# Enhancing information retrieval in electronic health records through collaborative filtering

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2020 · $541,639

## Abstract

Project Abstract
When we consider buying a book on Amazon's Website, we often benefit from items listed in a section called
"Customers also viewed." These recommendations, generated by a method called collaborative filtering (CF),
suggest items of possible interest based on what other customers have viewed and purchased. However,
when clinicians search the electronic health record (EHR) with regard to a particular patient problem, the EHR
does not make suggestions for potentially useful information. Instead, it requires clinicians to go through the
same manual, cumbersome and laborious process of searching for and retrieving information for similar
patients/problems every single time. This limitation is magnified in high-risk situations, such as managing chest
pain in the emergency department (ED). The goal of this project is to implement and evaluate CF as a method
to improve information retrieval from EHRs and reduce cognitive overload. The central hypothesis of our
proposal is that CF will (1) help clinicians retrieve and review the right patient information more efficiently and
effectively than current methods; and (2) score higher on usefulness and ease-of-use than current EHRs. We
will implement our CF algorithms in CareWeb Plus, a SMART-on-FHIR app we are currently building to
integrate relevant information from the Indiana Network for Patient Care (INPC), Indiana's major health
information exchange, with the ED workflow in Cerner/Epic. Our aims are to (1) extend CareWeb Plus to
support collaborative filtering; (2) design and implement collaborative filtering algorithms; (3) and implement
and evaluate CareWeb Plus in two adult emergency departments. Over 190 clinicians will use and evaluate
CareWeb Plus in the two busiest emergency departments in Indianapolis (> 200,000 patient visits/year
collectively), with more than 13,000 of them related to chest pain. We will evaluate (1) process measures, such
as CareWeb Plus use, information retrieval and viewing patterns, and time to key decisions (first order,
admission, discharge); (2) outcomes variables, such as lab/procedure utilization, ED length of stay and
admission rate; and (3) user perceptions and attitudes regarding usefulness and usability. Our project is
significant because it addresses two current, major limitations of EHRs in clinical practice. (1) Clinicians have
difficulty reviewing voluminous patient-specific information, especially from multiple sources, efficiently to find
relevant facts, especially in time-sensitive situations. (2) EHR users have little to no ability to change the static
and inflexible nature of EHR interfaces for information retrieval. Our proposal is innovative because it uses CF,
a method for tailoring information retrieval well-established in many fields except healthcare, to help solve
these two problems. Collaborative filtering will provide a continually adapting, dynamic paradigm of
informational retrieval and presentation that naturally follows the evol...

## Key facts

- **NIH application ID:** 9922983
- **Project number:** 5R01LM012605-03
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** XIA NING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $541,639
- **Award type:** 5
- **Project period:** 2018-06-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9922983

## Citation

> US National Institutes of Health, RePORTER application 9922983, Enhancing information retrieval in electronic health records through collaborative filtering (5R01LM012605-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9922983. Licensed CC0.

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