ライフサイエンスコーパス: EHR

1 EHR data from a large health care database spanning 15 y

2 EHR data were obtained from a health care system of more

3 EHR phenotypes related to type 2 diabetes mellitus, athe

4 EHR systems will increase in value when manufacturers in

5 EHR use has risen to high levels among hospitals in the

6 EHR use was associated with less frequent heparin overdo

7 EHR vs PRO diagnosis of current substance abuse was 13%

8 EHR was ascertained from medical records as >/=1 hospita

9 EHRs provide a powerful resource for high-throughput phe

10 ated hand-drawn sketches, whereas no (0/150) EHR notes contained drawings.

11 with patients on the following 3 activities: EHR use, conversation, and examination.

12 At 6 months after EHR transition, physicians also spent more time compared

13 months) and late (4-12 months) periods after EHR implementation.

14 re risks of GDM in the temporally aggregated EHRs.

15 ularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data

16 Although EHRs may be necessary for an increasingly high-tech, tra

17 SSs on patient outcomes is lacking, although EHRs with integrated CDSSs have demonstrated improvement

18 Ambulatory EHR adoption did not impact total cost (pre- to postimpl

19 ntation and 4,648,572 person-months after an EHR was being used by patients' physicians.

20 then stabilized, suggesting that although an EHR transition is not without consequences, these can be

21 However, it is possible to implement an EHR OR management system without serious negative impact

22 y implemented an EHR, 15% had implemented an EHR for some of their physicians or were in the process

23 emic ophthalmology department implemented an EHR system in 2006.

24 ractices surveyed had already implemented an EHR, 15% had implemented an EHR for some of their physic

25 ctice and economic effect of implementing an EHR into an ophthalmic practice is warranted.

26 applied to a retrospective test cohort in an EHR data warehouse at a large Veterans Affairs facility,

27 med presence-the notion that inclusion in an EHR is not random but rather indicates that the subject

28 sing the basic data input capabilities of an EHR does not translate into the greater opportunity that

29 le overall costs, and 55% would recommend an EHR to a fellow ophthalmologist.

30 We apply these methods to an EHR data set from a university medical center covering t

31 ific critical findings using paper versus an EHR system.

32 Among those with an EHR in their practice, 49% were satisfied or extremely s

33 Physicians with an EHR that met meaningful use criteria were significantly

34 using separate logistic regression analyses (EHR vs PRO).

35 netic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models.

36 Time stamps from the medical record and EHR audit log were analyzed to measure the length of tim

37 time with patients during office visits, and EHR use requires a substantial portion of that time.

38 nal health data using birth certificates and EHRs to determine prenatal medication exposures.

39 Although computers and EHRs can facilitate and even improve clinical documentat

40 Using commercially available EHRs in community practices seems to modestly slow ambul

41 Proportion of physicians who have a basic EHR and meet meaningful use criteria and ease of use of

42 43.5% of physicians reported having a basic EHR, and 9.8% met meaningful use criteria.

43 , we analyzed 4,997,585 person-months before EHR implementation and 4,648,572 person-months after an

44 models showed a positive association between EHR use and billing level and a negative association bet

45 tatistically significant association between EHR use and office visit rates.

46 We examined the association between EHR use and unfavorable clinical events (ED visits and h

47 ing level and a negative association between EHR use per encounter and clinic volume.

48 Documentation speed for both EHR strategies was slower than with paper.

49 MI, outcomes did not significantly differ by EHR status.

50 Usual care, EHR-linked mailings ("automated"), automated plus teleph

51 Compared with usual care, a centralized, EHR-linked, mailed CRC screening program led to twice as

52 Use of a commercially available certified EHR was associated with improved drug treatment intensif

53 Use of a commercially available certified EHR.

54 on, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes.

55 Conclusion EHR-based triggers can be used to identify patients with

56 sis-3) criteria for objective and consistent EHR-based surveillance.

57 re volume, capital and implementation costs, EHR incentive payments received, and coding volumes (inc

58 Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacoge

59 equired more circulating nurses in the early EHR (mean, 1.9 nurses/procedure) and late EHR (mean, 1.5

60 bsolute mean documentation time in the early EHR period (16.7 minutes) vs paper baseline (7.5 minutes

61 e was a worsening in total POTD in the early EHR period (83%) vs paper baseline (41%) (P < .001).

62 Epic EHR software (Epic Systems Corporation) is currently one

63 AF teledermatology workflows within the Epic EHR system.

64 vings per member per month (PMPM), excluding EHR adoption costs.

65 orkflow within the Epic system, the existing EHR system of Parkland Health and Hospital System (Dalla

66 cipients were much more likely to experience EHR (45.8% vs. 28.0%, p = 0.005), regardless of age.

67 ing) and mouse (2.2 +/- 0.7 seconds/finding) EHR compared with the paper strategy (2.0 +/- 0.8 second

68 use of E/M codes increased (14.7%) following EHR implementation (P < .001).

69 erences in revenue or productivity following EHR conversion in this clinical setting.

70 total time required by ophthalmologists for EHR use.

71 gth of time required by ophthalmologists for EHR use.

72 anagement, and demonstrate the potential for EHR systems to advance research in this area.

73 xamine ophthalmologist time requirements for EHR use.

74 general-purpose patient representation from EHR data that facilitates clinical predictive modeling.

75 This study linking diagnoses from EHRs to claims data collected valid information on PAR m

76 Drug allergy data were obtained from EHRs of patients who visited two large tertiary care hos

77 were seen in hospitals implemented with full EHRs; however, in ST-segment-elevation MI, differences i

78 A total of 511 hospitals had EHRs by the end of the study period.

79 In our sample of GWTG-Stroke hospitals, EHRs were not associated with higher-quality care or bet

80 Evidence is limited on how EHR use affects clinical care and outcomes.

81 h-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and

82 treated at hospitals with fully implemented EHR (n=43 527), partially implemented EHR (n=72 029), an

83 -ST-segment-elevation AMI, fully implemented EHR use was associated with lower risk of major bleeding

84 mented EHR (n=43 527), partially implemented EHR (n=72 029), and no EHR (n=9270).

85 treated at hospitals with fully implemented EHRs had fewer heparin overdosing errors (45.7% versus 7

86 In addition, frailty improved EHR risk prediction by improving the area under the rece

87 However, as interoperability pains in EHR/EMR, HIE and other collaboration-centric life scienc

88 HR implementation and the expected return in EHR incentive payments were evaluated.

89 There is variability in EHR use patterns among ophthalmologists.

90 One of the key sources of bias in EHRs is what we term informed presence-the notion that i

91 es and patient characteristics documented in EHRs of a large healthcare network over the last two dec

92 s systematically different from those not in EHRs.

93 es that the subject is ill, making people in EHRs systematically different from those not in EHRs.

94 occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using th

95 s from 380,000 patients in our institutional EHR, these putative interactions were either refuted or

96 retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable)

97 tion strategies were developed: (1) keyboard EHR, (2) mouse EHR, and (3) paper.

98 Sensitivity was 89.1% for the keyboard EHR, 87.2% for mouse EHR, and 88.6% for the paper strate

99 he positive ratio was 99.4% for the keyboard EHR, 98.9% for mouse EHR, and 99.9% for the paper strate

100 ly EHR (mean, 1.9 nurses/procedure) and late EHR (mean, 1.5 nurses/procedure) periods than in the pap

101 This improved to baseline levels by the late EHR period (46%, P = .28), although POTD in the cataract

102 This improved in the late EHR period (9.2 minutes) but remained worse than in the

103 Longitudinal EHR data were collected in six health centers in the Mid

104 Longitudinal EHR data, commonly available in clinical settings, can b

105 Only 24% of carriers met EHR-based presequencing criteria for probable or definit

106 were developed: (1) keyboard EHR, (2) mouse EHR, and (3) paper.

107 % for the paper strategy (P < .001 for mouse EHR vs paper; no significant differences between other p

108 89.1% for the keyboard EHR, 87.2% for mouse EHR, and 88.6% for the paper strategy (no statistically

109 99.4% for the keyboard EHR, 98.9% for mouse EHR, and 99.9% for the paper strategy (P < .001 for mous

110 Documentation speed of the mouse EHR strategy worsened with repetition.

111 linician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state dea

112 inic volumes also were unaffected by the new EHR.

113 No EHR-classified control subject received a diagnosis of b

114 partially implemented EHR (n=72 029), and no EHR (n=9270).

115 lizations per 1000 patients annually with no EHR to 238.50 per 1000 patients annually when using the

116 dence interval, 0.69-0.97]) compared with no EHR.

117 d with patients treated at hospitals with no EHR.

118 Numerous EHRs and CDSSs are available and have the potential to e

119 ease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with hig

120 es are needed to quantify the association of EHR use with changes in costs.

121 ss US hospitals; however, the association of EHR use with quality of care and outcomes after acute my

122 In particular, the impact of EHR operating room (OR) management systems on clinical e

123 horts, NLP could reduce by 90% the number of EHR charts abstracted to identify confirmed breast cance

124 The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses wa

125 t serve as a novel, independent predictor of EHR in KT recipients of all ages.

126 t, transplant and center-level predictors of EHR are limited, and novel predictors are needed.

127 ackle variability in quality and quantity of EHR data and importance of maintaining patient privacy a

128 The unadjusted rate of EHR by center ranged from 18% to 47%, but conventional c

129 y independently predicted 61% higher risk of EHR (adjusted RR = 1.61, 95% CI: 1.18-2.19, p = 0.002).

130 n and solicited to participate in a study of EHR use, practice management, and image management syste

131 Difficulties in use of EHR data include: data availability, missing data, incor

132 l haplotypes, and demonstrate the utility of EHR data in evolutionary analyses.

133 encounter that manifested within 2 weeks of EHR transition and then stabilized, suggesting that alth

134 The adoption of EHRs by ophthalmology practices more than doubled from 2

135 vey included questions about the adoption of EHRs, available functionality, benefits, barriers, satis

136 The rapid growth of EHRs provides opportunities for spreading this model bro

137 Literature evaluating the impact of EHRs and CDSSs on patient outcomes is lacking, although

138 Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients

139 n annual survey to determine the presence of EHRs.

140 ] plus organizational changes) from those of EHRs alone.

141 Although the use of EHRs has potential to improve patient health outcomes, t

142 me on direct clinical face time and 37.0% on EHR and desk work.

143 Data on EHR use were collected from the American Hospital Associ

144 y ophthalmologists directly with patients on EHR use, conversation, and examination as well as total

145 ients, nearly 2 additional hours is spent on EHR and desk work within the clinic day.

146 (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.

147 ime with patients and 49.2% of their time on EHR and desk work.

148 l physician time requirements for ophthalmic EHRs are required.

149 patients with diabetes, use of an outpatient EHR in an integrated delivery system was associated with

150 d a code status documented in the outpatient EHR compared with 14.5% (n = 12/83) of historical contro

151 Overall EHR use increased from 82.1% (183/223) hospitals in 2007

152 The overall EHR rate was 31%, and 19 independent patient-level facto

153 In this paper, EHR data is used to discover novel relationships between

154 was conducted comparing the pre-EHR and post-EHR time periods at the Cole Eye Institute, Cleveland, O

155 in the pre-EHR period and 14,191 in the post-EHR period).

156 ontrol study was conducted comparing the pre-EHR and post-EHR time periods at the Cole Eye Institute,

157 ncounters were identified (13,969 in the pre-EHR period and 14,191 in the post-EHR period).

158 es of patient portals tethered to a provider EHR that addressed patient outcomes, satisfaction, adher

159 n, namely the effective hydrodynamic radius (EHR) that provides an average measure of the size of the

160 ably, a small effective hydrodynamic radius (EHR; <4A) is obtained, providing a picture in which gate

161 achieved using representations based on raw EHR data and alternative feature learning strategies.

162 Early hospital readmission (EHR) after kidney transplantation (KT) is associated wit

163 clinical notes from 1,472 patients receiving EHR-documented care in an integrated health care system

164 ad adoption of the Electronic Health Record (EHR) by physicians will create a larger and more valuabl

165 ective analysis of electronic health record (EHR) data from 50,515 adult primary care patients was co

166 notype analyses on Electronic Health Record (EHR) data have recently drawn benefits across many areas

167 ome sequencing and electronic health record (EHR) data of 50,726 individuals were used to assess the

168 diagnoses from an electronic health record (EHR) data set (n=19 093).

169 13; n = 1,847,165) electronic health record (EHR) data sets.

170 interest in using electronic health record (EHR) data to assess quality of care, the accuracy of suc

171 ions from existing electronic health record (EHR) notes.

172 in the outpatient electronic health record (EHR) of patients with advanced lung cancers.

173 n the CHOP unified electronic health record (EHR) system (July 1, 2010, to June 30, 2014) were select

174 ning the impact of electronic health record (EHR) system migration in ophthalmology, a study evaluati

175 tine visits via an Electronic Health Record (EHR) system.

176 s tied to provider electronic health record (EHR) systems are increasingly popular.

177 Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare

178 Although electronic health record (EHR) systems have potential benefits, such as improved s

179 Electronic health record (EHR) systems have transformed the practice of medicine.

180 ical data from the electronic health record (EHR) systems of diverse hospitals.

181 monly available in electronic health record (EHR) systems, can be used to predict patients' future ri

182 hnologies, such as electronic health record (EHR) systems, have led to further changes in the clinica

183 ta embedded in the electronic health record (EHR) to define the phenome.

184 odification of the electronic health record (EHR) to include FBSE as a recommended preventive service

185 he availability of electronic health record (EHR)-based phenotypes allows for genome-wide association

186 and the impact of electronic health record (EHR)-based reminders on adherence to vaccination guideli

187 pose To develop an electronic health record (EHR)-based trigger algorithm to identify delays in follo

188 iants to over 1000 electronic health record (EHR)-derived phenotypes in ~28,000 adults of European an

189 linical face time, electronic health record [EHR] and desk work, administrative tasks, and other task

190 ata from PROs and Electronic Health Records (EHR) are lacking.

191 e identified from electronic health records (EHR), and each diagnostic group was matched 1:3 with chi

192 se event reports, electronic health records (EHR), and laboratory experiments, the goal of this study

193 ndings drawn from Electronic Health Records (EHR).

194 pread adoption of electronic health records (EHRs) across US hospitals; however, the association of E

195 Electronic health records (EHRs) and clinical decision support systems (CDSSs) have

196 Electronic health records (EHRs) are an increasingly utilized resource for clinical

197 Electronic health records (EHRs) are being increasingly utilized and form a unique

198 nwide adoption of electronic health records (EHRs) but lacks robust empirical evidence to anticipate

199 Electronic health records (EHRs) contain information on each feature of this triad.

200 g availability of electronic health records (EHRs) creates opportunities for automated extraction of

201 o validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying co

202 secondary use of electronic health records (EHRs) in early pregnancy.

203 Electronic health records (EHRs) may be key tools for improving the quality of heal

204 ies documented in electronic health records (EHRs) of large patient populations is understudied.

205 Secondary use of electronic health records (EHRs) promises to advance clinical research and better i

206 a using patients' electronic health records (EHRs) remain sparse.

207 s within existing electronic health records (EHRs) should be the standard for large health care organ

208 a or their use of electronic health records (EHRs) to manage patient populations.

209 thermore, whether electronic health records (EHRs) with chronic disease management capabilities suppo

210 ypically involves electronic health records (EHRs), organizational practice change, and payment refor

211 Using Electronic Health Records (EHRs), we identified in 2010 two cohorts of PAR patients

212 from free text in electronic health records (EHRs).

213 meaningful use of electronic health records (EHRs).

214 sing longitudinal electronic health records (EHRs).

215 H (which involves electronic health records [EHRs] plus organizational changes) from those of EHRs al

216 tient monitoring and intervention may reduce EHR rates.

217 ant terms extracted from Marshfield Clinic's EHR.

218 data from a large health care organization's EHR between 2000 and 2013, we determined the prevalence

219 ided all participating clinics with the same EHR software with decision support and patient registry

220 comorbidities by learning from a large-scale EHR database comprising more than 35 million inpatient c

221 They parsed and scored EHR documentation for 2,484 admissions covering 2,010 pa

222 A small EHR further suggests that the kinetics of gate interconv

223 Among small EHR-enabled clinics, a P4P incentive program compared wi

224 Analysis linking such EHR-assigned disease labels to a biospecimen repository

225 er mean documentation scores with paper than EHR notes.

226 owever, physicians have raised concerns that EHR time requirements have negatively affected their pro

227 This study supports the contention that EHR implementation can be accomplished in an ophthalmolo

228 Given the critical importance that EHR data will play in multiple aspects of the healthcare

229 The findings also suggest that EHR-based clinical data provide more objective estimates

230 The EHR IHD phenotype was most strongly correlated with ARIC

231 The EHR IHD risk profile differed from ARIC and indicates th

232 The EHR incentive payments did not offset costs of implement

233 The EHR notes included more complete documentation of examin

234 The EHR was also associated with 13.10 fewer hospitalization

235 The EHR was also associated with statistically significant r

236 The EHR was implemented in a setting with strong baseline pe

237 The EHR-based classifications were used to accrue 4,500 bipo

238 imates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards r

239 patients' perceptions of their visit and the EHR remained largely unchanged.

240 correctly identified as not eligible by the EHR data (specificity).

241 ew) and who were classified correctly by the EHR data as passing (sensitivity).

242 are distinct from but may be enabled by the EHR.

243 reminder was automatically generated by the EHR.

244 ignificant effect of this pair in either the EHR or in the electrophysiology experiments.

245 her clinically relevant information from the EHR that can be used for PheWAS analyses and discovery.

246 od allergy and intolerance documented in the EHR allergy module.

247 d timing of code status documentation in the EHR and warrant further investigation.

248 in the paper group and 6% higher than in the EHR group (adjusted P < 0.01 for each).

249 In the EHR model, neither substance abuse (OR = 1.25; 95% CI =

250 Concussion visits in the EHR were defined based on International Classification o

251 In the EHR, we found that patients taking both ceftriaxone and

252 linician documentation of code status in the EHR.

253 Instead, the EHR systems documented clinical findings using textual d

254 In addition, the total costs of the EHR implementation and the expected return in EHR incent

255 The small magnitude of the EHR provides strong evidence that the I-N interconversio

256 In multivariable analyses, use of the EHR was associated with a statistically significantly de

257 analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance

258 over time than either the paper group or the EHR group for 4 of the 10 measures (by 1 to 9 percentage

259 was significantly lower with paper than the EHR system (P </= 0.004).

260 Our experiments establish that the EHR is less than 4 A, on the order of the size of one to

261 r studying food allergy, suggesting that the EHR's allergy module has the potential to be used for cl

262 ther an informatics algorithm applied to the EHR could electronically identify patients with AERD.

263 Three time points relative to the EHR transition were evaluated: a 2-week period before th

264 ard clinical practice or who did not use the EHR were excluded.

265 al ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinati

266 per 1000 patients annually without using the EHR to 490.32 per 1000 patients when using the EHR.

267 ) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.

268 50 per 1000 patients annually when using the EHR.

269 R to 490.32 per 1000 patients when using the EHR.

270 ract estimates of the RDoC dimensions in the EHRs of a large health system.

271 For each patient, the EHRs were linked to corresponding claims data with MRU a

272 loped an informatics algorithm to search the EHRs of patients aged 18 years and older from the Partne

273 satisfaction of ophthalmologists with their EHR and their perception of beneficial effects on produc

274 ured accessible secure data captured through EHR systems provide mechanisms through which EHRs can fa

275 190 patients by trained clinicians blind to EHR diagnosis.

276 Linking these data to EHR-derived clinical phenotypes, we find clinical associ

277 ter-hours work each night, devoted mostly to EHR tasks.

278 dings indicate that deep learning applied to EHRs can derive patient representations that offer impro

279 payment of care may prompt clinicians to use EHRs in ways that result in more substantial savings.

280 on) is currently one of the most widely used EHR system in the United States, and development of a su

281 oke electronic quality measures (eQMs) using EHR data.

282 genomic and pharmacogenomic research, using EHR data for phenotypic information.

283 spread practice of predictive modeling using EHRs.

284 ve differences in the nature of paper versus EHR documentation of ophthalmic findings in this study.

285 Qualitative differences in paper versus EHR documentation were illustrated by selecting represen

286 n of qualitative differences in paper versus EHR documentation.

287 vider on different dates, using paper versus EHR methods.

288 d was significantly lower using paper versus EHR notes (P </= 0.022).

289 EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (

290 endent patient-level factors associated with EHR were identified: recipient factors included older ag

291 ded criteria donor) were not associated with EHR.

292 s and provided similar quality compared with EHRs and paper records.

293 >4 days was slightly lower at hospitals with EHRs (OR: 0.97; 95% CI: 0.95 to 0.99; p=0.01).

294 tudy was to determine whether hospitals with EHRs differed on quality or outcome measures for ischemi

295 ristics, patients admitted to hospitals with EHRs had similar odds of receiving "all-or-none" care (o

296 Hospitals with EHRs were larger and were more often teaching hospitals

297 study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized

298 Healthcare systems with EHRs should consider using electronic data to evaluate c

299 information, however, remains locked within EHR narrative text documents, including clinical notes a

300 sures for ischemic stroke from those without EHRs.