Population Health Management: A Bibliometric Analysis of Literature on Data Analytics and Public Health Interventions
DOI:
https://doi.org/10.58812/wsis.v2i04.796Keywords:
Population Health Management, Dana Analytics, Public Health Interventions, Bibliometric AnalysisAbstract
Population health management (PHM) has emerged as a pivotal strategy in healthcare, aiming to address the health needs of communities while enhancing care quality and outcomes. Recent years have witnessed a surge in research at the intersection of data analytics and public health interventions within the realm of PHM. This paper presents a comprehensive bibliometric analysis of the literature on data analytics and public health interventions in the context of population health management. Through systematic data collection from reputable scholarly databases and rigorous analysis, this study aims to map out the trends, key contributors, and emerging themes shaping the discourse in this domain. The analysis encompasses publication trends, authorship patterns, citation metrics, and network visualizations to provide insights into the landscape of literature. Notable findings include a high citation rate per paper and author, indicative of scholarly interest and collaboration within the research community. Key themes identified include outcomes, interventions, population health improvement, determinants, and health disparities. Additionally, temporal analysis reveals a shift towards non-communicable diseases and the increasing role of data in patient care. The author collaboration network highlights a core cluster of researchers with extensive collaborative ties, alongside peripheral authors and topics representing specialized or nascent areas. The density visualization map identifies potential areas for future research, including health disparities, women's health issues, and geographic-specific studies.
References
A. van Ede, N. Minderhout, M. Bruijnzeels, M. Numans, and V. Stein, “Mechanisms for a successful implementation of a Population Health Management approach: a scoping review.,” Int. J. Integr. Care, vol. 22, 2022.
H. Mansoor et al., “Exploratory Data Analysis of Population Level Smartphone-Sensed Data,” in International Joint Conference on Computer Vision, Imaging and Computer Graphics, Springer, 2021, pp. 206–231.
N. Mirin, H. Mattie, L. Jackson, Z. Samad, and R. Chunara, “Data science in public health: building next generation capacity,” arXiv Prepr. arXiv2208.03461, 2022.
S. Mohammed, T.-H. Kim, R.-S. Chang, and C. Ramos, “Guest Editorial: Data Analytics for Public Health Care,” IEEE J. Biomed. Heal. Informatics, vol. 26, no. 4, pp. 1409–1410, 2022.
J. H. Porembka et al., “Radiologists’ increasing role in population health management: AJR expert panel narrative review,” Am. J. Roentgenol., vol. 218, no. 1, pp. 7–18, 2022.
S. Tirunagari, S. Mohan, D. Windridge, and Y. Balla, “Addressing Challenges in Healthcare Big Data Analytics,” in International Conference on Multi-disciplinary Trends in Artificial Intelligence, Springer, 2023, pp. 757–765.
N. Berros, F. El Mendili, Y. Filaly, and Y. El Bouzekri El Idrissi, “Enhancing digital health services with big data analytics,” Big data Cogn. Comput., vol. 7, no. 2, p. 64, 2023.
A. Mitra, B. Soman, R. Gaitonde, T. Bhatnagar, E. Nieuhas, and S. Kumar, “Data science approaches to public health: case studies using routine health data from India,” in International Conference on Data Management, Analytics & Innovation, Springer, 2023, pp. 913–940.
B. Preim and K. Lawonn, “A survey of visual analytics for public health,” in Computer Graphics Forum, Wiley Online Library, 2020, pp. 543–580.
M. Abraham, M. J. Lim, W. S. Tan, and J. Cheah, “Global trends towards population health management and key lessons and initiatives in the Singapore context,” Int. J. Integr. Care, vol. 22, no. 3, 2022.
L. Bridle, P. Cross, S. Danielli, H. Ashrafian, and A. Darzi, “Using Population Health Management to improve screening of women with history of gestational diabetes in an urban setting,” 2022.
B. Steenkamer, E. de Weger, H. Drewes, K. Putters, H. Van Oers, and C. Baan, “Implementing population health management: an international comparative study,” J. Health Organ. Manag., vol. 34, no. 3, pp. 273–294, 2020.
A. S. Moumtzoglou and A. Pouliakis, “Population Health Management and the Science of Individuality in the Era of Quantum Computing,” in Quality of Healthcare in the Aftermath of the COVID-19 Pandemic, IGI Global, 2022, pp. 8–41.
S. Thomas, “Population health management and neurology services,” Br. J. Neurosci. Nurs., vol. 18, no. 5, pp. 236–239, 2022.
C. E. Earley, “Data analytics in auditing: Opportunities and challenges,” Bus. Horiz., vol. 58, no. 5, pp. 493–500, 2015.
V. Charles, A. Emrouznejad, T. Gherman, and J. Cochran, “Why data analytics is an art,” Significance, vol. 19, no. 6, pp. 42–45, 2022.
T. A. Runkler, Data analytics. Springer, 2020.
J. W. Tukey, Exploratory data analysis, vol. 2. Reading, MA, 1977.
V. V. Joseph, “The impact of public health interventions in a developing nation: an overview,” South Sudan Med. J., vol. 15, no. 4, pp. 147–151, 2022.
C. Shaw and C. Cleghorn, “Public health interventions: the elephant in the room of the health system crisis,” New Zeal. Med. J., vol. 136, no. 1568, pp. 8–11, 2023.
M. A. Schaffer, S. Strohschein, and K. Glavin, “Twenty years with the public health intervention wheel: Evidence for practice,” Public Health Nurs., vol. 39, no. 1, pp. 195–201, 2022.
J. Wienert, T. Jahnel, and L. Maaß, “What are digital public health interventions? First steps toward a definition and an intervention classification framework,” J. Med. Internet Res., vol. 24, no. 6, p. e31921, 2022.
J. Wu, F. Scarabel, B. Majeed, N. L. Bragazzi, and J. Orbinski, “The impact of public health interventions on delaying and mitigating against replacement by SARS-CoV-2 variants of concern,” 2021.
A. D. Lopez, C. D. Mathers, M. Ezzati, D. T. Jamison, and C. J. L. Murray, “Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data,” Lancet, vol. 367, no. 9524, pp. 1747–1757, 2006.
W. H. Organization, The world health report 2000: health systems: improving performance. World Health Organization, 2000.
D. Nutbeam, “Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century,” Health Promot. Int., vol. 15, no. 3, pp. 259–267, 2000.
K. Sørensen et al., “Health literacy and public health: a systematic review and integration of definitions and models,” BMC Public Health, vol. 12, pp. 1–13, 2012.
M. Marmot and R. Wilkinson, Social determinants of health. Oup Oxford, 2005.
D. M. Berwick, T. W. Nolan, and J. Whittington, “The triple aim: care, health, and cost,” Health Aff., vol. 27, no. 3, pp. 759–769, 2008.
T. Young, P. E. Peppard, and D. J. Gottlieb, “Epidemiology of obstructive sleep apnea: a population health perspective,” Am. J. Respir. Crit. Care Med., vol. 165, no. 9, pp. 1217–1239, 2002.
R. Rabin and F. de Charro, “EQ-SD: a measure of health status from the EuroQol Group,” Ann. Med., vol. 33, no. 5, pp. 337–343, 2001.
M. Prince et al., “No health without mental health,” Lancet, vol. 370, no. 9590, pp. 859–877, 2007.
E. A. Finkelstein, J. G. Trogdon, J. W. Cohen, and W. Dietz, “Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates: Amid calls for health reform, real cost savings are more likely to be achieved through reducing obesity and related risk factors.,” Health Aff., vol. 28, no. Suppl1, pp. w822–w831, 2009.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Loso Judijanto, Yocki Yuanti, Supriandi Supriandi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
+62 821 1557 5700
westscience.journals@gmail.com