The measurement of process and the role of information technology

1993 ◽  
Vol 101 ◽  
pp. 31-53
Author(s):  
M. P. Ryan ◽  
W. Dodd
Keyword(s):  
Information Technology ◽  
Quality Assurance ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Sets ◽  
Process Of Care ◽  
Operational Systems ◽  
History Of ◽  
Computer Based ◽  
English Speaking

SynopsisProcess of care is the most immediate, relevant and susceptible to improvement of Donabedian's three elements of quality assurance. To place the study of process in context, the history of quality assurance in English-speaking countries is reviewed, with particular emphasis on the U.S.A. A range of methods are examined in detail and examples are provided to illustrate their strengths and weaknesses. Particular attention is paid to the use of explicit and implicit criteria. The importance of information technology in monitoring the process of care cannot be over-stated. Limited audit is possible with manual records but all substantial projects require computer support. The value of capturing data from operational systems rather than from dedicated projects is emphasised. Attention is drawn to the key importance of structured records and minimum data sets; these allow information to be pooled and process studies to be generalised. Examples are given of quality assurance projects which have used information technology. Finally potential future developments are reviewed with particular reference to clinical guidelines and computer-based clinical decision support systems.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

Interactive, Flexible, and Adaptable Decision Support Systems

2009 ◽  
pp. 557-562
Author(s):  
John Wang ◽  
James Yao ◽  
Jeffrey Hsu
Keyword(s):  
Information Technology ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Massachusetts Institute Of Technology ◽  
Organizational Decision Making ◽  
History Of ◽  
Computer Based ◽  
Institute Of Technology ◽  
The Many

Over the four decades of its history, decision support systems (DSSs) have moved from a radical movement that changed the way information systems were perceived in business, to a mainstream commercial information technology movement that all organizations engage. This interactive, flexible, and adaptable computer-based information system derives from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute in the 1950s and early 1960s as well as the technical work on interactive computer systems which was mainly performed by the Massachusetts Institute of Technology (Keen & Morton, 1978). DSSs began due to the importance of formalizing a record of ideas, people, systems, and technologies implicated in this sector of applied information technology. But the history of this system is not precise due to the many individuals involved in different stages of DSSs and various industries while claiming to be pioneers of the system (Arnott & Pervan, 2005; Power, 2003). DSSs have become very sophisticated and stylish since these pioneers began their research. Many new systems have expanded the frontiers established by these pioneers yet the core and basis of the system remains the same. Today, DSSs are used in the finance, accounting, marketing, medical, as well as many other fields.

scholarly journals Accounting And Economic Implications Of Healthcare Information Technology

2011 ◽  
Vol 6 (7) ◽  
Author(s):  
Paul J. Carruth ◽  
Ann K. Carruth
Keyword(s):  
Information Technology ◽  
Healthcare Delivery ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Population Level ◽  
Clinical Decision ◽  
Healthcare Information Technology ◽  
Healthcare Information ◽  
Economic Implications

There is a growing consensus that interoperability of health care technology is greatly needed so that data can be effectively and accurately exchanged across provider groups and organizations to provide quality healthcare while reducing the cost of healthcare delivery. Benefits are expected for physician groups, organizations, and patients.  Perhaps most exciting is the opportunity for integrated patient and population level data leading to clinical decision support systems which assist with the management of patients by integrating medical knowledge with patient characteristics and generating computer based reminders, alerts, and guidelines.  While research supports a multitude of benefits, challenges exist that require long-term commitment before the reality of an integrated healthcare information technology will exist. This article explores the benefits, the barriers, and the long term impact of Healthcare Information Technology on future healthcare delivery systems.

Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

2015 ◽  
Vol 4 (2) ◽  
pp. 1-11 ◽  
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos F Baeta ◽  
Carlos Solano-Rodriguez
Keyword(s):  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical History ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory ◽  
Single Text ◽  
Discourse Representation Structure ◽  
History Of ◽  
Representation Structure

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT (Discourse Representation Theory) structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox).

Guideline Representation Ontologies for Evidence-Based Medicine Practice

2010 ◽  
pp. 234-254 ◽  
Author(s):  
Kai Zheng ◽  
Rema Padman ◽  
Michael P. Johnson ◽  
Sharique Hasan
Keyword(s):  
Clinical Practice ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Decision Criterion ◽  
Evidence Based ◽  
Medical Decisions ◽  
Computer Based ◽  
Interchange Format ◽  
Key Steps ◽  
Based Medicine

An ontology in the context of guideline representation is a specification of conceptualizations that constitutes evidence-based clinical practice guidelines. It represents the elements of a guideline by specifying its attributes and defining the relationships that hold among them. For example, a guideline representation ontology would define a set of medical decisions and actions (concepts), as well as a set of rules (relationships) that relate the evaluation of a decision criterion to further reasoning steps or to its associated actions. A rigorously defined computational ontology provides considerable promise of producing computable representations that can be visualized, edited, executed, and shared using computer-based systems. A widely acknowledged ontology, or standard representation schema, is the key to facilitating the dissemination of guidelines across computer systems and healthcare institutions. The first part of this chapter presents the evolution of ontology research in guideline representation. Several representative ontologies are reviewed and discussed, with in-depth analyses of two popular models: GLIF (Guideline Interchange Format) and PROforma. The second part of the chapter analyzes seven key elements constituting a guideline representation. It also discusses the criteria for evaluating competing ontologies and some known limitations in the existing models. At the end of this chapter, four key steps are outlined that converts a guideline into computerized representation, which can be then used in Clinical Decision Support Systems (CDSSs).

Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering

2018 ◽  
pp. 51-77
Author(s):  
Omer Deperlioglu
Keyword(s):  
Medical Information ◽  
Intelligent System ◽  
Medical Science ◽  
Healthcare Providers ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Patient Specific ◽  
Real Problem ◽  
History Of ◽  
The Right

Managing medical information and knowledge is becoming an increasing problem for healthcare professionals. Medical science that contains ever-increasing amounts of knowledge, such as the medical history of a patient, medical data about diseases, diagnosis and treatment methods, should be necessarily a science of information. The real problem faced by patients and healthcare providers is finding and using relevant knowledge at the right time. In this context, in the middle of 1950s, intelligent computer systems, called clinical decision support systems (CDSS), were introduced as a new concept. CDSS is defined as an active intelligent system that can help medical experts to make decisions by taking specific recommendations. Also, it provides decisions based on resolving patient-specific information and related medical truths. The objective of this chapter is to focus on these systems and explain relations with the field of artificial intelligence methods, approaches, or techniques in this manner.

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