The Development of an RFID Solution to Facilitate the Traceability of Patient and Pharmaceutical Data

Research on contactless intelligent medication pickup mode selection based on a hospital in China under COVID-19

BACKGROUND

During an outbreak such as COVID-19, hospital staff needs to be in close contact with all types of patients visiting the hospital and the risk of cross-infection is extremely high. Payment and medication pickup is a mandatory part of a patient's hospital visit, with direct contact between healthcare workers and patients, and long waiting times in the hospital area, which can easily lead to the spread of disease infection.

OBJECTIVE

This paper designed the prototype of a contactless smart medicine cabinet based on RFID technology and optimized the patient consultation and medication pickup process to address these problems.

METHODS

We conducted a 50-day field observation of patients for consultation and medication pickup at the First Hospital in H city, Jiangsu Province, China, and randomly timed 1600 sets of data from Surgery (ophthalmology) and Internal patients, then we designed the prototype of a contactless smart medicine cabinet based on RFID technology, optimized the patient consultation and medication pickup process, comparing the traditional and intelligent models using AnyLogic.

RESULTS

The results show that this contactless medicine cabinet was able to reduce the time taken by patients in consultation and medicine pickup by 18.74 minutes, increasing the overall efficiency of the consultation by 32.20%. The simulation revealed that this contactless intelligent medication pickup model was able to reduce the time taken by patients in consultation and medicine pickup, increasing the overall efficiency of the consultation, effectively reducing the frequency of contact between healthcare workers and patients, and reducing the risk of disease infection.

CONCLUSION

The proposed technical model provides a new idea to solve the problems of long queues, low efficiency and high risk of infection for patients to consult and get medicine during epidemics. Especially within hospitals it has important theoretical and practical implications for epidemic prevention and control as well as future hospital management.

Incorporating RTLS-Based Spatiotemporal Information in Studying Physical Activities of Clinical Staff. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:2386-2391. [PMID: 34891762 DOI: 10.1109/embc46164.2021.9630597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Clinicians and staff who work in intense hospital settings such as the emergency department (ED) are under an extended amount of mental and physical pressure every day. They may spend hours in active physical pressure to serve patients with severe injuries or stay in front of a computer to review patients' clinical history and update the patients' electronic health records (EHR). Nurses on the other hand may stay for multiple consecutive days of 9-12 working hours. The amount of pressure is so much that they usually end up taking days off to recover the lost energy. Both of these extreme cases of low and high physical activities are shown to affect the physical and mental health of clinicians and may even lead to fatigue and burnout.In this study Real-Time location systems (RTLS) are used for the first time, to study the amount of physical activity exerted by clinicians. RTLS systems have traditionally been used in hospital settings for locating staff and equipment, whereas our proposed method combines both time and location information together to estimate the duration, length, and speed of movements within hospital wards such as the ED. It is also our first step towards utilizing non-wearable devices to measure sedentary behavior inside the ED. This information helps to assess the workload on the care team and identify means to reduce the risk of performance compromise, fatigue, and burnout.We used one year worth of raw RFID data that covers movement records of 38 physicians, 13 residents, 163 nurses, 33 staff in the ED. We defined a walking path as the continuous sequences of movements and stops and identified separate walking paths for each individual on each day. Walking duration, distance, and speed, along with the number of steps and the duration of sedentary behavior, are then estimated for each walking path. We compared our results to the values reported in the literature and showed despite the low spatial resolution of RTLS, our non-invasive estimations are closely comparable to the ones measured by Fitbit or other wearable pedometers.Clinical Relevance- Adequate assessment of workload in a dynamic care delivery space plays an important role in ensuring safe and optimal care delivery [7]. Systems capable of measuring physical activities on a continuous basis during daily work can provide precious information for a variety of purposes including automated assessment of sedentary behaviors and early detection of work pressure. Such systems could help facilitate targeted changes in the number of staff, duration of their working shifts leading to a safer and healthier environment for both clinicians and patients.

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.

Knowledge-Based Decision Support in Healthcare via Near Field Communication

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

Protocol for Streaming Data from an RFID Sensor Network †

Currently, there is an increasing interest in the use of Radio Frequency Identification (RFID) tags which incorporate passive or battery-less sensors. These systems are known as computational RFID (CRFID). Several CRFID tags together with a reader set up an RFID sensor network. The reader powers up the tags’ microcontroller and their attached sensor using radio frequency waves, and tags backscatter, not only their EPC code but also the value of those sensors. The current standard for interrogating these CRFID tags is the EPC global Class 1 Generation 2 (EPC C1G2). When several tags are located inside the reader interrogation area, the EPC C1G2 results in very poor performance to obtain sensor data values. To solve this problem, a novel protocol called Sensor Frmed Slotted Aloha (sFSA) for streaming sensor data dealing with the tag collisions is presented. The proposed protocol increases the Sensor Read Rate (SRR), defined as the number of sensor data reads per second, compared to the standard. Additionally, this paper presents a prototype of an RFID sensor network to compare the proposed sFSA with the standard, increasing the SRR by more than five times on average. Additionally, the proposed protocol keeps a constant sensor sampling frequency for a suitable streaming of these tag sensors.

False Positive RFID Detection Using Classification Models

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

RFID Technology for Management and Tracking: e-Health Applications

Radio frequency identification (RFID) has become a key technology in the logistics and management industry, thanks to distinctive features such as the low cost of RFID tags, and the easiness of the RFID tags’ deployment and integration within the items to be tracked. In consequence, RFID plays a fundamental role in the so-called digital factory or 4.0 Industry, aiming to increase the level of automatization of industrial processes. In addition, RFID has also been found to be of great help in improving the tracking of patients, medicines, and medical assets in hospitals, where the digitalization of these operations improves their efficiency and safety. This contribution reviews the state-of-the-art of RFID for e-Health applications, describing the contributions to improve medical services and discussing the limitations. In particular, it has been found that a lot of effort has been put into software development, but in most of the cases a detailed study of the physical layer (that is, the characterization of the RFID signals within the area where the system is deployed) is not properly conducted. This contribution describes a basic RFID system for tracking and managing assets in hospitals, aiming to provide additional details about implementation aspects that must be considered to ensure proper functionality of the system. Although the scope of the RFID system described in this contribution is restricted to a small area of the hospital, the architecture is fully scalable to cover the needs of the different medical services in the hospital. Ultra high-frequency (UHF) RFID technology is selected over the most extended near-field communication (NFC) and high-frequency (HF) RFID technology to minimize hardware infrastructure. In particular, UHF RFID also makes the coverage/reading area conformation easier by using different kinds of antennas. Information is stored in a database, which is accessed from end-user mobile devices (tablets, smartphones) where the position and status of the assets to be tracked are displayed.

Traceability in Patient Healthcare through the Integration of RFID Technology in an ICU in a Hospital

Patient safety is a principal concern for health professionals in the care process and it is, therefore, necessary to provide information management systems to each unit of the hospital, capable of tracking patients and medication to reduce the occurrence of adverse events and therefore increase the quality of care received by patients during their stay in hospital. This work presents a tool for the Intensive Care Unit (ICU), a key service with special characteristics, which computerises and tracks admissions, care plans, vital monitoring, the prescription and medication administration process for patients in this service. To achieve this, it is essential that innovative and cutting-edge technologies are implemented such as Near Field Communication (NFC) technology which is now being implemented in diverse environments bringing a range of benefits to the tasks for which it is employed.

Emergency Department Crowding and Time at the Bedside: A Wearable Technology Feasibility Study

INTRODUCTION

ED crowding is a public health crisis, limiting quality and access to lifesaving care. The purpose of this study was to (1) evaluate the feasibility of radio-frequency identification tags to measure clinician-patient contact and (2) to test the relationship between ED occupancy and clinician-patient contact time.

METHODS

In this 4-week observational study, radio-frequency identification tags were worn by emergency clinicians in a 21-bay urban teaching hospital emergency department. The time-motion data were merged with electronic medical repository patient information (N = 3,237) to adjust for occupancy, age, gender, and acuity. Qualitative themes were generated from focus group (N = 39) debriefings of the quantitative results.

RESULTS

Data were collected on 56,342 total clinician events. Adjusting for patient age, increasing ED occupancy increased the number of times the attending physician entered and left the patient room (b = 0 .008, 95% confidence interval [CI] = [0.001-0.016], P = 0.03). There was no relationship for patient gender, triage acuity, shift at arrival, disposition to home, or discharge diagnosis category with either total minutes or number of encounters per patient visit. No time-motion and occupancy associations were observed for nurses, residents, or nurse practitioners/physician assistants. Debriefings indicated occupancy influenced the quality of care, despite maintaining the same quantity of contact time.

DISCUSSION

The physical environment and clinician privacy concerns limit the feasibility of wearable tracking technology in the emergency setting. Attending physician care becomes more fragmented with increasing ED occupancy. Other clinicians report changes in the quality of care, whereas the quantity of time and encounters were unchanged with occupancy rates.