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Results for “Decision Support Systems, Management”

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At least 19 recordsLinked to original sources
PubMed1995

Business modeling tools for managing decision support systems.

We sought to investigate the role of business modeling tools in facilitating the implementation of interactive decision support systems (DSS) for laboratory test ordering. We have recently shown that Ripple Down Rules (RDR), a new strategy for building expert systems (ES), enables institutions to build DSS maintained by local experts without knowledge engineering support. While DSS hold great promise for improving health outcomes by providing doctors with timely access to care guidelines, implementation will require careful planning and change management. The Situated, Strategic, and AI-Enhanced method was used to define and assess a DSS-driven process change. DECmodelTM, a graphical business modeling application, was used to build representations of current (paper-based) and future (ES-supported) test ordering environments. Models described both the interactions between new processes and the impact on healthcare quality of ES-supported DSS. Models were able to represent the key processes involved in test ordering and the impact of DSS. Users could interact with the model to examine the time and cost implications of the transition from current to future states. The level of detail in which processes could be examined was readily controlled by the user. We have shown that graphical business modeling applications are useful tools for enabling institutions to view the impact of DSS. Accuracy of models should be maximized in workshops involving key management personnel. Whilst the impact of modeling on the cost and quality of DSS implementation remains to be established, we believe these tools are valuable for institutions planning to align DSS technologies to their service needs.

Australia↗
PubMedDate not supplied

INFORM: development of information management and decision support systems for High Dependency Environments.

The long-term aim in the INFORM Project is to develop, evaluate and implement a new generation of Information Systems for hospital High Dependency Environments (HDE-Intensive Care Units, Neonatal Units, Burns Units. Operating and Recovery Rooms, and other specialised areas). The distinguishing feature of the HDE is the very large amount of data that is collected through monitors and paper records about the state of critically ill patients; this has made the role of the staff a technical one in addition to a caring one. The INFORM System will integrate Decision Support with on-line, off-line and observed patient data and, in addition, will incorporate and integrate unit management features. In the Exploratory Phase of the Project, functional requirements have been set out. These are based on four components: conceptual model of the HDE; evaluation of existing HDE Information Systems; development of a novel software architecture using a Knowledge-Based Systems (KBS) methodology, and based on a critical review of KBS applied to the HDE: monitoring of appropriate leading-edge technological developments. The conceptual model has two components: a patient-related information model, and a department-related cost model. The patient-related model is identifying key and difficult areas of decision making. A key aspect of INFORM is integration of clinical Decision Support for these areas into the Information System through a layered software architecture. The lower layers are concerned with monitoring and alarming and the higher levels with patient assessment and therapy planning. The functionality and interconnection of these layers are being determined.

Decision Support Systems, Management↗
PubMed1997

Information management & decision support systems in the intensive care unit.

Management of the vast array and sheer volume of clinical data generated during the care of the critically ill patient is a formidable task. Not only must the data be acquired accurately, but it must be retrievable readily and in an interpretable form. Such characteristics are essential features, but the mere uncritical compilation of biomedical data barely takes advantage of the enormous computing power that is available at the bedside. A complete medical informatics package can assist the caregiver with clinical decision making, suggest or even carry out changes in management, and provide continuous, always vigilant surveillance for both dangerous alterations in physiology or ill-advised orders. For optimal implementation, knowledge of the origin, synthesis, and utility of clinical information is essential. A successful implementation using the skills of physicians, nurses, biomedical engineers, computer scientists, and hospital administrators must be collaborative. The characteristics of a clinical information system were defined recently by the Institute of Medicine of the National Academy of Sciences (Table 1). However, even the ideal system, implemented and administered by a harmonious and committed team, will not ensure clinical acceptance. The issue of accessibility can become paramount. If the system is not responsive to the needs of the occasional user, a great deal of expensive hardware will not be used to its full capability.

Journal Article↗
PubMed1993

Informed clinical management: decision support systems and physicians.

In addition to providing value today, information systems should pave the way for greater systems' value tomorrow. An organization that begins now to transform its administrative decision support system into a clinically oriented decision support system has taken one significant step in the direction of the future.

Clinical Medicine↗
PubMed1994

Integration of data driven decision support into the HELIOS environment.

The development of large-scale, clinically accepted decision support systems (DSS) calls for powerful and commonly available methods and tools for knowledge acquisition, system realisation, and knowledge base maintenance. The paper addresses problems associated with the integration of knowledge-based systems within the clinical setting with special reference to (i) data driven decision support, (ii) the Arden Syntax as a knowledge representation format and, (iii) the HELIOS software engineering environment. Architecture of a DSS based on Arden Syntax and its integration in the HELIOS environment are presented. Realisation of the DSS is discussed in relation to client-server architecture and object-oriented databases, which are essential concepts of the HELIOS environment. Sharability and reusability of the knowledge, together with commonality of used software tools are also discussed.

Database Management Systems↗
PubMed2005

Medical guidelines presentation and comparing with Electronic Health Record.

Electronic Health Record (EHR) systems are now being developed in many places. More advanced systems provide also reminder facilities, usually based on if-then rules. In this paper we propose a method how to build the reminder facility directly upon the guideline interchange format (GLIF) model of medical guidelines. The method compares data items on the input of EHR system with medical guidelines GLIF model and is able to reveal if the input data item, that represents patient diagnosis or proposed patient treatment, contradicts with medical guidelines or not. The reminder facility can be part of EHR system itself or it can be realized by a stand-alone reminder system (SRS). The possible architecture of stand-alone reminder system is described in this paper and the advantages of stand-alone solution are discussed. The part of the EHR system could be also a browser that would present graphical GLIF model in easy to understand manner on the user screen. This browser can be data driven and focus attention of user to the relevant part of medical guidelines GLIF model.

Czech Republic↗
PubMed1997

Managing the gap: balancing advances in technology with advances in management practice.

Expenditure on information systems is widely anticipated to lead to improved management of health care resources. Despite large investments in hardware and software, these expectations are difficult to realise. Part of the difficulty lies in the manner in which information systems are applied to, rather than integrated within, organisations. This paper considers some of the the personal and organisational issues that need to be addressed to 'manage the gap' in balancing advances in information technology with advances in management practice. The issues identified are consistent with the concept of a learning organisation dealing with environmental change.

Case Management↗
PubMedDate not supplied

Risk management. Betting on good information.

Eating potato salad left out in the sun, flying a kite in a rainstorm, sticking a fork in a light socket--we don't need an actuarial table to know that these and many other actions are downright life-threatening. Most of us grow up learning these basic lessons and think of them as common sense, not as a measure of risk. But what about having a heart attack? Contracting a fatal disease? Experiencing unusual side effects from medicine? These risks aren't so obvious. But in each situation, information is available that can identify the chances of any one of us experiencing a stroke or food poisoning. In fact, conclusions about the incidence and potential health risks are key in the daily business decisions of healthcare. Just knowing the gambles, however, is not what risk in healthcare is all about. Instead, it is about the potential costs of those risks to health planners footing the bill.

Catalogs, Commercial as Topic↗
PubMed1995

The Philippine management information system for public health programs, vital statistics, mortality and notifiable diseases.

Strengthening the information support for decision making has been identified as an important first step toward improving the efficiency, effectiveness, and equitability of the health care system in the Philippines. A Philippine-German Cooperation is in partnership toward developing a need-responsive and cost-effective Health and Management Information System (HAMIS). Four information baskets are being strengthened specifically to address these needs in a cost-effective way: public health information systems, hospital information systems, information systems on economics and financing, information systems on good health care management. BLACKBOX is the management information system for public health programs, vital statistics, mortality and notifiable diseases of the Philippines. It handles and retrieves all data that is being collected by public health workers routinely all over the Philippines. The eventual aim of BLACKBOX is to encourage the development of an information culture in which health managers actively utilise information for rational planning and decision making for a knowledge based health care delivery.

Cost-Benefit Analysis↗
PubMed2000

Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care.

This paper describes the ATHENA Decision Support System (DSS), which operationalizes guidelines for hypertension using the EON architecture. ATHENA DSS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to comorbid diseases. ATHENA DSS has an easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant comorbid diseases, guideline-recommended drug classes for patients with comorbid disease, preferred drugs within each drug class, and clinical messages. Because evidence for best management of hypertension evolves continually, ATHENA DSS is designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities. Together with its database mediator Athenaeum, ATHENA DSS has physical and logical data independence from the legacy Computerized Patient Record System (CPRS) supplying the patient data, so it can be integrated into a variety of electronic medical record systems.

Artificial Intelligence↗
PubMed1991

INFORM: integrated support for decisions and activities in intensive care.

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.

Artificial Intelligence↗
PubMed1989

Advanced Multi-Attribute Scoring Technique (AMAST): a model scoring methodology for the request for proposal (RFP).

The continued demand for more efficient and functionally rich automated systems will force health care facilities into a changing and complex marketplace. As the Request for Proposal (RFP) will be the key document in the systems acquisition process, it is imperative that a systematic process must be established to evaluate the vast array of data collected. It is suggested in this paper that the effective application of the Advanced Multi-Attribute Scoring Technique (AMAST) will assist in this process, enhance the decision making process and reduce the risk associated with systems development and procurement.

Competitive Bidding↗
PubMed2005

Development of SOVAT: a numerical-spatial decision support system for community health assessment research.

INTRODUCTION: The development of numerical-spatial routines is frequently required to solve complex community health problems. Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting the development of numerical-spatial routines. BACKGROUND: Currently, there is no decision support system (DSS) that is effectively able to accomplish this task as the majority of public health geospatial information systems (GIS) are based on traditional (relational) database architecture. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. OLAP alone is not sufficient for solving numerical-spatial problems that frequently occur in CHA research. Coupling it with GIS technology offers the potential for a very powerful and useful system. METHODOLOGY: A community health OLAP cube was created by integrating health and population data from various sources. OLAP and GIS technologies were then combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT). RESULTS: The synergy of numerical and spatial environments within SOVAT is shown through an elaborate and easy-to-use drag and drop and direct manipulation graphical user interface (GUI). Community health problem-solving examples (routines) using SOVAT are shown through a series of screen shots. DISCUSSION: The impact of the difference between SOVAT and existing GIS public health applications can be seen by considering the numerical-spatial problem-solving examples. These examples are facilitated using OLAP-GIS functions. These functions can be mimicked in existing GIS public applications, but their performance and system response would be significantly worse since GIS is based on traditional (relational) backend. CONCLUSION: OLAP-GIS system offer great potential for powerful numerical-spatial decision support in community health analysis. The functionality of an OLAP-GIS system has been shown through a series of example community health numerical-spatial problems. Efforts are now focused on determining its usability during human-computer interaction (HCI). Later work will focus on performing summative evaluations comparing SOVAT to existing decision support tools used during community health assessment research.

Decision Support Systems, Management↗
PubMedDate not supplied

Developing decision support systems: a change in emphasis.

Against a backdrop of many demonstrably proficient expert systems that are not routinely used, and a user community that is skeptical of the benefits of using such technology in healthcare, the AIM funded Dilemma project is attempting to introduce decision support technology into shared care environments within scenarios from the specialties of oncology and cardiology. This paper outlines the experiences of one work-package of the Dilemma project which is concerned with the development of applications with a decision support component for use in shared care of coronary artery disease patients. We suggest reasons why expert systems have failed to gain acceptance in the past, and conclude that a shift in emphasis from building expert systems to building clinically useful applications that have an expert system component may improve the chances of acceptance of this technology in the future.

Coronary Disease↗
PubMed2000

Using data warehousing and OLAP in public health care.

The paper describes the possibilities of using data warehousing and OLAP technologies in public health care in general and then our own experience with these technologies gained during the implementation of a data warehouse of outpatient data at the national level. Such a data warehouse serves as a basis for advanced decision support systems based on statistical, OLAP or data mining methods. We used OLAP to enable interactive exploration and analysis of the data. We found out that data warehousing and OLAP are suitable for the domain of public health and that they enable new analytical possibilities in addition to the traditional statistical approaches.

Ambulatory Care↗