ライフサイエンスコーパス: claims data

1 measures in the model and adding historical claims data.

2 iagnosed by serial study ECGs or by Medicare claims data.

3 ords, study electrocardiograms, and Medicare claims data.

4 between 2005 and 2014, using administrative claims data.

5 edications was obtained from Medicare Part D claims data.

6 o December 31, 2010 using commercial insurer claims data.

7 tion and linked these patients with Medicare claims data.

8 een 2009 and 2015 derived from 100% Medicare claims data.

9 sing clinical registry versus administrative claims data.

10 ent Database together with VA administrative claims data.

11 went colorectal surgery using administrative claims data.

12 osis codes, death certificates, and Medicare claims data.

13 egistry linked with Medicare fee-for-service claims data.

14 with sickle cell anemia using administrative claims data.

15 l infarction (AMI) for which we had complete claims data.

16 are & Medicaid Services (CMS) administrative claims data.

17 to identify high-risk surgical procedures in claims data.

18 e Centers for Medicare and Medicaid Services claims data.

19 rumoxytol as reported in outpatient Medicare claims data.

20 of HDV in the United States using real-world claims data.

21 lyses from medical records or administrative claims data.

22 adjustment was limited to that available in claims data.

23 and actionable cohorts using linked Medicare claims data.

24 osclerotic CVD events identified in Medicare claims data.

25 cases and interventions were identified from claims data.

26 and End Results registry linked to Medicare claims data.

27 udinal Centers for Medicare & Medicaid (CMS) claims data.

28 between 2004 and 2008 and linked to Medicare claims data.

29 over 6-month rolling windows, using pharmacy claims data.

30 ident from prevalent cases identified in the claims data.

31 data had a higher specificity than all-payer claims data.

32 ospitals were linked with Medicare inpatient claims data.

33 media reports, and 9 (20%) from catastrophic claims data.

34 in pharmacoepidemiologic studies relying on claims data.

35 l and febuxostat: a study using the Medicare claims data.

36 performance metric with the use of Medicare claims data.

37 ipant report, electronic health records, and claims data.

38 013-2017 Mississippi Medicaid administrative claims data.

39 trols aged > 65 years in 2009 using Medicare claims data.

40 We used 5% Medicare inpatient claims data (1996-2008) to identify patients aged >/= 66

41 , observational study using health insurance claims data (1997-2013: Medicaid) from Florida, Iowa, Ka

42 We used 100% Texas Medicare claims data (2000-2008) to identify women older than 66

43 In a 5% sample of Medicare claims data, 2803 patients underwent preoperative stress

44 In linked clinical trial and Medicare claims data, 4 International Classification of Diseases,

45 Using administrative claims data, a commercially/Medicare-insured population

46 Oncology Group clinical records to Medicare claims data according to Social Security number, sex, an

47 In general, reports of claims data analyses should include clear descriptions o

48 ence to general guidance on the reporting of claims data analyses, as outlined in this article, is im

49 In our claims-data analysis of 4 common ocular conditions, a di

50 network of human diseases using large-scale claims data and analyze the associations between diagnos

51 ospitalization based on medical and pharmacy claims data and birth certificates.

52 I) participants aged >/=65 years to Medicare claims data and compared hospitalizations that had diagn

53 R include disease registries, administrative claims data and electronic medical records.

54 Here we model high-volume city-level medical claims data and human mobility proxies to explore the dr

55 tive cohort study, we used national Medicare claims data and identified patients undergoing bariatric

56 for Medicare and Medicaid Services inpatient claims data and International Classification of Diseases

57 light the limitations of using only Medicare claims data and suggest that population-based cohorts ma

58 ertain clinical parameters in administrative claims data and the inability to evaluate surrogate outc

59 NAFLD was identified using Medicare claims data, and controls were selected among participan

60 CathPCI Registry linked with administrative claims data, and validated using comparable 2005 data.

61 asure is not directly observed, but Medicare claims data are available.

62 ly, the diagnostic codes from administrative claims data are being used as clinical outcomes.

63 Administrative claims data are commonly used for sepsis surveillance, r

64 Claims data are currently not valid data sets for compar

65 NFIP claims data are messy, but the size of the dataset provi

66 As of 2008, claims data are used to deny payment for certain hospita

67 The use of diagnostic codes from claims data as clinical events, especially when restrict

68 Comparison of claims data-ascertained cases with the NLP demonstrated

69 We analyzed Medicare fee-for-service paid claims data between 1994-2012 to determine the number of

70 Medical and pharmacy claims data between 2007 and 2012 were analyzed to ident

71 We then examine how large-volume medical claims data can, with great spatiotemporal resolution, h

72 These claims data cases were then compared with NMSC identifie

73 In this study based on claims data collected in South Korea, we aimed to explor

74 This study linking diagnoses from EHRs to claims data collected valid information on PAR managemen

75 Claims data could be considered to track and report anti

76 ce-in-differences study using administrative claims data covering 6.7% of US adults.

77 Healthcare records for insurance claims data, detailing medical services incurred by mili

78 ssion diagnosis listed in the administrative claims data differed from the clinical diagnosis in 97 r

79 Claims data estimating rates of glaucoma medication adhe

80 set linking transplant registry and Medicare claims data for 12,803 liver transplant recipients was d

81 ant registry, pharmacy records, and Medicare claims data for 16 308 kidney transplant recipients tran

82 Health Study cohort Medicare enrollment and claims data for 1986-2004.

83 Using 100% Medicare claims data for 2010 to 2013, we identified patients age

84 (SEER) -Medicare linked databases to analyze claims data for 202,299 patients dying as a result of lu

85 PCI Registry data with longitudinal Medicare claims data for 250 350 patients undergoing PCI from 200

86 A retrospective study of Medicaid claims data for 28,794 unique pediatric patients coverin

87 rollment and nationwide MarketScan insurance claims data for 678220 privately insured patients receiv

88 Next, 2009-2010 Medicare claims data for 954,926 surgical patient discharges from

89 s between 1996 and 2006 using enrollment and claims data for a 5% national sample of Medicare benefic

90 G, AND PATIENTS: A retrospective analysis of claims data for a 5% representative sample of Medicare f

91 pective analysis of Medicare fee-for-service claims data for adults admitted for ischemic stroke from

92 United States Renal Data Systems patient and claims data for all adult renal transplant recipients be

93 id Analytic Extract database, which includes claims data for all children enrolled in Medicaid throug

94 Some advantages of using claims data for analyses include large, diverse sample s

95 The sensitivity of inpatient claims data for detecting ACS-NSQIP complications ranged

96 The reliability of claims data for documenting outcomes is unknown.

97 Variation in the accuracy of claims data for identifying sepsis and organ dysfunction

98 performed a retrospective cohort analysis of claims data for Medicaid beneficiaries, aged 18 to 64 ye

99 HSS to a hospital 30-day risk model based on claims data for Medicare beneficiaries with acute ischem

100 Using hospital claims data for Medicare beneficiaries, we calculated sp

101 ata Warehouse, which includes administrative claims data for over 100 million commercially insured an

102 ment was routinely poor between clinical and claims data for patient-level complications.

103 The data set was merged with claims data for patients in accountable care organizatio

104 y was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma

105 Clinical data have greater validity than claims data for quality measurement but can be burdensom

106 The use of claims data for research is expected to increase with th

107 A hybrid approach using claims data for risk adjustment and clinical data for co

108 corresponding hazard ratios based solely on claims data for the same hormone trial participants.

109 We aggregated claims data for the years 2004 and 2005 from four health

110 rvational cohort study, using administrative claims data from 14 commercial health care plans coverin

111 In this analysis of claims data from 145527 patients, wide variation in qual

112 Claims data from 19 927 newly diagnosed OAG patients enr

113 follow-up interviews were linked to Medicare claims data from 1993-2005.

114 ." The outcome was assessed by using medical claims data from 1998 to 2012.

115 We used Medicare claims data from 1999 through 2006 to measure trends in

116 Claims data from 1999 to 2006 were used.

117 -cause dementia using Medicare Parts A and B claims data from 1999 to 2009.

118 lorectal cancer using Medicare Parts A and B claims data from 1999 to 2009; the analysis was conducte

119 Using U.S. national Medicaid administrative claims data from 2000 through 2005, we identified a coho

120 Using national Medicare claims data from 2000 through 2009, we examined mortalit

121 tients with cancer were captured in Medicare claims data from 2005 through 2009 nationally and assess

122 pective cross-sectional study using Medicaid claims data from 2005 to 2010.

123 National Medicare claims data from 2006 to 2007 were used to examine the r

124 Using 100% Texas Medicare Claims Data from 2006 to 2011, we identified patients un

125 We analyzed Medicare claims data from 2006 to 2012 for patients discharged af

126 With the Guidelines) were linked to Medicare claims data from 2007 to 2010.

127 Using Medicare fee-for-service 5% claims data from 2007 to 2013, we analyzed treatment and

128 We used national Medicare claims data from 2009 and 2010 to examine geographic var

129 Using Medicare administrative claims data from 2010, we examined the relationship betw

130 ve cohort study was conducted using Medicaid claims data from 4 geographically diverse, large states

131 Using claims data from 4 participating health plans, we compar

132 pective analysis of fee-for-service Medicare claims data from 761 acute care hospitals providing inpa

133 We analyzed claims data from a 20% national Medicare sample of patie

134 We analyzed claims data from a 20% national sample of Medicare benef

135 rospective, observational cohort study using claims data from a 20% random sample of 2005-2008 Medica

136 Using claims data from a 20% random sample of Medicare benefic

137 Claims data from a 20% sample of enrollees in fee-for-se

138 Using prescription drug and medical claims data from a 5% random sample of Medicare benefici

139 This was a retrospective cohort study using claims data from a 5% random sample of Medicare benefici

140 Claims data from a large California Medicaid managed car

141 This analysis used insurance claims data from a large national database to identify p

142 Claims data from a large national United States managed

143 ive cohort study was conducted using medical claims data from a large national US insurer (N = 4,387,

144 AND PARTICIPANTS: Retrospective analysis of claims data from a large US commercial insurer, represen

145 Claims data from a managed care network were analyzed to

146 Using 2006--2007 claims data from a sample of private and public health p

147 2012 and December 2017 using administrative-claims data from across the United States (accessed thro

148 We performed a case-control study using claims data from all nonfederal emergency departments an

149 A retrospective cohort study using medical claims data from ambulatory care centers across the Unit

150 e cohort study using enhanced administrative claims data from approximately 20% of patients hospitali

151 We analyzed claims data from Clinformatics DataMart Database for pat

152 d a retrospective analysis of administrative claims data from community hospital and postdischarge am

153 s retrospective, inception cohort study used claims data from fee-for-service Medicare beneficiaries

154 statin with temperature dependence, by using claims data from five US Medicaid programs supplemented

155 rospective cohort study using administrative claims data from January 1, 2006 to December 31, 2015.

156 or inflammatory bowel disease using Medicare claims data from January 1, 2006, through December 31, 2

157 tudy using inpatient and outpatient Medicare claims data from January 1, 2008, through December 31, 2

158 Using claims data from January 2001 to December 2009, we obser

159 andardized health care costs from commercial claims data from January 2005 to June 2009, including to

160 alysis of publicly available Medicare Part B claims data from January 2012 to December 2014 includes

161 study was performed by using fee-for-service claims data from Medicare and a commercial carrier (Blue

162 By use of claims data from Medicare beneficiaries in the USA betwe

163 gnoses using commercial and Medicare medical claims data from Optum Labs (Cambridge, MA).

164 Setting: claims data from Optum Labs Data Warehouse, a longitudin

165 We used Medicaid claims data from Oregon and exploited the quasi-random a

166 trospective cohort study using U.S. Medicare claims data from patients undergoing pulmonary artery pr

167 Using national health insurance claims data from private preferred provider organization

168 We analyzed Medicare FFS claims data from surgeons who billed Medicare for 1 or m

169 All-payer claims data from the 2014 New York and Florida Healthcar

170 The claims data from the Bureau of National Health Insurance

171 es only in the primary position for Medicare claims data from the Center for Medicare & Medicaid Serv

172 ntific Registry of Transplant Recipients and claims data from the Centers for Medicare and Medicaid S

173 nd the STS Adult Cardiac Surgery Database to claims data from the Centers for Medicare and Medicaid S

174 rospective, cross-sectional study using 2011 claims data from the largest Minnesota health plan.

175 was a retrospective study of administrative claims data from the MarketScan Commercial and Medicare

176 rticipants: Retrospective cohort study using claims data from the Medicare Provider Analysis and Revi

177 A retrospective population-based study using claims data from the National Health Insurance Research

178 tion-based case-control design using medical claims data from the National Health Insurance Research

179 vey who were linked to the 2009 through 2013 claims data from the National Health Insurance system.

180 ropensity-matched study using administrative claims data from the OptumLabs Data Warehouse of private

181 is retrospective study, using administrative claims data from the OptumLabs Data Warehouse, we identi

182 pective cohort analysis utilizing healthcare claims data from the period 2007-2010 to compare rates o

183 nd effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract

184 nd effect estimate precision using insurance claims data from the Pharmaceutical Assistance Contract

185 lthcare payer perspective, we used insurance claims data from the Truven Health MarketScan 2014 Resea

186 ive cohort study and analyzed administrative claims data from Truven Health Analytics MarketScan Rese

187 011-2017 in US adults aged >=60 years, using claims data from Truven Health MarketScan Research Datab

188 Using patient-level claims data from US commercial and Medicare payers, we i

189 etrospective longitudinal cohort analysis of claims data from women 50 years or older enrolled in a U

190 In this analysis of claims data, from 2001 to 2009, a period during which th

191 HMO health plan claims data had a higher specificity than all-payer clai

192 a to risk-adjust complications identified by claims data had routinely poor agreement with all clinic

193 ratio, 0.84 [95% CI, 0.65-1.09]; P=0.33) and claims data (hazard ratio, 0.86 [95% CI, 0.66-1.11]; P=0

194 % for TAVR and 12.5% for SAVR patients using claims data (hazard ratio, 1.02 [95% CI, 0.73-1.41]; P=0

195 armacy sales), and the MarketScan Commercial Claims data (healthcare use) for 75 designated market ar

196 Addition of outpatient claims data improved sensitivity slightly but also great

197 ries 66-90 years of age from the 5% Medicare claims data in 2000 (n = 1,137,311) and tracked each sub

198 We conducted a study using claims data in the Taiwan National Health Insurance Rese

199 Claims data in the timeframe 2012-2014 were combined wit

200 Adding historical claims data increased the number of comorbidities identi

201 At 60 months, product-specific analyses of claims data indicated that between 34.6% and 55.4% of pa

202 ucted using Missouri Medicaid administrative claims data (January 1, 2010, to December 31, 2012) link

203 Using Medicare claims data linked to the North Carolina Central Cancer

204 etween 2002 and 2013 using Vizient inpatient claims data linked to the United Network for Organ Shari

205 After linking to Medicare claims data, long-term outcomes of CABG (up to 18 years

206 Furthermore, coding practices in claims data may influence findings.

207 However, estimates from claims data may lack clinical fidelity and can be affect

208 subset from calendar year 2009 with service claims data (n = 53,896).

209 This retrospective review analyzed the claims data of 145527 patients who underwent bariatric s

210 We analyzed prospective claims data of infants from Bavaria, Germany, born betwe

211 Using claims data of participating Sentinel/PRISM data-providi

212 Retrospective analysis of administrative claims data of patients discharged following a major sur

213 From the insurance claims data of patients with periodontal disease who wer

214 Based on claims data of the Helsana-Group, prevalence of IBD was

215 Retrospective analysis of health insurance claims data of two large Swiss basic health insurance pl

216 Using claims data on all discharges from nonfederal emergency

217 rospective cohort study using administrative claims data on patients hospitalized for surgery (as def

218 between clinical registry and administrative claims data on the occurrence of postoperative complicat

219 Measures were applied using clinical data, claims data, or a hybrid of both data sources.

220 ross-sectional, drawn from administrative or claims data, or based on qualitative work in limited geo

221 spitalizations) and Medicare fee-for-service claims data (out-of-system hospitalizations).

222 evel multivariable analysis of 2011 Medicare claims data (Part A hospital and Part B physician) for a

223 ted hospital variation in the sensitivity of claims data relative to clinical data from electronic he

224 Catheter-associated UTI rates determined by claims data seem to be inaccurate and are much lower tha

225 Using a nationwide insurance claims data set from 2013 to 2014, we identified US adul

226 data from SDI Health, a large administrative claims data set, to conduct a retrospective cohort study

227 delines-Stroke registry linked with Medicare claims data set, we examined whether 30-day and 1-year o

228 ith comparison series study using a national claims data set.

229 t differences between ACS-NSQIP and Medicare claims data sets for measuring surgical complications.

230 linked to Medicare inpatient and outpatient claims data sets.

231 Within 2 commercial and 1 federal (Medicare) claims data sources in the United States, we identified

232 Poor accuracy potentially makes claims data suboptimal for evaluating surgical complicat

233 ages include (a) the inherent limitations of claims data, such as incomplete, inaccurate, or missing

234 Insurance claims data suggest that the use of extended courses of

235 nce in cases identified by NLP compared with claims data, suggesting that formal surveillance efforts

236 re more closely concordant between trial and claims data than nonprocedural outcomes (eg, stroke, ble

237 Data included 100% Medicare claims data that covered admissions between 2000 and 200

238 study, linked to Medicare Benefits Schedule claims data, the cancer registry, and hospital admission

239 n Cancer Database, National Health Insurance Claims Data, the National Death Registry, and the bundle

240 By using the 5% sample of Medicare claims data, the study assessed risks of 3 adverse outco

241 hospital discharge records and from Medicare claims data through December 31, 2010.

242 ial designs have proposed the use of medical claims data to ascertain clinical events; however, the a

243 We analyzed insurance claims data to better characterize histoplasmosis testin

244 This pharmacoepidemiology study uses claims data to characterize angiotensin receptor blocker

245 This study uses Medicare Parts A and B claims data to compare hospitalizations for and spending

246 study uses commercial and Medicare Advantage claims data to compare medication fills, outpatient visi

247 We used commercial insurance claims data to compare vertical sleeve gastrectomy (VSG)

248 This study uses administrative claims data to describe trends in use of ICD-10-CM diagn

249 This study uses national pharmacy claims data to describes trends in prescriptions for HIV

250 Although caveats must be considered in using claims data to estimate prevalence in a population, thes

251 The use of claims data to evaluate resource use and efficiency and

252 We used linked Medicare claims data to examine postdischarge outcomes of 39 136

253 Adding electronically-available patient claims data to facility models consistently improved ant

254 from the US Renal Data System with Medicare claims data to identify 17,511 patients >/=67 years old

255 used Medicare fee-for-service administrative claims data to identify acute care hospitalizations for

256 se-control study to administrative insurance claims data to identify cases with nAMD and risk-set sam

257 y 1, 2007, to December 31, 2009, to Medicare claims data to obtain 1-year follow-up and medication ad

258 on Guidelines (CRUSADE) Registry to Medicare claims data to obtain longitudinal outcomes.

259 We used Medicare claims data to perform 3 cohort studies of medication in

260 proportional-hazard models that use Medicare claims data to predict life expectancy and risk of death

261 Hybrid models using claims data to risk-adjust complications identified by c

262 We used insurance claims data to track any use of trastuzumab in the 12 mo

263 METHODS AND Using commercial claims data (Truven Health Analytics MarketScan), we per

264 009 were matched to Medicare fee-for-service claims data using indirect patient identifiers.

265 07 and 31 December 2008 were identified from claims data using three ascertainment strategies.

266 he comparative performance of administrative claims data versus clinician-triggered event adjudicatio

267 tudinal cohort analysis of national Medicaid claims data was conducted of adults 21-64 years of age w

268 The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-5

269 ive analysis of the Medicare fee-for-service claims data was performed for elderly patients admitted

270 ent on hospital quality between clinical and claims data was poor.

271 ession analysis of national health insurance claims data was used to evaluate health care utilization

272 The primary referent, determined from claims data, was the first observed outpatient nephrolog

273 and Results Using 2007 to 2014 US healthcare claims data, we ascertained a retrospective cohort of wo

274 h Centers for Medicare and Medicaid Services claims data, we ascertained vital status from date of su

275 Using hospital claims data, we compared differences in coronary revascu

276 sing clinical registry data and longitudinal claims data, we developed a long-term survival predictio

277 Using a 100% sample of Medicare claims data, we evaluated Medicare beneficiaries (N = 19

278 y CathPCI registry data linked with Medicare claims data, we examined the association between operato

279 Using Medicare claims data, we followed up with patients up to 5 years

280 Using MarketScan(R) claims data, we identified AF patients without coronary

281 In this observational study using Medicare claims data, we identified incident cases with a primary

282 er Valve Therapy Registry linked to Medicare claims data, we identified patients >/=65 years old unde

283 gistry of Transplant Recipients and Medicare claims data, we studied 6780 HCV+ and 139 681 HCV- KT re

284 Using SRTR and Medicare claims data, we studied 6780 HCV+ and 139,681 HCV- KT re

285 Claims data were analyzed to assess trends in visual fie

286 tched, de-identified reviews and malpractice claims data were available for 264 surgeons from 2000 to

287 Demographic and medical claims data were compiled and prevalence estimates for P

288 urance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Cli

289 Medicaid claims data were obtained from the following states: Flo

290 Medicare claims data were used for confirmatory analyses.

291 LIMITATION: Medicare claims data were used for risk adjustment.

292 Medicare claims data were used to assign costs for postacute care

293 Administrative, budgetary, and service claims data were used to calculate and summarize costs f

294 Aggregated Medicare claims data were used to determine utilization of biopsi

295 e utilization of nursing home care; Medicare claims data were used to identify costs paid by Medicare

296 Medicare administrative and claims data were used to identify the date and cause of

297 ment Advisory (MedPAC) has recommended using claims data wherever possible to measure clinical qualit

298 t ophthalmology-related investigations using claims data will likely continue to evolve as health ser

299 cal measures leading to frequent reliance on claims data with its flaws in determining quality, fragm

300 tient, the EHRs were linked to corresponding claims data with MRU and costs during years 2011 to 2013