Public Health Decision Support Systems

Dixon, Brian E.; Kharrazi, Hadi; Sangareddy, Sridhar R. Papagari

doi:10.1007/978-3-030-41215-9_20

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Given the frequent changes of clinical practice guidelines, especially during public health emergencies such as the COVID-19 pandemic, CKATs should also offer more automated features to incorporate up-to-date knowledge from both clinical and public health sources. 71 Although public health decision support systems are differentiated from CDS systems, CKATs are needed to author, revise, maintain, and update population-level knowledge models. 71 Additionally, primary care settings, which often use preventative CDS systems, 72 will benefit from merging existing public health guidelines into local CDS systems, especially when dealing with public health emergencies.…”

Section: Discussionmentioning

confidence: 99%

“… 71 Although public health decision support systems are differentiated from CDS systems, CKATs are needed to author, revise, maintain, and update population-level knowledge models. 71 Additionally, primary care settings, which often use preventative CDS systems, 72 will benefit from merging existing public health guidelines into local CDS systems, especially when dealing with public health emergencies. 73 , 74 Consequently, CKATs should support not only the curation and maintenance of clinical knowledge but also the creation and management of population and public health knowledge models.…”

Section: Discussionmentioning

confidence: 99%

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A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

Nair

Doijad

et al. 2021

JAMIA Open

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Objective Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. Materials and Methods We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. Results Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. Conclusion Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

Nair

Doijad

et al. 2021

JAMIA Open

Self Cite

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“…Public health precision requires efficient and effective targeting of interventions to those most in need using the best available evidence [ 1 – 3 ]. Spatial decision support systems (SDSS) represent critical tools to achieve this goal by transforming disease data into information and knowledge useful for decision-making [ 3 ]. The spatial component is essential to enable the prioritization of resources and efficient and equitable delivery of interventions.…”

Section: Introductionmentioning

confidence: 99%

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea

et al. 2022

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Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity.

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