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Chapter 4 - How to Collect Authentic Data

Published online by Cambridge University Press:  13 July 2023

Ramalingam Shanmugam
Affiliation:
Texas State University, San Marcos
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Summary

In this chapter, methods to collect data from several reliable sources are articulated first. Then the importance of checking the authenticity of the data source is stated. Storing the collected data in Excel spreadsheets is vital. Refer to Hardin and Kotz (2020) for suggestions on improving data collection and amenability. Surging in popularity, mobile health (mHealth) apps foster research, clinical regimens, and individual well-being.

These procedures encourage proactivity and ongoing accountability for healthcare. For the purpose of addressing pertinent healthcare inquiries and quantifying health outcomes, information gathering and assessment on selected variables within a structure coalesce into a process called data collection, which is an essential step in research in all fields, including healthcare. While methods vary across disciplines, the emphasis of all data collection should be accuracy.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Selected References

Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1), 118.Google Scholar
Boddy, A., Hurst, W., Mackay, M. et al. (2019). An investigation into healthcare-data patterns. Future Internet, 11(2), 30.Google Scholar
Bravata, D. M., McDonald, K. M., Smith, W. M. et al. (2004). Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Annals of Internal Medicine, 140(11), 910922.CrossRefGoogle ScholarPubMed
Centers for Disease Control (CDC). (1985). Revision of the case definition of acquired immunodeficiency syndrome for national reporting: United States. Morbidity and Mortality Weekly Report, 34(25), 373375.Google Scholar
Consoli, S., Recupero, D. R., & Petković, M. (eds.). (2019). Data Science for Healthcare: Methodologies and Applications. New York: Springer.Google Scholar
Deverka, P. A., Majumder, M. A., Villanueva, A. G. et al. (2017). Creating a data resource: what will it take to build a medical information commons? Genome Medicine, 9(1), 15.Google Scholar
Dhillon, A., & Singh, A. (2019). Machine learning in healthcare data analysis: a survey. Journal of Biology and Today’s World, 8(6), 110.Google Scholar
Fazli, K., & Behboodian, J. (2002). A construction method for measures of central tendency and dispersion. International Journal of Mathematical Education in Science and Technology, 33(2), 299302.Google Scholar
General, United States (2001). Fiscal Year 2003 Budget Request.Google Scholar
Hardin, T., & Kotz, D. (2020). Amanuensis: information provenance for health-data systems. Information Processing & Management, 58(2), 102460.CrossRefGoogle Scholar
Jiang, S., Fang, S., Bloomquist, S. et al. (2016). Healthcare data visualization: geospatial and temporal integration. In Proceedings of the 11th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 2) (pp. 214221), edited by Magnenat-Thalmann, N., Richard, P., Linsen, L. et al. https://doi.org/10.5220/0005714002120219.Google Scholar
Jones, S., & Groom, F. M. (eds.). (2011). Information and Communication Technologies in Healthcare. Boca Raton, FL: CRC Press.Google Scholar
Jothi, N., & Husain, W. (2015). Data mining in healthcare: a review. Procedia Computer Science, 72, 306313.CrossRefGoogle Scholar
Khatri, I., & Shrivastava, V. K. (2016). A survey of big data in healthcare industry. In Advanced Computing and Communication Technologies (Vol. 1), edited by Mandal, J. K., Bhattacharyya, D., & Auluc, N. (pp. 245257). Singapore: Springer.Google Scholar
Kjelvik, M. K., & Schultheis, E. H. (2019). Getting messy with authentic data: exploring the potential of using data from scientific research to support student data literacy. CBE: Life Sciences Education, 18(2), es2.Google ScholarPubMed
Kostkova, P., Brewer, H., de Lusignan, S. et al. (2016). Who owns the data? Open data for healthcare. Frontiers in Public Health, 4(7).CrossRefGoogle ScholarPubMed
Kumar, S., & Singh, M. (2018). Big data analytics for healthcare industry: impact, applications, and tools. Big Data Mining and Analytics, 2(1), 4857.Google Scholar
Lee, E. T., & Wang, J. W. (2003). Statistical Methods for Survival Data Analysis. 3rd edition. Hoboken, NJ: Wiley.Google Scholar
Li, X., Huang, X., Li, C., Yu, R., & Shu, L. (2019). EdgeCare: leveraging edge computing for collaborative data management in mobile healthcare systems. IEEE Access, 7, 2201122025.Google Scholar
Lindsey, K. (1997). Modelling Frequency and Count Data. 1st edition. Oxford: Oxford University Press.Google Scholar
Liu, J., Bier, E., Wilson, A. et al. (2016). Graph analysis for detecting fraud, waste, and abuse in healthcare data. AI Magazine, 37(2), 3346.CrossRefGoogle Scholar
Liu, P., Lei, L., Yin, J. et al. (2006). Healthcare data mining: prediction inpatient length of stay. In 2006 3rd International IEEE Conference Intelligent Systems (pp. 832837). London: Institute of Electrical and Electronics Engineers.Google Scholar
Lockwood, K. J., Harding, K. E., Boyd, J. N., & Taylor, N. F. (2020). Home visits by occupational therapists improve adherence to recommendations: process evaluation of a randomized controlled trial. Australian Occupational Therapy Journal, 67(4), 287296.Google Scholar
Lu, H. M., Wei, C. P., & Hsiao, F. Y. (2016). Modeling healthcare data using multiple-channel latent Dirichlet allocation. Journal of Biomedical Informatics, 60, 210223.Google Scholar
Mohammed, N., Fung, B. C., Hung, P. C., & Lee, C. K. (2009). Anonymizing healthcare data: a case study on the blood transfusion service. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 12851294). New York: Association for Computing Machinery.Google Scholar
Oral, E. (2019). Surveying sensitive topics with indirect questioning. In Statistical Methodologies. London: Intech Open.Google Scholar
Park, Y., & Ghosh, J. (2012). A probabilistic imputation framework for predictive analysis using variably aggregated, multi-source healthcare data. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (pp. 445454). New York: Association for Computing Machinery.Google Scholar
Ponce, J., Hernández, A., Ochoa, A. et al. (2009). Data mining in Web applications. International Journal of Mathematical Education in Science and Technology, 32(6), 873886.Google Scholar
Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112134.Google Scholar
Ramanathan, A., Pullum, L. L., Steed, C. A. et al. (2013). Integrating heterogeneous healthcare datasets and visual analytics for disease bio-surveillance and dynamics. In 3rd IEEE Workshop on Visual Text Analytics. London: Institute of Electrical and Electronics Engineers.Google Scholar
Shanmugam, R. (1989). Asymptotic homogeneity tests for mean exponential family distributions. Journal of Statistical Planning and Inference, 23, 227241.CrossRefGoogle Scholar
Shanmugam, R. (1996). Effective sample size in length biased data. Applied Statistical Science, 1, 89100.Google Scholar
Shanmugam, R. (2013a). Mosaic masonries to interpret diagnostic test results. American Medical Journal, 4(1), 1220.Google Scholar
Shanmugam, R. (2013b). Shortage level of matching kidney and pancreas organs for implant is estimated. International Journal of Research in Nursing, 4(2), 4046.Google Scholar
Shanmugam, R. (2013c). Informatics about fear to report rapes using bumped-up Poisson model. American Journal of Biostatistics, 3(1), 1729.Google Scholar
Shanmugam, R. (2013d). Alzheimer’s disease prognosis is captured by a down-upsized incidence Poisson distribution. American Medical Journal, 4(2),150159.Google Scholar
Shanmugam, R. (2013e). Does smoking delay pregnancy? Data analysis by a tweaked geometric distribution answer. International Journal of Research in Medical Sciences, 1(4), 343348.Google Scholar
Shanmugam, R. (2013f). Tweaking exponential distribution to estimate the chance for more survival time if a cancerous kidney is removed. International Journal of Research in Nursing, 4(1), 2933.Google Scholar
Shanmugam, R. (2013g). Unified survival functions are derived and illustrated using hospitals’ preparedness data to treat anthrax cases. International Journal of Statistics and Economics, 12(3), 8295.Google Scholar
Shanmugam, R. (2013h). Probabilistic health-informatics and bioterrorism. International Journal of Communication and Computer, 10, 2832.Google Scholar
Shanmugam, R. (2013i). Hacking-Vigilance distribution with application to assess cyber insecurity level. International Journal of Information and Education Technology, 3(3), 300303.CrossRefGoogle Scholar
Shanmugam, R. (2013j). Alternate to traditional goodness of fit test with illustration using service duration to patients in hospitals. International Journal of Statistics and Economics, 11(2), 3143.Google Scholar
Shanmugam, R. (2013k). Odds to quicken reporting already delayed cases: AIDS incidences are illustrated. International Journal of Nursing in Research, 4(1), 113.Google Scholar
Shanmugam, R. (2013l). Is cancer recurrence postponed by a treatment? A new model answers. American Medical Journal, 4(1), 4362.Google Scholar
Shanmugam, R. (2013m). Does over or under dispersion in inverse binomial data suggest anything? A case in point is the waiting time for both heart-lung transplants. American Journal of Biostatistics, 3(2), 3037.Google Scholar
Shanmugam, R. (2014a). Health broken woven Poisson spheres to manage deadly Ebola incidences. American Journal of Infectious Diseases, 10, 143154.CrossRefGoogle Scholar
Shanmugam, R. (2014b). “Bivariate distribution” for infrastructures among operative, natural, and no menopauses. American Journal of Biostatistics, 4, 3444.Google Scholar
Shanmugam, R. (2014c). How do queuing concepts and tools help to effectively manage hospitals when the patients are impatient? A demonstration. International Journal of Research in Medical Sciences, 2, 10761084.Google Scholar
Shanmugam, R. (2014d). A bivariate probability model to identify “honesty” versus “cheating” in economic surveys: xenophobia is illustrated. American Journal of Economics and Business Administration, 6, 4248.Google Scholar
Shanmugam, R. (2014e). C (∝) method to check daunting over/under variances to understand times to aftershocks since a major earthquake. Computer, Electronics, Electrical, and Communication, 59, 190193.Google Scholar
Shanmugam, R. (2014f). Data guided public healthcare decision making. In Encyclopedia of Business Analytics and Optimization (Vol. 2), edited by Wang, J. (pp. 3043). New York: IGI Global.Google Scholar
Shanmugam, R. (2014g). Probing non-adherence to prescribed medicines? A bivariate distribution with information nucleus clarifies, American Medical Journal, 5, 5460.Google Scholar
Shanmugam, R. (2014h). Data envelopment analysis for operational efficiency. In Encyclopedia of Business Analytics and Optimization (Vol. 2), edited by Wang, J. (pp. 1828). New York: IGI Global.Google Scholar
Shanmugam, R. (2014i). Stochastic frontier analysis and cancer survivability. In Encyclopedia of Business Analytics and Optimization (Vol. 5), edited by Wang, J. (pp. 1826). New York: IGI Global.Google Scholar
Shanmugam, R. (2014j). Tweaked binomial distribution to capture the impact of drilling to cure bioterror victims in hospitals. International Journal of Statistics and Economics, 13(1), 4045.Google Scholar
Shanmugam, R. (2014k). An assessment of nurses’ sufficient immunity when treating infectious patients using bumped-up binomial model. International Journal of Research in Medical Sciences, 2(1), 132138.Google Scholar
Shanmugam, R. (2015). Refined randomized response model for suspicious answers: illicit drug users in U.S.A. are illustrated. International Journal of Ecological Economics & Statistics, 36, 1527.Google Scholar
Shanmugam, R. (2016). Entropy in Nucleus to tab data information and its illustration with Wolfram syndrome cases. International Journal of Ecological Economics and Statistics, 37(3), 4463.Google Scholar
Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(2), 250255.Google Scholar
Thall, P. F. and Vail, S. C. (1990). Some covariance models for longitudinal count data with over dispersion. Biometrics, 46, 657671.CrossRefGoogle Scholar
Thara, D. K., Premasudha, B. G., Ram, V. R., & Suma, R. (2016). Impact of big data in healthcare: a survey. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 729735). New York: Institute of Electrical and Electronics Engineers.Google Scholar
Tracy, S. J. (2019). Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact. Hoboken, NJ: Wiley.Google Scholar
Weiner, J. (2020). Why AI/data science projects fail: how to avoid project pitfalls. Synthesis Lectures on Computation and Analytics, 1(1), i77.Google Scholar
Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149153.CrossRefGoogle Scholar
Xu, H. D., & Basu, R. (2020). How the United States flunked the COVID-19 test: some observations and several lessons. American Review of Public Administration, 50(6–7), 568576.Google Scholar
Zamani Forooshani, M. (2020). A Tool for Integrating Dynamic Healthcare Data Sources (Master’s thesis, Universität Politècnica de Catalunya).Google Scholar
Zenuni, X., Raufi, B., Ismaili, F., & Ajdari, J. (2015). State of the art of semantic web for healthcare. Procedia: Social and Behavioral Sciences, 195, 19901998.Google Scholar

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