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Decision Systems Group, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115; greenes{at}harvard.edu
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Abstract |
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KEY WORDS: Clinical information systems • decision support • quality • error reduction • knowledge representation • clinical practice guidelines
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Introduction |
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A Major Requirement: Separation of Knowledge from the System |
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In this paper, we will discuss some of the kinds of successes in the use of knowledge for decision support (the first goal) and the approaches that have been pursued to manage the knowledge (the second goal). As the reader will note, there has been much more success with the former than with the latter. Reasons are mainly that, to date, the burden has been largely on demonstrating that the use of knowledge for decision support was feasible and cost-effective, with less concern for long-term management. Yet as the applications have been shown to be beneficial, developers have gone on to yet other applications rather than re-engineering already successfully demonstrated ones for ease of management. The only way to achieve both goals is to use consistent standards-based methods for representing the knowledge, developing authoring tools that manage the knowledge in this form, and also developing methods for integrating the knowledge in this form into different host platforms.
For knowledge to be effectively integrated into clinical information systems, it needs to have certain qualities that enable it to be evaluated in and applied to clinical settings.
How do we get there? The processes of acquisition, documentation, and updating of knowledge are difficult, even when dealing with non-executable content, in terms of the tasks of evaluating the evidence, synthesizing it to develop the knowledge, organizing and indexing it, retrieving it when needed, and updating it on a timely basis. This problem becomes substantially more difficult if we further require that the knowledge be executable. It is even more problematic to adapt knowledge to particular settings, in terms of modifying its representation for the application contexts in which it will be used, platforms in which it will be executed, and local constraints or requirements that may alter its interpretation.
Unfortunately, because the traditional focus of clinical information systems was not on the problems of knowledge management and decision support, and because a plethora of systems has evolved with differing platforms and limited embrace of standards, there is no strong base on which to build a knowledge management framework.
Nonetheless, several achievements to which we can point are beginning to show the promise of this capability. I will illustrate the issues with the process we have pursued in developing a sharable representation for clinical guidelines, and its status and challenges. I will close with a model and a potential agenda for action.
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Knowledge Management Initiatives |
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Addressing Health Care Safety and Quality Concerns |
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How to Respond |
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In our own health care system, Partners HealthCare is the integrated delivery network that is the parent of Massachusetts General Hospital and Brigham and Women’s Hospital (BWH), as well as several community hospitals and practices in Eastern Massachusetts. The Partners clinical information systems have received considerable attention. The Brigham Integrated Computing System (BICS), for example, is a very comprehensive, feature-laden system (Teich et al., 1999). BICS has an elaborate computerized physician order entry (CPOE) system, which provides timely warnings, advice and information about conflicts and drug interactions, and recommended changes for elderly patients or those with renal insufficiency. Alerts and reminders about abnormal results or other significant clinical events are provided (Kuperman et al., 1997). Studies of the efficacy of error-checking, alerts, and reminders in the CPOE system, by Bates and colleagues (1998), have demonstrated a considerable decrease in medication errors, a decrease in redundant labs, and more appropriate care of renal patients, with associated decreases in costs.
Making progress in the adaptation of clinical guidelines for clinical practice has been more difficult. Guidelines have been around, of course, for decades. Recent efforts have focused on computer-based implementation of guidelines so that they can be interpreted and delivered at the point of care, with the ultimate goal of integrating them into applications that deliver patient-specific recommendations (Shiffman et al., 1999).
An executable guideline representation can serve as a core technology for a variety of applications—to drive decision support, consultation, alerts and reminders, clinical protocols, utilization review rule triggering, and a range of workflow-oriented applications. This versatility is because the guidelines describe the decision logic and the process flow that must occur for integration into any application to be effective. Although investigators have been working steadily in this area and are seeing some improvements, this is still at a very early stage.
As noted earlier, a formidable impediment to integrating knowledge into clinical information systems is the abundance of hard-coding or ad hoc representation of the knowledge, which is not easy to maintain or adapt. The knowledge is embedded into many systems rather than being separate so as to facilitate authoring, editing, and updating. These issues have been true at Partners’ systems as well as most other hospital-based and commercial information systems. The successes, failures, and other practical experiences of systems like that at Partners have thus been difficult to extract, generalize, and replicate where positive. Commercialization attempts have been difficult, and there is very little sharing and re-use of successfully engineered knowledge bases.
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The Solution Requires a Major National/International Effort |
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GuideLine Interchange Format (GLIF): An Example of the Challenges |
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My group at Harvard worked with colleagues at Stanford and Columbia in a project funded by the NLM, the US Army, and AHRQ, known as the InterMed Collaboratory to develop the GuideLine Interchange Format (GLIF). GLIF was intended as a format for the sharing and dissemination of executable guidelines. Our experience in this process illustrates some of the challenges that must be faced in creating an NHII. With GLIF, guidelines are developed in sharable form, and then imported and executed within proprietary environments by adhering to standards. Consider a simple guideline for flu vaccination, as shown in Fig. 1
. The decision logic in the middle box can be represented in GLIF in a formal manner (Fig. 2). The author never sees that, since he or she uses a graphically oriented tool to create the flowchart with pop-up windows to specify details of the flowchart elements. GLIF uses RDF (Resource Description Format, from the W3 Consortium, and based on Extensible Markup Language [XML]) for internal representation and exchange.
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Several different approaches exist for authoring of computer-interpretable guidelines, ranging from mark-up of narrative guidelines and extraction and encoding of steps to using templates or graphic authoring tools to encode guidelines and decision logic directly. As part of the authoring phase, note that a testing and validation process must be included to ensure that the logic is correct, that the data references are valid, etc.
Testing and validation are followed by a dissemination phase where the generic guideline representation is exported—for instance, in XML or another agreed-upon format. As noted earlier, GLIF uses RDF, since it provides ontology support not available in XML itself.
With respect to implementation, guidelines must address a variety of platform-specific features and constraints. Not only are there differences in vendor systems and in target applications, but even within similar applications in which guidelines are to be embedded, vendors have "architected" them differently. In addition, the target sites themselves have different resources and settings, as a result of which the guidelines need to be adapted to those realities and to preferences.
A limiting factor in the implementation of guidelines is the paucity of experience demonstrating their effective integration in clinical practice. If there is a best practice that is represented by a guideline, somehow we ought to be able to build systems that implement or foster that behavior. But whether we deliver this as a recommendation to a doctor or a demand, or if the guideline influences what elements pop up on-screen first, there are many possible ways that may be effective and others that are ineffective, irritating, or otherwise counterproductive. That is why we need an ongoing process of experimentation and feedback. We need to build guidelines into applications to see what works, and then determine from that experience how the model should be refined, whether new features are required, and how the authoring tools should be modified to make that kind of application easier to support.
Two kinds of changes may be required as a result of the evaluation: (1) Depending on feedback and experience, the process for modeling and authoring of guidelines may need to be changed to facilitate incorporation of positive features and elimination or improvement of negative ones; and (2) medical knowledge changes, as a result of which the guidelines themselves must be updated.
Considering these tasks together, we view GLIF as facilitating a life-cycle process (Greenes et al., 2001) for guideline modeling, authoring, dissemination, implementation, and experience with use (Fig. 3).
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Focusing on Components |
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The purpose of the above tasks is to break down a larger problem, on which agreement is more difficult, into smaller components that could become industry building blocks which could be used by all parties in building their models and implementing their systems.
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Generalizing to the Knowledge Management Challenge |
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A huge investment is required to generate high-quality evidence-based knowledge in a form that is ready to be integrated into applications. There are many settings and applications in which knowledge could be potentially useful, yet the efficacy of most is unproven. Thus, we need a life-cycle process similar to that described above for sharable guideline representation, to facilitate experimental activity aimed at implementation of applications using knowledge and their evaluation. Further, to achieve a common framework for this process will require, as it has with the guideline effort, a focus on component elements.
To move this process forward requires, in my opinion, a broad consensus and funding supporting it. Once the priorities have been identified, we can focus on building the tools and knowledge resources that are needed. With a joint effort, a knowledge inventory would be the basis for a comparison, to establish a consensus on a set of priorities that all or most of our systems need, and we could then focus on the knowledge components for the next generation of clinical information systems.
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Conclusion |
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Acknowledgments |
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Footnotes |
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References |
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Teich JM, Glaser JP, Beckley RF, Aranow M, Bates DW, Kuperman GW, et al. (1999). The Brigham integrated computing system (BICS): advanced clnical systems in an academic hospital environment. Int J Med Inf 54:197–208.[Medline]
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Abstract
Introduction
