We have located links that may give you full text access.
Relevant priors prefetching algorithm performance for a picture archiving and communication system.
Proper prefetching of relevant prior examinations from a picture archiving and communication system (PACS) archive, when a patient is scheduled for a new imaging study, and sending the historic images to the display station where the new examination is expected to be routed and subsequently read out, can greatly facilitate interpretation and review, as well as enhance radiology departmental workflow and PACS performance. In practice, it has proven extremely difficult to implement an automatic prefetch as successful as the experienced fileroom clerk. An algorithm based on defined metagroup categories for examination type mnemonics has been designed and implemented as one possible solution to the prefetch problem. The metagroups such as gastrointestinal (GI) tract, abdomen, chest, etc, can represent, in a small number of categories, the several hundreds of examination types performed by a typical radiology department. These metagroups can be defined in a table of examination mnemonics that maps a particular mnemonic to a metagroup or groups, and vice versa. This table is used to effect the prefetch rules of relevance. A given examination may relate to several prefetch categories, and preferences are easily configurable for a particular site. The prefetch algorithm metatable was implemented in database structured query language (SQL) using a many-to-many fetch category strategy. Algorithm performance was measured by analyzing the appropriateness of the priors fetched based on the examination type of the current study. Fetched relevant priors, missed relevant priors, fetched priors that were not relevant to the current examination, and priors not fetched that were not relevant were used to calculate sensitivity and specificity for the prefetch method. The time required for real-time requesting of priors not previously prefetched was also measured. The sensitivity of the prefetch algorithm was determined to be 98.3% and the specificity 100%. Time required for on-demand requesting of priors was 9.5 minutes on average, although this time varied based on age of the prior examination and on the time of day and database traffic. A prefetch algorithm based on metatable examination mnemonic categories can pull the most appropriate relevant priors, reduce the number of missed relevant priors, and therefore reduce the time involved for the manual task of on-demand requests of priors. Network and database traffic can be reduced as well by decreasing the number of priors selected from the archive and subsequently transmitted to the display stations, through elimination of transactions on examinations not relevant to the current study.
Full text links
Related Resources
Trending Papers
Heart failure with preserved ejection fraction: diagnosis, risk assessment, and treatment.Clinical Research in Cardiology : Official Journal of the German Cardiac Society 2024 April 12
Proximal versus distal diuretics in congestive heart failure.Nephrology, Dialysis, Transplantation 2024 Februrary 30
World Health Organization and International Consensus Classification of eosinophilic disorders: 2024 update on diagnosis, risk stratification, and management.American Journal of Hematology 2024 March 30
Efficacy and safety of pharmacotherapy in chronic insomnia: A review of clinical guidelines and case reports.Mental Health Clinician 2023 October
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app