Evidence map›Paper›PMID 27417208›Full record

ReviewJournal of digital imaging2016

Orders- Versus Encounters-Based Image Capture: Implications Pre- and Post-Procedure Workflow, Technical and Build Capabilities, Resulting, Analytics and Revenue Capture: HIMSS-SIIM Collaborative White Paper.

Dawn Cram, Christopher J Roth, Alexander J Towbin

Abstract readReviewConsensus Statement
In one paragraph

Review in Journal of digital imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Implementing a Photodocumentation Program.Journal of imaging informatics in medicine · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. The Enterprise Imaging Value Proposition.Journal of digital imaging · 2020
    Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Review
  18. Review
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  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dawn CramDepartment of Information Technology, University of Miami Health System, 1425 N.W. 10th Avenue, Miami, FL, 33136, USA. dcram@med.miami.edu.
Christopher J RothDuke Health Technology Solutions, Hock Plaza, 2424 Erwin Road, Durham, NC, 27705, USA.
Alexander J TowbinDepartment of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5031, Cincinnati, OH, 45229, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The decision to implement an orders-based versus an encounters-based imaging workflow poses various implications to image capture and storage. The impacts include workflows before and after an imaging procedure, electronic health record build, technical infrastructure, analytics, resulting, and revenue. Orders-based workflows tend to favor some imaging specialties while others require an encounters-based approach. The intent of this HIMSS-SIIM white paper is to offer lessons learned from early adopting institutions to physician champions and informatics leadership developing strategic planning and operational rollouts for specialties capturing clinical multimedia.

Indexed as

Diagnostic ImagingElectronic Health RecordsMultimediaWorkflowAmbulatory CareGoalsHumansReimbursement MechanismsClinical multimediaClinical photographyDermatologyDICOMECMEHREncounterEncounters-basedEnterprise archiveEnterprise imagingImage captureImagingImaging informaticsImaging workflowMedical photographyMWLnon-DICOMOpthalmic imagingOrders-basedPACSScope videoVisible light imagingVNAXDSXDS-i

Identifiers

PMID27417208
PMCPMC5023529

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.