ライフサイエンスコーパス: electronic health record

1 er tool into clinician workflows through the electronic health record.

2 hea were defined based on lab results in the electronic health record.

3 ns; and incorporation of the Bundle into the Electronic Health Record.

4 Data were abstracted from the electronic health record.

5 and team level were difficult when using the electronic health record.

6 a, and outcomes data were collected from the electronic health record.

7 vailable in, and easily abstracted from, the electronic health record.

8 es were abstracted from the VA comprehensive electronic health record.

9 All clinical and laboratory variables in the electronic health record.

10 istics, and outcomes were retrieved from the electronic health record.

11 patients meeting Sepsis-3 criteria from the electronic health record.

12 ting and consulting providers share a common electronic health record.

13 hs based on measured weights recorded in the electronic health record.

14 g data, and mortality was extracted from our electronic health record.

15 a primary prevention cohort derived from the electronic health record.

16 lood culture order using routine data in the electronic health record.

17 rbidity Index scores were extracted from the electronic health record.

18 the Penn Medicine Biobank with accompanying electronic health records.

19 ata centre of a major vendor of primary care electronic health records.

20 uding bisphosphonate use, were obtained from electronic health records.

21 d outcomes were captured from the hospitals' electronic health records.

22 ctober 2013 were deterministically linked to electronic health records.

23 to validate our findings using cytometry and electronic health records.

24 ses and medication status based on available electronic health records.

25 20 potentially predictive variables from the electronic health records.

26 dent dementia ascertained through linkage to electronic health records.

27 linked set of digital mammography images and electronic health records.

28 otics in a cohort of young adults drawn from electronic health records.

29 l diagnoses ascertained via billing codes in electronic health records.

30 er-recorded diagnosis of dementia within the electronic health records.

31 s a single-center, retrospective analysis of electronic health records.

32 nal cohort analysis of geocoded longitudinal electronic health records.

33 ions, alarm management, and documentation in electronic health records.

34 ract information from clinical narratives in electronic health records.

35 stoperative PVR using clinical data from the electronic health records.

36 nd in-hospital mortality) were captured from electronic health records.

37 HF seen at Geisinger from 2008 to 2015 using electronic health records.

38 recent work that models adverse events from electronic health records(2-17) and using acute kidney i

39 plete common nursing tasks documented in the electronic health record, 2) nursing-related tasks that

40 patient, 2) cognitive work of navigating the electronic health record, 3) use of cognitive tools, 4)

41 o conduct medical research with deidentified electronic health records (96.8% v 87.7%; P = .01) and l

42 and research from big data sources, such as electronic health records, administrative claims databas

43 We externally validated an electronic health record AF (EHR-AF) score in IBM Explor

44 om the enterprise data warehouse and patient electronic health records after institutional review boa

45 n of National Early Warning Score within the electronic health record and associated best practice al

46 We used electronic health record and billing data for 16 EDs in

47 NS) remains unknown and requires large scale electronic health record and genomic data sets.

48 The phrase "goals of care" is common in the electronic health record and is used to indicate poor pr

49 ily ICU rounds compared with data within the electronic health record and on presenters' paper prerou

50 r imaging, combined with "big data" from the electronic health record and pathology, is likely to bet

51 ied how nurses and other clinicians used the electronic health record and perceived its impact: 1) fo

52 ey instrument integrated into the outpatient electronic health record and then linked to information

53 ary academic medical ICU with an established electronic health record and where physician trainees ar

54 sources (such as dengue and ILI case counts, electronic health records and frequency of multiple inte

55 f claims data relative to clinical data from electronic health records and its impact on outcome comp

56 ith increasing biobanking efforts connecting electronic health records and national registries to ger

57 018 were identified retrospectively from our electronic health records and patient administration sys

58 inal IOP measurements were collected through electronic health records and, in total, 356,987 measure

59 model and an a priori model developed using electronic health records) and a least absolute shrinkag

60 udicated with the use of participant report, electronic health records, and claims data.

61 were ascertained through a manual review of electronic health records, and completion was compared a

62 Google search frequencies, information from electronic health records, and historical flu trends wit

63 with widespread use of digital technologies, electronic health records, and mobile health has created

64 Using large-scale electronic health records, Artzi and colleagues develope

65 urrent best practice advisories built in the electronic health record as a clinical decision support

66 cy, and virtual visits) were identified from electronic health records at Kaiser Permanente Southern

67 cy, and virtual visits) were identified from electronic health records at Kaiser Permanente Southern

68 We implemented an electronic health record-based "best practice advisory"

69 Emerging data suggest that electronic health record-based clinical surveillance, su

70 score predicts incident HF in a real-world, electronic health record-based cohort.

71 ominantly European descent, but with limited electronic health record-based evidence of phenotypic as

72 f this study were to assess the impact of an electronic health record-based prompt on hepatitis C vir

73 lure Assessment) criteria may enhance timely electronic health record-based sepsis identification.

74 Introduction As the electronic health record becomes more sophisticated, com

75 primary care settings (repeated mailing, an electronic health record best practice alert [BPA], and

76 at 5-year intervals and linked these data to electronic health records between baseline (Aug 7, 1991,

77 e learning algorithms applied to data in the electronic health record can learn models that accuratel

78 epts (complaints) captured from unstructured electronic health record clinical notes.

79 on of medication errors, including review of electronic health records, clinical checklists at care t

80 ional rounding script and a well-established electronic health record, clinician laboratory data retr

81 literature search focused on 3 concepts: the electronic health record, cognition, and nursing practic

82 Data from electronic health records could be used to improve risk

83 hs after device implantation or 1 year after electronic health record data availability.

84 CHA(2)DS(2)-VASc score was assessed using electronic health record data before the index date.

85 Retrospective analysis of electronic health record data for 7762 operations from 2

86 The electronic health record data for all study eyes were an

87 e current literature on the secondary use of electronic health record data for clinical research conc

88 We analyzed timestamped electronic health record data from 16,612 encounters ide

89 Medicine Biobank between 2008 and 2017 using electronic health record data from 1996 to 2017.

90 Biobank enrolled between 2007 and 2015 using electronic health record data from 2007 to 2018.

91 Thus far, BP Track has collected electronic health record data from over 826 000 eligible

92 different machine learning techniques to the electronic health record data of 15,930 patients in the

93 ze on the emerging availability of streaming electronic health record data or capture time-sensitive

94 We used electronic health record data to calculate quick Sequent

95 This study uses electronic health record data to describe primary care s

96 ed a time-to-event analysis of retrospective electronic health record data using Cox proportional haz

97 ecting SSI using structured and unstructured electronic health record data was tested to perform semi

98 RFI among pregnant women, administrative and electronic health record data were analyzed from retrosp

99 end-points, e.g. by connecting genomics and electronic health record data within healthcare systems

100 gagement of health systems and collection of electronic health record data, and the Eureka Research P

101 rest classifier, derived and validated using electronic health record data, was deployed both silentl

102 with de-identified administrative claims and electronic health record data, was used to identify enro

103 Using longitudinal electronic health record data, we assessed the relative

104 Using detailed electronic health record data, we examined patterns of t

105 Using electronic health record data, we performed a genome-wid

106 y-derived pragmatic BP control metrics using electronic health record data, with a focus on tracking

107 tive model-using machine learning methods on electronic health record data-to identify which PAD pati

108 remia and fungemia using routinely collected electronic health record data.

109 tural language processing to domains such as electronic health record data.

110 unds presentations when compared with source electronic health record data.

111 th system's patients using readily available electronic health record data.

112 tial Organ Failure Assessment criteria using electronic health record data.

113 gy, radiology, genomics, and the analysis of electronic health record data.

114 ventilation, and death) were collected from electronic health record data.

115 s sparse representation of time series data, electronic health records data (for ILI) and Google Tren

116 In this study, we used large-scale electronic health records data from multiple linked UK d

117 semi-automated algorithm based on structured electronic health records data to reliably identify CIED

118 Electronic health records data were analyzed before and

119 d death using readily available preoperative electronic health records data.

120 with de-identified administrative claims and electronic health records data.

121 ipants from a primary care, population-based electronic health record database (Information System fo

122 ge by using BioVU, Vanderbilt's deidentified electronic health record database.

123 ort of twenty 121 patients in a Chicago-wide electronic health record database.

124 twork of six administrative claims and three electronic health record databases.

125 Data from the Optum electronic health record deidentified database (2007-201

126 Sepsis was defined using electronic health record-derived clinical indicators of

127 emonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses i

128 dings from this review have implications for electronic health record design.

129 mographic, insurance, and clinical data from electronic health records, determined each patient's nei

130 A novel strategy integrating electronic health records, discarded clinical specimens,

131 large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effecti

132 To measure the effect of an electronic health record (EHR) alert on chronic hepatiti

133 The authors developed and tested an electronic health record (EHR) algorithm to identify chi

134 r CVD event prediction by using longitudinal electronic health record (EHR) and genetic data.

135 Large-scale use of electronic health record (EHR) data can help to understa

136 a large-scale, nation-wide registry based on electronic health record (EHR) data from participating u

137 The second leveraged electronic health record (EHR) data from the Vanderbilt

138 d diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging.

139 This study uses electronic health record (EHR) data to evaluate differen

140 an automated prediction algorithm that uses electronic health record (EHR) data to identify individu

141 dvancements in biobanking, computer science, electronic health record (EHR) data, and more affordable

142 eleven clinical features extracted from the Electronic Health Record (EHR) database of 150 patients

143 In this study, we combined electronic health record (EHR) derived phenotypes and ge

144 on of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as

145 We interrogated the electronic health record (EHR) information of >1.3 milli

146 linical outcomes extracted from unstructured electronic health record (EHR) provider notes is integra

147 Electronic health record (EHR) systems can be configured

148 Adoption of Electronic Health Record (EHR) systems has led to collec

149 Electronic health record (EHR) systems have transformed

150 benefits from the significant investment in electronic health record (EHR) systems in the United Sta

151 dinal historical data, commonly available in electronic health record (EHR) systems, can be used to p

152 study analyzed and quantified the sources of electronic health record (EHR) text documentation in oph

153 he number of patient records displayed in an electronic health record (EHR) to 1 at a time, although

154 ter, 2011-2017, comparing data from hospital Electronic Health Record (EHR) to State Uniform Hospital

155 Using 81 electronic health record (EHR) variables, we applied lea

156 l keratitis morphology measurements from the electronic health record (EHR) was 75-96% sensitive and

157 Electronic health record (EHR)-based AF risk prediction

158 ozygosity rate is a significant predictor of electronic health record (EHR)-based estimates of 10-yea

159 Electronic health record (EHR)-based interventions to im

160 The availability of electronic health record (EHR)-based phenotypes allows f

161 ociations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across

162 By contrasting Electronic Health Record (EHR)-derived symptoms of COVID

163 ranscriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderb

164 We abstracted demographic data from the electronic health record (EHR).

165 cale biobanks with extensive phenotypes from electronic health records (EHR) and genotypes across mil

166 Electronic health records (EHR) are rich heterogeneous c

167 examined complete blood count values in the electronic health records (EHR) of 45 068 patients with

168 Electronic health records (EHR) represent a rich resourc

169 deep neural sequence transduction model for electronic health records (EHR), capable of simultaneous

170 of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation f

171 nited States has seen widespread adoption of electronic health records (EHRs) and a transition from m

172 outcomes in stable adult patients by mining electronic health records (EHRs) and linked blood donor

173 Electronic health records (eHRs) are a source of such bi

174 Electronic health records (EHRs) are an increasingly imp

175 Increasingly, electronic health records (EHRs) are being linked to pat

176 Electronic health records (EHRs) are now widely adopted

177 Electronic health records (EHRs) are quickly becoming om

178 Electronic health records (EHRs) are ubiquitous yet stil

179 With the current wide adoption of electronic health records (EHRs) by ophthalmologists, th

180 his is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 uniq

181 Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent infor

182 n models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnos

183 data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcar

184 Wide adoption of electronic health records (EHRs) has raised the expectat

185 er it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy.

186 e integration of genomic and other data with electronic health records (EHRs) in the United States an

187 systems now offer patient portals to access electronic health records (EHRs) in the United States, b

188 plications assessed by double abstraction of electronic health records (EHRs) of 600 incident patient

189 plications assessed by double abstraction of electronic health records (EHRs) of 600 incident patient

190 bserved phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients,

191 Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients

192 The transition from paper to electronic health records (EHRs) over the past decade ha

193 Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity

194 ng, but epidemiological data using patients' electronic health records (EHRs) remain sparse.

195 nt of internal SAF workflows within existing electronic health records (EHRs) should be the standard

196 ch world's ability to use data captured from electronic health records (EHRs) to address pressing med

197 he amount of time that providers spend using electronic health records (EHRs) to support the care del

198 Electronic health records (EHRs) were reviewed for indiv

199 so be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may

200 rbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Exp

201 gram protocol include health questionnaires, electronic health records (EHRs), physical measurements,

202 r patients to be estimated from free text in electronic health records (EHRs).

203 ied organ dysfunction criteria optimized for electronic health records (eSOFA).

204 iatric Sepsis Outcomes Sepsis was defined by electronic health record evidence of suspected infection

205 hysician care coordinators, decision support electronic health records facilitating physician treatme

206 By combining healthcare claims, electronic health records, familial whole-exome sequence

207 Clinician decision-making discourse in the electronic health record followed a single, consistent p

208 Conclusions Using the electronic health record for cost accounting in nursing

209 The authors used electronic health records for 106,160 patients from four

210 ls and Methods For this retrospective study, electronic health records for 829 asymptomatic patients

211 Pushed by the growing availability of Electronic Health Records for data mining, the identific

212 he data consist of approximately one million electronic health records for dogs and cats, collected f

213 man Services data to analyze surveillance of electronic health records for patient safety issues to i

214 Nationwide Children's Hospital electronic health records from 1/1/2008 to 6/30/2018 wer

215 We used linked electronic health records from 1997 to 2016 to recreate

216 We searched electronic health records from 4 September 2010 through

217 y, we used linked primary and secondary care electronic health records from England (Health Data Rese

218 Dementia diagnoses obtained from electronic health records from January 1, 1996, to Octob

219 o share genetic data and the availability of electronic health records from large cohorts for researc

220 ospective cohort study using UK primary care electronic health records from the Clinical Practice Res

221 METHODS AND We used linked electronic health records from the United Kingdom Clinic

222 Electronic health records from the Veterans Health Admin

223 Analysis of longitudinal electronic health records from USRetina.

224 analysis computer algorithms, as well as the electronic health record, genomics, and other disparate

225 mbined with information technologies such as electronic health records, has the potential to cause a

226 Electronic health records have become instrumental in ef

227 Electronic health records have potential to become tools

228 Using longitudinal IOP measurements from electronic health records improves our power to identify

229 arch in Sight) Registry database, drawn from electronic health records in ophthalmology practices acr

230 ta sources towards this objective, including electronic health records, intraoperative physiological

231 Natural language processing of electronic health records is increasingly used to study

232 e and facility ZIP codes from the anonymised electronic health records, linking patient-level residen

233 Passive data gathering via electronic health record macros resulted in extremely co

234 as well as the availability of extensive VA electronic health records, make it a promising resource

235 Integration of HF risk into the electronic health record may allow for risk-based discus

236 ion of ICU Liberation Bundle elements in the Electronic Health Record may help facilitate team commun

237 sing (HVBP) Program method computed with the electronic health record measure.

238 p convened in Washington, DC, to examine how electronic health record, mobile, and wearable technolog

239 Nevertheless, the growing use of electronic health records, mobile applications, and wear

240 ta) from 2005 to 2015 were obtained from the electronic health records (N = 465).

241 one had an existing diagnosis of ARVC in the electronic health record, nor significant differences in

242 Co-morbidity was evaluated using electronic health records obtained from the PennOmics da

243 onducted qualitative content analysis of the electronic health record of 52 adult patients, admitted

244 ing by evaluating clinician discourse in the electronic health record of critically ill adults who de

245 Text mining the electronic health records of 14,017 patients, we matched

246 Using the electronic health records of 733,804 UK adults, we emula

247 Data were collected from electronic health records of IRIS(R) Registry participat

248 We extracted data from the electronic health records of more than 600,000 participa

249 ational study involved a review of data from electronic health records of patients aged >=18 years wi

250 Duke Glaucoma Registry, a large database of electronic health records of patients from the Duke Eye

251 We analyzed data from electronic health records of patients tested for HCV ant

252 Longitudinal electronic health records on 99,785 Genetic Epidemiology

253 t of a best practice alert (BPA) through the electronic health records on the rates of electrophysiol

254 d and unstructured data abstraction from the electronic health record or wearable monitoring technolo

255 p of clinical characteristics derivable from electronic health records or administrative claims recor

256 pitals met data quality criteria across four electronic health record platforms.

257 interventions, but the wealth of data in the electronic health record poses unique modeling challenge

258 A new study of deep learning based on electronic health records promises to forecast acute kid

259 Summary reports and handoff tools in the electronic health record proved insufficient as stand-al

260 Detailed ventilator records in the electronic health record provide a unique window for eva

261 Tumor registry and other electronic health records provided information on sociod

262 udy is ascertainment of dementia status from electronic health records rather than in-person assessme

263 Conclusions Delivering a BPA through the electronic health record recommending to providers consi

264 Electronic health record redesign and new documentation

265 Clinician discourse in the electronic health record reveals that patient physiology

266 Electronic health record review identified IR procedures

267 view was to synthesize the literature on the electronic health record's impact on nurses' cognitive w

268 ut few studies have sought to understand the electronic health record's impact on these dimensions of

269 ulated from data derived from an algorithmic electronic health record search.

270 matory response syndrome resulted in earlier electronic health record sepsis identification in greate

271 This electronic health records study assessed the association

272 Complementary analysis using electronic health records suggested that thiamine labora

273 In addition to the electronic health record, supplementary sources of data

274 accurately identify patients with PAD in an electronic health record system compared with a structur

275 researchers, clinicians, informaticians and electronic health record systems around the world.

276 The wide adoption of electronic health record systems in health care generate

277 are not routinely recorded in all hospitals' electronic health record systems, limiting its utility f

278 able triggers are difficult to abstract from electronic health record systems.

279 n that can be extracted from most hospitals' electronic health record systems.

280 eveloped on a large, longitudinal dataset of electronic health records that cover diverse clinical en

281 Using the electronic health record, the authors identified patient

282 With more widespread use of electronic health records, there is an enormous unmet op

283 anizations in the United States have adopted electronic health records, they may be ill prepared to a

284 ssover trial using software tools within the electronic health record to compare saline to balanced c

285 en up to age 7 years from Geisinger Clinic's electronic health record to conduct a sex- and age-match

286 ibitors, and leveraging real-world data from electronic health records to begin to understand the cli

287 eryRisk) that uses existing clinical data in electronic health records to forecast patient-level prob

288 We queried electronic health records to identify patients meeting p

289 events, highlighting the great potential of electronic health records to provide automated risk stra

290 al trainee oversight and education, improved electronic health record tools, and novel academic round

291 d data abstractor; 2) education campaign; 3) electronic health record tools; and 4) a Modified Early

292 elp inform targeted strategies for improving electronic health record use in ophthalmology.

293 ed prediction model of ten readily available electronic health record variables to accurately predict

294 n digital health technologies, including the electronic health record, virtual visits, mobile health,

295 The electronic health record was perceived by nurses as an i

296 sing national all-payer claims and data from electronic health records, we conducted a cross-sectiona

297 Using linked electronic health records, we evaluated associations of

298 In this study of UK population-based electronic health records, we found no association betwe

299 In addition, electronic health records were used to assess the incide

300 this study came from Clalit Health Services' electronic health records, which are integrated in a cen