ライフサイエンスコーパス: decision support

1 sources for diagnosis and might benefit from decision support.

2 w promise as important elements of physician decision support.

3 tware prototype that can facilitate clinical decision support.

4 ance improvement, resource use, and clinical decision support.

5 could be more relevant to individual patient decision support.

6 udies and database development, and clinical decision support.

7 tion from thousands of patients for accurate decision support.

8 te views; 112 users (38%) completed clinical decision support.

9 rch and practice for knowledge discovery and decision support.

10 al data analysis, knowledge engineering, and decision support.

11 r understanding of these beliefs may improve decision support.

12 ent may enable more customized and effective decision support.

13 el fMRI and ERP results during memory-guided decisions support a key role of the PFC in top-down bias

14 as been implemented to allow straightforward decision support across a variety of decision-making con

15 We evaluated the effect of a computer-based decision support algorithm in delivering patient-specifi

16 owing volume and complexity of imaging data, decision-support algorithms will be required to help phy

17 in interoperability, process automation, and decision support all promise to alter both the structure

18 can serve as a flexible framework for future decision support and can be a step toward more sustainab

19 ercent of the 236 studies evaluated clinical decision support and computerized provider order entry,

20 Strong evidence supports the use of clinical decision support and computerized provider order entry.

21 ssimilate the tools of ASPs into EMRs, using decision support and feedback.

22 serve as a resource to evaluate new clinical decision support and monitoring algorithms.

23 ting clinics with the same EHR software with decision support and patient registry functionalities an

24 d findings could be used to improve clinical decision support and personalize trajectories, thereby d

25 indicators/measures; (5) developing clinical decision support and quality improvement tools; and (6)

26 ut rtPA in hyperacute stroke were relational decision support and situationally-sensitive knowledge t

27 Policy decisions, support and interventions will need to be tar

28 critical illness, enable real-time clinical decision support, and improve both clinical outcomes and

29 ovel data presentation formats, computerized decision support, and smart alarms are used to enhance e

30 (3) computerized guideline application with decision support (APP).

31 en the basis for the development of clinical decision support applications that promise to integrate

32 Ns and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Su

33 It is an individual-level decision support approach which results in the emergence

34 Cognitive aids and other methods of decision support are receiving increased interest by the

35 s-which are the basis of day-to-day clinical decision support-are often used to guide the clinical ma

36 n requesting and conducting imaging studies; decision support at point of care; education of referrin

37 ovement intervention comprising computerized decision support, audit/feedback tools, and staff traini

38 By advocating rational decisions, supported by accurate information, scientists

39 Computerized protocols used to deliver decision support can be configured to contain much more

40 In future, software for decision support can be developed based on the DW and it

41 Universal decision support can better standardize a decisional app

42 cess in evolution to integrate ASP tools and decision support capacities directly into Epic's EMR.

43 rovider overrides of evidence-based clinical decision support (CDS) for ordering computed tomographic

44 Self-triage using web-based decision support could be a useful way to encourage appr

45 Decision support during medication administration provid

46 ve, 4-center, randomized controlled trial of decision support effectiveness.

47 omprising nonphysician care coordinators and decision-support electronic health records.

48 increase, it will become critical to provide decision support for genomic medicine.

49 The results can be used as decision support for local farmers and authorities.

50 aking surrounding DT LVAD should incorporate decision support for patients and caregivers.

51 ent provided by trained nurses, and clinical decision support for PCPs by consulting physicians.

52 nge on environmental services and to provide decision support for policy.

53 f an institutional protocol and computerized decision support for red cell transfusion in the critica

54 The further development of clinical decision support for renal anemia requires the character

55 he results were bridged to humans to provide decision support for setting breakpoints for voriconazol

56 ps understand SOC dynamics but also provides decision support for sustainable biofuel development.

57 iscovery has great potential application for decision-support for basic research and clinical problem

58 A decision support framework for the management of lagoon

59 ibes the development and test of a potential decision support framework that combines elements from s

60 tion with existing systems and sophisticated decision support functionality are important decisive fa

61 A computerized, interactive decision-support guide and access to prenatal testing wi

62 guidelines using a computerized, interactive decision-support guide in the absence of financial barri

63 ance improvement, resource use, and clinical decision support has been less well studied.

64 Historically, probabilistic models for decision support have focused on discrimination, e.g., m

65 formation systems, delivery system redesign, decision support, health care organization, and communit

66 Decision support improved processes of care but not depr

67 h may be used to tailor therapies or provide decision support in a clinical setting where the choice

68 Probability-based decision support in LCA is a way to help stakeholders in

69 n of functional genomics experiments and for decision support in precision medicine.

70 The possible role of decision support in preventing the emergence of resistan

71 ding an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

72 Computerized clinical decision support integrated with the electronic health r

73 When used as decision support, interactive use of CAD for malignant m

74 1.65; 95% CI, 1.26 to 2.16), educational or decision support intervention (provided v not, OR = 0.21

75 n was implemented in a randomized trial of a decision support intervention to manage depression among

76 were randomized to the computerized clinical decision support intervention, aimed at physicians, and

77 will be used in further research to develop decision support interventions to assist with the assess

78 The early work in this field of decision support is encouraging but there are many quest

79 spect is especially important when automated decision support is used for patient care over substanti

80 re crafted in part using the Guidelines Into Decision Support methodology.

81 his paper, we develop a regional water reuse decision-support model (RWRM) using the general algebrai

82 LED and ORBIT scores and should be useful as decision support on anticoagulation treatment in patient

83 omized to CDS, pediatric clinicians received decision support on obesity management, and patients and

84 ion and were randomly assigned to depression decision support or usual care.

85 formation systems, delivery system redesign, decision support, organizational support, and community

86 ology, patient-centered and -driven clinical decision support, patient engagement, culture change, cl

87 We have developed a computerized decision-support program linked to computer-based patien

88 Conclusions: Computer-assisted decision support programs that use local clinician-deriv

89 sus guidelines embedded in computer-assisted decision support programs.

90 ics, aided by electrocardiograph (ECG)-based decision support, randomized 911 (871 enrolled) patients

91 The fraction of low-yield (decision support score, 1-3) examinations requested thro

92 implemented: Examinations that had low-yield decision support scores could not be scheduled when the

93 equests for examinations with higher initial decision support scores that were not affected by the po

94 Results were correlated with user status and decision support scores.

95 ented with the Veterans Heath Administration Decision Support Services hemoglobin A1c (HbA(c)) and se

96 d differential diagnosis, patient and family decision support, shared decision making and triage, tre

97 knowledge base, and development of clinical decision support software are needed in addition to auto

98 ss the impact of smart pumps with integrated decision support software on the incidence and nature of

99 telephone and was supported by computerized decision support software.

100 ive effect of computer-generated prompts and decision support software.

101 s reproducible sampling methods and examines decision support strategies that lead to quality decisio

102 ure was higher with CDSS alerts than with no decision support system (adjusted odds ratio 3.18, 95% C

103 ted the effectiveness of a computer clinical decision support system (CDSS) for reducing the risk of

104 A clinical decision support system (CDSS) is a computer program tha

105 A clinical decision support system (CDSS) is a computer program, wh

106 Use of a clinical decision support system (CDSS) might improve outcomes.

107 Could a clinical decision support system (CDSS), interposed at the time o

108 ducted to determine the effect of a tailored decision support system (DSS) that provides individualiz

109 The Veterans Health Administration Decision Support System and VA Surgical Quality Improvem

110 loped an open-source, generalizable clinical decision support system called Electronic Support for Pu

111 rce, electronic health record-based clinical decision support system can increase AE detection and re

112 ith the use of a reliable outcome management/decision support system designed for acute psychiatric s

113 A decision support system evaluated the effects of various

114 This research proposes an intelligent decision support system for acute lymphoblastic leukaemi

115 protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSS

116 ture CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop

117 yze the efficacy of a computerized open-loop decision support system for burn resuscitation compared

118 Implementation of a computer decision support system for burn resuscitation in the in

119 he Veterans Affairs Medical SAS Datasets and Decision Support System for entire cohort and separately

120 Real-time computerized decision support system for prescribing drugs in patient

121 his platform demonstrate EMMA's promise as a decision support system for therapeutic management of mu

122 sequent patients with severe burns (computer decision support system group) and compared with the Mod

123 nary output goals was higher in the computer decision support system group.

124 rface area) were also lower for the computer decision support system group.

125 e unit volume were all lower in the computer decision support system group.

126 Quality Improvement Program database and the Decision Support System pharmacy database were linked to

127 mapping, Geographic Information System, and Decision Support System technologies, and progress in sp

128 The addition of computer decision support system technology improved patient care

129 Use of a computerized clinical decision support system to automate the identification a

130 Using a computerized clinical decision support system to automate the screening of chi

131 e potential role for a computerized clinical decision support system to enable non-specialists rapidl

132 pic medication guidelines and a computerized decision support system to facilitate shared decision ma

133 and effectiveness of a computerized clinical decision support system to identify pediatric patients a

134 igned to be integrated within a computerized decision support system to improve patient flow manageme

135 The computer decision support system was used to resuscitate 32 subse

136 levels were regulated by a computer-assisted decision support system.

137 omputerized open-loop algorithm and computer decision support system.

138 adding a T2D module to an existing computer decision support system.

139 involving a PSP and its associated web-based Decision Support System.

140 adding a DSS module to an existing computer decision support system.

141 ted in guideline adherence by a computerized decision-support system.

142 Computerized clinical decision support systems (CCDSSs) have been implemented

143 Clinical decision support systems (CDSS) can scan the patient's e

144 ed provider order entry (CPOE) with clinical decision support systems (CDSS) in transfusion medicine

145 Antibiotic computerised decision support systems (CDSSs) are shown to improve an

146 lectronic health records (EHRs) and clinical decision support systems (CDSSs) have the potential to e

147 Clinical decision support systems (CDSSs) might be beneficial for

148 the Chernobyl Nuclear Power Plant accident, decision support systems (DSS) for supporting response o

149 Computer-based diagnostic decision support systems (DSSs) were developed to improv

150 n demonstrates a way in which computer-based decision support systems can improve care.

151 scalable data-driven algorithmic management decision support systems for crowdsourcing.

152 Demand for clinical decision support systems in medicine and self-diagnostic

153 plication of Convex-NMF in computer assisted decision support systems is expected to facilitate furth

154 Patients' educational needs and decision support systems need a lot more research.

155 Most studies addressed decision support systems or electronic health records.

156 Meaningful decision support systems rely on accurate data analysis

157 ts of interventions--Therapy, Harm, Clinical Decision Support Systems, and Summarizing the Evidence g

158 uce or prevent alert fatigue within clinical decision support systems.

159 uce or prevent alert fatigue within clinical decision support systems.

160 nance play in modern medical information and decision support systems; it also discusses the implicat

161 port improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care

162 increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and

163 Decision-support systems (DSSs) are interactive computer

164 The depression decision support team, which consisted of a psychiatrist

165 ata must be interpreted by multidisciplinary decision-support teams to determine mutation actionabili

166 soning is the basis of an explicit method of decision support that allows the rigorous evaluation of

167 we propose a model of "universal" electronic decision support that can be easily adapted by clinician

168 ce, precisely when they felt they had made a decision, supports the idea that conscious awareness occ

169 nd the subsequent analysis of these data for decision support; this practice is termed radiomics.

170 lementation of an institutional protocol and decision support through a computerized provider order e

171 f LDKT and may benefit from patient-centered decision support to facilitate a risk-benefit assessment

172 This pilot study suggests that web-based decision support to help parents and adult caregivers se

173 inicians provide effective and compassionate decision support to patients with advanced dementia and

174 decision analysis (MCDA) move beyond ad hoc decision support to quantitatively and holistically rank

175 er of processed food products and to provide decision support to the stakeholders.

176 ler, with the aim of providing environmental decision-support to the retailer and establishing life c

177 oad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of publi

178 of a multimodality-appropriate use criteria decision support tool (AUC-DST) on rates of appropriate

179 emented and studied the impact of a clinical decision support tool (CDST) to decrease the number of u

180 This study examined the impact of a video decision support tool and patient checklist on advance c

181 and evaluation in the context of a clinical decision support tool are warranted.

182 CONCLUSIONS/SIGNIFICANCE: The diagnostic decision support tool covered the majority of diagnoses

183 , difficulties, and gaps in genomic clinical decision support tool development.

184 tment recommended by the Atrial Fibrillation Decision Support Tool in 931 patients.

185 e ABC score should be considered an improved decision support tool in the care of patients with atria

186 This study examined the effect of a video decision support tool on CPR preferences among patients

187 Use of a decision support tool that integrates patient-specific s

188 We then created a decision support tool that recommends performing no bloo

189 tation and could be a useful educational and decision support tool to assist physicians in the diagno

190 protocol was designed using the EVA tool, a decision support tool to help in the development of an e

191 evaluated the predictive validity of a brief decision support tool to screen veterans for problems wi

192 : We evaluated whether a low-cost diagnostic decision support tool would lead to changes in clinical

193 Use of the decision support tool would result in 38% fewer blood cu

194 atment recommended by an Atrial Fibrillation Decision Support Tool.

195 onjugate pneumococcal vaccine as an accurate decision support tool.

196 tential transplant recipient with a valuable decision support tool.

197 s adjusted every 3 months by using an online decision support tool.

198 that recommended by the Atrial Fibrillation Decision Support Tool.

199 sibly providing a laboratory-based objective decision-support tool for determination of an APE diagno

200 elp in the evaluation of each case we used a decision-support tool that was previously developed for

201 gical risk calculator has been proposed as a decision-support tool to predict complication risk after

202 r-writing applications, and a computer-based decision-support tool, were available both in the ICU an

203 orting clinician order entry and of clinical decision support tools (CDSTs) has provided expanded opp

204 In addition, a new generation of decision support tools are needed to further the practic

205 Decision support tools are required to assess the relati

206 serve as the engine for a new generation of decision support tools for individuals with diabetes.

207 ldren with BMI >/= 85th percentile, clinical decision support tools for pediatric weight management,

208 ting practices of BCID results with clinical decision support tools providing interpretation guidance

209 e quantitative output of these algorithms as decision support tools to aid with image interpretation.

210 the strengthening of primary care practice, decision support tools to avoid inappropriate services,

211 Use of clinical decision support tools to enable substitution of the mos

212 es for tPA administration, provided clinical decision support tools, facilitated hospital participati

213 Interventions included clinical decision support tools, structured improvement strategie

214 For computerized methods to be useful as decision support tools, they need to be resilient to dat

215 certainty and without the help of structured decision support tools.

216 ons supports use of cognitive aids and other decision support tools.

217 The use of decision-support tools is a response to both the informa

218 The assessment of land use impacts in decision-support tools such as life cycle assessment (LC

219 Better data, decision-support tools, and incentives will be needed to

220 To provide decision support toward a green process design, a resear

221 In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family

222 consecutive orders for examinations in which decision support was provided that were placed from Apri

223 d hence to inform development of appropriate decision support we interviewed patients, their family a

224 ocumentation tools, order sets, and clinical decision support were designed to improve efficiency, op

225 ordering prophylaxis vs routine care without decision support were included.

226 ystems (eg, increase patient activation with decision support) whereas other levers must be tailored

227 der different treatments enables therapeutic decision support, which is available in exploratory form

228 Decision support will guide adherence to achievement and

229 clinical information systems, and integrated decision support, with accompanying changes in delivery