Real-Time Person Identification in a Hospital Setting: A Systematic Review

Usability and usefulness of (electronic) patient identification systems-A cross-sectional evaluation in German-speaking radiation oncology departments

PURPOSE

Patient misidentification in radiation oncology (RO) is a significant concern due to the potential harm to patient health and the burden on healthcare systems. Electronic patient identification systems (ePIS) are increasingly being used as an alternative or supplement to organizational systems (oPIS). The objective of this study was to assess the usability and usefulness of ePIS and oPIS in German-speaking countries.

METHODS

A cross-sectional survey was designed by a group of experts from various professional backgrounds in RO. The survey consisted of 38 questions encompassing quantitative and qualitative data on usability, user experience, and usefulness of PIS. It was available between August and October 2022.

RESULTS

Of 118 eligible participants, 37% had implemented some kind of ePIS. Overall, 22% of participants who use an oPIS vs. 10% of participants who use an ePIS reported adverse events in terms of patients' misidentification in the past 5 years. Frequent or very frequent drop-outs of electronic systems were reported by 31% of ePIS users. Users of ePIS significantly more often affirmed a positive cost-benefit ratio of ePIS as well as an improvement of workflow, whereas users of oPIS more frequently apprehended a decrease in staffs' attention through ePIS. The response rate was 8%.

CONCLUSION

The implementation of ePIS can contribute to efficient PI and improved processes. Apprehensions by oPIS users and assessments of ePIS users differ significantly in aspects of the perceived usefulness of ePIS. However, technical problems need to be addressed to ensure the reliability of ePIS. Further research is needed to assess the impact of different PIS on patient safety in RO.

A Clinical Trial Evaluating the Efficacy of Deep Learning-Based Facial Recognition for Patient Identification in Diverse Hospital Settings

BACKGROUND

Facial recognition systems utilizing deep learning techniques can improve the accuracy of facial recognition technology. However, it remains unclear whether these systems should be available for patient identification in a hospital setting.

METHODS

We evaluated a facial recognition system using deep learning and the built-in camera of an iPad to identify patients. We tested the system under different conditions to assess its authentication scores (AS) and determine its efficacy. Our evaluation included 100 patients in four postures: sitting, supine, and lateral positions, with and without masks, and under nighttime sleeping conditions.

RESULTS

Our results show that the unmasked certification rate of 99.7% was significantly higher than the masked rate of 90.8% (p < 0.0001). In addition, we found that the authentication rate exceeded 99% even during nighttime sleeping. Furthermore, the facial recognition system was safe and acceptable for patient identification within a hospital environment. Even for patients wearing masks, we achieved a 100% success rate for authentication regardless of illumination if they were sitting with their eyes open.

CONCLUSIONS

This is the first systematical study to evaluate facial recognition among hospitalized patients under different situations. The facial recognition system using deep learning for patient identification shows promising results, proving its safety and acceptability, especially in hospital settings where accurate patient identification is crucial.

Review of Smart Hospital Services in Real Healthcare Environments

Objectives: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. Methods: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group interview with experts in hospital information systems, and ultimately derived eight smart hospital service types.Results: Smart hospital services can be classified into the following types: services based on location recognition and tracking technology that measures and monitors the location information of an object based on short-range communication technology; high-speed communication network-based services based on new wireless communication technology; Internet of Things-based services that connect objects embedded with sensors and communication functions to the internet; mobile health services such as mobile phones, tablets, and wearables; artificial intelligence-based services for the diagnosis and prediction of diseases; robot services provided on behalf of humans in various medical fields; extended reality services that apply hyper-realistic immersive technology to medical practice; and telehealth using ICT. Conclusions: Smart hospitals can influence health and medical policies and create new medical value by defining and quantitatively measuring detailed indicators based on data collected from existing hospitals. Simultaneously, appropriate government incentives, consolidated interdisciplinary research, and active participation by industry are required to foster and facilitate smart hospitals.

Analysis of Managers' Perception Regarding the Use of Traceability Tools in the Context of Brazilian Health

Our society has advanced in terms of technology, and health could not be different. Despite the benefits and advantages that such improvements entail, it is unknown what contributions have been added to the hospital environment and whether such technological engineering has managed to generate value and adapt to different factors within such institutions' professional culture to establish relevance to the base of utilitarian nature. The use of tools can be conditioned to the view that the managerial sectors have such instruments. The work aims to identify and understand the perception that health managers have traceability tools such and their view on their efficiency and effectiveness in the hospital environment. The results direct us that the traceability tools have a significant expression in the hospital context, collaborating for efficiency and efficacy. Traceability tools can help the entire health system to be more uniform in service, in accountability, and in inspection processes.