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

1 fect) and empirical data (i.e., derived from Electronic Health Records).

2 rd the use of existing resources such as the electronic health record.

3 racterization of clinical phenotypes from an electronic health record.

4 cal, pharmacy, and surgical records from the electronic health record.

5 ceived clinical relevance and entered in the electronic health record.

6 betes patient panels were extracted from the electronic health record.

7 chemic stroke that are integrated within the electronic health record.

8 ost SR adherence programs integrated with an electronic health record.

9 d setting characteristics extracted from the electronic health record.

10 ted from a remote office, facilitated by the electronic health record.

11 t the patient that might not be coded in the electronic health record.

12 Data were extracted from the electronic health record.

13 porary threats to documentation posed by the electronic health record.

14 re performed using software tools within the electronic health record.

15 academic medical ICU with a well-established electronic health record.

16 atients, and 70.9% had HCV documented in the electronic health record.

17 ician care coordinators and decision-support electronic health records.

18 tments, and clinical outcomes using national electronic health records.

19 ication, use, and CVD risk was captured from electronic health records.

20 nal cohort analysis of geocoded longitudinal electronic health records.

21 treatment data were obtained from patients' electronic health records.

22 sease, was identified using validated linked electronic health records.

23 Data were collected from electronic health records.

24 ms for diabetes case identification by using electronic health records.

25 f clinical diabetes, which we extracted from electronic health records.

26 cted a cohort study spanning 2001-2013 using electronic health records.

27 hysician "group intelligence" that exists in electronic health records.

28 and patient care environments, including all electronic health records.

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

30 ral capabilities as performance feedback and electronic health records.

31 of the United States were more likely to use electronic health records.

32 st effect on decisions about the adoption of electronic health records.

33 p efforts, and feasibility in hospitals with electronic health records.

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

35 Baseline covariates were collected from electronic health records.

36 dings, health outcomes, and integration with electronic health records.

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

38 We used linked electronic health records (1995 to 2015) in The Health I

39 1654 [47.7%], P < .001), and at least basic electronic health records (80 [6.5%] vs 445 [13.9%], P <

40 Electronic Health Records aggregated in Clinical Data Wa

41 diction treatment with health care using the electronic health record and a patient activation approa

42 older at diagnosis with data recorded in the electronic health record and follow-up after diagnosis.

43 medical and demographic information from the electronic health record and from parent answers to ques

44 oaches have been increasingly applied to the electronic health record and have led to the discovery o

45 and laboratory data were extracted from the electronic health record and investigated as potential p

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

47 ened 660 CIEDI cases from 2005 to 2015 using electronic health records and a CIEDI institutional data

48 Electronic health records and care management systems ca

49 vailability of detailed phenotypic data from electronic health records and epidemiological studies, t

50 Inclusion of PROs in electronic health records and hospital patient portals,

51 We used electronic health records and natural language processin

52 atient information gathered from high-volume electronic health records and participatory surveillance

53 Electronic health records and pharmacy dispensing data w

54 of "high-throughput clinical phenotyping" of electronic health records and speculates on the impact s

55 The faster adoption of electronic health records and their use in England to as

56 demographic information were abstracted from electronic health records and Wisconsin Division of Heal

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

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

59 engagement in health care (by interview and electronic health record), and alcohol, drug, and depres

60 aking process, (2) integrating PROs into the electronic health record, and (3) measuring quality in s

61 equencing and the advent of medical imaging, electronic health records, and "omics" technologies have

62 ompt but careful transition to measures from electronic health records, and allocation of sufficient

63 mentia diagnosis by general practitioners in electronic health records, and needs to be taken into ac

64 Electronic health records are being increasingly used by

65 ature of genomic information, which existing electronic health records are ill equipped to manage.

66 our novel conceptual framework that uses the electronic health record as a platform on which external

67 alth information technology resources in the electronic health record, as well as facilitated communi

68 teristics (within 2 years of diagnosis) from electronic health records, as well as information about

69 Using a definition of electronic health records based on expert consensus, we

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

71 face prompting was superior to an unprompted electronic health record-based checklist at reducing emp

72 rompting mechanism with a more generalizable electronic health record-based checklist.

73 An open-source, electronic health record-based clinical decision support

74 ctronic Cardiac Arrest Risk Triage score, an electronic health record-based early warning score.

75 The electronic health record-based methodology demonstrated

76 ss potential sample selection bias in future electronic health record-based periodontitis research wa

77 Implementation of an electronic health record-based prompt increased HCV scre

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

79 Electronic health record-based retrospective cohort stud

80 An electronic health record-based retrospective cohort stud

81 We tested whether prospective use of electronic health record-based trigger algorithms to ide

82 equires electronic reporting of quality from electronic health records, beginning in 2014.

83 Physicians who use electronic health records believe such systems improve t

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

85 eir incident prescriptions from primary care electronic health records between 2006 and 2009 linked t

86 dapting to new technologies and implementing electronic health records, but the efforts need to be al

87 recorded in the immunization registry via an electronic health record by March 31, 2011.

88 The electronic health record can be used to feed decisions a

89 t characteristics derived from review of the electronic health record can be used to refine risk pred

90 linical decision support integrated with the electronic health record can improve appropriate use of

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

92 mpting for evidence-based practices using an electronic health record could impact ICU care delivery

93 e emergence of population-based resources in electronic health records, coupled with the rapid expans

94 ed CVD at baseline (51% women), using linked electronic health records covering primary care, hospita

95 health records (n=12), and secondary use of electronic health record data (n=19).

96 Three other models, 1 developed in electronic health record data and 2 developed in adminis

97 challenges in and the potential for merging electronic health record data and genomics for cardiovas

98 Electronic health record data can identify patients with

99 Cross-sectional analysis of electronic health record data collected between January

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

101 The electronic health record data for each of the 3 groups w

102 We used electronic health record data from 162 patients enrolled

103 This study used longitudinal electronic health record data from 2005 through 2010 for

104 Alternatively, the growing availability of electronic health record data has facilitated the possib

105 care patients in the Geisinger Clinic using electronic health record data.

106 ating ICU transfer as a competing risk using electronic health record data.

107 lergy and other allergies in childhood using electronic health record data.

108 47690 patients with HS were identified using electronic health record data.

109 ithms for heart failure identification using electronic health record data: (1) heart failure on prob

110 a nationally representative UK sample using electronic health records data collected between January

111 Electronic health records data from 446,480 Geisinger Cl

112 Using electronic health records data to predict events and ons

113 Electronic health records data were then linked with New

114 e onset predictive models using longitudinal electronic health records data.

115 in primary care patients using longitudinal electronic health records data.

116 Data on these children were obtained from an electronic health record database.

117 etwork database, a population-representative electronic health records database from the United Kingd

118 ough meetings, Delphi processes, analysis of electronic health record databases, and voting, followed

119 that information extracted from cloud-based electronic health records databases, in combination with

120 ity inherent in disease histories of a large electronic health records dataset with over half a milli

121 Using a multihospital, electronic health record-derived data set, we identified

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

123 ove clinical practice, information exchange, electronic health record documentation, harmonization of

124 The widespread adoption of the Electronic Health Record (EHR) by physicians will create

125 A retrospective analysis of electronic health record (EHR) data from 50,515 adult pr

126 Data-driven phenotype analyses on Electronic Health Record (EHR) data have recently drawn

127 Exome sequencing and electronic health record (EHR) data of 50,726 individual

128 try subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093).

129 California (KPNC) (2009-2013; n = 1,847,165) electronic health record (EHR) data sets.

130 Despite interest in using electronic health record (EHR) data to assess quality of

131 of extracting these dimensions from existing electronic health record (EHR) notes.

132 ns to document code status in the outpatient electronic health record (EHR) of patients with advanced

133 An electronic health record (EHR) review of 1,062 unique RA

134 al visits for concussion in the CHOP unified electronic health record (EHR) system (July 1, 2010, to

135 of previous reports examining the impact of electronic health record (EHR) system migration in ophth

136 were collected during routine visits via an Electronic Health Record (EHR) system.

137 Patient portals tied to provider electronic health record (EHR) systems are increasingly

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

139 Although electronic health record (EHR) systems have potential be

140 Electronic health record (EHR) systems have transformed

141 trends using detailed clinical data from the electronic health record (EHR) systems of diverse hospit

142 are systems are increasingly adopting robust electronic health record (EHR) systems that not only can

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

144 ently, new information technologies, such as electronic health record (EHR) systems, have led to furt

145 onal barriers that inhibit their adoption of electronic health record (EHR) systems.

146 approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome.

147 We used an integrated electronic health record (EHR) to identify characteristi

148 icians and patients, and modification of the electronic health record (EHR) to include FBSE as a reco

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

150 referral hepatology clinic and the impact of electronic health record (EHR)-based reminders on adhere

151 Purpose To develop an electronic health record (EHR)-based trigger algorithm t

152 n of common Neandertal variants to over 1000 electronic health record (EHR)-derived phenotypes in ~28

153 Comparisons between data from PROs and Electronic Health Records (EHR) are lacking.

154 land health-care system were identified from electronic health records (EHR), and each diagnostic gro

155 sing a combination of adverse event reports, electronic health records (EHR), and laboratory experime

156 ation is vital for study findings drawn from Electronic Health Records (EHR).

157 on 4 activities (direct clinical face time, electronic health record [EHR] and desk work, administra

158 legislation promoted wide-spread adoption of electronic health records (EHRs) across US hospitals; ho

159 Electronic health records (EHRs) and clinical decision s

160 Electronic health records (EHRs) are an increasingly uti

161 Electronic health records (EHRs) are being increasingly

162 Electronic health records (EHRs) are highlighted as a cr

163 is aiming to achieve nationwide adoption of electronic health records (EHRs) but lacks robust empiri

164 Electronic health records (EHRs) contain information on

165 The increasing availability of electronic health records (EHRs) creates opportunities f

166 The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar

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

168 Electronic health records (EHRs) may be key tools for im

169 e prevalence of drug allergies documented in electronic health records (EHRs) of large patient popula

170 The ability to mine the electronic health records (EHRs) of patients followed as

171 have shown few quality-related advantages of electronic health records (EHRs) over traditional paper

172 Secondary use of electronic health records (EHRs) promises to advance cli

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

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

175 meet meaningful use criteria or their use of electronic health records (EHRs) to manage patient popul

176 Furthermore, whether electronic health records (EHRs) with chronic disease ma

177 The PCMH typically involves electronic health records (EHRs), organizational practic

178 Using Electronic Health Records (EHRs), we identified in 2010

179 grated health care system using longitudinal electronic health records (EHRs).

180 ncentives to encourage the meaningful use of electronic health records (EHRs).

181 nts for meaningful use of complete certified electronic health records (EHRs).

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

183 nguished effects of the PCMH (which involves electronic health records [EHRs] plus organizational cha

184 ter-generated random number sequence, to use electronic health records either alone (control) or with

185 and fellows were trained once to complete an electronic health record-embedded checklist daily for ea

186 An electronic health record-embedded, cluster-randomized, m

187 The electronic health record enables long-term outlooks on h

188 Among 1.3 million electronic health record encounters from January 1, 2010

189 nitial work has confirmed the utility of the electronic health record for understanding mechanisms an

190 n California (KSPC) cancer registry data and electronic health records for 663 AYA patients with eith

191 upport systems (CDSS) can scan the patient's electronic health records for clinical risk factors pred

192 ESP-VAERS monitors patients' electronic health records for new diagnoses, changes in

193 earched and retrieved comprehensive clinical electronic health records for over 200 000 patients from

194 orting the successful integration and use in electronic health records for two standardized nursing t

195 The use of electronic health records for vision and eye health surv

196 We used linked electronic health records from 1997 to 2010 in the CALIB

197 We used linked electronic health records from 1997 to 2010 in the CALIB

198 , with HealthLNK, a 2006 to 2012 database of electronic health records from 6 Chicago health systems.

199 Our sample consisted of electronic health records from 9310 HIV-infected and 510

200 We utilized electronic health records from a large New England healt

201 ing nationally representative United Kingdom electronic health records from January 1, 1995, until De

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

203 s cohort and nested case-control study using electronic health records from January 1, 2004, to Decem

204 Electronic health records from January 2011 to April 201

205 Information in the electronic health records from managed health care organ

206 hat could be reliably detected in anonymised electronic health records from South London and Maudsley

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

208 The two major data sources are electronic health records from traditional health system

209 We used electronic health records, genetic co-heritability analy

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

211 ncer from 1995 to 2007 using UK primary care electronic health records (GPRD).

212 mic data to the information contained in the electronic health record has been demonstrated.

213 Electronic health records have the potential to improve

214 Electronic health records hold great promise for clinica

215 m by applying natural language processing to electronic health records (i2b2 cohort).

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

217 integrated health-care delivery systems with electronic health records in 10 US states.

218 The Health Improvement Network primary care electronic health records in the UK.

219 The very low levels of adoption of electronic health records in U.S. hospitals suggest that

220 e estimates of the prevalence of adoption of electronic health records in U.S. hospitals.

221 The federal Electronic Health Record Incentive Program requires elec

222 Recommendations for incorporating electronic health records into such a system are present

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

224 ns but tools contained only 78% of available electronic health record laboratory data.

225 By linking electronic health records, laboratory and imaging data,

226 Application of electronic health record-leveraged SR interventions may

227 Electronic health records may provide additional informa

228 or older, younger, and all patients using an electronic health record measure of AMI mortality endors

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

230 Applying the previously validated electronic health records model to our study sample yiel

231 n of standardized nursing terminologies into electronic health records (n=12), and secondary use of e

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

233 then conducted a manual chart review in the electronic health record of all patients with a code for

234 d clinical information through review of the electronic health record of each patient.

235 Electronic health records of >1.6 million adult patients

236 We reviewed the electronic health records of 392 patients with noninfect

237 study, we used linked primary and secondary electronic health records of 4 million individuals from

238 used geocoded residential address data from electronic health records of 49,770 children and adolesc

239 We reviewed the electronic health records of 500 patients with scleritis

240 The electronic health records of 500 patients with scleritis

241 data were extracted from administrative and electronic health records of 623,358 patients aged 6-19

242 Other recent developments include access to electronic health records of daytime primary care practi

243 ducted using data obtained from the complete electronic health records of Kaiser Permanente Southern

244 antidiabetic medications were extracted from electronic health records of Kaiser Permanente Southern

245 hallenges by collecting information from the electronic health records of large numbers of patients w

246 hallenges by collecting information from the electronic health records of large numbers of patients w

247 Electronic health records offer the potential to assess

248 Longitudinal electronic health records on 99,785 Genetic Epidemiology

249 The data were obtained from electronic health records, operative reports, discharge

250 Electronic health record OR management system implementa

251 Electronic health record OR management system implementa

252 s for prescribing antibiotics into patients' electronic health records; peer comparison sent emails t

253 Subsequent linkage to electronic health records permitted analysis of major in

254 e financial incentives for meaningful use of electronic health records, physicians and hospitals will

255 ighlight how the expansion of patient-facing electronic health record portals could exacerbate existi

256 ar research using LInked Bespoke studies and Electronic health Records) programme to assemble a cohor

257 ar research using linked bespoke studies and electronic health records) programme to investigate the

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

259 major limitation of our study is the use of electronic health records rather than comprehensive deme

260 Intervention steps included queries of the electronic health record repository for patients with ab

261 A search of electronic health records revealed an odds ratio of 2.4

262 The study also included an electronic health records review approved by the institu

263 We used data from electronic health records routinely entered in the Clini

264 f eligibility for specialist consultation by electronic health record searches for triggers was most

265 Electronic health records should be used to facilitate b

266 l and process measures derived from a common electronic health record system provided real-time feedb

267 ening algorithms that are integrated into an electronic health record system.

268 Second is the emerging use of electronic health record systems and other large clinica

269 lyses of the vast phenotypic repositories in electronic health record systems and population-based bi

270 tes that support of such analytics in future electronic health record systems can improve cohort iden

271 The spread of electronic health record systems has been the basis for

272 Adoption of electronic health record systems has increased the avail

273 s intervention can be easily integrated into electronic health record systems to increase HCV diagnos

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

275 Using a definition for electronic health records that was based on expert conse

276 ion-research and having them use a dedicated Electronic-Health-Record that provides feedback, improve

277 assessed physicians' adoption of outpatient electronic health records, their satisfaction with such

278 arning Score and could be implemented in the electronic health record to alert caregivers with real-t

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

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

281 e system (IHS) that uses case management and electronic health records to determine mortality from CV

282 We used electronic health records to extract prescribing data an

283 ort outpatient quality measures from data in electronic health records to facilitate care improvement

284 bjects were passively followed through their electronic health records to identify HZ incidence.

285 hat applies a set of rules to data stored in electronic health records to offer actionable recommenda

286 lso examined the relationship of adoption of electronic health records to specific hospital character

287 ice physiological data, and information from electronic health records to ultimately provide better c

288 curated medical phenome, often derived from electronic health records, to search for associations be

289 We used anonymized electronic health records totaling >100 million person-y

290 stratification tool using commonly collected electronic health record variables in a large multicente

291 In January 2012, the default in the electronic health record was changed for IM providers fr

292 Using electronic health records, we defined a cohort of 2479 a

293 Last available data in the electronic health record were used to assess end points.

294 ructured data retrospectively extracted from electronic health records were analyzed for 3 months fol

295 Abstracted electronic health records were assembled from inpatients

296 Frequently used multifunctional electronic health records were associated with higher pe

297 ural capabilities of primary care practices, electronic health records were associated with higher pe

298 Baseline data from comprehensive electronic health records were linked with vital status

299 rge quantities of digital content within the electronic health record, which is potentially a valuabl

300 s is an important aspect of text mining from electronic health records, which are increasingly recogn