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Chapter 22 - Health Information and Information Technology

The Path from Data to Decision

from Section 2 - Transforming Health Systems: Confronting Challenges, Seizing Opportunities

Published online by Cambridge University Press:  08 December 2022

Sameen Siddiqi
Affiliation:
Aga Khan University
Awad Mataria
Affiliation:
World Health Organization, Egypt
Katherine D. Rouleau
Affiliation:
University of Toronto
Meesha Iqbal
Affiliation:
UTHealth School of Public Health, Houston
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Summary

This Chapter describes principles of information management for health systems and the need to focus on key data items required to improve individual and population health. It discusses the collection and analysis of relevant, high-quality data and the importance of agreeing on health programme aims before defining the minimum data set. We review the derivation of health indicators, focusing on WHO indicators. Many indicators rely on linking data from different sources, which requires accurate personal identifiers. Data is useless unless reports based on it can be shared and understood, so data analysts should use different visualization techniques to facilitate and support user decisions such as self-service dashboards. We also review the many high quality, open source, free to use data capture, analysis and data sharing tools that can support health systems, concluding that it is rarely necessary to develop an information system from scratch. Finally, while big data analytics, artificial intelligence and machine learning capture many headlines, health system can achieve much using simple tools to capture relevant, high-quality data and turn it into actionable knowledge to support their decision makers.

Type
Chapter
Information
Making Health Systems Work in Low and Middle Income Countries
Textbook for Public Health Practitioners
, pp. 336 - 353
Publisher: Cambridge University Press
Print publication year: 2022

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References

Shortliffe, E. H., Perreault, L. E., Wiederhold, G., et al. Medical Informatics: Computer Applications in Health Care. Reading, MA, Addison-Wesley, 1990.Google Scholar
World Health Organization. Toolkit on Monitoring Health Systems Strengthening: health information systems. 2008. www.who.int/healthinfo/statistics/toolkit_hss/EN_PDF_Toolkit_HSS_InformationSystems.pdf (accessed January 11, 2021).Google Scholar
Puttkammer, N., Baseman, J. G., Devine, E. B., et al. An assessment of data quality in a multi-site electronic medical record system in Haiti. Int J Med Inform 2016; 86: 104116.Google Scholar
World Health Organization. 2018 Global reference list of 100 core health indicators (plus health-related SDGs). 2018. https://apps.who.int/iris/handle/10665/259951 (accessed November 22, 2021).Google Scholar
World Health Organization. Maternal mortality ratio (per 100 000 live births). 2021. www.who.int/data/gho/indicator-metadata-registry/imr-details/26 (accessed November 22, 2021).Google Scholar
ISO 9000. Glossary of words used in the ISO9000 family of standards. 2019. www.iso.org/files/live/sites/isoorg/files/standards/docs/en/terminology-ISO9000-family.pdf (accessed August 20, 2022).Google Scholar
Wyatt, J. C., Sullivan, F.. What is health information? BMJ 2005; 331(7516): 566568.Google Scholar
World Health Organization. International statistical classification of diseases and related health problems (ICD). 2021. www.who.int/standards/classifications/classification-of-diseases (accessed November 22, 2021).Google Scholar
Lawrence, N. D.. Data readiness levels. 2017. https://arxiv.org/abs/1705.02245 (accessed August 20, 2022 ).Google Scholar
Fraser, H., Coiera, E., Wong, D.. Safety of patient-facing digital symptom checkers. Lancet 2018; 392(10161): 22632264.Google Scholar
Open Health News. District Health Information System 2 (DHIS2). 2020. www.openhealthnews.com/resources/district-health-information-system-2-dhis2 (accessed November 22, 2021).Google Scholar
Stroux, L., Martinez, B., Coyote, I. E., et al. An mHealth monitoring system for traditional birth attendant-led antenatal risk assessment in rural Guatemala. J Med Eng Technol 2016; 40(7–8): 356371.Google Scholar
Digital Square. Addressing the need for a thriving marketplace for digital health. 2021. https://digitalsquare.org (accessed November 22, 2021).Google Scholar
Kaphle, S., Chaturvedi, S., Chaudhuri, I., et al. Adoption and usage of mHealth technology on quality and experience of care provided by frontline workers: observations from rural India. JMIR Mhealth Uhealth 2015; 3(2): e61.Google Scholar
Mamlin, B. W., Biondich, P. G., Wolfe, B. A., et al. Cooking up an open source EMR for developing countries: OpenMRS – a recipe for successful collaboration. AMIA Annu Symp Proc 2006; 2006: 529533.Google Scholar
Bacher, I., Mankowski, P., White, C., et al. A new FHIR-based API for OpenMRS. Poster presented at AMIA Clinical Informatics Conference, May 2021.Google Scholar
Medfloss. OpenClinic GA. 2020. www.medfloss.org/node/722 (accessed August 20, 2022).Google Scholar
Tom-Aba, D., Silenou, B. C., Doerrbecker, J., et al. The Surveillance Outbreak Response Management and Analysis System (SORMAS): digital health global goods maturity assessment. JMIR Public Health Surveill 2020; 6(2): e15860.CrossRefGoogle ScholarPubMed
OpenELIS global. Homepage. 2020. https://openelis-global.org (accessed August 20, 2022 ).Google Scholar
Open Logistics Management Information System. Homepage. https://openlmis.org (accessed May 29, 2022).Google Scholar
iDart. iDart pharmacy dispensing system. www.cell-life.org/idart (accessed May 28, 2022).Google Scholar
OpenBoxes. Homepage. https://openboxes.com (accessed May 28, 2022).Google Scholar
Tambo, E., Kazienga, A., Talla, M., et al. Digital technology and mobile applications impact on Zika and Ebola epidemics data sharing and emergency response. J Health Med Inform 2017; 8: 254.Google Scholar
Agarwal, S.. Digital solutions for COVID-19 response: an assessment of digital tools for rapid scale-up for case management and contact tracing. 2020. www.comminit.com/covid/content/digital-solutions-covid-19-response-assessment-digital-tools-rapid-scale-case-management (accessed March 19, 2020).Google Scholar
Muinga, N., Magare, S., Monda, J., et al. Digital health systems in Kenyan public hospitals: a mixed-methods survey. BMC Med Inform Decis Mak 2020; 20(1): 2.Google Scholar
Muthee, V., Bochner, A. F., Osterman, A., et al. The impact of routine data quality assessments on electronic medical record data quality in Kenya. PLoS One 2018; 13(4): e0195362.Google Scholar
Saripalle, R., Runyan, C., Russell, M.. Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform 2019; 94: 103188.Google Scholar
Baskaya, M., Yuksel, M., Erturkmen, G. B. L., et al. Health4Afrika: implementing HL7 FHIR based interoperability. Stud Health Technol Inform 2019; 264: 2024.Google ScholarPubMed
Zeeberg, B. R., Riss, J., Kane, D. W., et al. Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics. BMC Bioinformat 2004; 5: 80.Google Scholar
Porta, M.. A dictionary of epidemiology. Revista española de salud pública 2008; 82(4): 433.Google Scholar
NHS e-Referral Service Open Data Dashboard. UK online referrals dashboard. 2021. https://digital.nhs.uk/dashboards/ers-open-data (accessed March 19, 2020).Google Scholar
Fraser, H. S., Mugisha, M., Remera, E., et al. User perceptions and use of an enhanced electronic health record in Rwanda with and without clinical alerts: cross-sectional survey. JMIR Med Informat 2022; 10(5): e32305.Google Scholar
World Health Organization. Data collection and analysis tools. 2021. www.who.int/healthinfo/tools_data_analysis/en/ (accessed March 15, 2020).Google Scholar
O’Neil, C.. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London, Penguin Books, 2017.Google Scholar
Sheron, N., Moore, M., O’Brien, W., et al. Feasibility of detection and intervention for alcohol-related liver disease in the community: the Alcohol and Liver Disease Detection study (ALDDeS). Br J Gen Pract 2013; 63(615): e698e705.Google Scholar
Bae, J. M., Jung, Y. S., Jung, H. M., et al. Classification of facial vitiligo: a cluster analysis of 473 patients. Pigment Cell Melanoma Res 2018; 31(5): 585591.Google Scholar
Gray, E., Marti, J., Wyatt, J. C., et al. Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer: a retrospective cohort study. PLoS Med 2019; 16(12): e1003006.Google Scholar
Topol, E. J.. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25(1): 4456.Google Scholar
Hart, A., Wyatt, J.. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Med Inform (Lond) 1990; 15(3): 229236.Google Scholar
Wyatt, J.. Nervous about artificial neural networks? Lancet 1995; 346(8984): 1175–1177.CrossRefGoogle ScholarPubMed
Liu, X., Faes, L., Kale, A. U., et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019; 1(6): e271e297.Google Scholar
Esteva, A., Kuprel, B., Novoa, R. A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115118.Google Scholar
Narla, A., Kuprel, B., Sarin, K., et al. Automated classification of skin lesions: from pixels to practice. J Invest Dermatol 2018; 138(10): 21082110.Google Scholar
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24(9): 13421350.Google Scholar
Rajkomar, A., Dean, J., Kohane, I.. Machine learning in medicine. N Engl J Med 2019; 380(14): 13471358.Google Scholar
Vyas, D. A., Eisenstein, L. G., Jones, D. S.. Hidden in plain sight: reconsidering the use of race correction in clinical algorithms. N Engl J Med 2020; 383(9): 874882.Google Scholar
Kannel, W. B., Dawber, T. R., Kagan, A., et al. Factors of risk in the development of coronary heart disease: six year follow-up experience – the Framingham study. Ann Intern Med 1961; 55: 3350.Google Scholar

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