-billing systems in these practices to understand how to reduce the increasing amount of time physicians spent on business administration. Their work addresses the “gap” in previous research, which tended to focus mainly on hospitals and Health Management Organizations (HMOs), but not private physician practices. Accordingly, the authors reported on a follow up of their 1994 study of group medical practices and again used mailed survey as a way to build a “longitudinal” picture of the IT services adoption patterns by these private group practices. Results of the study provide insights on key issues and challenges faced by these practices, including the types of IT applications used, the different types of savings arising from IT adoption, the percentage of time spent by physicians on business administration and the different sources of information they drew upon to work on the business aspects of their practice.
The next survey-based contribution is discussed in Chapter 10, “Understanding Physicians’ Acceptance of Computerized Physician Order Entry,” authored by Liang and colleagues. Computerized physician order entry (CPOE) holds the potential to reduce medical errors, improve care quality, and cut healthcare costs. Based on a series of hypotheses generated by the Technology Acceptance Model (TAM), these authors attempted to evaluate physician user acceptance of CPOE in a large general hospital setting in China. Items in the questionnaire are adapted from previous research and designed to measure perceptions of physicians about CPOE. Under the condition of high CPOE experience, the authors found perceived ease of use had no effect on attitude, whereas under the condition of low CPOE experience, perceived ease of use was shown to positively affect attitude. In other words, as physician users become more experienced with CPOE, the issue of usability becomes less important. Yet, this result does not diminish the significance of the need for designers to pay attention to the usability factor.
The empirical study covered in Chapter 11 by Davis and Thakkar used a two-phase approach to identify the status of Electronic Health Records Systems (EHR) in U.S. hospitals. They first interviewed seven healthcare and information systems professionals from three hospitals to develop a sound instrument, identifying and drawing relevant measures from the extant literature. They wanted to know if there was a significant relationship between perceived levels of benefit and risk with the use of each “core functionality” in an EHR system, as well as between the status of the EHR system and size of hospital. Core functionality was defined as “health information, results management, order try/management, decision support, electronic communication, patient support, administrative processes, reporting, and population health management functionalities of an EHR system.” Their results showed a significant positive correlation between perceived benefits and risks in all of the eight core functionalities but no significant relationship found between status of EHR system and size of hospitals. They concluded that each of these eight core functionalities might be adopted by hospitals either individually or as an entire EHR system. Like the preceding contributions, the usefulness of conducting appropriately managed surveys (as well as interviews) as HISI methodologies is clearly documented in this study.
The penultimate chapter for this section, Chapter 12, features again the use of an integrated survey-interview approach including stakeholder questionnaire and 12 key informant interviews, combined with an extensive literature review in “Telehealth Organizational Implementation Guidelines Issues: A Canadian Perspective” by Maryan Yeo and Penny A. Jennett. To develop theoretically sound and empirically based perspectives, their research uses a triangulation of common methodologies. Results of their study are categorized into the four major themes of organizational readiness, accountability, quality assurance, and continuity. The authors pointed out that a vast number of their findings relate mostly to the former two themes whereas the latter two themes have gained only scattered attention. The findings and recommendations are useful in the evolution of telehealth services and their successful management. This chapter leads us naturally to the third section on HISI implementation, evaluation and practice.
Part III, comprising Chapters 13 through 16, links HISI research to real-world implementation and understanding models to support HISI evaluation and practice. Briefly, Apostolakis, Valsamos and Varlamis demonstrated a practical implementation of a Greek telemedicine system at a national level in Chapter 13; Chang, Lutes, Braswell, and Nielsen evaluated the use of an emerging IT solution to overcome inefficiencies in providing quality nursing care by nurses working in hospitals supported by paper-based systems in Chapter 14; David Parry concentrated on the diffusion of e-health technology and the evaluation of a Fuzzy Ontology-Based Medical Information System in Chapter 15; and Wickramasinghe, Misra, Jenkins, and Vogel advocated the use of a “Competitive Forces” framework to provide a unified system for evaluating various e-health initiatives in the closing chapter of this section.
In terms of HISI practice, Apostolakis et al. in “Decentralization of the Greek National Telemedicine System through the upgrading of the Regional Telemedicine Centers” recorded and analyzed the shortcomings and difficulties of the existing Greek Telemedicine system and suggested the upgrading of Regional Telemedicine Centers’ role in order to become the cornerstone of the new system. In this context, they highlighted the necessary actions at technical, operational and organizational level for the smooth transition to a new system, and argued for the advantages of this new structure. Their analysis uncovered the shortcomings and inefficiencies in the usage of Telemedicine Centers in the National Health System (NHS) over the years and dictated the development of the Regional Telemedicine Centers (RTC). Because the binding of the new RTC with the existing telemedicine system must be performed with the minimum cost, this presumed recording and reuse of the existing infrastructure, training of personnel and the smooth transition to the new telemedicine structure. Based on recent experiences, the authors presented an action plan that illustrates different technical and organizational aspects for the development and successful incorporation and management of a National Telemedicine System.
Chang et al. in their contribution, “Nurses’ Perceptions of Using a Pocket PC for Shift Reports and Patient Care,” introduced the use of an emerging IT solution to deal with nursing care inefficiencies and evaluated it for use by nurses working in traditional hospital paper-based systems. An integrated IT system, consisting of Pocket PCs and a desktop PC interfaced to a hospital’s mainframe system, was developed and suggested as the IT solution. The goal was to apply mobile IT to give nurses easier access to patient information. The authors described the development of the prototype and reported the results of a pilot test comparing the time spent in taking and giving shift reports before and after the study and nurses’ perceptions of the mobile IT system. Nurses appeared to provide strong verbal support for the use of the system. The readers are therefore left with answering the key question of returns on investments (ROI) with the deployment of such a system. This study demonstrated that the potential of mobile technology, integrated with a hospital’s mainframe system, could significantly improve the efficiency of communication in shift reports and in accessing information relevant to patient care.
In Chapter 15, David Parry’s contribution on “Evaluation of a fuzzy ontology based medical information system” dealing with evidence-based medicine (EBM) shows that appropriate information should be made available to clinicians at the point of care. While electronic sources of information are typically known to fulfill this need, they require a high level of skill to use successfully. More specifically, the author described the rationale and initial testing of a system to allow collaborative search and ontology construction for professional groups in the health sector. The approach, which is based on the use of a browser linked to a fuzzy ontology rooted in the National Library of Medicine (NLM) Unified Medical Language System (UMLS), is seen to provide high quality information for professionals in making future EBM decisions.
Wickramasinghe et al., in Chapter 16, attempted to provide a unified system for evaluating a growing number of e-health initiatives. The authors assessed the relative strengths and deficiencies among these initiatives in realizing improved access, quality, and value of healthcare services. The evaluation system is based on focusing on three key components: 1) understanding how e-health can modify the interactions between the various players such as regulators, payers, providers, healthcare organizations, suppliers, and patients, as well as create added value healthcare services; 2) understand the competitive forces facing e-health organizations and the role of the Internet in modifying these forces; and 3) introduce a framework that serves to identify the key forces facing e-health. Their work also provided some suggestions of how a healthcare organization can structure itself to be e-health prepared. What becomes critical then is the sustainability of the e-health initiatives and their ability to bring benefits to the key actor in health care, the patient.
Given the significance of HISI research and practice, Part IV closes the volume with an attempt to show how these two components can be linked. Here, a gleam of HISI policies and knowledge dissemination and transfers is provided through four chapters in this section. Chapter 17 provides a framework for detailing health web services privacy and security policy. Chapter 18 highlights the current alternative paradigms to e-health pedagogy while Chapter 19 takes a novel approach to examining healthcare information technology (HIT) usage and user satisfaction in healthcare organizations. Finally, Chapter 20 shows how to go about translating theory into best practices in the area of healthcare technology management within a Canadian teaching hospital setting.
With “HIPAA Compliant Access Control Model for Web Services,” Cheng and Hung focus on the growing demands of Web services-based healthcare applications and the need for Health Insurance Portability and Accessibility Act (HIPAA) privacy rules to be standardized in Web services. As no comprehensive solutions to the various privacy issues have been defined in this area, they proposed a vocabulary-based Web services privacy policy framework with Role-based Access Control (RBAC) and privacy extensions. They also discussed about the HIPAA compliance for such a framework.
In Chapter 18, Wilson in his “Building Better E-Health Through a Personal Health Informatics Pedagogy,” reviewed and compared the current alternative paradigms to e-health pedagogy. Adopting a Personal Health Informatics (PHI) perspective, Wilson integrated three previously held paradigms, namely, the e-commerce paradigm, the personal health record paradigm, and the consumer health informatics paradigm. In this light, he attempted to develop a conceptual model of a new paradigm, the PHI. Accordingly, this paradigm incorporates the significant features of previous paradigms by the integration of multiple perspectives of informatics, personal, and healthcare. The significance of this work lies in providing a model for enhancing knowledge dissemination in e-health design, development, and management. Overall, this work helps further advance our contemporary understanding of the various health informatics issues as well as points out future directions for HISI research and practice, in particular, the design and development of encompassing e-health.
Next, Hikmet and Bhattacherjee in their contribution on “The Impact of Professional Certifications on Healthcare Information Technology Use” took a novel approach to examine HIT usage and user satisfaction in healthcare organizations. The authors looked at the effect of professional certifications such as that of the Joint Commission of Accreditation of Healthcare Organizations (JCAHO) in motivating HIT use among healthcare administrators. Their survey-based approach concluded that these types of certifications indeed enhance HIT usage and user satisfaction. Their study raised potentially interesting questions regarding the effect of external entities and factors on HIT use. It examined only two of several organizational factors, namely professional certifications and facility type (size). Since HIT usage tends to be positively correlated with user satisfaction, this divergence of effects is theoretically perplexing.
To conclude this volume, Tan and Eisler in “Translating Theory into Healthcare Technology Management (HCTM) Practice within a Canadian Teaching Hospital Setting” showed how the different aspects of HISI research, practice and knowledge transfers could be brought together. Their work illustrates how theory-based research drives the development of a sound measuring instrument, which can in turn be applied to improve HCTM practice in the field; in essence, the entire process of HISI knowledge transfers from the laboratory to the field. Using a triangulation method, combining expert panel review, survey and cross-validation of their research findings with study results from an independent external source, they showed that the development of a valid HCTM instrument could be used to guide policy formulation, dictate future HISI research and practice, and provide a model for structuring HISI education and knowledge diffusion. In this context, the instrument they developed to measure HCTM practice entails key performance indicators that differentiated among high and low performing health organizations.
Owing to the rapid diffusion of digital library capabilities and the proliferation of Internet-based data exchanges in an age of increased globalization, we are quickly witnessing an explosion of knowledge in HISI. The HISI advanced series represent the cross-pollination of ideas from trained experts across a myriad of disciplines. This volume, in and of itself, is the beginning of an accumulation of contributions that is germinating an emergent body of HISI knowledge to inform future HISI research and practice. Contributors are primarily researchers and practitioners from many walks of life with expertise in the area of healthcare computing, industrial processes and biomedical engineering, nursing informatics, health information sciences, management information systems, operations research, applied systems sciences, digital networks, web standards and services, mobile, wireless and sensor networks, e-medicine and e-home healthcare delivery systems. A key purpose of the HISI advanced series is therefore to cross-fertilize HISI conceptual, methodological, evaluative, and practical breakthroughs and to organize and connect some of the more current HISI-focused empirical research and practitioner-based studies with the goal of improving future HISI implementation, evaluation and best practices. As the Editor-in-Chief of this volume, I sincerely hope that the compiled chapters of this first volume provide a testament to fulfilling this aim.