Clinical Decision Support Systems for the Practice of Evidence-based Medicine

Sim, Ida; Gorman, Paul N.; Greenes, Robert A.; Haynes, R. Brian; Kaplan, Bonnie; Lehmann, Harold P.; Tang, Paul C.

doi:10.1136/jamia.2001.0080527

Cited by 547 publications

(388 citation statements)

References 36 publications

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“…The explosive growth of data requires them to learn and remember many details so they can prescribe the right medication, in the right amount, for the right patient. The possible approaches to dealing with this problem are by means of electronic medical records (EMR) [1][2][3][4][5] and clinical decision support systems (CDSS) [6][7][8][9][10]. Through knowledge discovery from these disciplines of medical informatics, the medical prescription process can be facilitated and hence the quality of prescription decisions can be improved [46].…”

Section: Introductionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

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ObjectiveWith the increased complexity and uncertainty in drug information, issuing medical prescriptions has become a vexing issue. As many as 240,000 medicines are available on the market, so this paper proposes a novel approach to the issuing of medical prescriptions. The proposed process will provide general practitioners (GPs) with medication advice and suggest a range of medicines for specific medical conditions by taking into consideration the collective pattern as well as the individual preferences of physicians' prescription decisions. Methods and MaterialA hybrid approach is described that uses a combination of case-based reasoning (CBR) and Bayesian reasoning. In the CBR process, all the previous knowledge retrieved via similarity measures is made available for the reference of physicians as to what medicines have been prescribed (to a particular patient) in the past. After obtaining the results from CBR, Bayesian reasoning is then applied to model the prescription experience of all physicians within the organization. By comparing the two sets of results, more refined recommendations on a range of medicines are suggested along with the ranking for each recommendation. ResultsTo validate the proposed approach, a Hong Kong medical center was selected as a testing site. Through application of the hybrid approach in the medical center for a period of one month, the results demonstrated that the approach produced satisfactory performance in terms of user satisfaction, ease of use, flexibility and effectiveness. In addition, the proposed approach yields better results and a faster learning rate than This is the Pre-Published Version. 2 when either CBR or Bayesian reasoning are applied alone. ConclusionEven with the help of a decision support system, the current approach to anticipating what drugs are to be prescribed is not flexible enough to cater for individual preferences of GPs, and provides little support for managing complex and dynamic changes in drug information. Therefore, with the increase in the amount of information about drugs, it is extremely difficult for physicians to write a good prescription. By integrating CBR and Bayesian reasoning, the general practitioners' prescription practices can be retrieved and compared with the collective prescription experience as modeled by probabilistic reasoning. As a result, physicians can select the drugs which are supported by informed evidential decisions. That is, they can take into consideration the pattern of decisions made by other physicians in similar cases.

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Section: Introductionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

View full text Add to dashboard Cite

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“…The final SQIs are presented in Table 2. [38][39][40][41][42][43][44][45][46][47][48] It maps the indicator, the SQI-related evidence, SQI numerator and denominator, and trigger values for each indicator.…”

Section: Resultsmentioning

confidence: 99%

“…The set includes five specific and defined SQIs related to decision support (e.g., the availability of policies, procedures, or protocols outlining care management of older people with cognitive impairment in ED) for ED staff. These SQIs are based on the evidence that organizational policies or procedures support the staff in clinical decision-making for the practice of evidencebased care, 38,39 and expert opinion, including the view of a patient representative who is often overlooked in quality initiative development groups, 49 gives these SQIs content and face validity. These SQIs are the result of a structured approach combining available scientific evidence with expert opinion, which has been described as an appropriate methodology for developing optimal quality indicators for care evaluation.…”

Section: Discussionmentioning

confidence: 99%

Structural Quality Indicators to Support Quality of Care for Older People With Cognitive Impairment in Emergency Departments

Schnitker

Martin‐Khan

Burkett

et al. 2015

Academic Emergency Medicine

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Objectives: The purpose of this study was to identify the structural quality of care domains and to establish a set of structural quality indicators (SQIs) for the assessment of care of older people with cognitive impairment in emergency departments (EDs).Methods: A structured approach to SQI development was undertaken including: 1) a comprehensive search of peer-reviewed and gray literature focusing on identification of evidence-based interventions targeting structure of care of older patients with cognitive impairment and existing SQIs; 2) a consultative process engaging experts in the care of older people and epidemiologic methods (i.e., advisory panel) leading to development of a draft set of SQIs; 3) field testing of drafted SQIs in eight EDs, leading to refinement of the SQI set; and 4) an independent voting process among the panelists for SQI inclusion in a final set, using preestablished inclusion and exclusion criteria.Results: At the conclusion of the process, five SQIs targeting the management of older ED patients with cognitive impairment were developed: 1) the ED has a policy outlining the management of older people with cognitive impairment during the ED episode of care; 2) the ED has a policy outlining issues relevant to carers of older people with cognitive impairment, encompassing the need to include the (family) carer in the ED episode of care; 3) the ED has a policy outlining the assessment and management of behavioral symptoms, with specific reference to older people with cognitive impairment; 4) the ED has a policy outlining delirium prevention strategies, including the assessment of patients' delirium risk factors; and 5) the ED has a policy outlining pain assessment and management for older people with cognitive impairment.Conclusions: This article presents a set of SQIs for the evaluation of performance in caring for older people with cognitive impairment in EDs.ACADEMIC EMERGENCY MEDICINE 2015;22:273-284

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“…Computerized Clinical Decision Support Systems (CDSS) are information systems designed to improve clinical decision making [3]. More specifically, CDSSs are "software that is designed to be a direct aid to clinical decision-making in which the characteristics of an individual patient are matched to a computerized clinical knowledge base, and patientspecific assessments or recommendations are then presented to the clinician and/or the patient for a decision" [6]. The work of many authors supports the evidence that decision support systems can be extremely effective in improving the quality and safety of healthcare (e.g.…”

Section: Introductionmentioning

confidence: 99%

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

et al. 2011

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Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

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Clinical Decision Support Systems for the Practice of Evidence-based Medicine

Abstract: Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.

Cited by 547 publications

References 36 publications

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Structural Quality Indicators to Support Quality of Care for Older People With Cognitive Impairment in Emergency Departments

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

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