Cristiano André da Costa1, Cristian F Pasluosta2, Björn Eskofier3, Denise Bandeira da Silva4, Rodrigo da Rosa Righi5. 1. Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil. Electronic address: cac@unisinos.br. 2. Machine Learning and Data Analytics Lab., Department of Computer Science, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen 91058, Germany; Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, University of Freiburg, Georges-Koehler-Allee 102, Freiburg 79110, Germany. Electronic address: cristian.pasluosta@imtek.uni-freiburg.de. 3. Machine Learning and Data Analytics Lab., Department of Computer Science, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen 91058, Germany. Electronic address: bjoern.eskofier@fau.de. 4. Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil. Electronic address: bandeira@unisinos.br. 5. Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil. Electronic address: rrrighi@unisinos.br.
Abstract
BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. METHODS: This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. RESULTS: As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. CONCLUSION: It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.
BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. METHODS: This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. RESULTS: As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. CONCLUSION: It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.
Authors: Mohammad Nasajpour; Seyedamin Pouriyeh; Reza M Parizi; Mohsen Dorodchi; Maria Valero; Hamid R Arabnia Journal: J Healthc Inform Res Date: 2020-11-12
Authors: Gabriel Souto Fischer; Rodrigo da Rosa Righi; Cristiano André da Costa; Guilherme Galante; Dalvan Griebler Journal: Sensors (Basel) Date: 2019-09-02 Impact factor: 3.576