Browsing by Author "Özcanhan, Mehmet Hilal"

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Citation - WoS: 8
Citation - Scopus: 10
Automated personnel-assets-consumables-drug tracking in ambulance services for more effective and efficient medical emergency interventions
(ELSEVIER IRELAND LTD, 2016) Semih Utku; Mehmet Hilal Ozcanhan; Mehmet Suleyman Unluturk; Unluturk, Mehmet Suleyman; Utku, Semih; Özcanhan, Mehmet Hilal
Patient delivery time is no longer considered as the only critical factor in ambulatory services. Presently five clinical performance indicators are used to decide patient satisfaction. Unfortunately the emergency ambulance services in rapidly growing metropolitan areas do not meet current satisfaction expectations, because of human errors in the management of the objects onboard the ambulances. But human involvement in the information management of emergency interventions can be reduced by electronic tracking of personnel assets consumables and drugs (PACD) carried in the ambulances. Electronic tracking needs the support of automation software which should be integrated to the overall hospital information system. Our work presents a complete solution based on a centralized database supported by radio frequency identification (RFID) and bluetooth low energy (BLE) identification and tracking technologies. Each object in an ambulance is identified and tracked by the best suited technology. The automated identification and tracking reduces manual paper documentation and frees the personnel to better focus on medical activities. The presence and amounts of the PACD are automatically monitored warning about their depletion non-presence or maintenance dates. The computerized two way hospital-ambulance communication link provides information sharing and instantaneous feedback for better and faster diagnosis decisions. A fully implemented system is presented with detailed hardware and software descriptions. The benefits and the clinical outcomes of the proposed system are discussed which lead to improved personnel efficiency and more effective interventions. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 6
Neural network-supported patient-adaptive fall prevention system
(Springer, 2020) Mehmet Hilal Özcanhan; Semih Utku; Mehmet Suleyman Ünlütürk; Unluturk, Mehmet Suleyman; Utku, Semih; Özcanhan, Mehmet Hilal
Patient falls due to unattended bed-exits are costly to patients healthcare personnel and hospitals. Numerous researches based on up to three predetermined factors have been conducted for preventing falls. The present comprehensive proposal is based on four sub-systems that synthesize six factors. A parameter is assigned to each factor with a coefficient specifically determined for each individual patient and per admittance. The parameters are aggregated in equations that lead to an early warning about a probable bed-exit or an alarm about an imminent bed-exit. The ultimate aim of our proposal is the generation of the earliest possible warning to grant the longest time for nurse intervention. Thus the probable fall of high-risk patients can be prevented by stopping the unattended bed-exits. The proposal is supported by a prototype multi-tier system design and the results of laboratory patient bed-exit scenarios carried out using the design. Comparison of the obtained results with previous work shows that our proposed solution is unmatched in providing the longest time for nurse intervention (up to 15.7 ± 1.1 s) because of the comprehensive six-factor synthesis specific to each individual patient and each admittance. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 6
Wireless transmission of vital body data and ambient magnetic field with wearable IoT device attached smart textile
(SAGE Publications Ltd, 2025) Hakan Dalkiliç; Hakan Özdemir; Mehmet Hilal Özcanhan; Özdemir, Hakan; Dalkılıç, Hakan; Özcanhan, Mehmet Hilal
The use of smart textiles is expanding. The wearer’s data are transferred to the Cloud by a mobile device and shared with authorized parties. The study aims to monitor continuously and share our wearable smart textile’s heartbeat body temperature and the surrounding magnetic field data providing early intervention before negative health events occur or a high magnetic field is of concern to its wearer. A heartbeat sensor a temperature sensor and an ESP32 module with a built-in Hall effect sensor were integrated with a special conductive wire woven fabric. The data measured by the sensors were sent to the cloud server wirelessly by the ESP32. Our custom-made software analyzes the collected data with statistical methods enabling the generation of predictions and early warnings. The generated reports can be sent to the smart textile user doctors and authorized third-party health institutions and relevant magnetic field authorities. Our study shows that the body temperature reported by the designed smart textile has less than a 2.0% error compared with the actual value. On the other hand the reported heartbeat has a 11.0% error as it largely depends on sensor quality and placement location. In addition to these continuous monitoring of the ambient magnetic field has been achieved with smart textiles. Our smart textile design sends the wearer’s body temperature heartbeat and surrounding magnetic field information to a cloud server automatically and wirelessly. Our custom-made software and mobile application use the data to provide early warnings and live reports on users’ mobile devices. © 2025 Elsevier B.V. All rights reserved.