Collaborative search in electronic health records

Abstract

Objective: A full-text search engine can be a useful tool for augmenting the reuse value of unstructured narrative data stored in electronic health records (EHR). A prominent barrier to the effective utilization of such tools originates from users' lack of search expertise and/or medical-domain knowledge. To mitigate the issue, the authors experimented with a 'collaborative search' feature through a homegrown EHR search engine that allows users to preserve their search knowledge and share it with others. This feature was inspired by the success of many social information-foraging techniques used on the web that leverage users' collective wisdom to improve the quality and efficiency of information retrieval.

Design: The authors conducted an empirical evaluation study over a 4-year period. The user sample consisted of 451 academic researchers, medical practitioners, and hospital administrators. The data were analyzed using a social-network analysis to delineate the structure of the user collaboration networks that mediated the diffusion of knowledge of search.

Results: The users embraced the concept with considerable enthusiasm. About half of the EHR searches processed by the system (0.44 million) were based on stored search knowledge; 0.16 million utilized shared knowledge made available by other users. The social-network analysis results also suggest that the user-collaboration networks engendered by the collaborative search feature played an instrumental role in enabling the transfer of search knowledge across people and domains.

Conclusion: Applying collaborative search, a social information-foraging technique popularly used on the web, may provide the potential to improve the quality and efficiency of information retrieval in healthcare.

Figure 1

User participation in bundle creation, sharing, and using: (A) number of bundles created versus number of bundles shared; and (B) number of others' bundles used versus number of bundles shared.

Figure 2

Network plots. (A) Department–Department Network. Circles, academic departments; dots, search-terms bundles; gray areas, zones encompassing all bundles created by users from the same department; edges (gray), connecting a bundle to the department of its creator; edges (red), connecting a bundle to the department(s) with which its consumer(s) are affiliated. (B) Consumer–Consumer Network. Dots, search engine users; Edges (red), connecting user groups wherein all members had used at least two same bundles in common. The width of an edge is proportional to the number of bundles used in common by the two users connected.