Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings
Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CD...
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Veröffentlicht in: | Global health science and practice 2023-08, Vol.11 (4), p.e2200439 |
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creator | Beynon, Fenella Guérin, Frédérique Lampariello, Riccardo Schmitz, Torsten Tan, Rainer Ratanaprayul, Natschja Tamrat, Tigest Pellé, Karell G Catho, Gaud Keitel, Kristina Masanja, Irene Rambaud-Althaus, Clotilde |
description | Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. The multistakeholder development processes involved syntheses of recommendations from existing guidelines and literature; creation and validation of clinical algorithms; and iterative development, implementation, and evaluation. The common themes surrounding adaptations of IMCI guidance highlight the complexities of digitalizing evidence-based recommendations and reinforce the rationale for leveraging standards for CDSS development, such as the World Health Organization's SMART Guidelines. Implementation through multistakeholder dialogue is critical to ensure CDSSs can effectively and equitably improve quality of care for children in resource-constrained settings. |
doi_str_mv | 10.9745/GHSP-D-22-00439 |
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Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. The multistakeholder development processes involved syntheses of recommendations from existing guidelines and literature; creation and validation of clinical algorithms; and iterative development, implementation, and evaluation. The common themes surrounding adaptations of IMCI guidance highlight the complexities of digitalizing evidence-based recommendations and reinforce the rationale for leveraging standards for CDSS development, such as the World Health Organization's SMART Guidelines. Implementation through multistakeholder dialogue is critical to ensure CDSSs can effectively and equitably improve quality of care for children in resource-constrained settings.</description><identifier>ISSN: 2169-575X</identifier><identifier>EISSN: 2169-575X</identifier><identifier>DOI: 10.9745/GHSP-D-22-00439</identifier><identifier>PMID: 37640492</identifier><language>eng</language><publisher>United States: Global Health: Science and Practice</publisher><subject>Algorithms ; Child ; Decision Support Systems, Clinical ; Humans ; Ichthyosiform Erythroderma, Congenital ; Lipid Metabolism, Inborn Errors ; Technical Note</subject><ispartof>Global health science and practice, 2023-08, Vol.11 (4), p.e2200439</ispartof><rights>Beynon et al.</rights><rights>Beynon et al. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c394t-283939f0641b9d788ad5e481fa99a09572dd9233b5c20c17ec3c2e008f6255e93</citedby><cites>FETCH-LOGICAL-c394t-283939f0641b9d788ad5e481fa99a09572dd9233b5c20c17ec3c2e008f6255e93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461705/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461705/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37640492$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Beynon, Fenella</creatorcontrib><creatorcontrib>Guérin, Frédérique</creatorcontrib><creatorcontrib>Lampariello, Riccardo</creatorcontrib><creatorcontrib>Schmitz, Torsten</creatorcontrib><creatorcontrib>Tan, Rainer</creatorcontrib><creatorcontrib>Ratanaprayul, Natschja</creatorcontrib><creatorcontrib>Tamrat, Tigest</creatorcontrib><creatorcontrib>Pellé, Karell G</creatorcontrib><creatorcontrib>Catho, Gaud</creatorcontrib><creatorcontrib>Keitel, Kristina</creatorcontrib><creatorcontrib>Masanja, Irene</creatorcontrib><creatorcontrib>Rambaud-Althaus, Clotilde</creatorcontrib><title>Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings</title><title>Global health science and practice</title><addtitle>Glob Health Sci Pract</addtitle><description>Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. The multistakeholder development processes involved syntheses of recommendations from existing guidelines and literature; creation and validation of clinical algorithms; and iterative development, implementation, and evaluation. The common themes surrounding adaptations of IMCI guidance highlight the complexities of digitalizing evidence-based recommendations and reinforce the rationale for leveraging standards for CDSS development, such as the World Health Organization's SMART Guidelines. Implementation through multistakeholder dialogue is critical to ensure CDSSs can effectively and equitably improve quality of care for children in resource-constrained settings.</description><subject>Algorithms</subject><subject>Child</subject><subject>Decision Support Systems, Clinical</subject><subject>Humans</subject><subject>Ichthyosiform Erythroderma, Congenital</subject><subject>Lipid Metabolism, Inborn Errors</subject><subject>Technical Note</subject><issn>2169-575X</issn><issn>2169-575X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUU1v1TAQjBCIVqVnbshHLmkdfyQxF1S9lNdKRSBeK3Gz_JxNYuTYwXYq2v_U_9j0g0fZy-5qZ2ZHmix7X-AjUTF-vD7bfM-bnJAcY0bFq2yfFKXIecV_vn4x72WHMf7CSwlWEFG_zfZoVTLMBNnP7hrTm6SsuTWuRytrnNHKovVsWlgWiJ_Q6Z8JggGnISLjUBoANXAN1k8juIR894_WgDbReIc28zT5kNCJ7X0waRgj6nxAX5VTPexog7Ht4H2Lzq1dXj3K_4Do56AhX3kXU1CLhxZtIKXFX3yXvemUjXD43A-yqy-nl6uz_OLb-nx1cpFrKljKSU0FFR0uWbEVbVXXquXA6qJTQigseEXaVhBKt1wTrIsKNNUEMK67knAOgh5kn590p3k7QqsXw0FZOQUzqnAjvTLy_4szg-z9tSwwK4sK80Xh47NC8L9niEmOJmqwVjnwc5Sk5rWoBS2rBXr8BNXBxxig2_0psHwIWj4ELRtJiHwMemF8eGlvh_8bK70HS_eotA</recordid><startdate>20230828</startdate><enddate>20230828</enddate><creator>Beynon, Fenella</creator><creator>Guérin, Frédérique</creator><creator>Lampariello, Riccardo</creator><creator>Schmitz, Torsten</creator><creator>Tan, Rainer</creator><creator>Ratanaprayul, Natschja</creator><creator>Tamrat, Tigest</creator><creator>Pellé, Karell G</creator><creator>Catho, Gaud</creator><creator>Keitel, Kristina</creator><creator>Masanja, Irene</creator><creator>Rambaud-Althaus, Clotilde</creator><general>Global Health: Science and Practice</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230828</creationdate><title>Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings</title><author>Beynon, Fenella ; Guérin, Frédérique ; Lampariello, Riccardo ; Schmitz, Torsten ; Tan, Rainer ; Ratanaprayul, Natschja ; Tamrat, Tigest ; Pellé, Karell G ; Catho, Gaud ; Keitel, Kristina ; Masanja, Irene ; Rambaud-Althaus, Clotilde</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c394t-283939f0641b9d788ad5e481fa99a09572dd9233b5c20c17ec3c2e008f6255e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Child</topic><topic>Decision Support Systems, Clinical</topic><topic>Humans</topic><topic>Ichthyosiform Erythroderma, Congenital</topic><topic>Lipid Metabolism, Inborn Errors</topic><topic>Technical Note</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beynon, Fenella</creatorcontrib><creatorcontrib>Guérin, Frédérique</creatorcontrib><creatorcontrib>Lampariello, Riccardo</creatorcontrib><creatorcontrib>Schmitz, Torsten</creatorcontrib><creatorcontrib>Tan, Rainer</creatorcontrib><creatorcontrib>Ratanaprayul, Natschja</creatorcontrib><creatorcontrib>Tamrat, Tigest</creatorcontrib><creatorcontrib>Pellé, Karell G</creatorcontrib><creatorcontrib>Catho, Gaud</creatorcontrib><creatorcontrib>Keitel, Kristina</creatorcontrib><creatorcontrib>Masanja, Irene</creatorcontrib><creatorcontrib>Rambaud-Althaus, Clotilde</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Global health science and practice</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Beynon, Fenella</au><au>Guérin, Frédérique</au><au>Lampariello, Riccardo</au><au>Schmitz, Torsten</au><au>Tan, Rainer</au><au>Ratanaprayul, Natschja</au><au>Tamrat, Tigest</au><au>Pellé, Karell G</au><au>Catho, Gaud</au><au>Keitel, Kristina</au><au>Masanja, Irene</au><au>Rambaud-Althaus, Clotilde</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings</atitle><jtitle>Global health science and practice</jtitle><addtitle>Glob Health Sci Pract</addtitle><date>2023-08-28</date><risdate>2023</risdate><volume>11</volume><issue>4</issue><spage>e2200439</spage><pages>e2200439-</pages><issn>2169-575X</issn><eissn>2169-575X</eissn><abstract>Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. 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source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; PubMed Central |
subjects | Algorithms Child Decision Support Systems, Clinical Humans Ichthyosiform Erythroderma, Congenital Lipid Metabolism, Inborn Errors Technical Note |
title | Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings |
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