Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review

Design, Implementation, and Usability of the Electronic Medical Record Search Engine (EMERSE) Tool

Free text forms of clinical documentation stored in electronic health records contain a trove of data for researchers and clinicians alike. However, often these data are challenging to use and not easily accessible. EMERSE, a clinical documentation search and data abstraction tool developed by the University of Michigan, helps users in the task of searching through free text notes in clinical documentation. This study evaluates the usability and user experience of the EMERSE system, and draws inferences for the design of such systems. The study was conducted in 3 phases. In Phase 1, interviews with site administrators investigated factors that facilitate or hinder the implementation and adoption of EMERSE. Phase 2 employed semi-structured interviews to understand the uses, benefits, and limitations of the system from the perspective of experienced users. In Phase 3, system-naive users performed a set of basic workflow tasks, then completed post-activity questions and surveys to evaluate the intuitiveness and usability of the system. Participants rated the system exceptionally high on usability, user interface satisfaction, and perceived usefulness. Feedback also indicated that improvements could be made in visual contrast, affordances, and scope of notes indexed. These results indicate that tools such as EMERSE should be highly intuitive, attractive, and moderately customizable. This paper discusses some aspects of what may contribute to a system having such characteristics.

Evaluating the Impact on Clinical Task Efficiency of a Natural Language Processing Algorithm for Searching Medical Documents: Prospective Crossover Study

Background

Information retrieval (IR) from the free text within electronic health records (EHRs) is time consuming and complex. We hypothesize that natural language processing (NLP)–enhanced search functionality for EHRs can make clinical workflows more efficient and reduce cognitive load for clinicians.

Objective

This study aimed to evaluate the efficacy of 3 levels of search functionality (no search, string search, and NLP-enhanced search) in supporting IR for clinical users from the free text of EHR documents in a simulated clinical environment.

Methods

A clinical environment was simulated by uploading 3 sets of patient notes into an EHR research software application and presenting these alongside 3 corresponding IR tasks. Tasks contained a mixture of multiple-choice and free-text questions. A prospective crossover study design was used, for which 3 groups of evaluators were recruited, which comprised doctors (n=19) and medical students (n=16). Evaluators performed the 3 tasks using each of the search functionalities in an order in accordance with their randomly assigned group. The speed and accuracy of task completion were measured and analyzed, and user perceptions of NLP-enhanced search were reviewed in a feedback survey.

Results

NLP-enhanced search facilitated more accurate task completion than both string search (5.14%; P=.02) and no search (5.13%; P=.08). NLP-enhanced search and string search facilitated similar task speeds, both showing an increase in speed compared to the no search function, by 11.5% (P=.008) and 16.0% (P=.007) respectively. Overall, 93% of evaluators agreed that NLP-enhanced search would make clinical workflows more efficient than string search, with qualitative feedback reporting that NLP-enhanced search reduced cognitive load.

Conclusions

To the best of our knowledge, this study is the largest evaluation to date of different search functionalities for supporting target clinical users in realistic clinical workflows, with a 3-way prospective crossover study design. NLP-enhanced search improved both accuracy and speed of clinical EHR IR tasks compared to browsing clinical notes without search. NLP-enhanced search improved accuracy and reduced the number of searches required for clinical EHR IR tasks compared to direct search term matching.

Information handoffs in critical care and their implications for information quality: A socio-technical network approach

OBJECTIVE

The design of health ICTs, as well as human factors, have been shown to influence patient information quality. The aim of this study was to understand how patterns of interaction between these factors influence information quality aspects in a critical care environment.

METHOD

We conducted an ethnographic study of socio-technical information handoffs in a critical care unit. Data collection methods included non-participant observations and semi-structured interviews. Methodological principles from network analysis (SNA, VNA) were used to develop visual network diagrams, as well as to analyze the composition of the information network and its influence on patient information quality.

RESULTS

The network patterns that emerged uncover that human actors have many information processing and dissemination roles at the critical care unit. However, ICTs play key network roles, acting as information intermediaries and gatekeepers. We further identify three types of information handoffs in the critical care environment - human-human, human-ICT and ICT-human. On the one hand, we find that human-human and ICT-human handoffs influence contextual and intrinsic aspects of patient information, such as information completeness and accuracy. On the other hand, human-ICT handoffs influence information accessibility and representational quality, such as consistency and interpretability.

DISCUSSION

The results suggest that standardizing change of shift handoff communication may not be sufficient to prevent information decay in complex care trajectories. In particular, we argue that ensuring information consistency and interpretability across disciplines and professions is as important as ensuring information completeness and accuracy during change of shift handoffs. ICT and workflow design opportunities are discussed as means to address overlapping or conflicting information needs across disciplines and professions, increase information consistency, and reduce information redundancy across the network.