schliessen

Filtern

 

Bibliotheken

Computable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data

Computable phenotypes (CPs) are an increasingly important structured and reproducible method of using electronic health record data to classify people. CPs have the potential to provide important benefits to health information management (HIM) professionals in their everyday work. A CP is a precise... Full description

Journal Title: Perspectives in Health Information Management Fall 2018, pp.1-8
Main Author: Verchinina, Lilia
Other Authors: Ferguson, Lisa , Flynn, Allen , Wichorek, Michelle , Markel, Dorene
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: © ProQuest LLC All rights reserved
ID: ISSN: 1559-4122
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: proquest2133763092
title: Computable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data
format: Article
creator:
  • Verchinina, Lilia
  • Ferguson, Lisa
  • Flynn, Allen
  • Wichorek, Michelle
  • Markel, Dorene
subjects:
  • Patients
  • Electronic Health Records
  • Genomes
  • Data Bases
  • Algorithms
  • Health Informatics
  • Queries
  • Genotype & Phenotype
  • Information Management
  • American Medical Informatics Association
ispartof: Perspectives in Health Information Management, Fall 2018, pp.1-8
description: Computable phenotypes (CPs) are an increasingly important structured and reproducible method of using electronic health record data to classify people. CPs have the potential to provide important benefits to health information management (HIM) professionals in their everyday work. A CP is a precise algorithm, including inclusion and exclusion criteria, that can be used to identify a cohort of patients with a specific set of observable and measurable traits. With the use of CPs, a series of technical steps can be taken to automatically identify people with specific traits, such as people with a particular disease or condition. CPs were first used outside of the HIM domain for clinical trials and network-based research. Because CPs are becoming more easily shareable, they have the potential to be used by HIM professionals to help improve coding, reporting, management, sharing, and reuse of clinical information.
language: eng
source: © ProQuest LLC All rights reserved
identifier: ISSN: 1559-4122
fulltext: fulltext_linktorsrc
issn:
  • 15594122
  • 1559-4122
url: Link


@attributes
ID1030278238
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid2133763092
sourceidproquest
recordidTN_proquest2133763092
sourcesystemPC
pqid2133763092
display
typearticle
titleComputable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data
creatorVerchinina, Lilia ; Ferguson, Lisa ; Flynn, Allen ; Wichorek, Michelle ; Markel, Dorene
ispartofPerspectives in Health Information Management, Fall 2018, pp.1-8
identifierISSN: 1559-4122
subjectPatients ; Electronic Health Records ; Genomes ; Data Bases ; Algorithms ; Health Informatics ; Queries ; Genotype & Phenotype ; Information Management ; American Medical Informatics Association
descriptionComputable phenotypes (CPs) are an increasingly important structured and reproducible method of using electronic health record data to classify people. CPs have the potential to provide important benefits to health information management (HIM) professionals in their everyday work. A CP is a precise algorithm, including inclusion and exclusion criteria, that can be used to identify a cohort of patients with a specific set of observable and measurable traits. With the use of CPs, a series of technical steps can be taken to automatically identify people with specific traits, such as people with a particular disease or condition. CPs were first used outside of the HIM domain for clinical trials and network-based research. Because CPs are becoming more easily shareable, they have the potential to be used by HIM professionals to help improve coding, reporting, management, sharing, and reuse of clinical information.
languageeng
source
0© ProQuest LLC All rights reserved
1Medical Database
2Healthcare Administration Database
3Health & Medical Collection (Alumni edition)
4Medical Database (Alumni edition)
5Nursing & Allied Health Database (Alumni edition)
6Health & Medical Collection
7Nursing & Allied Health Database
8Healthcare Administration Database (Alumni)
9Publicly Available Content Database
10ProQuest Nursing & Allied Health Source
11ProQuest Central
12ProQuest Hospital Collection
13Hospital Premium Collection (Alumni edition)
14ProQuest Health & Medical Complete
15ProQuest Medical Library
16ProQuest Central (new)
17ProQuest Central Korea
18Health Research Premium Collection
19Health Research Premium Collection (Alumni edition)
20ProQuest Central Essentials
21ProQuest Central China
22ProQuest One Academic
oafree_for_read
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
linktorsrc$$Uhttp://search.proquest.com/docview/2133763092/?pq-origsite=primo$$EView_record_in_ProQuest_(subscribers_only)
search
creatorcontrib
0Verchinina, Lilia
1Ferguson, Lisa
2Flynn, Allen
3Wichorek, Michelle
4Markel, Dorene
titleComputable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data
descriptionComputable phenotypes (CPs) are an increasingly important structured and reproducible method of using electronic health record data to classify people. CPs have the potential to provide important benefits to health information management (HIM) professionals in their everyday work. A CP is a precise algorithm, including inclusion and exclusion criteria, that can be used to identify a cohort of patients with a specific set of observable and measurable traits. With the use of CPs, a series of technical steps can be taken to automatically identify people with specific traits, such as people with a particular disease or condition. CPs were first used outside of the HIM domain for clinical trials and network-based research. Because CPs are becoming more easily shareable, they have the potential to be used by HIM professionals to help improve coding, reporting, management, sharing, and reuse of clinical information.
subject
0Patients
1Electronic Health Records
2Genomes
3Data Bases
4Algorithms
5Health Informatics
6Queries
7Genotype & Phenotype
8Information Management
9American Medical Informatics Association
10813910
general
0English
1American Health Information Management Association
2Medical Database
3Healthcare Administration Database
4Health & Medical Collection (Alumni edition)
5Medical Database (Alumni edition)
6Nursing & Allied Health Database (Alumni edition)
7Health & Medical Collection
8Nursing & Allied Health Database
9Healthcare Administration Database (Alumni)
10Publicly Available Content Database
11ProQuest Nursing & Allied Health Source
12ProQuest Central
13ProQuest Hospital Collection
14Hospital Premium Collection (Alumni edition)
15ProQuest Health & Medical Complete
16ProQuest Medical Library
17ProQuest Central (new)
18ProQuest Central Korea
19Health Research Premium Collection
20Health Research Premium Collection (Alumni edition)
21ProQuest Central Essentials
22ProQuest Central China
23ProQuest One Academic
sourceidproquest
recordidproquest2133763092
issn
015594122
11559-4122
rsrctypearticle
creationdate2018
addtitlePerspectives in Health Information Management
searchscope
01000273
11006481
21006761
31006762
41006763
51007067
61007107
71007293
81007945
91008886
101009127
111009240
121009386
1310000020
1410000039
1510000047
1610000117
1710000118
1810000119
1910000155
2010000156
2110000157
2210000158
2310000255
2410000256
2510000258
2610000270
2710000271
2810000281
2910000300
3010000302
3110000348
3210000360
33proquest
scope
01000273
11006481
21006761
31006762
41006763
51007067
61007107
71007293
81007945
91008886
101009127
111009240
121009386
1310000020
1410000039
1510000047
1610000117
1710000118
1810000119
1910000155
2010000156
2110000157
2210000158
2310000255
2410000256
2510000258
2610000270
2710000271
2810000281
2910000300
3010000302
3110000348
3210000360
33proquest
lsr43
01000273true
11006481true
21006761true
31006762true
41006763true
51007067true
61007107true
71007293true
81007945true
91008886true
101009127true
111009240true
121009386true
1310000020true
1410000039true
1510000047true
1610000117true
1710000118true
1810000119true
1910000155true
2010000156true
2110000157true
2210000158true
2310000255true
2410000256true
2510000258true
2610000270true
2710000271true
2810000281true
2910000300true
3010000302true
3110000348true
3210000360true
startdate20181001
enddate20181001
citationpf 1 pt 8
sort
titleComputable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data
authorVerchinina, Lilia ; Ferguson, Lisa ; Flynn, Allen ; Wichorek, Michelle ; Markel, Dorene
creationdate20181001
lso0120181001
facets
frbrgroupid7124755534837233321
frbrtype6
newrecords20181120
languageeng
creationdate2018
topic
0Patients
1Electronic Health Records
2Genomes
3Data Bases
4Algorithms
5Health Informatics
6Queries
7Genotype & Phenotype
8Information Management
9American Medical Informatics Association
collection
0Medical Database
1Healthcare Administration Database
2Health & Medical Collection (Alumni edition)
3Medical Database (Alumni edition)
4Nursing & Allied Health Database (Alumni edition)
5Health & Medical Collection
6Nursing & Allied Health Database
7Healthcare Administration Database (Alumni)
8Publicly Available Content Database
9ProQuest Nursing & Allied Health Source
10ProQuest Central
11ProQuest Hospital Collection
12Hospital Premium Collection (Alumni edition)
13ProQuest Health & Medical Complete
14ProQuest Medical Library
15ProQuest Central (new)
16ProQuest Central Korea
17Health Research Premium Collection
18Health Research Premium Collection (Alumni edition)
19ProQuest Central Essentials
20ProQuest Central China
21ProQuest One Academic
prefilterarticles
rsrctypearticles
creatorcontrib
0Verchinina, Lilia
1Ferguson, Lisa
2Flynn, Allen
3Wichorek, Michelle
4Markel, Dorene
jtitlePerspectives in Health Information Management
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextfulltext_linktorsrc
addata
aulast
0Verchinina
1Ferguson
2Flynn
3Wichorek
4Markel
aufirst
0Lilia
1Lisa
2Allen
3Michelle
4Dorene
au
0Verchinina, Lilia
1Ferguson, Lisa
2Flynn, Allen
3Wichorek, Michelle
4Markel, Dorene
atitleComputable Phenotypes: Standardized Ways to Classify People Using Electronic Health Record Data
jtitlePerspectives in Health Information Management
risdate20181001
spage1
epage8
pages1-8
issn1559-4122
formatjournal
genrearticle
ristypeJOUR
abstractComputable phenotypes (CPs) are an increasingly important structured and reproducible method of using electronic health record data to classify people. CPs have the potential to provide important benefits to health information management (HIM) professionals in their everyday work. A CP is a precise algorithm, including inclusion and exclusion criteria, that can be used to identify a cohort of patients with a specific set of observable and measurable traits. With the use of CPs, a series of technical steps can be taken to automatically identify people with specific traits, such as people with a particular disease or condition. CPs were first used outside of the HIM domain for clinical trials and network-based research. Because CPs are becoming more easily shareable, they have the potential to be used by HIM professionals to help improve coding, reporting, management, sharing, and reuse of clinical information.
copChicago
pubAmerican Health Information Management Association
urlhttp://search.proquest.com/docview/2133763092/
oafree_for_read
date2018-10-01