Song Jiyoun, Zolnoori Maryam, Scharp Danielle, Vergez Sasha, McDonald Margaret V, Sridharan Sridevi, Kostic Zoran, Topaz Maxim
Columbia University School of Nursing, New York, New York, USA.
Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, New York, USA.
JAMIA Open. 2022 May 26;5(2):ooac034. doi: 10.1093/jamiaopen/ooac034. eCollection 2022 Jul.
To assess the overlap of information between electronic health record (EHR) and patient-nurse verbal communication in home healthcare (HHC).
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.
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.
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.
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.
评估家庭医疗保健（HHC）中电子健康记录（EHR）与患者-护士口头沟通之间信息的重叠情况。
于2021年2月16日至2021年9月2日期间，对美国东北部一家机构服务的患者进行家访时的患者-护士口头沟通进行录音。转录了15名患者的22份录音。为比较信息重叠情况，对转录内容以及EHR中的信息（包括与HHC访视对应的结构化数据和临床记录）进行了问题和干预措施的人工标注。
约30%（1534/5118）的话语（即沉默或说话者转换之前/之后的口语）被确定包含问题或干预措施。通过该研究中所有患者的口头沟通共识别出216个问题和492项干预措施。口头沟通中讨论的问题约50.5%以及干预措施约20.8%未记录在EHR中。初步结果显示，在问题和干预措施的比较中观察到不同种族群体之间存在统计学差异。
本研究首次调查了HHC访视期间患者-护士口头沟通中提及问题和干预措施的程度，以及这些信息是否记录在EHR中。我们的分析发现了信息重叠方面的差距以及可能存在的种族差异。
我们的结果凸显了分析HHC患者与护士之间沟通的价值。未来的研究应探索利用自动语音识别在口头沟通中获取信息的方法。
