Radiology workflow and patient volume: Effect of picture archiving and communication systems on technologists and radiologists View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2000-05

AUTHORS

R. O. Redfern, S. C. Horii, E. Feingold, H. L. Kundel

ABSTRACT

This study was performed to evaluate the changes in workflow and efficiency in various clinical settings in the radiology department after the introduction of a picture archiving and communication system (PACS). Time and motion data were collected when conventional image management was used, and again after the introduction of a PACS. Changes in the elapsed time from examination request until the image dispatch to the radiologist, and from dispatch until report dictation, were evaluated. The relationship between patient volume and throughput was evaluated. The time from examination request until dispatch was significantly longer after the introduction of PACS for examinations taken on patients from the emergency department (ED) (pre-PACS, 20 minutes; post-PACS, 25 minutes; P < .0001), and for examinations taken on patients in the medical intensive care unit (MICU) (pre-PACS, 34 minutes; post-PACS, 42 minutes; P < .0001). The interval from image dispatch until report dictation shortened significantly after the introduction of PACS in the ED (pre-PACS, 38 minutes; post-PACS, 23 minutes; P < .0001) and in the outpatient department (OPD) (pre-PACS, 38 minutes; post-PACS, 20 minutes; P < .0001). Simple least squares regression showed a significant relationship between daily patient volume and the daily median time until report dictation (F = 43.42, P < .001). PACS slowed technologists by prolonging the quality-control procedure. Radiologist workflow was shortened or not affected. Efficiency is dependent on patient volume, and workflow improvements are due to a shift from batch to on-line reading that is enabled by the ability of PACS to route enough examinations to keep radiologists fully occupied. More... »

PAGES

97-100

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf03167635

DOI

http://dx.doi.org/10.1007/bf03167635

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042929839

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10847373


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Appointments and Schedules", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Efficiency", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Online Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiology Information Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Task Performance and Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Technology, Radiologic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Workload", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "General Electric (United States)", 
          "id": "https://www.grid.ac/institutes/grid.418143.b", 
          "name": [
            "Department of Radiology, M.I.G., University of Pennsylvania Medical Center, 3600 Market St, Suite 370, 19104, Philadelphia, PA", 
            "Integrated Imaging Solutions, General Electric Co, Mr Prospect, IL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Redfern", 
        "givenName": "R. O.", 
        "id": "sg:person.01302446325.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302446325.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "General Electric (United States)", 
          "id": "https://www.grid.ac/institutes/grid.418143.b", 
          "name": [
            "Department of Radiology, M.I.G., University of Pennsylvania Medical Center, 3600 Market St, Suite 370, 19104, Philadelphia, PA", 
            "Integrated Imaging Solutions, General Electric Co, Mr Prospect, IL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Horii", 
        "givenName": "S. C.", 
        "id": "sg:person.01115174050.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115174050.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "General Electric (United States)", 
          "id": "https://www.grid.ac/institutes/grid.418143.b", 
          "name": [
            "Department of Radiology, M.I.G., University of Pennsylvania Medical Center, 3600 Market St, Suite 370, 19104, Philadelphia, PA", 
            "Integrated Imaging Solutions, General Electric Co, Mr Prospect, IL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Feingold", 
        "givenName": "E.", 
        "id": "sg:person.0601257540.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601257540.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "General Electric (United States)", 
          "id": "https://www.grid.ac/institutes/grid.418143.b", 
          "name": [
            "Department of Radiology, M.I.G., University of Pennsylvania Medical Center, 3600 Market St, Suite 370, 19104, Philadelphia, PA", 
            "Integrated Imaging Solutions, General Electric Co, Mr Prospect, IL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kundel", 
        "givenName": "H. L.", 
        "id": "sg:person.0757760240.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0757760240.23"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1117/12.274573", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009838824"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1117/12.239241", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014963892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1117/12.352760", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027570706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0720-048x(97)01168-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029779195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0895-6111(95)00010-n", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049865575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.207.2.9577498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083257428"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2000-05", 
    "datePublishedReg": "2000-05-01", 
    "description": "This study was performed to evaluate the changes in workflow and efficiency in various clinical settings in the radiology department after the introduction of a picture archiving and communication system (PACS). Time and motion data were collected when conventional image management was used, and again after the introduction of a PACS. Changes in the elapsed time from examination request until the image dispatch to the radiologist, and from dispatch until report dictation, were evaluated. The relationship between patient volume and throughput was evaluated. The time from examination request until dispatch was significantly longer after the introduction of PACS for examinations taken on patients from the emergency department (ED) (pre-PACS, 20 minutes; post-PACS, 25 minutes; P < .0001), and for examinations taken on patients in the medical intensive care unit (MICU) (pre-PACS, 34 minutes; post-PACS, 42 minutes; P < .0001). The interval from image dispatch until report dictation shortened significantly after the introduction of PACS in the ED (pre-PACS, 38 minutes; post-PACS, 23 minutes; P < .0001) and in the outpatient department (OPD) (pre-PACS, 38 minutes; post-PACS, 20 minutes; P < .0001). Simple least squares regression showed a significant relationship between daily patient volume and the daily median time until report dictation (F = 43.42, P < .001). PACS slowed technologists by prolonging the quality-control procedure. Radiologist workflow was shortened or not affected. Efficiency is dependent on patient volume, and workflow improvements are due to a shift from batch to on-line reading that is enabled by the ability of PACS to route enough examinations to keep radiologists fully occupied.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf03167635", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2435601", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1100894", 
        "issn": [
          "0897-1889", 
          "1618-727X"
        ], 
        "name": "Journal of Digital Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "Suppl 1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "name": "Radiology workflow and patient volume: Effect of picture archiving and communication systems on technologists and radiologists", 
    "pagination": "97-100", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cc7d71c19af0883264b28a234ebe18f945ac2f2528c1607b86ace5cdcb87bd10"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "10847373"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9100529"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf03167635"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042929839"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf03167635", 
      "https://app.dimensions.ai/details/publication/pub.1042929839"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:54", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89798_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF03167635"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bf03167635'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf03167635'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf03167635'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf03167635'


 

This table displays all metadata directly associated to this object as RDF triples.

147 TRIPLES      21 PREDICATES      44 URIs      30 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf03167635 schema:about N05c876b486a140b5855472511753401e
2 N0878bec78f8146e783811ab071f4ed01
3 N20f9596c396e450896d80aef6005bb64
4 N59bebd92037e4d66ada00be4216fa92a
5 Na2790eb4aafb433f858de55e31131b64
6 Nb5374a97b9b44639a8ec3903e00450c4
7 Nd01f9cb349a940319ac407baf82c84ce
8 Ndb95d6ef3e5f4341b4dedca4b686c6d8
9 Nfe991cdff7b641a3b4d1c8c65e3f2008
10 anzsrc-for:11
11 anzsrc-for:1117
12 schema:author N4e42684359a84474895374be5baec41f
13 schema:citation https://doi.org/10.1016/0895-6111(95)00010-n
14 https://doi.org/10.1016/s0720-048x(97)01168-6
15 https://doi.org/10.1117/12.239241
16 https://doi.org/10.1117/12.274573
17 https://doi.org/10.1117/12.352760
18 https://doi.org/10.1148/radiology.207.2.9577498
19 schema:datePublished 2000-05
20 schema:datePublishedReg 2000-05-01
21 schema:description This study was performed to evaluate the changes in workflow and efficiency in various clinical settings in the radiology department after the introduction of a picture archiving and communication system (PACS). Time and motion data were collected when conventional image management was used, and again after the introduction of a PACS. Changes in the elapsed time from examination request until the image dispatch to the radiologist, and from dispatch until report dictation, were evaluated. The relationship between patient volume and throughput was evaluated. The time from examination request until dispatch was significantly longer after the introduction of PACS for examinations taken on patients from the emergency department (ED) (pre-PACS, 20 minutes; post-PACS, 25 minutes; P < .0001), and for examinations taken on patients in the medical intensive care unit (MICU) (pre-PACS, 34 minutes; post-PACS, 42 minutes; P < .0001). The interval from image dispatch until report dictation shortened significantly after the introduction of PACS in the ED (pre-PACS, 38 minutes; post-PACS, 23 minutes; P < .0001) and in the outpatient department (OPD) (pre-PACS, 38 minutes; post-PACS, 20 minutes; P < .0001). Simple least squares regression showed a significant relationship between daily patient volume and the daily median time until report dictation (F = 43.42, P < .001). PACS slowed technologists by prolonging the quality-control procedure. Radiologist workflow was shortened or not affected. Efficiency is dependent on patient volume, and workflow improvements are due to a shift from batch to on-line reading that is enabled by the ability of PACS to route enough examinations to keep radiologists fully occupied.
22 schema:genre research_article
23 schema:inLanguage en
24 schema:isAccessibleForFree true
25 schema:isPartOf N3f85d94826a845e882972cb28bbbb8c1
26 N3fc96a6b3ed74e75a6dc26718241477c
27 sg:journal.1100894
28 schema:name Radiology workflow and patient volume: Effect of picture archiving and communication systems on technologists and radiologists
29 schema:pagination 97-100
30 schema:productId N12d2d4e21e87493ba661316cca511eb7
31 N4b8733268dce41cd9de075942e573076
32 N8b351a8d86604a5bbda16f0ac2c55cc9
33 Nbbeef205961446c1ab9d9e13dccebd2d
34 Ndb375d012d4d43f3ae4212e0761cf3e8
35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042929839
36 https://doi.org/10.1007/bf03167635
37 schema:sdDatePublished 2019-04-11T09:54
38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
39 schema:sdPublisher Nf2327ffe40ee4ba38bbfd25478809398
40 schema:url http://link.springer.com/10.1007/BF03167635
41 sgo:license sg:explorer/license/
42 sgo:sdDataset articles
43 rdf:type schema:ScholarlyArticle
44 N05c876b486a140b5855472511753401e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
45 schema:name Task Performance and Analysis
46 rdf:type schema:DefinedTerm
47 N0878bec78f8146e783811ab071f4ed01 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
48 schema:name Humans
49 rdf:type schema:DefinedTerm
50 N119681036fdd46aeb466f2e1aa24e362 rdf:first sg:person.0601257540.13
51 rdf:rest Nfa883816c6c846b9a197012d6828973f
52 N12d2d4e21e87493ba661316cca511eb7 schema:name doi
53 schema:value 10.1007/bf03167635
54 rdf:type schema:PropertyValue
55 N20f9596c396e450896d80aef6005bb64 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
56 schema:name Technology, Radiologic
57 rdf:type schema:DefinedTerm
58 N3f85d94826a845e882972cb28bbbb8c1 schema:volumeNumber 13
59 rdf:type schema:PublicationVolume
60 N3fc96a6b3ed74e75a6dc26718241477c schema:issueNumber Suppl 1
61 rdf:type schema:PublicationIssue
62 N4b8733268dce41cd9de075942e573076 schema:name dimensions_id
63 schema:value pub.1042929839
64 rdf:type schema:PropertyValue
65 N4e42684359a84474895374be5baec41f rdf:first sg:person.01302446325.53
66 rdf:rest N9af7a62715cd4ae78f787cb829646dbd
67 N59bebd92037e4d66ada00be4216fa92a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Workload
69 rdf:type schema:DefinedTerm
70 N8b351a8d86604a5bbda16f0ac2c55cc9 schema:name readcube_id
71 schema:value cc7d71c19af0883264b28a234ebe18f945ac2f2528c1607b86ace5cdcb87bd10
72 rdf:type schema:PropertyValue
73 N9af7a62715cd4ae78f787cb829646dbd rdf:first sg:person.01115174050.45
74 rdf:rest N119681036fdd46aeb466f2e1aa24e362
75 Na2790eb4aafb433f858de55e31131b64 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Radiology Information Systems
77 rdf:type schema:DefinedTerm
78 Nb5374a97b9b44639a8ec3903e00450c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Appointments and Schedules
80 rdf:type schema:DefinedTerm
81 Nbbeef205961446c1ab9d9e13dccebd2d schema:name pubmed_id
82 schema:value 10847373
83 rdf:type schema:PropertyValue
84 Nd01f9cb349a940319ac407baf82c84ce schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Efficiency
86 rdf:type schema:DefinedTerm
87 Ndb375d012d4d43f3ae4212e0761cf3e8 schema:name nlm_unique_id
88 schema:value 9100529
89 rdf:type schema:PropertyValue
90 Ndb95d6ef3e5f4341b4dedca4b686c6d8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Online Systems
92 rdf:type schema:DefinedTerm
93 Nf2327ffe40ee4ba38bbfd25478809398 schema:name Springer Nature - SN SciGraph project
94 rdf:type schema:Organization
95 Nfa883816c6c846b9a197012d6828973f rdf:first sg:person.0757760240.23
96 rdf:rest rdf:nil
97 Nfe991cdff7b641a3b4d1c8c65e3f2008 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Radiology
99 rdf:type schema:DefinedTerm
100 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
101 schema:name Medical and Health Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
104 schema:name Public Health and Health Services
105 rdf:type schema:DefinedTerm
106 sg:grant.2435601 http://pending.schema.org/fundedItem sg:pub.10.1007/bf03167635
107 rdf:type schema:MonetaryGrant
108 sg:journal.1100894 schema:issn 0897-1889
109 1618-727X
110 schema:name Journal of Digital Imaging
111 rdf:type schema:Periodical
112 sg:person.01115174050.45 schema:affiliation https://www.grid.ac/institutes/grid.418143.b
113 schema:familyName Horii
114 schema:givenName S. C.
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115174050.45
116 rdf:type schema:Person
117 sg:person.01302446325.53 schema:affiliation https://www.grid.ac/institutes/grid.418143.b
118 schema:familyName Redfern
119 schema:givenName R. O.
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302446325.53
121 rdf:type schema:Person
122 sg:person.0601257540.13 schema:affiliation https://www.grid.ac/institutes/grid.418143.b
123 schema:familyName Feingold
124 schema:givenName E.
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601257540.13
126 rdf:type schema:Person
127 sg:person.0757760240.23 schema:affiliation https://www.grid.ac/institutes/grid.418143.b
128 schema:familyName Kundel
129 schema:givenName H. L.
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0757760240.23
131 rdf:type schema:Person
132 https://doi.org/10.1016/0895-6111(95)00010-n schema:sameAs https://app.dimensions.ai/details/publication/pub.1049865575
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/s0720-048x(97)01168-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029779195
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1117/12.239241 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014963892
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1117/12.274573 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009838824
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1117/12.352760 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027570706
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1148/radiology.207.2.9577498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083257428
143 rdf:type schema:CreativeWork
144 https://www.grid.ac/institutes/grid.418143.b schema:alternateName General Electric (United States)
145 schema:name Department of Radiology, M.I.G., University of Pennsylvania Medical Center, 3600 Market St, Suite 370, 19104, Philadelphia, PA
146 Integrated Imaging Solutions, General Electric Co, Mr Prospect, IL
147 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...