Do nurses document all discussions of patient problems and nursing interventions in the electronic health record? A pilot study in home healthcare

Author:

Song Jiyoun1ORCID,Zolnoori Maryam1,Scharp Danielle1,Vergez Sasha2,McDonald Margaret V2,Sridharan Sridevi2,Kostic Zoran3,Topaz Maxim124

Affiliation:

1. Columbia University School of Nursing , New York, New York, USA

2. Center for Home Care Policy & Research, Visiting Nurse Service of New York , New York, New York, USA

3. Fu Foundation School of Engineering and Applied Science, Department of Electrical Engineering, Columbia University , New York, New York, USA

4. Data Science Institute, Columbia University , New York, New York, USA

Abstract

Abstract Objective To assess the overlap of information between electronic health record (EHR) and patient–nurse verbal communication in home healthcare (HHC). Methods Patient–nurse verbal communications during home visits were recorded between February 16, 2021 and September 2, 2021 with patients being served in an organization located in the Northeast United States. Twenty-two audio recordings for 15 patients were transcribed. To compare overlap of information, manual annotations of problems and interventions were made on transcriptions as well as information from EHR including structured data and clinical notes corresponding to HHC visits. Results About 30% (1534/5118) of utterances (ie, spoken language preceding/following silence or a change of speaker) were identified as including problems or interventions. A total of 216 problems and 492 interventions were identified through verbal communication among all the patients in the study. Approximately 50.5% of the problems and 20.8% of the interventions discussed during the verbal communication were not documented in the EHR. Preliminary results showed that statistical differences between racial groups were observed in a comparison of problems and interventions. Discussion This study was the first to investigate the extent that problems and interventions were mentioned in patient–nurse verbal communication during HHC visits and whether this information was documented in EHR. Our analysis identified gaps in information overlap and possible racial disparities. Conclusion Our results highlight the value of analyzing communications between HHC patients and nurses. Future studies should explore ways to capture information in verbal communication using automated speech recognition.

Funder

Columbia University School of Nursing

Columbia University Center of Artificial Intelligence Technology

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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