Adoption of Information and Technology Communication in Ulcer Pressure Prevention: A Narrative Review
Abstract
Patients who have immobilization and bedrest prone to have poor blood circulation to the area that contact the bed. This condition is triggering pressure ulcer that may impact on patients’ recovery process, burden, and conformity. In 4.0 industry, information, and communication technology (ICT) has implemented in many healthcare activities, including in the prevention of pressure ulcer. ICT provides a solution to prevent this unwanted condition in bedrest patients through many forms, but has not been mapped yet, especially for nursing. The purpose of this study is mainly to synthesize previous findings related to the adoption of ICT in pressure ulcer preventions. This study is a literature review which using several databases, namely Pubmed, Google Scholar, JMIR, IEEE, and Sage Journals. We used the PRISMA framework as a guideline to select the eligible articles that must be included to our study. We implemented Mixed Method Appraisal Tool to ensure the quality of articles in this review. Then, the data is synthesized and visualized in tabular. We gathered 2,081 articles from literature searching and obtained 12 eligible articles to be synthesized. Our Findings revealed that ICT adoption in ulcer pressure prevention was further than expected which the implementation of artificial intelligence (AI) was dominated in our findings. We also found that several technologies, such internet of things (IoT), were also implemented to send the patient’s data related to pressure ulcer periodically and provided the information for healthcare provider to formulate preventive interventions. Detection of pressure ulcer among patients may be done precisely by tomography technique in one study. Various ICT implementations in pressure ulcer prevention were promising to be adopted by healthcare providers, including nurses.
Keywords:
ICT, Pressure ulcer, Prevention
Downloads
Download data is not yet available.
References
Abdelmoghith, A., Shaaban, R., Alsheghri, Z., & Ismail, L.S. (2020). IoT-Based Healthcare Monitoring System: Bedsores Prevention. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 64-69.
Almathami, H. K. Y., Than Win, K., & Vlahu-Gjorgievska, E. (2020). Barriers and facilitators that influence telemedicine-based, real-time, online consultation at patients’ homes: Systematic literature review. Journal of Medical Internet Research, 22(2). https://doi.org/10.2196/16407
Amann, J., Fiordelli, M., Brach, M., Bertschy, S., Scheel-Sailer, A., & Rubinelli, S. (2020). Co-designing a self-management app prototype to support people with spinal cord injury in the prevention of pressure injuries: Mixed methods study. JMIR MHealth and UHealth, 8(7), 1–17. https://doi.org/10.2196/18018
Barakat-Johnson, M., Lai, M., Wand, T., Li, M., White, K., & Coyer, F. (2019). The incidence and prevalence of medical device-related pressure ulcers in intensive care: a systematic review. Journal of Wound Care, 28(8), 512–521.
Begum, T., Khan, S. M., Adamou, B., Ferdous, J., Parvez, M. M., Islam, M. S., Kumkum, F. A., Rahman, A., & Anwar, I. (2020). Perceptions and experiences with district health information system software to collect and utilize health data in Bangladesh: A qualitative exploratory study. BMC Health Services Research, 20(1), 1–13. https://doi.org/10.1186/s12913-020-05322-2
Calonaci, D. (2021). Designing User Interfaces: Exploring User interfaces, UI Elements, Design prototypes and Figma UI Design Tool. BPB Publications. http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203
Cicceri, G., De Vita, F., Bruneo, D., Merlino, G., & Puliafito, A. (2020). A deep learning approach for pressure ulcer prevention using wearable computing. Human-Centric Computing and Information Sciences, 10(1). https://doi.org/10.1186/s13673-020-0211-8
Cramer, E. M., Seneviratne, M. G., Sharifi, H., Ozturk, A., & Hernandez-Boussard, T. (2019). Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning. EGEMs (Generating Evidence & Methods to Improve Patient Outcomes), 7(1), 49. https://doi.org/10.5334/egems.307
Crunden, E. A., Worsley, P. R., Coleman, S. B., & Schoonhoven, L. (2022). Barriers and facilitators to reporting medical device-related pressure ulcers: A qualitative exploration of international practice. International Journal of Nursing Studies, 135, 104326.
Du, Y.-L., Ma, C.-H., Liao, Y.-F., Wang, L., Zhang, Y., & Niu, G. (2021). Is Clinical Scenario Simulation Teaching Effective in Cultivating the Competency of Nursing Students to Recognize and Assess the Risk of Pressure Ulcers? Risk Management and Healthcare Policy, 2887–2896.
Ebenso, B., Okusanya, B., Okunade, K., Akeju, D., Tukur, J., Abdullahi, M. K., Okuzu, O., & Allsop, M. J. (2021). What Are the Contextual Enablers and Impacts of Using Digital Technology to Extend Maternal and Child Health Services to Rural Areas? Findings of a Qualitative Study From. Frontiers in Global Women’s Health, 2, 1–16. https://doi.org/10.3389/fgwh.2021.670494
Ferrão, J. C., Oliveira, M. D., Janela, F., Martins, H. M. G., & Gartner, D. (2021). Can Structured EHR Data Support Clinical Coding? A Data Mining Approach. Health Systems, 10(2), 138–161. https://doi.org/10.1080/20476965.2020.1729666
Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E. T., Wu, J. T., Welt, J., Foote, J., Moseley, E. T., Grant, D. W., Tyler, P. D., & Celi, L. A. (2018). Comparing Deep Learning and Concept Extraction Based Methods for Patient Phenotyping from Clinical narratives. PLoS ONE, 13(2), 1–19. https://doi.org/10.1371/journal.pone.0192360
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Gurkan, A., Kirtil, I., Aydin, Y. D., & Kutuk, G. (2022). Pressure injuries in surgical patients: a comparison of Norton, Braden and Waterlow risk assessment scales. Journal of Wound Care, 31(2), 170–177.
Han, J. H., & Lee, J. Y. (2021). Digital healthcare industry and technology trends. Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021, 375–377. https://doi.org/10.1109/BigComp51126.2021.00083
Heuvel, J. F. M. Van Den, Ayubi, S., Franx, A., & Bekker, M. N. (2020). Home-Based Monitoring and Telemonitoring of Complicated Pregnancies: Nationwide Cross-Sectional Survey of Current Practice in the Netherlands. JMIR MHealth and UHealth, 8(10). https://doi.org/10.2196/18966
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M. C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) Version 2018 for Information Professionals and Researchers. Education for Information, 34(4), 285–291.
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., Rousseau, M.-C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT) Version 2018: User Guide. McGill, 1–11. http://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/fetch/127916259/MMAT_2018_criteria-manual_2018-08-01_ENG.pdf%0Ahttp://mixedmethodsappraisaltoolpublic.pbworks.com/
Inal, Y., Wake, J. D., Guribye, F., & Nordgreen, T. (2020). Usability Evaluations of Mobile Mental Health Technologies: Systematic Review. Journal of Medical Internet Research, 22(1). https://doi.org/10.2196/15337
Jones, O. T., Calanzani, N., Saji, S., Duffy, S. W., Emery, J., Hamilton, W., Singh, H., de Wit, N. J., & Walter, F. M. (2021). Artificial Intelligence Techniques that May be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review. Journal of Medical Internet Research, 23(3). https://doi.org/10.2196/23483
Khac, A. Do, Jourdan, C., Fazilleau, S., Palayer, C., Laffont, I., Dupeyron, A., Verdun, S., & Gelis, A. (2021). mHealth app for pressure ulcer wound assessment in patients with spinal cord injury: Clinical validation study. JMIR MHealth and UHealth, 9(2). https://doi.org/10.2196/26443
Kottner, J., Cuddigan, J., Carville, K., Balzer, K., Berlowitz, D., Law, S., Litchford, M., Mitchell, P., Moore, Z., & Pittman, J. (2020). Pressure ulcer/injury classification today: An international perspective. Journal of Tissue Viability, 29(3), 197–203.
Lau, C. H., Yu, K. H.-O., Yip, T. F., Luk, L. Y. F., Wai, A. K. C., Sit, T.-Y., Wong, J. Y.-H., & Ho, J. W. K. (2022). An artificial intelligence-enabled smartphone app for real-time pressure injury assessment. Frontiers in Medical Technology, 4(September), 1–10. https://doi.org/10.3389/fmedt.2022.905074
Liu, S., Xu, Y., Chen, S., Hu, Q., & Dong, F. (2022). Detection of Pressure Ulcers Using Electrical Impedance Tomography. Conference Record - IEEE Instrumentation and Measurement Technology Conference, 1–6. https://doi.org/10.1109/I2MTC48687.2022.9806603
Mandracchia, F., Llauradó, E., Tarro, L., Valls, R. M., & Solà, R. (2020). Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS). JMIR MHealth and UHealth, 8(9), 1–14. https://doi.org/10.2196/18339
McBride, S., & TIetze, M. (2016). Nursing Informatics for The Advanced Practice Nurse. 2nd ed. Springer.
McKeown, E., McGraw, C., Holder, P., Shand, J., & Hirani, S. P. (2022). Acceptability and Impact of an Educational App (iCare) for Informal Carers Looking After People at Risk of Pressure Ulceration: Mixed Methods Pilot Study. JMIR Formative Research, 6(9). https://doi.org/10.2196/36517
Miller, M. W., Emeny, R. T., Snide, J. A., & Freed, G. L. (2020). Patient-specific factors associated with pressure injuries revealed by electronic health record analyses. Health Informatics Journal, 26(1), 474–485. https://doi.org/10.1177/1460458219832053
Monroy, E. B., Polo Rodríguez, A., Espinilla Estevez, M., & Medina Quero, J. (2020). Fuzzy monitoring of in-bed postural changes for the prevention of pressure ulcers using inertial sensors attached to clothing. Journal of Biomedical Informatics, 107(June), 103476. https://doi.org/10.1016/j.jbi.2020.103476
Nurmambetova, E., Pan, J., Zhang, Z., Wu, G., Lee, S., Southern, D. A., Martin, E. A., Ho, C., Xu, Y., & Eastwood, C. A. (2023). Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models. Jmir Ai, 2(Ml), e41264. https://doi.org/10.2196/41264
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. The BMJ, 372(n71). https://doi.org/10.1136/bmj.n71
Rangarajan, S., Lee, Y., Johnson, V., Schorger, K., Lee, H., Nguyen, D., Behfar, M. H., Jansson, E., Rekila, J., Hiltunen, J., Vin, E., & Obraczka, K. (2022). A Novel IoT System For Patient-Centric Pressure Ulcer Prevention Using Sensor Embedded Dressings. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events, PerCom Workshops 2022, 42–45. https://doi.org/10.1109/PerComWorkshops53856.2022.9767294
Roehrs, A., Da Costa, C. A., Da Rosa Righi, R., & De Oliveira, K. S. F. (2017). Personal health records: A systematic literature review. Journal of Medical Internet Research, 19(1). https://doi.org/10.2196/jmir.5876
Sahay, S., Sundararaman, T., & Braa, J. (2017). Public Health Informatics: Design for Change - A Developing Country Perspective. Oxford University Press.
Siramungklanon, N., Yaibuates, M., Pravesjit, S., & Kantawong, K. (2022). The Surveillance Systems to Prevent Pressure Bedsores and Clinical Guidelines for Bedridden Patients and Caregivers. 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022, 33–36. https://doi.org/10.1109/ECTIDAMTNCON53731.2022.9720381
WHO. (2021). Global Strategy on Digital Health 2020-2025. In Indian Pediatrics (Vol. 57, Issue 4). WHO. https://doi.org/10.1007/s13312-020-1789-7
Willcox, M., Moorthy, A., Mohan, D., Romano, K., Hutchful, D., Mehl, G., Labrique, A., & LeFevre, A. (2019). Mobile Technology for Community Health in Ghana: Is Maternal Messaging and Provider Use of Technology Cost-Effective in Improving Maternal and Child Health Outcomes at Scale? Journal of Medical Internet Research, 21(2). https://doi.org/10.2196/11268
Almathami, H. K. Y., Than Win, K., & Vlahu-Gjorgievska, E. (2020). Barriers and facilitators that influence telemedicine-based, real-time, online consultation at patients’ homes: Systematic literature review. Journal of Medical Internet Research, 22(2). https://doi.org/10.2196/16407
Amann, J., Fiordelli, M., Brach, M., Bertschy, S., Scheel-Sailer, A., & Rubinelli, S. (2020). Co-designing a self-management app prototype to support people with spinal cord injury in the prevention of pressure injuries: Mixed methods study. JMIR MHealth and UHealth, 8(7), 1–17. https://doi.org/10.2196/18018
Barakat-Johnson, M., Lai, M., Wand, T., Li, M., White, K., & Coyer, F. (2019). The incidence and prevalence of medical device-related pressure ulcers in intensive care: a systematic review. Journal of Wound Care, 28(8), 512–521.
Begum, T., Khan, S. M., Adamou, B., Ferdous, J., Parvez, M. M., Islam, M. S., Kumkum, F. A., Rahman, A., & Anwar, I. (2020). Perceptions and experiences with district health information system software to collect and utilize health data in Bangladesh: A qualitative exploratory study. BMC Health Services Research, 20(1), 1–13. https://doi.org/10.1186/s12913-020-05322-2
Calonaci, D. (2021). Designing User Interfaces: Exploring User interfaces, UI Elements, Design prototypes and Figma UI Design Tool. BPB Publications. http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203
Cicceri, G., De Vita, F., Bruneo, D., Merlino, G., & Puliafito, A. (2020). A deep learning approach for pressure ulcer prevention using wearable computing. Human-Centric Computing and Information Sciences, 10(1). https://doi.org/10.1186/s13673-020-0211-8
Cramer, E. M., Seneviratne, M. G., Sharifi, H., Ozturk, A., & Hernandez-Boussard, T. (2019). Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning. EGEMs (Generating Evidence & Methods to Improve Patient Outcomes), 7(1), 49. https://doi.org/10.5334/egems.307
Crunden, E. A., Worsley, P. R., Coleman, S. B., & Schoonhoven, L. (2022). Barriers and facilitators to reporting medical device-related pressure ulcers: A qualitative exploration of international practice. International Journal of Nursing Studies, 135, 104326.
Du, Y.-L., Ma, C.-H., Liao, Y.-F., Wang, L., Zhang, Y., & Niu, G. (2021). Is Clinical Scenario Simulation Teaching Effective in Cultivating the Competency of Nursing Students to Recognize and Assess the Risk of Pressure Ulcers? Risk Management and Healthcare Policy, 2887–2896.
Ebenso, B., Okusanya, B., Okunade, K., Akeju, D., Tukur, J., Abdullahi, M. K., Okuzu, O., & Allsop, M. J. (2021). What Are the Contextual Enablers and Impacts of Using Digital Technology to Extend Maternal and Child Health Services to Rural Areas? Findings of a Qualitative Study From. Frontiers in Global Women’s Health, 2, 1–16. https://doi.org/10.3389/fgwh.2021.670494
Ferrão, J. C., Oliveira, M. D., Janela, F., Martins, H. M. G., & Gartner, D. (2021). Can Structured EHR Data Support Clinical Coding? A Data Mining Approach. Health Systems, 10(2), 138–161. https://doi.org/10.1080/20476965.2020.1729666
Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E. T., Wu, J. T., Welt, J., Foote, J., Moseley, E. T., Grant, D. W., Tyler, P. D., & Celi, L. A. (2018). Comparing Deep Learning and Concept Extraction Based Methods for Patient Phenotyping from Clinical narratives. PLoS ONE, 13(2), 1–19. https://doi.org/10.1371/journal.pone.0192360
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Gurkan, A., Kirtil, I., Aydin, Y. D., & Kutuk, G. (2022). Pressure injuries in surgical patients: a comparison of Norton, Braden and Waterlow risk assessment scales. Journal of Wound Care, 31(2), 170–177.
Han, J. H., & Lee, J. Y. (2021). Digital healthcare industry and technology trends. Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021, 375–377. https://doi.org/10.1109/BigComp51126.2021.00083
Heuvel, J. F. M. Van Den, Ayubi, S., Franx, A., & Bekker, M. N. (2020). Home-Based Monitoring and Telemonitoring of Complicated Pregnancies: Nationwide Cross-Sectional Survey of Current Practice in the Netherlands. JMIR MHealth and UHealth, 8(10). https://doi.org/10.2196/18966
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M. C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) Version 2018 for Information Professionals and Researchers. Education for Information, 34(4), 285–291.
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., Rousseau, M.-C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT) Version 2018: User Guide. McGill, 1–11. http://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/fetch/127916259/MMAT_2018_criteria-manual_2018-08-01_ENG.pdf%0Ahttp://mixedmethodsappraisaltoolpublic.pbworks.com/
Inal, Y., Wake, J. D., Guribye, F., & Nordgreen, T. (2020). Usability Evaluations of Mobile Mental Health Technologies: Systematic Review. Journal of Medical Internet Research, 22(1). https://doi.org/10.2196/15337
Jones, O. T., Calanzani, N., Saji, S., Duffy, S. W., Emery, J., Hamilton, W., Singh, H., de Wit, N. J., & Walter, F. M. (2021). Artificial Intelligence Techniques that May be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review. Journal of Medical Internet Research, 23(3). https://doi.org/10.2196/23483
Khac, A. Do, Jourdan, C., Fazilleau, S., Palayer, C., Laffont, I., Dupeyron, A., Verdun, S., & Gelis, A. (2021). mHealth app for pressure ulcer wound assessment in patients with spinal cord injury: Clinical validation study. JMIR MHealth and UHealth, 9(2). https://doi.org/10.2196/26443
Kottner, J., Cuddigan, J., Carville, K., Balzer, K., Berlowitz, D., Law, S., Litchford, M., Mitchell, P., Moore, Z., & Pittman, J. (2020). Pressure ulcer/injury classification today: An international perspective. Journal of Tissue Viability, 29(3), 197–203.
Lau, C. H., Yu, K. H.-O., Yip, T. F., Luk, L. Y. F., Wai, A. K. C., Sit, T.-Y., Wong, J. Y.-H., & Ho, J. W. K. (2022). An artificial intelligence-enabled smartphone app for real-time pressure injury assessment. Frontiers in Medical Technology, 4(September), 1–10. https://doi.org/10.3389/fmedt.2022.905074
Liu, S., Xu, Y., Chen, S., Hu, Q., & Dong, F. (2022). Detection of Pressure Ulcers Using Electrical Impedance Tomography. Conference Record - IEEE Instrumentation and Measurement Technology Conference, 1–6. https://doi.org/10.1109/I2MTC48687.2022.9806603
Mandracchia, F., Llauradó, E., Tarro, L., Valls, R. M., & Solà, R. (2020). Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS). JMIR MHealth and UHealth, 8(9), 1–14. https://doi.org/10.2196/18339
McBride, S., & TIetze, M. (2016). Nursing Informatics for The Advanced Practice Nurse. 2nd ed. Springer.
McKeown, E., McGraw, C., Holder, P., Shand, J., & Hirani, S. P. (2022). Acceptability and Impact of an Educational App (iCare) for Informal Carers Looking After People at Risk of Pressure Ulceration: Mixed Methods Pilot Study. JMIR Formative Research, 6(9). https://doi.org/10.2196/36517
Miller, M. W., Emeny, R. T., Snide, J. A., & Freed, G. L. (2020). Patient-specific factors associated with pressure injuries revealed by electronic health record analyses. Health Informatics Journal, 26(1), 474–485. https://doi.org/10.1177/1460458219832053
Monroy, E. B., Polo Rodríguez, A., Espinilla Estevez, M., & Medina Quero, J. (2020). Fuzzy monitoring of in-bed postural changes for the prevention of pressure ulcers using inertial sensors attached to clothing. Journal of Biomedical Informatics, 107(June), 103476. https://doi.org/10.1016/j.jbi.2020.103476
Nurmambetova, E., Pan, J., Zhang, Z., Wu, G., Lee, S., Southern, D. A., Martin, E. A., Ho, C., Xu, Y., & Eastwood, C. A. (2023). Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models. Jmir Ai, 2(Ml), e41264. https://doi.org/10.2196/41264
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. The BMJ, 372(n71). https://doi.org/10.1136/bmj.n71
Rangarajan, S., Lee, Y., Johnson, V., Schorger, K., Lee, H., Nguyen, D., Behfar, M. H., Jansson, E., Rekila, J., Hiltunen, J., Vin, E., & Obraczka, K. (2022). A Novel IoT System For Patient-Centric Pressure Ulcer Prevention Using Sensor Embedded Dressings. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events, PerCom Workshops 2022, 42–45. https://doi.org/10.1109/PerComWorkshops53856.2022.9767294
Roehrs, A., Da Costa, C. A., Da Rosa Righi, R., & De Oliveira, K. S. F. (2017). Personal health records: A systematic literature review. Journal of Medical Internet Research, 19(1). https://doi.org/10.2196/jmir.5876
Sahay, S., Sundararaman, T., & Braa, J. (2017). Public Health Informatics: Design for Change - A Developing Country Perspective. Oxford University Press.
Siramungklanon, N., Yaibuates, M., Pravesjit, S., & Kantawong, K. (2022). The Surveillance Systems to Prevent Pressure Bedsores and Clinical Guidelines for Bedridden Patients and Caregivers. 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022, 33–36. https://doi.org/10.1109/ECTIDAMTNCON53731.2022.9720381
WHO. (2021). Global Strategy on Digital Health 2020-2025. In Indian Pediatrics (Vol. 57, Issue 4). WHO. https://doi.org/10.1007/s13312-020-1789-7
Willcox, M., Moorthy, A., Mohan, D., Romano, K., Hutchful, D., Mehl, G., Labrique, A., & LeFevre, A. (2019). Mobile Technology for Community Health in Ghana: Is Maternal Messaging and Provider Use of Technology Cost-Effective in Improving Maternal and Child Health Outcomes at Scale? Journal of Medical Internet Research, 21(2). https://doi.org/10.2196/11268
Statistics
841 Views | 617 Downloads
How to Cite
Ahmalia, R., Aljaberi, M., & Said, M. (2024). Adoption of Information and Technology Communication in Ulcer Pressure Prevention: A Narrative Review. International Journal of Advancement in Life Sciences Research, 7(1), 15-23. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i01.002
Section
Review Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
.