Relevant priors prefetching algorithm performance for a picture archiving and communication system

Abstract

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 detarmined 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.