Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study.

BACKGROUND: Documentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation. OBJECTIVE: This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses. METHODS: An electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022. RESULTS: A total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group. CONCLUSIONS: The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

An Automatic Image Collection System for Multicenter Clinical Studies.

The acquisition of medical images from multiple medial institutions has become important for high-quality clinical studies. In recent years, electronic data submission has enabled the transmission of image data to independent institutions more quickly and easily than before. However, the selection, anonymization, and transmission of medical images still require human resources in the form of clinical research collaborators. In this study, we developed an image collection system that works with the electronic data capture (EDC) system. In this image collection system, medical images are selected based on EDC input information, patient ID is anonymized to a subject ID issued by the EDC, and the selected anonymized images are transferred to the research institute without human intervention. In the research institute, clinical information registered by the EDC and clinical images collected by the image collection system are managed by the same subject ID and can be used for clinical studies. In October 2019, our image collection system was introduced to 13 medical institutions and has now begun collecting medical images from the in-hospital picture archiving and communication system (PACS) of those institutions.

Case Report Form Reporter: A Key Component for the Integration of Electronic Medical Records and the Electronic Data Capture System.

To improve the efficiency of clinical research, we developed a system to integrate electronic medical records (EMRs) and the electronic data capture system (EDC). EDC is divided into case report form (CRF) reporter and CDMS with CRF receiver with data communication using the operational data model (ODM). The CRF reporter is incorporated into the EMR to share data witth the EMR. In the data transcription type, doctors enter data using a progress note template, which are transmitted to the reporter template. It then generates the ODM. In the direct record type, reporter templates open from the progress note and generate narrative text to make record in the progress note. The configuration files for a study are delivered from the contents server to minimize the setup. This system has been used for 15 clinical studies including 3 clinical trials. This system can save labor and financial costs in clinical research.

Interconnection of electronic medical record with clinical data management system by CDISC ODM.

EDC system has been used in the field of clinical research. The current EDC system does not connect with electronic medical record system (EMR), thus a medical staff has to transcribe the data in EMR to EDC system manually. This redundant process causes not only inefficiency but also human error. We developed an EDC system cooperating with EMR, in which the data required for a clinical research form (CRF) is transcribed automatically from EMR to electronic CRF (eCRF) and is sent via network. We call this system as "eCRF reporter". The interface module of eCRF reporter can retrieves the data in EMR database including patient biography data, laboratory test data, prescription data and data entered by template in progress notes. The eCRF reporter also enables users to enter data directly to eCRF. The eCRF reporter generates CDISC ODM file and PDF which is a translated form of Clinical data in ODM. After storing eCRF in EMR, it is transferred via VPN to a clinical data management system (CDMS) which can receive the eCRF files and parse ODM. We started some clinical research by using this system. This system is expected to promote clinical research efficiency and strictness.