Internet of things in healthcare for patient safety: an empirical study.

INTRODUCTION: This study evaluates the impact of an Internet of Things (IoT) intervention in a hospital unit and provides empirical evidence on the effects of smart technologies on patient safety (patient falls and hand hygiene compliance rate) and staff experiences. METHOD: We have conducted a post-intervention analysis of hand hygiene (HH) compliance rate, and a pre-and post-intervention interrupted time-series (ITS) analysis of the patient falls rates. Lastly, we investigated staff experiences by conducting semi-structured open-ended interviews based on Roger's Diffusion of Innovation Theory. RESULTS: The results showed that (i) there was no statistically significant change in the mean patient fall rates. ITS analysis revealed non-significant incremental changes in mean patient falls (- 0.14 falls/quarter/1000 patient-days). (ii) HH compliance rates were observed to increase in the first year then decrease in the second year for all staff types and room types. (iii) qualitative interviews with the nurses reported improvement in direct patient care time, and a reduced number of patient falls. CONCLUSION: This study provides empirical evidence of some positive changes in the outcome variables of interest and the interviews with the staff of that unit reported similar results as well. Notably, our observations identified behavioral and environmental issues as being particularly important for ensuring success during an IoT innovation implementation within a hospital setting.

Evaluation framework for automatic privacy auditing tools for hospital data breach detections: A case study.

OBJECTIVE: We aim to 1) design an evaluation framework to examine the accuracy of automatic privacy auditing tools, 2) apply the evaluation method at a hospital to validate the performance of an auditing tool that uses a machine learning algorithm to automate user access auditing, and 3) recommend further improvements in auditing for the hospital. MATERIALS AND METHODS: Using the black box method of user acceptance testing, we have designed an evaluation framework consisting of appropriate and inappropriate behaviour scenarios to examine the privacy auditing tools. The scenarios were designed from clinical and non-clinical hospital staff perspective, taking expert opinions from the privacy officers and considering examples from the Information and Privacy Commission (IPC) and were tested using Mackenzie Richmond Hill Hospital's data. RESULTS: The case study using this evaluation framework found that on average 98.09 % of total accesses of the hospital were identified as appropriate and the tool was unable to explain the remaining 1.91 % of accesses. In addition, a statistically significant (P < 0.05) increasing trend on categorizing appropriate accesses by the tool have been observed. Furthermore, an analysis of unexplained accesses revealed the contributing factors and found issues related to hospital workflows and data quality (information was missing about staff roles and departments). CONCLUSION: Given that adoption of these machine learning tools is increasing in hospitals, this research provides an evaluation framework and an empirical evidence on the effectiveness of automated privacy auditing and detecting anomalies for dynamic hospital workflows.