Hybrid collaborative filtering methods for recommending search terms to clinicians

Ren, Zhiyun; Peng, Bo; Schleyer, Titus; Ning, Xia

doi:10.1016/j.jbi.2020.103635

“…Collaborative filtering algorithms have been proposed for medical and health care settings, 49 50 51 52 53 54 55 56 57 58 although none have been integrated into a clinical setting. Some proposed collaborative filtering interventions use patient EHR data (e.g., to support rare disease diagnosis 51 ).…”

Section: Discussionmentioning

confidence: 99%

“…57 58 Additionally, one article and a brief conference proceedings paper propose using collaborative filtering to support EHR search and discuss the methods by which such a system could be developed. 55 56 We believe that implementing collaborative filtering within EHR search functions is a promising direction for future work.…”

Section: Discussionmentioning

confidence: 99%

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

Hill

¹

,

Visweswaran

²

,

Ning

³

et al. 2021

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Objective Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record. Conclusion Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.

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“…Since we developed the DmCF , we have implemented and evaluated an additional method termed H ybrid C ollaborative F iltering M ethod for H ealthcare ( HCFMH ) [ 42 ]. HCFMH was inspired by the DmCF to integrate different collaborative filtering components.…”

Section: Discussionmentioning

confidence: 99%

Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

Ning

¹

,

Fan

²

,

Burgun³

et al. 2021

PLoS ONE

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Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.

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“…Collaborative filtering algorithms have been proposed for medical and health care settings, [49][50][51][52][53][54][55][56][57][58] although none have been integrated into a clinical setting. Some proposed collaborative filtering interventions use patient EHR data (e.g., to support rare disease diagnosis 51 ).…”

Section: Discussionmentioning

confidence: 99%

Untitled

2021

Appl Clin Inform

0

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Objective Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record. Conclusion Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.

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Hybrid collaborative filtering methods for recommending search terms to clinicians

Cited by 6 publications

References 18 publications

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

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

Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

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