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1  clinical information were recorded from the electronic medical record.
2 a a patient scoring tool integrated into the electronic medical record.
3 08 and December 2019 through a search of the electronic medical record.
4 and pharmacy data were extracted from the VA electronic medical record.
5 ations to primary care providers through the electronic medical record.
6  care was obtained from documentation in the electronic medical record.
7 telet count variables were obtained from the electronic medical record.
8 l centers were entered prospectively into an electronic medical record.
9 on and retrospective analysis as part of the electronic medical record.
10 examination findings, was reviewed using the electronic medical record.
11 a manual, retrospective chart reviews of the electronic medical record.
12 ckup data were obtained through the existing electronic medical record.
13 ior asthma treatments were obtained from the electronic medical record.
14   Clinical information was obtained from the electronic medical record.
15 adiotherapy by using predictors from routine electronic medical record.
16 , and home address were obtained through the electronic medical record.
17 d pathologic reports were extracted from the electronic medical record.
18  from hospitalized patients, alongside their electronic medical records.
19 nosis and procedure codes from comprehensive electronic medical records.
20 l Leuven were identified via a search of the electronic medical records.
21  linking diseases and symptoms directly from electronic medical records.
22 cal information was extracted from subjects' electronic medical records.
23            Clinical data were extracted from electronic medical records.
24 reasing, especially with the introduction of electronic medical records.
25 topathologic outcomes were obtained from the electronic medical records.
26 and surgery details were obtained from their electronic medical records.
27 s trapped in the free-text narratives within electronic medical records.
28 and March 31, 2011, were identified from the electronic medical records.
29 g documented HCV infection was obtained from electronic medical records.
30            Clinical data were collected from electronic medical records.
31 time to onset of cirrhotic decompensation in electronic medical records.
32 period that ended in 2011 and verified using electronic medical records.
33 ccination during pregnancy was obtained from electronic medical records.
34 ineligibility was assessed through review of electronic medical records.
35 iovascular risk factors were identified from electronic medical records.
36 d using information extracted from patients' electronic medical records.
37 and clinical covariates were determined from electronic medical records.
38 s to register a diagnosis of HD in patients' electronic medical records.
39 readily transferable to modern comprehensive electronic medical records.
40 e registries, administrative claims data and electronic medical records.
41 ss to their history of Facebook statuses and electronic medical records.
42 eases (ICD)-9 and ICD-10 codes documented in electronic medical records.
43 l data were retrieved from the corresponding electronic medical records.
44 abstracted clinical and laboratory data from electronic medical records.
45  databank and extracted outcomes from linked electronic medical records.
46 d from Kaiser Permanente Southern California electronic medical records.
47 bed over 2 consecutive days through the same electronic medical records.
48 % vs 22.0%, P = .03) and a fully implemented electronic medical record (12.6% vs 17.8%, P = .03).
49 l of 726 (70.3%) had a weight entered in the electronic medical record 7 or more years after surgery
50 udied by retrieving patient information from electronic medical records after ethical approval.
51 iduals without cardiac disease selected from electronic medical record algorithms at 5 sites in the E
52                                        Using electronic medical records, all participants were follow
53 , which uses routinely collectible data from electronic medical records, along with brief clinical as
54 ware that allows integration of FHH with the electronic medical record and clinical decision support
55                      The two CLIA-accredited Electronic Medical Record and Genomics Network sequencin
56 apy was set to discontinue after 48 h in the electronic medical record and the duration of therapy fo
57                                              Electronic medical records and all available imaging stu
58                                              Electronic medical records and all available imaging stu
59 ta from Wake Forest Baptist Medical Center's electronic medical records and annotated with BioCarta s
60                              A review of the electronic medical records and dictated reports identifi
61 atients of European or African ancestry with electronic medical records and exome chip data to compar
62 viduals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network
63 ploratory Research (CSER) Consortium and the Electronic Medical Records and Genomics (eMERGE) Network
64  medical record algorithms at 5 sites in the Electronic Medical Records and Genomics (eMERGE) network
65 cases and 7,763 controls identified from the Electronic Medical Records and Genomics (eMERGE) network
66 ober 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmaco
67 zed Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we appl
68 es with asthma and control subjects from the Electronic Medical Records and Genomics network.
69 ith monocyte count in 11 014 subjects in the electronic Medical Records and Genomics Network.
70  modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978).
71 ut of care in an urban HIV care clinic using electronic medical records and geospatial data.
72                     The authors reviewed the electronic medical records and imaging reports for all p
73 d postdelivery infant feeding practices from electronic medical records and in-person surveys.
74 and interpret data, and integration into the electronic medical records and medical practice.
75                                              Electronic medical records and patient interviews were r
76                                              Electronic medical records and radiology reports were re
77 ta were collected from the Parkland Hospital electronic medical records and the US census, constituti
78 ocol development and implementation into the electronic medical record, and (5) ongoing review of dat
79 llection time, time of result entry into the electronic medical record, and turnaround time were comp
80                              Patient facing, electronic medical record, and web-enabled FHH platforms
81   Patient characteristics were obtained from electronic medical records, and ASCVD events were ascert
82      Surgery-related data were collected via electronic medical records, and complications were calcu
83 ofiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health
84 ser integration of psychiatric genetics with electronic medical records, and the development of the n
85 otic prescribing frequency, especially where electronic medical records are not available.
86 estimates, derived from large collections of electronic medical records, are useful for interpreting
87                                              Electronic medical records at the Cleveland Clinic were
88 posures and medication adherence) as well as electronic medical records augmented with clinical decis
89 tive single-center cohort study utilizing an electronic medical record based database of patients who
90 n-dialysis-dependent CKD population using an electronic medical record-based CKD registry in a large
91 left ventricular ejection fraction, a simple electronic medical record-based intervention directed to
92                                 We also sent electronic medical record-based messages shortly before
93 ations of the FUT6 p.Glu274Lys variant in an electronic medical record-based phenome-wide association
94  drug reactions at our hospital, we utilized electronic medical records-based automated trigger tools
95  We sought to determine whether an automated electronic medical record best practice alert (BPA) base
96 btained and confirmed by review of paper and electronic medical records between 2015 and 2017.
97 ged 18-90 years with COVID-19 coded in their electronic medical records between January 20, 2020, and
98 TM) captures data contained in retrospective electronic medical records between September 2012 and Ja
99 ake updates to laboratory information system/electronic medical record builds in the setting of limit
100                              We reviewed the electronic medical record by analyzing the histological,
101               SSTIs were identified from the electronic medical record by use of International Classi
102                                 However, the electronic medical record can reliably be used to identi
103                      Information overload in electronic medical records can impede providers' ability
104  utilization, the widespread availability of electronic medical records capable of supporting clinici
105                                     National electronic medical records compiled by the Veterans Heal
106                                          The electronic medical record contains a wealth of informati
107                                          The electronic medical record contains an abundance of unuse
108                        The current growth of electronic medical records coupled with machine learning
109                      We benefit from data in electronic medical records covering all hospital encount
110 characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as fami
111 apability; data was cyber-delivered into our electronic medical record daily.
112  All transmitted data are transferred to the electronic medical record daily.
113 measurements from a single blood sample with electronic medical record data (EMR) for the identificat
114  We measured implementation metrics based on electronic medical record data and evaluated the impact
115                                      We used electronic medical record data and supplemented these wi
116                                  We analyzed electronic medical record data from a 4-site retrospecti
117 a text-processing method was used to analyze electronic medical record data from an academic medical
118         Retrospective analysis of nationwide electronic medical record data from the US Veterans Heal
119 lemented text-processing pipeline to analyze electronic medical record data in a retrospective cohort
120                                              Electronic medical record data included gestational week
121 nal Surgical Quality Improvement Program and electronic medical record data obtained on diverticuliti
122                             The structure of electronic medical record data prevents easy population-
123 ates the utility of novel methods to analyze electronic medical record data to generate practice-base
124                                          The electronic medical records data from Narayana Nethralaya
125  to incorporate Internet-source and hospital electronic medical records data into surveillance system
126 study determined by an initial search of the electronic medical record database of Kaiser Permanente
127                                           An electronic medical record database was used to identify
128      From a multi-institutional longitudinal electronic medical record database, we identified patien
129 , Titusville, NJ) were identified through an electronic medical record database.
130 nd risk score associations in an independent electronic medical records database (n = 192,868) reveal
131 om The Health Improvement Network (THIN), an electronic medical records database broadly representati
132                          A population-based, electronic medical records database from the Hong Kong C
133 e collected from the healthcare organization electronic medical record databases and some comorbiditi
134 inistrative, clinical, laboratory, drug, and electronic medical record databases using encoded person
135 ricular tachycardia (VT)-were ascertained by electronic medical records, defibrillator interrogation,
136                          We examined a large electronic medical record (EMR) containing health record
137 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each ot
138                                              Electronic medical record (EMR) data from patients 10 ye
139             The growth of biobanks linked to electronic medical record (EMR) data has both facilitate
140                       This cohort study uses electronic medical record (EMR) data to examine the asso
141 ypothesized that analysis of highly-detailed electronic medical record (EMR) data would demonstrate t
142 data were ascertained retrospectively from a electronic medical record (EMR) dataset and analyzed.
143                                          The electronic medical record (EMR) has huge potential for f
144 ate documentation of patient symptoms in the electronic medical record (EMR) is important for high-qu
145 e led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of
146                    Retrospective analysis of electronic medical record (EMR) notes (OpenEyes) and pap
147 ters in the United Kingdom that use the same electronic medical record (EMR) system (Medisoft Ophthal
148               We used data from the national electronic medical record (EMR) system in Zambia to enum
149 ively collected clinical data using a single electronic medical record (EMR) system, with automatic e
150 ngdom centers to a central database using an electronic medical record (EMR) system.
151                                              Electronic medical record (EMR) systems have become wide
152 iscover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologi
153 nformation system (LIS) can be linked to the electronic medical record (EMR) to enable adaptive alert
154 , patient-level administration data from the electronic medical record (EMR), and patient-level admin
155                                              Electronic medical record (EMR)-based reflex strategy sc
156 agnosis by combining these measurements with electronic medical record (EMR).
157 ientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices.
158 inistrative claims, physician databases, and electronic medical records (EMR) from 1455 patients (50-
159                                Although, the electronic medical records (EMR) system is the digital s
160                                The advent of Electronic Medical Records (EMR) with large electronic i
161 g methods that combine chart data, including electronic medical records (EMR), with PRO symptoms may
162 ine learning algorithms applied to patients' electronic medical records (EMR).
163                      Retrospective review of electronic medical records (EMRs) in an integrated healt
164 medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrativ
165                          Phenotype data from electronic medical records (EMRs) may provide a resource
166                    A retrospective review of Electronic Medical Records (EMRs) of patients admitted f
167 lore integrating NGS with clinical data from electronic medical records (EMRs), immune profiling data
168 ia to enable an automated chart review using electronic medical records (EMRs).
169  included provider and patient education, an electronic medical record-enabled best practice alert, a
170 lopmental Cohort is a genotyped sample, with electronic medical records, enrolled in the study of bra
171                                              Electronic medical record follow-up was conducted for 30
172 veitis was determined through a query of the electronic medical record followed by individual medical
173       The mean time of result entry into the electronic medical record for CT was 3.5 days earlier th
174 roencephalogram (EEG) data and evaluated the electronic medical record for evidence of epilepsy and d
175 s-sectional analysis of routine primary care electronic medical records for 1 424 378 adults in the U
176                                              Electronic medical records for 223,502 US deliveries wer
177 rs, and we retrieved diabetes diagnoses from electronic medical records for 8 years.
178            Clinical data were extracted from electronic medical records for all study patients, inclu
179 opulation-based genealogy resource linked to electronic medical records for health care systems acros
180          With the increased use of data from electronic medical records for research, it is important
181 data were abstracted from individual patient electronic medical records for the 24 hours before the r
182 aset of >10 million individuals derived from electronic medical records from 1995 through 2013 in the
183 e of aggregated, longitudinal, de-identified electronic medical records from a geographically and dem
184 y using nationwide pharmacy claims linked to electronic medical records from a nationwide data wareho
185 duled visit) as determined from clinic-based electronic medical records from a probability sample of
186 l signatures extracted from over 13 years of electronic medical records from a tertiary hospital, inc
187 ntional radiology database and of individual electronic medical records from an academic tertiary med
188 r Infirmary Ocular Inflammation Database and electronic medical records from March 1, 2008, to Decemb
189                                  We analysed electronic medical records from the Clinical Practice Re
190 cal and demographic data were extracted from electronic medical records from the University of Califo
191   Two primary outcomes were assessed through electronic medical records: >=2 urine drug tests and any
192  in digital technology and increasing use of electronic medical records in health systems have led to
193 ultisite, observational study (2002-2008) of electronic medical records in the United States.
194 e clinical factors (>200) extracted from the electronic medical record included medications, comorbid
195 y was ascertained via resurvey or linkage to electronic medical records (including hospital admission
196     Electronic data were abstracted from the electronic medical record, including demographics, syste
197  and unconventional structured data from the electronic medical record, including need for medical in
198                                              Electronic medical record information was used to compar
199 ation strategies (repeated-mailing outreach, electronic medical record-integrated provider best pract
200 ological approach to quality improvement and electronic medical record integration has potential to s
201                      Vanderbilt University's electronic medical record linked to a DNA biorepository
202 ealth Administration's integrated, national, electronic medical record linked to Organ Procurement an
203  (BioVU; n = 786), the Vanderbilt University electronic medical record-linked DNA biobank.
204  HumanExome BeadChip v.1.0 in the Vanderbilt electronic medical record-linked DNA repository, BioVU.
205  N = 133,413 for eGFR, N = 117,165 for CKD), electronic-medical-record-linked UK Biobank data (N = 33
206 hrough epidemiological investigations and an electronic medical records match, and summarized descrip
207  and their skin check partners drawn from an electronic medical record melanoma registry and advertis
208 April 10, 2020, were identified by using the electronic medical record (n = 326; mean age, 59 years +
209 2006, and May 30, 2012, were identified from electronic medical records (n = 830).
210 umentation of end-of-life discussions in the electronic medical record, no single intervention type o
211                                              Electronic medical records, nowadays routinely collected
212 eriod determined by an initial search of the electronic medical record of Kaiser Permanente Hawaii an
213                                          The electronic medical record of patients born in or after 1
214 , based on secondary data extracted from the electronic medical record of the Hospital Italiano of Bu
215                                          The electronic medical record of trauma patients undergoing
216                        Materials and Methods Electronic medical records of 185 patients referred to t
217                         Authors reviewed the electronic medical records of 3 treatment-naive HCV geno
218 emerged from our qualitative analysis of the electronic medical records of 340 cohort members with no
219  VFs and IOP measurements extracted from the electronic medical records of 5 regionally different gla
220                Data for this study came from electronic medical records of a level III neonatal care
221       Potential subjects were screened using electronic medical records of a regional Veterans Affair
222  graft survival by retrospective analysis of electronic medical records of a single-center cohort of
223              We retrospectively reviewed the electronic medical records of all children younger than
224 We carried out a retrospective review of the electronic medical records of all patients undergoing GM
225                                          The electronic medical records of all patients with an Inter
226 nset were collected retrospectively from the electronic medical records of ALS patients.
227                                        Using electronic medical records of an integrated delivery sys
228 ction of detailed phenotype information from electronic medical records of cancer patients.
229 were determined using standard criteria with electronic medical records of laboratory, diagnosis, and
230                                          The electronic medical records of patients aged 21 years or
231 design was used to analyse data derived from electronic medical records of patients enrolled in the C
232                              We reviewed the electronic medical records of patients positive for seve
233 this retrospective, single-center study, the electronic medical records of patients who were primaril
234                  We retrospectively analyzed electronic medical records of patients with Ehlers-Danlo
235  Duke Glaucoma Registry, a large database of electronic medical records of patients with glaucoma and
236 nd ventilator days from individual review of electronic medical records of sequential adult patients
237 ded in Kaiser Permanente Northern California electronic medical records on at least 2 occasions any t
238 of routine care are reviewed: (1) effects of electronic medical records on the patient interview; (2)
239 characteristics, identified full adoption of electronic medical records (OR 4.74), home health progra
240 ine (n = 736; 65.7%), through development of electronic medical record orders (n = 626; 55.8%), or wi
241       A retrospective review and analysis of electronic medical records over a 12-week period examine
242                        Data sources included electronic medical records, pharmacy databases, state bi
243  were obtained from full review of paper and electronic medical records, prescriptions, and investiga
244  with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03).
245 n review of the insulin/glucose chart in the electronic medical record, recommendations for insulin c
246                                              Electronic medical record review for International Class
247 le pathologic analyses were assessed through electronic medical record review.
248 gh questionnaires with confirmation using an electronic medical record review.
249  information from patient questionnaires and electronic medical records review, three models were dev
250  compared to information availability in the electronic medical record (secondary outcome).
251 cians of the burden of checking boxes in the electronic medical record so that they can devote their
252 order to extract meaningful information from electronic medical records, such as signs and symptoms,
253 om the population-based cancer registry with electronic medical records supporting ART delivery in Ma
254  extracted from 14 UK centers using the same electronic medical record system (EMR).
255 ing the development and implementation of an electronic medical record system and a new league-wide i
256                                    Follow-up electronic medical record system data were available for
257 rovider decision-making support tools in the electronic medical record system may potentially improve
258 f documentation pertaining to hospice in the electronic medical record system of the Department of Ve
259 ively collected clinical data using a single electronic medical record system, with automatic extract
260 and clinical data were collected through the electronic medical record system.
261 , OPCRIT+, that is being introduced into the electronic medical records system of the South London an
262 reoretinal surgery were recorded on the same electronic medical records system.
263 tates centers to a central database using an electronic medical records system.
264 sease pairs that are overrepresented in both electronic medical record systems and in VARIMED come fr
265                 Widespread implementation of electronic medical record systems has inadvertently led
266  routinely collected, anonymized data within electronic medical record systems were extracted remotel
267                   THIN database comprises UK electronic medical records taken from 787 general practi
268 medical record tool was implemented into the electronic medical record to aid preoperative clinic pro
269                                      We used electronic medical records to ascertain data for antibio
270               We applied machine-learning to electronic medical records to better characterize the he
271  the time to utilize information recorded in electronic medical records to develop innovative disease
272                                      We used electronic medical records to identify all office visits
273 a validated model that uses information from electronic medical records to identify hospitalized pati
274 frequently used clinical concepts within the electronic medical record, to improve the efficiency of
275 ticenter cohort with genotype data linked to electronic medical records, to estimate the diagnostic r
276                           A clinically based electronic medical record tool was implemented into the
277 rt, recent stressor exposures, and, from the electronic medical record, treatment history, and Charls
278 ecause the largest source of clinical data - Electronic Medical Records - typically contains noisy, s
279                     A separate search in the electronic medical record was also performed to identify
280                                          The electronic medical record was reviewed for each patient
281                                          The electronic medical record was used to identify neutropen
282               A phenotyping algorithm mining electronic medical records was developed and validated t
283                A comprehensive search of the electronic medical records was performed using a proprie
284 h at least 12 months follow-up and available electronic medical records was used to identify 37 T2D p
285 tion to mortality data obtained from routine electronic medical records, we intensively traced a rand
286 ervation procedures: all AEs recorded in the electronic medical record were extracted and retrospecti
287 ervation Procedures: All AEs recorded in the electronic medical record were extracted and retrospecti
288                                              Electronic medical records were examined for a history o
289 e from Kaiser Permanente Northern California electronic medical records were included.
290                              Serial prenatal electronic medical records were obtained from women who
291                                              Electronic medical records were reviewed for 102 patient
292                                              Electronic medical records were reviewed for a 5-year pe
293                                              Electronic medical records were reviewed for abnormal fi
294                                              Electronic medical records were reviewed for OTRs diagno
295                                              Electronic medical records were reviewed for patients wi
296                                              Electronic medical records were reviewed to determine th
297                                              Electronic medical records were searched to verify test
298  Limitations of our study include the use of electronic medical records, which could have resulted in
299 zation of health care is best exemplified by electronic medical records, which have been far from fav
300               Current models for correlating electronic medical records with -omics data largely igno

 
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