Health Information Management: Integrating Information Technology ...

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DESIGNING INTERACTIONS 99 the way the software is interpreted by users, and that interpretation is dependent upon the user’s specific context, culture, knowledge and resources. In this chapter, the communication space will be introduced as a major component of the overall interaction space for humans working in complex organizations. The gap between the current focus on interactions outside of the communication space and the overall interaction needs of humans will also be highlighted. A framework will be developed next, based upon the notion of bounded mutual knowledge, or common ground, that is shared between communicating agents. This will help us decide whether an interaction was best served by communication or information technology. Finally we will turn to the task of designing systems to support interactions, whether in the communication space or not and specifically look at how, through a formal theory of interaction design, we can design and implement technological systems to support individual interactions by modelling the wider interaction space within which individuals operate. THE COMMUNICATION SPACE The current decision-support paradigm in health informatics is a computational one. The computer sits at the centre of information systems that acquire, manipulate, store and present data to clinicians. Computational models of clinical problems allow computers to make inferences and create views on data, or perhaps prompt, critique or actually make clinical decisions. In this computational paradigm, human information processes are shaped into a form dictated by technological structure. Yet as we have seen in the previous chapters, the development of technology is socially shaped. The value of any particular information technology can only be determined with reference to the social context within which it is used, and more precisely, with reference to those who use the technology. For example, in one study the strongest predictor of e-mail adoption in an organization had nothing to do with system design or function, but with whether the e-mail user’s manager also used e-mail (Markus 1994). Further, a highly structured view of human processes sits uneasily with the clinical workplace. It is not just that people have difficulty accepting information technology in a social setting because their interactions are loosely structured. We know that people will treat computers and media as if they were people. Consequently, they superimpose social expectations on technological interactions. So, should we recast the tasks of acquiring and presenting clinical information socially? In the computational paradigm, when faced with a decision problem, clinicians turn to computer-based systems for support. However, if we examine what actually happens clinically, it is clear that people preferentially turn to each other for information and decision support. It is through the multitude of conversations that pepper the clinical day that clinicians examine, present and interpret clinical data, and ultimately decide upon clinical actions. In
100 STARTING POINTS contrast to the computational view of decision support, this conversational view emphasizes social interaction within health care, and sees the sharing and interpretation of information as an interactive process that emerges out of communication. Rather than ‘acquiring’ and ‘presenting’ data in some mechanistic way, conversations are better characterized with the fluid and interactive notions of asking and telling, inquiring and explaining. While there are few studies that have attempted to directly quantify the size of the communication space which contains the direct interactions between clinicians, we do know some fundamental issues. Rather than document sources, about 50 per cent of information requests by clinicians in the clinic is met by colleagues (Covell et al. 1985). Furthermore, about 60 per cent of clinician time is devoted to talk (Tange et al. 1996). Safran et al. reviewed the information transactions in a hospital with a mature computer-based record system, and still found about 50 per cent of information transactions occurred face-to-face between colleagues, with e-mail and voice-mail accounting for about another quarter of the total. Only about 10 per cent of the information transactions occurred through the EMR (1998). Not only is the communication space huge in terms of the total information transactions and clinician time, it is also a source of significant morbidity and mortality. Communication failures are a large contributor to adverse clinical events and outcomes. In a retrospective review of 14,000 in-hospital deaths, communication errors were found to be the lead cause, twice as frequent as errors due to inadequate clinical skill (Wilson et al. 1995). Further, about 50 per cent of all adverse events detected in a study of primary care physicians were associated with communication difficulties (Bhasale et al. 1998). If we look beyond the raw numbers, the clinical communication space is interruption-driven, has poor communication systems and poor practices. So, in summary, the communication space is apparently the largest part of the health system’s information space. It contains a substantial proportion of the health system information ‘pathology’, but it is usually ignored in our informatics thinking. Yet it seems to be where most of the information in the clinical workplace is acquired and presented. The biggest information repository in health care lies within the people working in it and the biggest information system is the web of conversations that link the actions of these individuals. POSSIBLE RESPONSES TO THE COMMUNICATION PARADIGM How do we respond to the idea that information exchanges within the social communication space are primary, and that therefore this is where substantial informatics efforts need to be focused? There seem to be four plausible responses, depending upon how one views communication tasks and what technical interventions are considered to support those tasks:
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Health Information Management In al
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Health Information Management Integ
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Contents List of illustrations vii
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Illustrations FIGURES 3.1 Medicatio
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Contributors Marc Berg is a physici
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Series preface In almost all Wester
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Acknowledgements I would like to th
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Chapter 1 Introduction In all Weste
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INTRODUCTION 3 touch upon the deplo
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INTRODUCTION 5 can draw upon to mai
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INTRODUCTION 7 Physician Order Entr
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Part I Starting points Understandin
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WAITING FOR GODOT 11 KEY TERMS ■
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WAITING FOR GODOT 13 Netherlands an
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WAITING FOR GODOT 15 facts to show
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WAITING FOR GODOT 17 (Lewinski-Corw
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WAITING FOR GODOT 19 In most cases,
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WAITING FOR GODOT 21 (Anonymous 191
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WAITING FOR GODOT 23 Because of the
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WAITING FOR GODOT 25 they simply sa
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WAITING FOR GODOT 27 expected. Stil
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WAITING FOR GODOT 29 organize their
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WAITING FOR GODOT 31 performance in
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WAITING FOR GODOT 33 different HIS
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WAITING FOR GODOT 35 CONCLUSION: TH
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WAITING FOR GODOT 37 assumption tha
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WAITING FOR GODOT 39 Anonymous (191
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WAITING FOR GODOT 41 Berg, M. (1997
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Chapter 3 Health care work and pati
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HEALTH CARE WORK AND INFORMATION SY
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HEALTH CARE WORK AND INFORMATION SY
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Page 96 and 97: STARTING POINTS 81 ‘gender, race,
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Page 102 and 103: STARTING POINTS 87 Figure 5.2 Incre
Page 104 and 105: STARTING POINTS 89 already overflow
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Page 108 and 109: STARTING POINTS 93 answer the quest
Page 110 and 111: STARTING POINTS 95 Kohn, L.T., J.M.
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Page 116 and 117: DESIGNING INTERACTIONS 101 1 Identi
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STRATEGY, IMPLEMENTATION AND EVALUA
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Guidelines for information system d
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Chapter 9 Implementing information
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IMPLEMENTING INFORMATION SYSTEMS 16
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Chapter 10 Project management of in
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Index accumulation 53-4, 61, 73-4,
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INDEX 223 contingent and emergent c
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INDEX 225 satisfaction 176 patient
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INDEX 227 support 164 technology de
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