TY - JOUR
T1 - A novel method for inferring RFID tag reader recordings into clinical events
AU - Chang, Yung Ting
AU - Syed-Abdul, Shabbir
AU - Tsai, Chung You
AU - Li, Yu Chuan
N1 - Funding Information:
We are thankful to Dr. Vimla L. Patel, Professor School of Biomedical Informatics, University of Texas for her useful comments. This study is partly funded by Center of Excellence for Cancer Research (CECR), grand number DOH100-TD-C-111-008 and Mobile Intelligent Personal Medication / Management Platform (PMP) NSC992218E038001.
PY - 2011/12
Y1 - 2011/12
N2 - Background: Nosocomial infections (NIs) are among the important indicators used for evaluating patients' safety and hospital performance during accreditation of hospitals. NI rate is higher in Intensive Care Units (ICUs) than in the general wards because patients require intense care involving both invasive and non-invasive clinical procedures. The emergence of Superbugs is motivating health providers to enhance infection control measures. Contact behavior between health caregivers and patients is one of the main causes of cross infections. In this technology driven era remote monitoring of patients and caregivers in the hospital setting can be performed reliably, and thus is in demand. Proximity sensing using radio frequency identification (RFID) technology can be helpful in capturing and keeping track on all contact history between health caregivers and patients for example. Objectives: This study intended to extend the use of proximity sensing of radio frequency identification technology by proposing a model for inferring RFID tag reader recordings into clinical events. The aims of the study are twofold. The first aim is to set up a Contact History Inferential Model (CHIM) between health caregivers and patients. The second is to verify CHIM with real-time observation done at the ICU ward. Method: A pre-study was conducted followed by two study phases. During the pre-study proximity sensing of RFID was tested, and deployment of the RFID in the Clinical Skill Center in one of the medical centers in Taiwan was done. We simulated clinical events and developed CHIM using variables such as duration of time, frequency, and identity (tag) numbers assigned to caregivers. All clinical proximity events are classified into close-in events, contact events and invasive events. During the first phase three observers were recruited to do real time recordings of all clinical events in the Clinical Skill Center with the deployed automated RFID interaction recording system. The observations were used to verify the CHIM recordings. In second phase the first author conducted 40 h of participatory observation in the ICU, and observed values that were used as golden standard to validate CHIM. Results: There were a total of 193 events to validate the CHIM in the second phase. The sensitivity, specificity, and accuracy of close-in events were 73.8%, 83.8%, and 81.6%; contact events were 81.4%, 78.8%, and 80.7%; and invasive events were 90.9%, 98.0%, and 97.5% respectively. Conclusion: The results of the study indicated that proximity sensing of the RFID detects proximity events effectively, and the CHIM can infer proximity events accurately. RFID technology can be used for recording complete clinical contact history between caregivers and patients thus assisting in tracing cause of NIs. Since this model could infer the ICU activities accurately, we are convinced that the CHIM can also be applied in other wards and can be used for additional purposes.
AB - Background: Nosocomial infections (NIs) are among the important indicators used for evaluating patients' safety and hospital performance during accreditation of hospitals. NI rate is higher in Intensive Care Units (ICUs) than in the general wards because patients require intense care involving both invasive and non-invasive clinical procedures. The emergence of Superbugs is motivating health providers to enhance infection control measures. Contact behavior between health caregivers and patients is one of the main causes of cross infections. In this technology driven era remote monitoring of patients and caregivers in the hospital setting can be performed reliably, and thus is in demand. Proximity sensing using radio frequency identification (RFID) technology can be helpful in capturing and keeping track on all contact history between health caregivers and patients for example. Objectives: This study intended to extend the use of proximity sensing of radio frequency identification technology by proposing a model for inferring RFID tag reader recordings into clinical events. The aims of the study are twofold. The first aim is to set up a Contact History Inferential Model (CHIM) between health caregivers and patients. The second is to verify CHIM with real-time observation done at the ICU ward. Method: A pre-study was conducted followed by two study phases. During the pre-study proximity sensing of RFID was tested, and deployment of the RFID in the Clinical Skill Center in one of the medical centers in Taiwan was done. We simulated clinical events and developed CHIM using variables such as duration of time, frequency, and identity (tag) numbers assigned to caregivers. All clinical proximity events are classified into close-in events, contact events and invasive events. During the first phase three observers were recruited to do real time recordings of all clinical events in the Clinical Skill Center with the deployed automated RFID interaction recording system. The observations were used to verify the CHIM recordings. In second phase the first author conducted 40 h of participatory observation in the ICU, and observed values that were used as golden standard to validate CHIM. Results: There were a total of 193 events to validate the CHIM in the second phase. The sensitivity, specificity, and accuracy of close-in events were 73.8%, 83.8%, and 81.6%; contact events were 81.4%, 78.8%, and 80.7%; and invasive events were 90.9%, 98.0%, and 97.5% respectively. Conclusion: The results of the study indicated that proximity sensing of the RFID detects proximity events effectively, and the CHIM can infer proximity events accurately. RFID technology can be used for recording complete clinical contact history between caregivers and patients thus assisting in tracing cause of NIs. Since this model could infer the ICU activities accurately, we are convinced that the CHIM can also be applied in other wards and can be used for additional purposes.
KW - Active radio frequency identification
KW - Intensive Care Unit
KW - Nosocomial infections
KW - Proximity sensing
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U2 - 10.1016/j.ijmedinf.2011.09.006
DO - 10.1016/j.ijmedinf.2011.09.006
M3 - Article
C2 - 22018605
AN - SCOPUS:84859988168
SN - 1386-5056
VL - 80
SP - 872
EP - 880
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
IS - 12
ER -