Infection Control in Digital Era: Future or Futile?
Article Sidebar
Main Article Content
Abstract
New technologies are being developed and marketed to healthcare facilities all over the world as a way to stop healthcare- associated infections. The Internet of Things and artificial intelligence have been created with a variety of capabilities to improve people's health, offer necessary services, and monitor their health The potential adoption of these technology in automated surveillance and automated hand hygiene compliance monitoring systems has a lot to offer health care systems. However, the success or failure of the use of technology will depend on the awareness of the challenge and the establishment of a strategy, goals, and processes to support technology deployment, maintenance, and training. System differences between nations and a lack of standardization in the application of digitalization in health care hinder this technology from providing the full range of potential benefits. In this review, we explore the use of technology in the areas of automated infection surveillance in healthcare-associated infection and hand hygiene compliance, with an emphasis on the difficulties in developing such technologies
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal of Pharmacy at https://journals.iium.edu.my/ktn/index.php/jp is licensed under a Creative Commons Attribution 4.0 International License.
References
Awwad, S., Tarvade, S., Piccardi, M., & Gattas, D. J. (2019). The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene. International journal for quality in health care : journal of the International Society for Quality in Health Care, 31(1), 36–42. https://doi.org/10.1093/intqhc/mzy099. DOI: https://doi.org/10.1093/intqhc/mzy099
Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020). Challenges to the reproducibility of machine learning models in health care. JAMA, 323(4), 305–306. https://doi.org/10.1001/jama.2019.20866. DOI: https://doi.org/10.1001/jama.2019.20866
Blacky, A., Mandl, H., Adlassnig, K. P., & Koller, W. (2011). Fully Automated Surveillance of Healthcare-Associated Infections with MONI-ICU: A Breakthrough in Clinical Infection Surveillance. Applied clinical informatics, 2(3), 365–372. https://doi.org/10.4338/ACI-2011-03-RA-0022. DOI: https://doi.org/10.4338/ACI-2011-03-RA-0022
Clancy C, Delungahawatta T, Dunne CP. (2021). Hand hygiene-related clinical trials reported between 2014 and 2020: a comprehensive systematic review. J Hosp Infect.;111:6–26. S0195-6701:00102-X. DOI: https://doi.org/10.1016/j.jhin.2021.03.007
Conway LJ. (2016) Challenges in implementing electronic hand hygiene monitoring systems. Am J Infect Contro;44(5 Suppl):7–12.doi: 10.1016/j.ajic.2015.11.031.S0196-6553(15)01224-9. DOI: https://doi.org/10.1016/j.ajic.2015.11.031
Cha, K. S., & Kim, K. M. (2020). Korean National Healthcare-Associated Infections Surveillance (KONIS): Hand hygiene surveillance. American journal of infection control, 48(3), 327–329. https://doi.org/10.1016/j.ajic.2019.08.014. DOI: https://doi.org/10.1016/j.ajic.2019.08.014
Char DS, Abràmoff MD, Feudtner C. Identifying ethical considerations for machine learning healthcare applications. Am J Bioeth. 2020 Nov 26;20(11):7–17. doi:10.1080/15265161.2020.1819469. DOI: https://doi.org/10.1080/15265161.2020.1819469
Degeling, C., Johnson, J., & Gilbert, G. L. (2019). Perspectives of Australian policymakers on the potential benefits and risks of technologically enhanced communicable disease surveillance - a modified Delphi survey. Health research policy and systems, 17(1), 35. https://doi.org/10.1186/s12961-019-0440-3. DOI: https://doi.org/10.1186/s12961-019-0440-3
Du, M., Xing, Y., Suo, J., Liu, B., Jia, N., Huo, R., Chen, C., & Liu, Y. (2014). Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital. BMC medical informatics and decision making, 14, 9. https://doi.org/10.1186/1472-6947-14-9 DOI: https://doi.org/10.1186/1472-6947-14-9
Fitzpatrick, F., Doherty, A. & Lacey, G. Using Artificial Intelligence in Infection Prevention. Curr Treat Options Infect Dis 12, 135–144 (2020). https://doi.org/10.1007/s40506-020-00216-7. DOI: https://doi.org/10.1007/s40506-020-00216-7
Gould DJ, Creedon S, Jeanes A, Drey NS, Chudleigh J, Moralejo D. (2017). Impact of observing hand hygiene in practice and research: a methodological reconsideration. J Hosp Infect; 95:169–174. doi: 10.1016/j.jhin.2016.08.008. DOI: https://doi.org/10.1016/j.jhin.2016.08.008
Gianfrancesco, M. A., & Goldstein, N. D. (2021). A narrative review on the validity of electronic health record-based research in epidemiology. BMC medical research methodology, 21(1), 234. https://doi.org/10.1186/s12874-021-01416-5. DOI: https://doi.org/10.1186/s12874-021-01416-5
Gilbert GL, Degeling C, Johnson J. Communicable disease surveillance ethics in the age of big data and new technology. Asian Bioeth Rev. 2019 Jun 10;11(2):173–187. doi: 10.1007/s41649-019-00087-1. DOI: https://doi.org/10.1007/s41649-019-00087-1
Javaid M., Haleem A., Vaishya R., Bahl S., Suman R., Vaish A.(2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev.;14:419–422. DOI: https://doi.org/10.1016/j.dsx.2020.04.032
Jones, T., Marimuthu, K., & Bearman, G. (2022). Virtual Infection Prevention and Control in Low- and Middle-Income Countries. International journal of infectious diseases: IJID : official publication of the International Society for Infectious Diseases, 117, 93–96. https://doi.org/10.1016/j.ijid.2022.01.065. DOI: https://doi.org/10.1016/j.ijid.2022.01.065
Kalhori SRN, Bahaadinbeigy K, Deldar K, Gholamzadeh M, Hajesmaeel-Gohari S, Ayyoubzadeh SM, et al. (2021). Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review. J Med Internet Res;23. DOI: https://doi.org/10.2196/19473
Kalkman, S., Mostert, M., Gerlinger, C., van Delden, J., & van Thiel, G. (2019). Responsible data sharing in international health research: a systematic review of principles and norms. BMC medical ethics, 20(1), 21. https://doi.org/10.1186/s12910-019-0359-9. DOI: https://doi.org/10.1186/s12910-019-0359-9
Kelly, D., Purssell, E., Wigglesworth, N., & Gould, D. J. (2021). Electronic hand hygiene monitoring systems can be well-tolerated by health workers: Findings of a qualitative study. Journal of infection prevention, 22(6), 246–251. https://doi.org/10.1177/17571774211012781. DOI: https://doi.org/10.1177/17571774211012781
Kruse C, Betancourt J, Ortiz S, Valdes Luna SM, Bamrah IK, Segovia N. (2019). Barriers to the Use of Mobile Health in Improving Health Outcomes in Developing Countries: Systematic Review. J Med Internet Res;21(10):e13263. doi: 10.2196/13263. DOI: https://doi.org/10.2196/13263
Lacey G, Zhou J, Li X, Craven C, Gush C. (2020). The impact of automatic video auditing with real-time feedback on the quality and quantity of handwash events in a hospital setting. Am J Infect Control;48(2):162–166. doi: 10.1016/j.ajic.2019.06.015. DOI: https://doi.org/10.1016/j.ajic.2019.06.015
Li, B. Y., Oh, J., Young, V. B., Rao, K., & Wiens, J. (2019). Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection. Open forum infectious diseases, 6(5), ofz186. https://doi.org/10.1093/ofid/ofz186. DOI: https://doi.org/10.1093/ofid/ofz186
Liao, Y. H., Wang, Z. C., Zhang, F. G., Abbod, M. F., Shih, C. H., & Shieh, J. S. (2019). Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit. Sensors (Basel, Switzerland), 19(8), 1866. https://doi.org/10.3390/s19081866. DOI: https://doi.org/10.3390/s19081866
Lowe, H., Woodd, S., Lange, I. L., Janjanin, S., Barnet, J., & Graham, W. (2021). Challenges and opportunities for infection prevention and control in hospitals in conflict-affected settings: a qualitative study. Conflict and health, 15(1), 94. https://doi.org/10.1186/s13031-021-00428-8. DOI: https://doi.org/10.1186/s13031-021-00428-8
Marques, R., Gregório, J., Pinheiro, F., Póvoa, P., da Silva, M. M., & Lapão, L. V. (2017). How can information systems provide support to nurses' hand hygiene performance? Using gamification and indoor location to improve hand hygiene awareness and reduce hospital infections. BMC medical informatics and decision making, 17(1), 15. https://doi.org/10.1186/s12911-017-0410-z. DOI: https://doi.org/10.1186/s12911-017-0410-z
Ni, K., Chu, H., Zeng, L., Li, N., & Zhao, Y. (2019). Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study. BMJ open, 9(7), e029314. https://doi.org/10.1136/bmjopen-2019-029314. DOI: https://doi.org/10.1136/bmjopen-2019-029314
Park, D. J., Park, M. W., Lee, H., Kim, Y. J., Kim, Y., & Park, Y. H. (2021). Development of machine learning model for diagnostic disease prediction based on laboratory tests. Scientific reports, 11(1), 7567. https://doi.org/10.1038/s41598-021-87171-5 DOI: https://doi.org/10.1038/s41598-021-87171-5
Parreco, J. P., Hidalgo, A. E., Badilla, A. D., Ilyas, O., & Rattan, R. (2018). Predicting central line-associated bloodstream infections and mortality using supervised machine learning. Journal of critical care, 45, 156–162. https://doi.org/10.1016/j.jcrc.2018.02.010. DOI: https://doi.org/10.1016/j.jcrc.2018.02.010
Pires, D., Gayet-Ageron, A., Guitart, C., Robert, Y. A., Fankhauser, C., Tartari, E., Peters, A., Tymurkaynak, F., Fourquier, S., Soule, H., Beuchat, R., Bellissimo-Rodrigues, F., Martin, Y., Zingg, W., & Pittet, D. (2021). Effect of Wearing a Novel Electronic Wearable Device on Hand Hygiene Compliance Among Health Care Workers: A Stepped-Wedge Cluster Randomized Clinical Trial. JAMA network open, 4(2), e2035331. https://doi.org/10.1001/jamanetworkopen.2020.35331. DOI: https://doi.org/10.1001/jamanetworkopen.2020.35331
Pollett, S., Althouse, B. M., Forshey, B., Rutherford, G. W., & Jarman, R. G. (2017). Internet-based biosurveillance methods for vector-borne diseases: Are they novel public health tools or just novelties?. PLoS neglected tropical diseases, 11(11), e0005871. https://doi.org/10.1371/journal.pntd.0005871. DOI: https://doi.org/10.1371/journal.pntd.0005871
Sadule-Rios, N., & Aguilera, G. (2017). Nurses' perceptions of reasons for persistent low rates in hand hygiene compliance. Intensive & critical care nursing, 42, 17–21. https://doi.org/10.1016/j.iccn.2017.02.005. DOI: https://doi.org/10.1016/j.iccn.2017.02.005
Samyoun, S., Shubha, S. S., Sayeed Mondol, M. A., & Stankovic, J. A. (2021). iWash: A smartwatch handwashing quality assessment and reminder system with real-time feedback in the context of infectious disease. Smart health (Amsterdam, Netherlands), 19, 100171. https://doi.org/10.1016/j.smhl.2020.100171. DOI: https://doi.org/10.1016/j.smhl.2020.100171
Singh H, Sittig DF. (2016). Measuring and improving patient safety through health information technology: The Health IT Safety Framework. BMJ Qual Saf;25(4):226–32. doi: 10.1136/bmjqs-2015-004486. DOI: https://doi.org/10.1136/bmjqs-2015-004486
Sittig, D. F., Wright, A., Coiera, E., Magrabi, F., Ratwani, R., Bates, D. W., & Singh, H. (2020). Current challenges in health information technology-related patient safety. Health informatics journal, 26(1), 181–189. https://doi.org/10.1177/1460458218814893. DOI: https://doi.org/10.1177/1460458218814893
Stangerup, M., Hansen, M. B., Hansen, R., Sode, L. P., Hesselbo, B., Kostadinov, K., Olesen, B. S., & Calum, H. (2021). Hand hygiene compliance of healthcare workers before and during the COVID-19 pandemic: A long-term follow-up study. American journal of infection control, 49(9), 1118–1122. https://doi.org/10.1016/j.ajic.2021.06.014 DOI: https://doi.org/10.1016/j.ajic.2021.06.014
Streefkerk, H., Willemsen, S. P., van der Hoeven, C. P., Vos, M. C., Verkooijen, R. P., & Verbrugh, H. A. (2019). Computer-assisted, high-frequency, hospital-wide point prevalence surveys of hospital-acquired infections in a tertiary care hospital, the Netherlands, 2013 to 2014. Euro surveillance: bulletin Europeen surles maladies transmissibles = European communicable disease bulletin, 24(13), 1800177. https://doi.org/10.2807/1560-7917.ES.2019.24.13.1800177. DOI: https://doi.org/10.2807/1560-7917.ES.2019.24.13.1800177
Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. (2019). Deep learning intervention for health care challenges: some biomedical domain considerations. JMIR Mhealth Uhealth. Aug 2;7(8):e11966. doi: 10.2196/11966 DOI: https://doi.org/10.2196/11966
Torous J, Jän Myrick K, Rauseo-Ricupero N, Firth J.(2020). Digital mental health and covid-19: using technology today to accelerate the curve on access and quality tomorrow. JMIR Ment Health;7(3):e18848. doi: 10.2196/18848. DOI: https://doi.org/10.2196/18848
Wang, C., Sarsenbayeva, Z., Chen, X., Dingler, T., Goncalves, J., & Kostakos, V. (2020). Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study. JMIR mHealth and uHealth, 8(3), e17001. https://doi.org/10.2196/17001. DOI: https://doi.org/10.2196/17001
Wu, K. S., Lee, S. S., Chen, J. K., Chen, Y. S., Tsai, H. C., Chen, Y. J., Huang, Y. H., & Lin, H. S. (2018). Identifying heterogeneity in the Hawthorne effect on hand hygiene observation: a cohort study of overtly and covertly observed results. BMC infectious diseases, 18(1), 369. https://doi.org/10.1186/s12879-018-3292-5. DOI: https://doi.org/10.1186/s12879-018-3292-5
Vaishya R, Javaid M, Khan IH, Haleem A (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr;14(4):337–339. doi: 10.1016/j.dsx.2020.04.012. DOI: https://doi.org/10.1016/j.dsx.2020.04.012
Villamarín-Bello, B., Uriel-Latorre, B., Fdez-Riverola, F., Sande-Meijide, M., & Glez-Peña, D. (2019). Gold Standard Evaluation of an Automatic HAIs Surveillance System. BioMed research international. 1049575. https://doi.org/10.1155/2019/1049575. DOI: https://doi.org/10.1155/2019/1049575
Vidal-Alaball J, Acosta-Roja R, Pastor Hernández N, Sanchez Luque U, Morrison D, Narejos Pérez S, Perez-Llano J, Salvador Vèrges A, López Seguí F. Telemedicine in the face of the COVID-19 pandemic. Aten Primaria. 2020;52:418–422. DOI: https://doi.org/10.1016/j.aprim.2020.04.003
Xu, Q., Liu, Y., Cepulis, D., Jerde, A., Sheppard, R. A., Tretter, K., Oppy, L., Stevenson, G., Bishop, S., Clifford, S. P., Liu, P., Kong, M., & Huang, J. (2021). Implementing an electronic hand hygiene system improved compliance in the intensive care unit. American journal of infection control, 49(12), 1535–1542. https://doi.org/10.1016/j.ajic.2021.05.014 DOI: https://doi.org/10.1016/j.ajic.2021.05.014
Xu, N., Liu, C., Feng, Y., Li, F., Meng, X., Lv, Q., & Lan, C. (2021). Influence of the Internet of Things management system on hand hygiene compliance in an emergency intensive care unit. The Journal of hospital infection, 109, 101–106. https://doi.org/10.1016/j.jhin.2020.12.009 DOI: https://doi.org/10.1016/j.jhin.2020.12.009
Yesmin, T., Carter, M. W., & Gladman, A. S. (2022). Internet of things in healthcare for patient safety: an empirical study. BMC health services research, 22(1), 278. https://doi.org/10.1186/s12913-022-07620-3 DOI: https://doi.org/10.1186/s12913-022-07620-3
Zhao, I. Y., Ma, Y. X., Yu, M., Liu, J., Dong, W. N., Pang, Q., Lu, X. Q., Molassiotis, A., Holroyd, E., & Wong, C. (2021). Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review. Journal of medical Internet research, 23(10), e32328. https://doi.org/10.2196/32328 DOI: https://doi.org/10.2196/32328