Knowledge attributes of public health management information systems used in health emergencies: a scoping review

Abstract

Introduction: Learning from public health emergencies has not always been possible due to suboptimal knowledge accrual from previous outbreaks. This study described the knowledge attributes of Health Management Information Systems (HMIS) that are currently used during health emergencies. It aims to inform the development of a "nuggets of knowledge" (NoK) platform to support agile decision-making and knowledge continuity following health emergencies.

Methods: A search was conducted on the Web of Science and Google Scholar, with no date restriction for articles that conveniently selected 13 HMIS and their knowledge attributes. Proportions were used to summarize HMIS distribution by countries' World Bank income status. Thematic content analysis was used to describe knowledge attributes of HMIS based on the knowledge attributes of Holsapple et al.

Results: Seven of the 13 HMIS contained tacit knowledge; the 7 HMIS were predominantly used in higher-income settings and developed after explicit knowledge containing HMIS. More HMISs that contained tacit knowledge were currently usable, universal, programmable, user-friendly, and relied on informal information sources than HMIS that contained explicit knowledge HMIS. Tacit and explicit knowledge containing HMIS were equally practical, accessible, and domain-oriented.

Conclusion: HMIS should continuously capture both tacit and explicit knowledge that is actionable and practical in HMIS, user-friendly, programmable, and accessible to persons in all geographical settings. HMIS that contain tacit knowledge have more favorable attributes than those that contain explicit knowledge, but they may not be available to all emergency responders globally, a distribution that may change as newer low-cost technologies become available. Future research should investigate the impact of the NoK platform on public health emergency management.

Keywords: experiential knowledge; health emergencies; knowledge dimensions; knowledge management systems; tacit knowledge.

Figure 1

A PRISMA-ScR flow chart. DHIS, District Health Information System; EOC, Emergency Operations Center; GIS, Geographical Information System; GLEWS, Global Early Warning System; GPHIN, Global Public Health Intelligence Network; HDX, Humanitarian Data Exchange; HealthMap, an automated electronic information system for monitoring, organizing, and visualizing reports of global disease outbreaks; mHealth-Mobile health applications; OpenWHO, World Health Organization’s online learning platform; ProMED, ProMED mail: Program for Monitoring Emerging Diseases; Telemed, telemedicine platforms; WHO COVID-19, World Health Organization COVID-19 dashboard; WHO GHO, World Health Organization Global Health Observatory.

Figure 2

Development timeline, knowledge modes and sources of knowledge for health management information systems (HMIS) used in health emergencies. Tacit knowledge containing HMIS contain knowledge that describes an aspect of reasoning knowledge that has not been articulated (green font) and explicit knowledge containing HMIS contain knowledge that has been articulated and formalized in documents or databases (black font). HMIS that obtain information from formal sources are highlighted in orange while those that obtain information from both formal and informal sources are highlighted in blue. HMIS that contain explicit knowledge (black font) predate HMIS that contain tacit knowledge (green font). More HMIS that contain tacit knowledge obtain knowledge from both formal and informal sources (blue highlights) when compared to HMIS that contain explicit knowledge (orange highlights). DHIS, District Health Information System; EOC, Emergency Operations Center; GIS, Geographical Information System; GLEWS, Global Early Warning System; GPHIN, Global Public Health Intelligence Network; HDX, Humanitarian Data Exchange; HealthMap, an automated electronic information system for monitoring, organizing, and visualizing reports of global disease outbreaks; mHealth-Mobile health applications; OpenWHO, World Health Organization’s online learning platform; ProMED, ProMED mail: Program for Monitoring Emerging Diseases; Telemed, telemedicine platforms; WHO COVID-19, World Health Organization COVID-19 dashboard; WHO GHO, World Health Organization Global Health Observatory (34, 35, 39, 41, 45, 47, 49, 53, 59, 61, 93, 142, 176, 194, 195, 196).

Figure 3

(A) Number of articles reviewed in this study for HMIS used in health emergencies containing tacit knowledge (upper panel) and explicit knowledge (lower panel). The figure represents the final number of articles selected for inclusion for each HMIS used in health emergencies. There was a balance between the number of articles reviewed for HMIS that contain tacit and explicit knowledge. A similar number of HMIS from each group had fewer or more articles reviewed. (B) HMIS year of development and number of articles published over time. The year of development and the proportion of articles published in different periods as a fraction of the total articles for each HMIS. The figure summarizes the year of inception of each HMIS and number of articles per HMIS reviewed that were published within each 5-year time-period between 2005 to 2024 as follows: before 2006, 2006-2010, 2011-2015, 2016-2020, 2021-2014. An increase in the overall number of publications over the years possibly signifies the development of more HMIS. GIS, EOC, ProMed, DHIS, mHealth and GLEWS had a general increase in the number of publications over the five periods. Telemedicine platforms had an initial decrease in the number of articles published in the first two periods followed by a drastic increase in the number of articles in the last two periods. WHO GHO, HDX, OpenWHO, WHO COVID-19 dashboard had articles published in the last two years only. HealthMap had a rapid increase in the number of articles published in the second period with a consistent number of articles in the following years. GPHIN had an initial increase followed by a decrease in the number of articles published over time. DHIS, District Health Information System; EOC, Emergency Operations Center; GIS, Geographical Information System; GLEWS, Global Early Warning System; GPHIN, Global Public Health Intelligence Network; HDX, Humanitarian Data Exchange; HealthMap, an automated electronic information system for monitoring, organizing, and visualizing reports of global disease outbreaks; mHealth-Mobile health applications; OpenWHO, World Health Organization’s online learning platform; ProMED, ProMED mail: Program for Monitoring Emerging Diseases; Telemed, telemedicine platforms; WHO COVID-19, World Health Organization COVID-19 dashboard; WHO GHO, World Health Organization Global Health Observatory.

Figure 4

Distribution, year of development and World Bank Income classification status of countries where HMIS containing tacit knowledge (upper panel) and HMIS containing explicit knowledge (lower pane) were used in the reviewed articles. HMIS were categorized by knowledge mode into two: HMIS that contain tacit knowledge, i.e. those that describe an aspect of reasoning knowledge that has not been articulated (upper panel) and HMIS that contain explicit knowledge, i.e., those that represent knowledge that has been articulated and formalized in documents or databases (lower panel). Countries where HMIS were used was obtained from each article and categorized based on 2022 World Bank income classification status. The distribution of HMIS containing tacit knowledge (upper panel) and HMIS containing explicit knowledge (lower panel) was initially limited to higher income settings but with time spread out to include all countries regardless of income status. DHIS, District Health Information System; EOC, Emergency Operations Center; GIS, Geographical Information System; GLEWS, Global Early Warning System; GPHIN, Global Public Health Intelligence Network; HDX, Humanitarian Data Exchange; HealthMap, an automated electronic information system for monitoring, organizing, and visualizing reports of global disease outbreaks; mHealth-Mobile health applications; OpenWHO, World Health Organization’s online learning platform; ProMED, ProMED mail: Program for Monitoring Emerging Diseases; Telemed, telemedicine platforms; WHO COVID-19, World Health Organization COVID-19 dashboard; WHO GHO, World Health Organization Global Health Observatory. LIC-low-income countries, LMIC- low middle income countries, UMIC- upper middle-income countries, HIC- high middle-income countries as per World Bank Classification.

Figure 5

(A) Number of HMIS used in health emergencies exhibiting different knowledge attributes. HMIS were characterised using the following attributes: (1) Domain: Animal health or human health or both. (2) Practicality: High (contains relevant information that is pertinent to the problem at hand) or moderate (partially contains relevant information that is pertinent to the problem at hand), low (contains very little relevant information that is pertinent to the problem). (3) User-friendliness: High (easy to navigate), moderate (slightly challenging to navigate), low (very challenging to navigate). (4) Applicability: Localized (only used within a country or countries for a specific purpose) or global (universally usable in routine and frequent circumstances). (5) Programmability: High (very easy to transfer for use), moderate (slightly challenging to transfer) to low (very challenging to transfer). (6) Immediacy: Potentially usable (latent) or currently usable. (7) Source: Formal (established sources) or informal (expert opinion, media, public etc) or both formal and informal sources. (8) Accessibility: Private (closed source-accessible to specific processors) & public (open source-accessible to any processor). (9) Mode/Type. Mode: Tacit (knowledge that has not been articulated and is contained in the heads of experts) & explicit (knowledge that has been articulated and formalized in documents or databases. Type: Data and information (descriptive) & reasoning knowledge (information that can be acted upon). Almost all HMIS reviewed covered both animal and human health domains (12/13) and were highly practical (11/13). More than three quarters were user-friendly (10/13), close to two thirds were global (9/13), highly programmable (8/13), currently actionable (8/13) and obtained information from both formal and informal sources (8/13). Slightly more than half of the HMIS evaluated were open to the public (7/13) and contained tacit knowledge (7/13). (B) Knowledge attributes of HMIS that contain tacit and explicit knowledge. Number of HMIS containing tacit knowledge and HMIS containing explicit knowledge used in health emergencies demonstrating select knowledge attributes. HMIS containing tacit knowledge were compared to HMIS containing explicit knowledge using the following knowledge attributes, domain, immediacy, source, applicability, practicality, programmability, user-friendliness and accessibility. Compared to HMIS that contain explicit knowledge, more HMIS containing tacit knowledge covered both animal and human domains, were currently usable, obtained information from formal and informal sources, were globally applicable, highly programmable, highly user-friendly and publicly accessible. But, both HMIS that contain tacit knowledge and HMIS that contain explicit knowledge used in emergencies were equally practical and accessible.

Figure 6

(A) The number of mentions per knowledge attribute for all HMIS evaluated in the study. The figure lists the number of mentions for each knowledge attribute in all the articles reviewed for all HMIS. Excluding mode and type, the highest number of mentions were for immediacy, applicability, and practicality. (B) The proportion of mentions of each knowledge attribute per HMIS. The proportion of mentions per knowledge attribute for each HMIS evaluated in the study. The proportion of mentions per attribute for each HMIS was computed as a fraction of the total number of mentions for all attributes for that specific HMIS. The top 5 HMIS with the highest mentions for accessibility were on OpenWHO (n = 18), WHO-COVID 19 dashboard (n= 11),DHIS (n = 11), mHealth (n = 8), and ProMed mail (n = 7); for applicability were on WHO GHO (31), mHealth (25), GPHIN (n = 24), OpenWHO (n = 18) and Telemedicine (n = 17); for domain were GLEWS (n = 15), GPHIN (n = 12), WHO COVID-19 dashboard (n = 11) mHealth (n = 10) and EOC (n = 9); and for immediacy were on GIS (n = 30), ProMed mail (n = 27), mHealth (n = 23), DHIS (n = 22) and Telemedicine platforms (n = 21) and WHO COVID-19 dashboard (n = 21). The top 5 HMIS with the highest mentions for mode/type were on GHO (n = 55), HDX (n = 50), EOC (n = 31), GIS (n = 26) and WHO-COVID-19 dashboard (n = 16); for practicality were on Telemedicine platforms (n = 40), GIS (n = 26), DHIS (n = 22), HealthMap (16) and mHealth (n = 13); for programmability were on WHO COVID-19 dashboard (n=16), OpenWHO (n = 16), HealthMap (n = 11), DHIS (n = 11) and HDX (n = 10); for source were on GPHIN (n = 21), GLEWS (n = 15), HealthMap (n = 13), ProMed mail (n = 12), and EOC (n = 6), and for user-friendliness were on OpenWHO (n = 18), DHIS (n = 11), WHO COVID-19 dashboard (n = 11), mHealth (n = 8) and ProMed mail (n = 7). DHIS, District Health Information System; EOC, Emergency Operations Center; GIS, Geographical Information System; GLEWS, Global Early Warning System; GPHIN, Global Public Health Intelligence Network; HDX, Humanitarian Data Exchange; HealthMap, an automated electronic information system for monitoring, organizing, and visualizing reports of global disease outbreaks; mHealth-Mobile health applications; OpenWHO, World Health Organization’s online learning platform; ProMED, ProMED mail: Program for Monitoring Emerging Diseases; Telemed, telemedicine platforms; WHO COVID-19, World Health Organization COVID-19 dashboard; WHO GHO, World Health Organization Global Health Observatory. programmability were on the WHO COVID-19 dashboard (n = 16), OpenWHO (n = 16), HealthMap (n = 11), DHIS (n = 11), and HDX (n = 10); for accessibility were on HealthMap (16), HDX (n = 13), GLEWS (n = 12), OpenWHO (n = 10), and ProMed mail (n = 10); for source were on GPHIN (n = 21), GLEWS (n = 15), HealthMap (n = 13), ProMed mail (n = 12), and EOC (n = 6); for domain were GLEWS (n = 15), GPHIN (n = 12), WHO COVID-19 dashboard (n = 11) mHealht (n = 10) and EOC (n = 9); and for user-friendliness was on OpenWHO (n = 12), DHIS (n = 11), WHO COVID-19 dashboard (n = 11), mHealth (n = 8), and ProMed mail (n = 7).