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1 Salient information was abstracted from the medical record.
2 the analysis of symptoms abstracted from the medical record.
3 rsitis symptom onset was abstracted from the medical record.
4 etrospective chart reviews of the electronic medical record.
5 ponding patient data were retrieved from the medical record.
6 ere obtained through the existing electronic medical record.
7 structured interviews or abstracted from the medical record.
8 treatments were obtained from the electronic medical record.
9 information was obtained from the electronic medical record.
10 address were obtained through the electronic medical record.
11 of 15 comorbidities were abstracted from the medical record.
12 c reports were extracted from the electronic medical record.
13 ions and procedures were abstracted from the medical record.
14 ependently analyzed blinded to the patients' medical record.
15 by using predictors from routine electronic medical record.
16 btained through abstraction of the inpatient medical record.
17 nformation were recorded from the electronic medical record.
18 mber 2019 through a search of the electronic medical record.
19 findings, was reviewed using the electronic medical record.
20 with clinical, chemo/radiotherapy data from medical records.
21 linical data were obtained through review of medical records.
22 Patient data were retrieved from the medical records.
23 Patient demographics were collected from medical records.
24 h were extracted from death certificates and medical records.
25 cific radiotherapy (RT) were abstracted from medical records.
26 -9 and ICD-10 codes documented in electronic medical records.
27 retrieved from the corresponding electronic medical records.
28 clinical and laboratory data from electronic medical records.
29 in our clinics and, by extension, within our medical records.
30 linical and outcome data were retrieved from medical records.
31 Clinical data were extracted from medical records.
32 ires and subsequently confirmed by reviewing medical records.
33 mes, and adverse events were abstracted from medical records.
34 atment data regarding TC were retrieved from medical records.
35 was self-reported and validated by review of medical records.
36 collected according to maternal and neonatal medical records.
37 th outcomes were abstracted at delivery from medical records.
38 spirometry, and mortality were recorded from medical records.
39 nd extracted outcomes from linked electronic medical records.
40 er Permanente Southern California electronic medical records.
41 re taken from well-maintained registries and medical records.
42 aemic heart disease were obtained from their medical records.
43 ACE inhibitor/ARB dose was abstracted from medical records.
44 consecutive days through the same electronic medical records.
45 Other patient information was retrieved from medical records.
46 talized patients, alongside their electronic medical records.
47 s post-MI were determined from all available medical records.
48 parameters were collected from the patients' medical records.
49 Diagnoses were reviewed and validated in medical records.
50 pecially with the introduction of electronic medical records.
51 details were obtained from their electronic medical records.
52 inical and laboratory data due to the use of medical records.
53 onset of GMG, and (4) had incomplete or lost medical records.
54 ) and visual fields (VFs) were collated from medical records.
55 uring pregnancy was obtained from electronic medical records.
56 Outcomes and results were extracted from medical records.
57 outcomes were abstracted from the patient's medical records.
58 history of Facebook statuses and electronic medical records.
60 derwent LPF at our institution, (2) complete medical records, (3) minimum follow-up of 12 months.
67 d in urban areas, and predominantly based on medical records abstraction and/or drug prescription aud
68 ory-confirmed influenza, 80 261 had complete medical record abstractions and available ICD codes (med
72 s routinely collectible data from electronic medical records, along with brief clinical assessments o
75 llows integration of FHH with the electronic medical record and clinical decision support capabilitie
78 atient outcomes, assessed at 30 days via the medical record and telephone calls, were evaluated using
81 boratory findings, side reactions from their medical records and carried out questionnaire survey abo
84 -consult inquiries was assessed by review of medical records and defined as meeting the following 4 c
92 me, time of result entry into the electronic medical record, and turnaround time were compared to tho
95 tes/hypertensive disorders in pregnancy from medical records, and extracted data on mood/anxiety diso
96 ancer cases (n = 11,943) were confirmed with medical records, and subtypes were determined by tissue
98 tion of psychiatric genetics with electronic medical records, and the development of the neuroscience
99 sed as the trigger to collect and adjudicate medical records, any event that is not reported by the p
102 medication adherence) as well as electronic medical records augmented with clinical decision support
104 he FUT6 p.Glu274Lys variant in an electronic medical record-based phenome-wide association scan of ov
105 ions at our hospital, we utilized electronic medical records-based automated trigger tools to alert p
106 to determine whether an automated electronic medical record best practice alert (BPA) based on procal
108 ears with COVID-19 coded in their electronic medical records between January 20, 2020, and May 26, 20
109 s data contained in retrospective electronic medical records between September 2012 and January 2018
110 to laboratory information system/electronic medical record builds in the setting of limited informat
112 ld be done to document their presence in the medical record, but such findings are common and follow-
114 SSTIs were identified from the electronic medical record by use of International Classification of
115 demonstrate that unspecialized and existing medical recording can be reliably turned into power of p
118 tics, socioeconomic status (SES), electronic medical records, criminality, as well as family history
120 is retrospective cohort study, we abstracted medical record data for 637 children/adolescents (5-17 y
121 l Quality Improvement Program and electronic medical record data obtained on diverticulitis colectomy
124 al system set and a comprehensive set adding medical record data-to develop Least Absolute Selection
125 Medical Examiner to genealogical records and medical records data available on over eight million ind
128 rate Internet-source and hospital electronic medical records data into surveillance systems seek to i
130 multi-institutional longitudinal electronic medical record database, we identified patients with new
131 re associations in an independent electronic medical records database (n = 192,868) reveal connection
132 from the healthcare organization electronic medical record databases and some comorbidities may be u
134 that analysis of highly-detailed electronic medical record (EMR) data would demonstrate that patient
136 resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of application
137 We used data from the national electronic medical record (EMR) system in Zambia to enumerate a lar
145 All intensivists reviewed a paper-based medical record for a hypothetical patient on ICU day 3 a
146 ean time of result entry into the electronic medical record for CT was 3.5 days earlier than the mean
147 gram (EEG) data and evaluated the electronic medical record for evidence of epilepsy and developmenta
153 A at ages 50 to 90 years was identified from medical records from 1996 to 2017 in the Utah Population
156 ducted a population-based cohort analysis of medical records from 389 primary care practices contribu
157 ated, longitudinal, de-identified electronic medical records from a geographically and demographicall
158 ) as determined from clinic-based electronic medical records from a probability sample of facilities.
159 ry outcomes were assessed through electronic medical records: >=2 urine drug tests and any early COT
160 technology and increasing use of electronic medical records in health systems have led to the dramat
161 orks considering their lifetime history from medical records in order to compare the network properti
162 zed as IDU cases if IDU was indicated in the medical records in the 12 months prior to the date of in
164 nic data were abstracted from the electronic medical record, including demographics, systemic antimic
167 Documentation of specific diagnoses in the medical record is important in the broad context of our
169 h December 31, 2014, were identified using a medical record linkage system that captures virtually al
170 Using the Rochester Epidemiology Project medical record-linkage system, the authors identified re
171 3 for eGFR, N = 117,165 for CKD), electronic-medical-record-linked UK Biobank data (N = 335,212), and
172 emiological investigations and an electronic medical records match, and summarized descriptively.
173 020, were identified by using the electronic medical record (n = 326; mean age, 59 years +/-17 [stand
175 of end-of-life discussions in the electronic medical record, no single intervention type or SEIPS dom
184 m our qualitative analysis of the electronic medical records of 340 cohort members with notes contain
185 spective cohort study involved examining the medical records of 433 patients who underwent transhiata
189 ed a retrospective case series, by reviewing medical records of 7 members of a pedigree with NTG caus
192 Data for this study came from electronic medical records of a level III neonatal care center in G
199 We retrospectively reviewed the electronic medical records of all children younger than 18 years ol
201 at all usual care visits were extracted from medical records of all participants until the point of d
216 an 19.8% of these had documentation in their medical records of inherited genetic disease risk, inclu
225 oma Registry, a large database of electronic medical records of patients with glaucoma and suspected
227 ective case-control study (1:1), we reviewed medical records of people with HIV infection on cART in
228 or days from individual review of electronic medical records of sequential adult patients with active
231 nd angiographic details were abstracted from medical records of their index procedure, and were compa
232 ts and food allergy histories (obtained from medical records) of participants who underwent OFC using
233 hree ART programmes were linked with routine medical records on mental health treatment from Jan 1, 2
234 care are reviewed: (1) effects of electronic medical records on the patient interview; (2) effects of
238 d on the basis of patient history, review of medical records, or baseline HbA(1c) or fasting serum gl
239 6; 65.7%), through development of electronic medical record orders (n = 626; 55.8%), or with integrat
242 Breast cancers were confirmed after central medical record review and serial National Death Index li
244 was that we lacked the resources to conduct medical record review for all the potential cases of Kaw
247 aboratory characteristics were abstracted by medical record review, and were compared with clinical c
248 tively evaluated screening approach involved medical record review, this was unnecessary to prevent e
254 macist-led penicillin allergy assessment via medical records review and patient interview improved gu
255 Cases meeting inclusion criteria underwent medical records review and, when available, independent
258 n from patient questionnaires and electronic medical records review, three models were developed to a
260 ospective clinical registries, retrospective medical record reviews, and analyses of the usual care a
261 e burden of checking boxes in the electronic medical record so that they can devote their energies to
262 lation-based cancer registry with electronic medical records supporting ART delivery in Malawi's 2 la
263 capabilities, including accessing full-text medical records, supporting randomized clinical trials e
265 tion pertaining to hospice in the electronic medical record system of the Department of Veterans Affa
268 Widespread implementation of electronic medical record systems has inadvertently led to clinicia
271 xtended blood group profile as part of their medical record to be used to inform selection of the opt
273 ocio-demographic factors were extracted from medical records to assess the associations of neuropsych
274 We applied machine-learning to electronic medical records to better characterize the heterogeneity
275 ersons who inject drugs (PWID), and reviewed medical records to compare clinical features and outcome
277 ts and all nonserious reports with available medical records to determine if they met the Brighton Co
278 model that uses information from electronic medical records to identify hospitalized patients at hig
279 tients to collect data on symptoms, reviewed medical records to obtain the presumptive diagnoses, and
282 A comprehensive search of the electronic medical records was performed using a proprietary python
283 tality data obtained from routine electronic medical records, we intensively traced a random sample o
284 ocedures: all AEs recorded in the electronic medical record were extracted and retrospectively review
285 ocedures: All AEs recorded in the electronic medical record were extracted and retrospectively review
298 s in a multicenter, Chicago-wide database of medical records with ICD-9 codes of cirrhosis and withou
299 s in a multicenter, Chicago-wide database of medical records with International Classification of Dis
300 ess and consultation notes documented in the medical record within 7 days of patient's ICU discharge