ライフサイエンスコーパス: deidentified

1 i-structured interviews were transcribed and deidentified.

2 m a clinical archive were used and data were deidentified.

3 oss-sectional study used publicly available, deidentified 2019 to 2022 data from the US Consumer Prod

4 The model is estimated using deidentified activity data on 1.1 million browsing and w

5 Methods: Deidentified adjudicated claims data for patients with o

6 ort study included retrospective analysis of deidentified administrative claims and electronic health

7 NG, AND PARTICIPANTS: This cohort study used deidentified administrative claims data for privately in

8 IPANTS: This retrospective cohort study used deidentified administrative claims data for privately in

9 IPANTS: This retrospective cohort study used deidentified administrative claims data for US adults wi

10 AND PARTICIPANTS: This cohort study analyzed deidentified administrative claims data from OptumLabs D

11 his retrospective cross-sectional study used deidentified administrative claims data from OptumLabs D

12 In this retrospective cohort study using deidentified administrative claims for Medicare Advantag

13 Deidentified administrative claims from the OptumLabs Da

14 from OptumLabs Data Warehouse, a database of deidentified administrative claims.

15 oard approval was obtained for this study of deidentified aggregate administrative data.

16 hort study is a population-based analysis of deidentified, aggregated electronic health record data c

17 Clinician EHR use was tracked by deidentified and aggregated metadata across a variety of

18 Interview transcripts were deidentified and analyzed using deductive thematic analy

19 Samples were deidentified and assayed and underwent masked analyses.

20 ected from January 2012 to January 2018 were deidentified and compiled into the publicly available v.

21 Each video clip was deidentified and distributed to at least 3 of the 24 bli

22 sets (0.6%; 95% CI, 0.0%-1.5%) were actually deidentified and publicly available as of April 10, 2020

23 All images were deidentified and randomized, and TA was scored with the

24 wardship program personnel at each hospital, deidentified and submitted in aggregate for benchmarking

25 Interviews were transcribed, deidentified, and analyzed to identify themes.

26 Clinical samples were deidentified, and laboratory personnel handling the samp

27 Interview transcripts were translated, deidentified, and then analyzed using thematic analysis.

28 n-based range of CK in newborns using 30,547 deidentified anonymous dried blood spot samples.

29 Deidentified applicant data for match cycles from 2010 t

30 AND PARTICIPANTS: This cohort study assessed deidentified application and matriculation data from the

31 AND PARTICIPANTS: This prognostic study used deidentified, archived colorectal cancer cases from Janu

32 Deidentified audio recordings were transcribed and trans

33 ily meetings were recorded, transcribed, and deidentified before being screened for discussion of dea

34 cause all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a public

35 Interleague, deidentified cardiac data were pooled for collective ana

36 In this retrospective cohort study, we used deidentified chargemaster data from 297 hospitals across

37 IPANTS: This retrospective cohort study used deidentified claims data from 2017 to 2024.

38 IPANTS: This retrospective cohort study used deidentified claims data from Blue Cross Blue Shield ben

39 IPANTS: This retrospective cohort study used deidentified claims data from commercially insured Blue

40 trospective cohort study was conducted using deidentified claims data from the MarketScan database.

41 the Wakely Consulting Group ACA database, a deidentified claims database built on data voluntarily s

42 used the OptumLabs Data Warehouse to analyze deidentified claims from approximately 74 million adults

43 fied Clinformatics Data Mart, which contains deidentified claims from patients with private insurance

44 rom Optum Labs Data Warehouse, a US national deidentified claims-based database.

45 cohort study used claims data from the Optum deidentified Clinformatics Data Mart Database between De

46 pective analysis of health data from Optum's deidentified Clinformatics Data Mart Database from 2016

47 used administrative claims data from Optum's deidentified Clinformatics Data Mart Database from Janua

48 e-series study used claims data from Optum's deidentified Clinformatics Data Mart database to assess

49 was conducted using US claims data (Optum's deidentified Clinformatics Data Mart database) for Janua

50 nwide commercial insurance database (Optum's deidentified Clinformatics Data Mart Database) from Janu

51 to September 30, 2015, obtained from Optum's deidentified Clinformatics Data Mart Database, a commerc

52 ohort study used medical claims from Optum's deidentified Clinformatics Data Mart Database, a commerc

53 tionwide data from 2004 to 2019 from Optum's deidentified Clinformatics Data Mart Database, an insura

54 were identified from the 2016 to 2020 Optum deidentified Clinformatics Data Mart Database, which is

55 nd prevalent AIH was identified from Optum's deidentified Clinformatics Data Mart Database.

56 study used 2017 to 2021 data from the Optum deidentified Clinformatics Data Mart Database.

57 and June 30, 2019, using data from the Optum deidentified Clinformatics Data Mart database.

58 cember 31, 2020, were extracted from Optum's deidentified Clinformatics Data Mart Database.

59 insurance claims databases-Medicaid, Optum's deidentified Clinformatics Data Mart, and Merative Marke

60 ted using 2017 to 2018 claims from the Optum deidentified Clinformatics Data Mart.

61 ening algorithms, test performance data, and deidentified clinical and laboratory information regardi

62 Deidentified clinical data and tests for Trichomonas vag

63 TS: This retrospective cohort study obtained deidentified clinical data for 5382 patients from a nati

64 Deidentified clinical data were analyzed to determine th

65 Access to deidentified clinical information relating genetic varia

66 Deidentified clinical information was collected.

67 In our study, 116 consecutive, deidentified, clinical nasopharyngeal swab samples were

68 Deidentified clinician-reported data from all respondent

69 Participants were deidentified clinicians who billed at least 10 Internati

70 defined biomarker subgroups used data from a deidentified clinicogenomic database and included patien

71 ned biomarker-stratified genomic data from a deidentified clinicogenomic database.

72 PANTS: A prespecified cohort study using the deidentified clinicogenomic Tempus database of patients

73 DESIGN, SETTING, AND PARTICIPANTS: Deidentified color fundus images taken after dilation we

74 Patient data were obtained from a deidentified commercial and Medicare Advantage medical c

75 : This cross-sectional analysis of national, deidentified commercial health insurance claims of youth

76 the OptumLabs Data Warehouse, a longitudinal deidentified commercial insurance claims database, from

77 terminants of health, while enabling secure, deidentified computation.

78 ETTING, AND PARTICIPANTS: This analysis used deidentified, cross-sectional data on patients with MS a

79 review board permission was obtained to use deidentified CT colonography data for this prospective r

80 The Dose Imaging Registry (DIR) collects deidentified CT data, including examination type and dos

81 ted detailed forms in which they could enter deidentified data and volume statistics pertaining to pa

82 modifications, level of difficulty obtaining deidentified data and waivers, experiences with multisit

83 Deidentified data collected as part of routine clinical

84 Retrospective deidentified data collected between 2005 and 2013 were e

85 We used anonymous/deidentified data collected between January 1, 1995, and

86 Retrospective study using deidentified data for the 1994-1999 national cohort of 9

87 Deidentified data from 1422 individuals from 14 publishe

88 trospective cohort study was conducted using deidentified data from a national commercial insurance d

89 NTS: This population-based cohort study used deidentified data from a nationwide electronic health re

90 In this cohort study, we analysed deidentified data from adults receiving first-line antir

91 NG, AND PARTICIPANTS: This cohort study used deidentified data from an electronic health record-deriv

92 ARTICIPANTS: This cross-sectional study used deidentified data from an online medical forum, in which

93 IPANTS: This retrospective cohort study used deidentified data from Optum Insight's Clinformatics Dat

94 set was curated from publicly available and deidentified data from patients with HNSCC treated at MD

95 October 1, 2011, and July 25, 2015, based on deidentified data from phase 3, multicenter, randomized

96 his retrospective cross-sectional study used deidentified data from the Association of American Medic

97 als and Methods This secondary analysis used deidentified data from the Association of American Medic

98 We obtained deidentified data from the Australia and New Zealand Dia

99 ls followed from birth to age 20 years using deidentified data from the Better Evidence Better Outcom

100 IPANTS: This retrospective cohort study used deidentified data from the Hawai'i Tumor Registry of 622

101 ARTICIPANTS: This cross-sectional study used deidentified data from the MarketScan Commercial Claims

102 TS: This retrospective cohort study examined deidentified data from the National Cancer Database betw

103 ARTICIPANTS: This cross-sectional study used deidentified data from the Optum Clinformatics Data Mart

104 retrospective longitudinal cohort study used deidentified data from the Optum electronic health recor

105 Deidentified data from the remote monitoring Merlin.net

106 Cohorts were constructed using deidentified data from the TriNetX Research Network.

107 een January 1, 2016, and June 30, 2019, used deidentified data from the US Optum Clinformatics Data M

108 IPANTS: This retrospective cohort study used deidentified data from US Veterans Health Administration

109 d a retrospective, observational study using deidentified data obtained from all consecutive patients

110 S: Retrospective cross-sectional study using deidentified data of medical graduates who completed the

111 ic health record-derived database to extract deidentified data of patients receiving care from US phy

112 his retrospective cross-sectional study used deidentified data on 37 610 medical students who matricu

113 We collected deidentified data on adult patients (> 18 yr) receiving

114 AND PARTICIPANTS: A cross-sectional study of deidentified data on outpatients throughout the US was c

115 Deidentified data on patient demographics and clinical c

116 ic Stroke (CERTAIN) collaboration comprising deidentified data on patients with ischemic stroke treat

117 p a web-based registry for the collection of deidentified data on the management and course of neonat

118 The study and use of anonymized and deidentified data received approval from the MedStar-Geo

119 RTICIPANTS: The study created cohorts from a deidentified data set composed of commercial laboratory

120 An aggregate deidentified data set will be made available to the rese

121 sing multiple cross-sectional analyses and a deidentified data set, we analyzed data from infants wit

122 Sites shared deidentified data sets, including previously collected s

123 Sites submitted deidentified data surrounding sociodemographics, cancer

124 We analyzed deidentified data through August 2014 from the Associati

125 retrospective cross-sectional analysis with deidentified data using the International Statistical Cl

126 Deidentified data were collected from 12 of 15 sites in

127 Deidentified data were entered into an electronic Resear

128 DESIGN, SETTING, AND PARTICIPANTS: Deidentified data were obtained from a nationally repres

129 Deidentified data were obtained from United Network for

130 multicenter retrospective cohort study used deidentified data with 6-year follow-up from the Medicar

131 Using deidentified data, we examined how 10.1 million U.S. Fac

132 a 14-year period where 171 ICUs contributed deidentified data.

133 Data from the Optum electronic health record deidentified database (2007-2017) were linked to the Med

134 he Flatiron Health electronic record-derived deidentified database diagnosed between 2011 and 2021, m

135 study used administrative claims data from a deidentified database of commercially insured and Medica

136 d the Optum Labs Data Warehouse, a national, deidentified database of electronic health records, to i

137 We retrospectively queried the deidentified database of patients hospitalized between J

138 ANTS: This cohort study used an EHR-derived, deidentified database that included patients with stage

139 A large deidentified database was queried for the Common Procedu

140 tabase, an electronic health record-derived, deidentified database with data from community and acade

141 ationwide, electronic health record-derived, deidentified database with median duration of follow-up

142 nationwide electronic health record-derived deidentified database, which includes data for approxima

143 nationwide electronic health record-derived, deidentified database.

144 included insurance claims of US adults in a deidentified database.

145 nwide electronic health record (EHR)-derived deidentified database.

146 aminations from 1997 through 2008 by using a deidentified database.

147 In this cohort study, data from 3 different deidentified databases containing electronic health reco

148 Deidentified DBS samples and data submitted to the New Y

149 Deidentified death records from the National Center for

150 small area estimation models were applied to deidentified death records from the National Center for

151 Deidentified death records from the National Center for

152 ARTICIPANTS: This cross-sectional study used deidentified death records from the National Vital Stati

153 Deidentified demographic and clinical data from each kid

154 Deidentified demographic and clinical data were used to

155 nth Revision code L40.1) identified in Optum deidentified EHR data between July 1, 2015, and June 30,

156 stitutional dataset containing statistically deidentified EHR data for over 21 million individuals (E

157 record (EHR) research network containing the deidentified EHR data of more than 103 million patients,

158 n Examination Surveys (NHANES) and the Optum deidentified electronic health record (EHR) data set of

159 was a retrospective cohort study aggregating deidentified electronic health record data from January

160 AND PARTICIPANTS: This prognostic study used deidentified electronic health record data from the Univ

161 rospective cohort study used data from Optum deidentified electronic health record data set (7.7 mill

162 used patient-level data from the nationwide deidentified electronic health record database Flatiron

163 n-based cohort study used Flatiron Health, a deidentified electronic health record database of patien

164 , race, and age by using BioVU, Vanderbilt's deidentified electronic health record database.

165 anderbilt University Medical Center (VUMC)'s deidentified electronic health record system.

166 dy used patient-level data from a nationwide deidentified electronic health record-derived database,

167 to be able to conduct medical research with deidentified electronic health records (96.8% v 87.7%; P

168 udy was conducted using data obtained from a deidentified electronic health records data set from Jan

169 Data were collected using TriNetX, a deidentified electronic health records research network.

170 s cross-sectional study used public data and deidentified electronic health records to describe the b

171 rch Collaborative, a centralized database of deidentified electronic medical record data from a netwo

172 ICIPANTS: This retrospective cohort study of deidentified electronic medical record data from the Tri

173 ed data from TriNetX, a national database of deidentified electronic medical records from both inpati

174 We analyzed deidentified ES data from 6,517 participants (2,204 Afri

175 TS: This retrospective cohort study used the deidentified Flatiron-Health electronic health record-de

176 m January 2013 to March 2015 using archived, deidentified, formalin-fixed, paraffin-embedded GCA-nega

177 Retrospective cross-sectional evaluation of deidentified fundus photographs matched to spectacle-cor

178 Deidentified German patient data were used for a retrosp

179 nions regarding acceptable secondary uses of deidentified health information and consent models for o

180 tion, was performed on a database containing deidentified health records of 1.28 million patients acr

181 n = 70-698) and controls (n = 808-3818) from deidentified health records.

182 ling algorithm with retrospectively obtained deidentified HIPAA-compliant data.

183 Materials and Methods Four deidentified HIPAA-compliant datasets were used in this

184 Data were captured from a deidentified, HIPAA-compliant data warehouse.

185 Publicly available deidentified hospital claim data for all surgical proced

186 tudy were derived from publicly available or deidentified human subject data.

187 cohort study, with external validation in a deidentified ICU database.

188 ata were centrally collated in a cloud-based deidentified image database.

189 ver performance study was performed by using deidentified images acquired between 2008 and 2011 with

190 approval and informed consent for the use of deidentified images were obtained.

191 approval because the 10 image data sets were deidentified in the Osteoarthritis Initiative database,

192 s with concomitant catheterization data, and deidentified individual and group results were shared at

193 Deidentified individual participant data from GlaxoSmith

194 ilable devices in the United States provided deidentified individual patient data for independent ana

195 Deidentified individual patient data were obtained throu

196 Deidentified individual records on causes of death for a

197 Deidentified individual-level data from participants (15

198 0 participants or more were invited to share deidentified individual-level data on the above four var

199 NG, AND PARTICIPANTS: This cohort study used deidentified individual-level faculty data from 136 uniq

200 Deidentified, individual-level data were merged into a m

201 A deidentified, individual-level database of PGY-1 residen

202 A+B FIA allows for surveillance of real-time deidentified influenza activity.

203 Comfort with the use of deidentified information from medical records varied by

204 The database contained deidentified information pertaining to a cohort of 61107

205 Deidentified, institution-level aggregate counts of annu

206 (EHR) linked to closed claims data (Optum's deidentified Integrated Claims-Clinical dataset, TriNetX

207 s an anonymous, self-reported, confidential, deidentified, internet-accessible medication error repor

208 radiologists independently interpreted twice deidentified mammograms obtained in 153 women (age range

209 re-matched, cross-sectional study used Optum deidentified Market Clarity Data (claims and electronic

210 RTICIPANTS: Retrospective cohort study using deidentified medical and pharmacy claims and enrollment

211 the OptumLabs Data Warehouse, which includes deidentified medical and pharmacy claims and enrollment

212 children 17 years of age or younger analyzed deidentified medical and pharmacy claims in OptumLabs Da

213 udy of 794 809 insured US men was drawn from deidentified medical claims between January 2011 and Dec

214 led measure of comfort with secondary use of deidentified medical information and evaluated its corre

215 oratory queries, and billing code queries of deidentified medical record data.

216 tors to get a patient's permission each time deidentified medical record information is used for rese

217 IPANTS: This retrospective cohort study used deidentified medical record review of 18 243 female-iden

218 s that most patients wish to be asked before deidentified medical records are used for research.

219 Based on over 30,000 deidentified medical records, we explore 336 abdominal d

220 eptions after deliberation related to use of deidentified medical-record data by insurance companies.

221 ts at least once whether researchers can use deidentified medical-records data for future research.

222 comparative effectiveness cohort study used deidentified Medicare claims data from August 1, 2014, t

223 CIPANTS: This cross-sectional study assesses deidentified medication abortion prescription fulfilment

224 Deidentified Medisoft Ophthalmology electronic medical r

225 NTS: This cross-sectional study analyzed the deidentified metadata of ambulatory care health systems

226 board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations

227 ing first-line ICI were abstracted using the deidentified nationwide Clinico-Genomic Database (CGDB)

228 solated from the dried blood spots of 36,124 deidentified newborn males.

229 Data were deidentified next-generation sequencing results from ind

230 RTICIPANTS: Using 2003 to 2017 data from the deidentified Optum Clinformatics Data Mart database from

231 etrospective cohort study used data from the deidentified Optum Cliniformatics Data Mart Database on

232 TS: This retrospective cohort study used the deidentified Optum Labs Data Warehouse, a claims databas

233 Medical billing claims data from the deidentified OptumLabs Data Warehouse were used.

234 TS: This retrospective cohort study obtained deidentified OptumLabs electronic health record claims d

235 Sites assembled deidentified packets, including physician notes and elec

236 For this analysis, we collected deidentified participant-level data from the MEMOIR data

237 January 1, 2023 METHODS: This study utilized deidentified patient data from the TriNetX database.

238 Deidentified patient data originated from a geographical

239 l was obtained for retrospective analysis of deidentified patient images.

240 was calculated from free-listing terms from deidentified patient interviews.

241 levels and lipids, we analyzed 4.06 million deidentified patient laboratory test results from Septem

242 cal Data Warehouse (STARR), which aggregates deidentified patient records from Stanford Health Care.

243 were compared against culture using remnant deidentified patient urine samples.

244 -10-CM code for post-COVID-19 condition used deidentified patient-level claims data aggregated by Hea

245 This retrospective analysis pooled deidentified patient-level data from 10 academic institu

246 Deidentified patient-level data from a US database (Flat

247 ted Network of Organ Sharing (UNOS) provided deidentified patient-level data.

248 ed Network for Organ Sharing (UNOS) provided deidentified patient-level data.

249 Sharing deidentified patient-level research data presents immens

250 from the Flatiron Health database containing deidentified, patient-level, electronic health record-de

251 Surgical discard tissue from deidentified patients and samples of normal skin from he

252 Deidentified peripheral blood (n = 33) and cord blood (n

253 Herein, deidentified plasma was collected from sepsis patients (

254 ughout the United States collected residual, deidentified positive blood culture samples for analysis

255 Retrospective analysis of a deidentified private insurance database from 2007 throug

256 tability Act-compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest d

257 tability Act-compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest d

258 ospective cohort study was performed using a deidentified, random sample of 4 999 999 fee-for-service

259 CIPANTS: We performed a cohort study using a deidentified, random sample of 4 999 999 fee-for-service

260 a data-driven, agent-based model informed by deidentified real-world hospitalization records.

261 Deidentified records of all cases of melanoma among Quee

262 lation-based cohort study included data from deidentified records of all invasive melanomas diagnosed

263 e on August 18, 2021, and included data from deidentified records of patients tested, using the Tempu

264 Deidentified records were obtained of all Queensland res

265 Two hundred deidentified remnant diarrheal stool specimens were test

266 ross-sectional, retrospective study analyzed deidentified results from blood lead tests performed at

267 he Optum Labs Data Warehouse, which contains deidentified retrospective administrative claims data an

268 titution, tertiary academic referral center, deidentified, retrospectively collected, ultra-widefield

269 Child care center directors reported weekly deidentified self-reported COVID-19 cases from all CCPs

270 ARTICIPANTS: This cross-sectional study used deidentified, self-reported data from 2003 to 2019 from

271 Fifty deidentified serum samples collected from 1986 to 1992 f

272 f the assay was evaluated using 211 residual deidentified stool samples tested with a GDH-and-toxin E

273 A total of 2,410 unformed, deidentified stool specimens were collected.

274 Contributing centres completed deidentified structured case report sheets to include va

275 Contributing centres completed deidentified structured case-report spreadsheets, adapte

276 DESIGN, SETTING, AND PARTICIPANTS: Using deidentified student-level data of allopathic US medical

277 A deidentified summary of clinical and radiological record

278 IPANTS: This cross-sectional study evaluated deidentified surgical videos of phacoemulsification cata

279 MA Combo 2 tests was assessed using unlinked/deidentified surplus clinical specimens previously analy

280 US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014.

281 in clinical blood plasma samples taken from deidentified TBI patients.

282 ween May 2020, and December 2022, within the deidentified, Tempus multimodal database, consisting of

283 en February 2021 and October 2023 within the deidentified, Tempus multimodal database, consisting of

284 Deidentified time logs were sourced from the EHR and all

285 Data were automatically deidentified to comply with Health Insurance Portability

286 combined with Virena software for automatic deidentified tracking of influenza activity across the L

287 recorded until reaching thematic saturation, deidentified, transcribed and translated, and analyzed u

288 search assistants independently coded all 30 deidentified transcripts and resolved discrepancies (kap

289 Deidentified transcripts from the interviews were analyz

290 Deidentified ultra-widefield color fundus photographs we

291 ugust-2020 and June-2022 were analyzed using deidentified United Network for Organ Sharing database.

292 ugh December 31, 2018, and data from a large deidentified US commercial health care database (Optum C

293 th outpatient MA-RSV infections from 3 large deidentified US databases across 6 RSV seasons, approxim

294 A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moder

295 DESIGN, SETTING AND PARTICIPANTS: Deidentified UWF images with mild or moderate nonprolife

296 rovement collaborative submitted an unedited deidentified video of a representative laparoscopic SG.

297 Deidentified videos were recorded of surgeons performing

298 s in evaluating over 130000 images that were deidentified with respect to age, sex, and race/ethnicit

299 med across 2 independent wound centers using deidentified wound photographs collected for routine car

300 Two blinded intensivists reviewed deidentified written transcripts of all simulated family