ライフサイエンスコーパス: claims data

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

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

3 e Centers for Medicare and Medicaid Services claims data.

4 rumoxytol as reported in outpatient Medicare claims data.

5 adjustment was limited to that available in claims data.

6 osclerotic CVD events identified in Medicare claims data.

7 and End Results registry linked to Medicare claims data.

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

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

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

11 ident from prevalent cases identified in the claims data.

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

13 ospitals were linked with Medicare inpatient claims data.

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

15 in pharmacoepidemiologic studies relying on claims data.

16 on Centers for Medicare & Medicaid Services claims data.

17 mbled by use of linked hospital and pharmacy claims data.

18 numerical laboratory data to administrative claims data.

19 ive incidence and rates were calculated from claims data.

20 examining the validity of gout diagnoses in claims data.

21 30-day mortality rates derived from Medicare claims data.

22 itored and nonmonitored exercise in Medicare claims data.

23 s, which were measured using Medicare Part A claims data.

24 measures in the model and adding historical claims data.

25 ital procedure volume as defined by Medicare claims data.

26 for ischemic stroke, determined by Medicare claims data.

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

28 lth care plans and geographic areas based on claims data.

29 rm Care Survey and merged them with Medicare claims data.

30 certificate and maternal and infant hospital claims data.

31 ertificate and maternal and newborn hospital claims data.

32 ords, study electrocardiograms, and Medicare claims data.

33 new breast cancer was identified with use of claims data.

34 between 2005 and 2014, using administrative claims data.

35 edications was obtained from Medicare Part D claims data.

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

37 tion and linked these patients with Medicare claims data.

38 sing clinical registry versus administrative claims data.

39 ent Database together with VA administrative claims data.

40 went colorectal surgery using administrative claims data.

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

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

43 with sickle cell anemia using administrative claims data.

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

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

46 es including medical record review, Medicare claims data, 1990 Census data, and a patient survey, we

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

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

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

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

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

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

53 egy were analyzed using linked SEER-Medicare claims data after adjusting for differences in comorbidi

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

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

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

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

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

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

60 ity of disease were determined from Medicare claims data and correlated with the entry period hematoc

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

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

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

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

65 For the comparison of models from claims data and medical record data, we used the Coopera

66 risk-standardized state mortality rates from claims data and rates derived from medical record data w

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

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

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

70 Claims data are currently not valid data sets for compar

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

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

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

74 Using information from the national Medicare claims data base and the Nationwide Inpatient Sample, we

75 Using information from the national Medicare claims data base for 1998 through 1999, we examined mort

76 on between 1989 and 1991 were sampled from a claims data base of the Surveillance, Epidemiology, and

77 to glaucoma medications by using a modified claims data-based measure of adherence, validation by ch

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

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

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

81 These claims data cases were then compared with NMSC identifie

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

83 Claims data could be considered to track and report anti

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

85 Medicare Part B claims data files from 1993, 1996, and 1999 were analyze

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

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

88 quality-of-care outcomes were measured with claims data for 18 309 patients (n = 178 to 2657 per pro

89 We analyzed Medicare claims data for 190,921 discharges from 69 rehabilitatio

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

91 n Sharing registry information with Medicare claims data for 1991-1996.

92 l transplant registry was merged to Medicare claims data for 1991-1997 by the United States Renal Dat

93 We used all Medicare fee-for-service claims data for 1998 through 2000 to determine the incid

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

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

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

97 iption opioid use was assessed from pharmacy claims data for 3 30-day periods immediately after the i

98 l transplant registry was merged to Medicare claims data for 42,868 cadaveric renal transplants perfo

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

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

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

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

103 The sensitivity of the claims data for a specific diagnosis was calculated as t

104 The positive predictive value of the claims data for a specific diagnosis was calculated as t

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

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

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

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

109 nt random sample of the 1993 Medicare Part B claims data for beneficiaries over the age of 65 who wer

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

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

112 We reviewed 1 year of claims data for extremity radiographic examinations perf

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

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

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

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

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

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

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

120 cian or referred to a radiology facility and claims data for related patient office visits.

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

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

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

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

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

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

127 Claims data from 19 927 newly diagnosed OAG patients enr

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

129 th Korea by using nationwide heath insurance claims data from 1997 to 1999.

130 hierarchical regression model using Medicare claims data from 1998 that produces hospital risk-standa

131 Data included Medicaid eligibility and claims data from 1998 to 2000 for San Diego County, Cali

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

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

134 Claims data from 1999 to 2006 were used.

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

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

137 Claims data from 1999-2003 were grouped into episodes of

138 Calendar year 2002 claims data from 2 large insurers in Washington state we

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

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

141 We analyzed Medicare claims data from 2003-2004 to describe the patterns of r

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

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

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

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

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

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

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

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

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

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

152 rospective, observational cohort study using claims data from a 20% random sample of 2004 Medicare fe

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

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

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

156 Claims data from a large California Medicaid managed car

157 a retrospective study, the authors analyzed claims data from a large health insurer in New England.

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

159 Claims data from a large national United States managed

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

161 Claims data from a large, multistate managed care organi

162 Claims data from a managed care network were analyzed to

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

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

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

166 rol analysis was performed with pharmacy and claims data from California Medicaid (Medi-Cal).

167 Medicare part B claims data from fiscal year 1992 were analyzed for CPT

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

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

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

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

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

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

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

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

176 Aggregated claims data from Medicare enrollees for all radiology pr

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

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

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

180 The claims data from the Bureau of National Health Insurance

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

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

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

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

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

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

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

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

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

190 We used longitudinal claims data from three large commercial health programs

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

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

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

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

195 led that 31% of subjects "new to therapy" in claims data had actually been previously treated; and th

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

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

198 Medicare claims data identified participants with osteoarthritis

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

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

201 rt of 9123 episodes of UGIH in 2004 Medicare claims data, including 3506 (38.4%) managed as outpatien

202 Adding historical claims data increased the number of comorbidities identi

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

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

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

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

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

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

209 Medical record reviews for validation of claims data may provide an inadequate gold standard to c

210 Although the use of claims data may underestimate the true incidence of lymp

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

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

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

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

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

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

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

218 Using claims data on all discharges from nonfederal emergency

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

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

221 tal surgical volume, as assessed by Medicare claims data, on overall survival and gastric cancer recu

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

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

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

225 erformance in Medicare: that with the use of claims data, patients can be assigned to a physician or

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

227 Medicare claims data seem to provide valuable information on the

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

229 Data were drawn from a Medicaid medical claims data set from Pittsburgh and the surrounding regi

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

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

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

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

234 linked to Medicare inpatient and outpatient claims data sets.

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

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

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

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

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

240 A model using administrative claims data that is suitable for profiling hospital perf

241 A model using administrative claims data that is suitable for profiling hospital perf

242 hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-

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

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

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

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

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

248 We used claims data to compare the utilization of and spending o

249 Medicare expenditures, we analyzed Medicare claims data to determine current patterns of use.

250 es Renal Data System (USRDS), using Medicare claims data to determine the incidence of new-onset gout

251 Survey data were combined with Medicare claims data to estimate intensity-adjusted daily cost.

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

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

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

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

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

257 omorbidity suggest limitations in the use of claims data to identify diabetes in the elderly.

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

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

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

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

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

263 We used claims data to study an incident cohort of breast cancer

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

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

266 009 were matched to Medicare fee-for-service claims data using indirect patient identifiers.

267 07 and 31 December 2008 were identified from claims data using three ascertainment strategies.

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

269 etrospective analysis of California Medicaid claims data was conducted on patients with SLE.

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

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

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

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

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 For hospital estimates derived from claims data, we developed a derivation model using 140,1

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

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

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

282 Claims data were analyzed to assess trends in visual fie

283 Claims data were analyzed to follow a cohort of 57,488 M

284 Pharmacy claims data were collected for all patients with a diagn

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

286 Aggregated claims data were obtained from four sources for up to ni

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

288 oncentrations were measured, and health care claims data were obtained.

289 Medicare claims data were used for confirmatory analyses.

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

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

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

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

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

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

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

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

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

299 We merged Medicare claims data with OPTIMIZE-HF (Organized Program to Initi