Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.
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ABSTRACT:
SUBMITTER: Dorea FC
PROVIDER: S-EPMC7994248 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.
| S-EPMC7301503 | biostudies-literature
Project description:BackgroundThe increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.PurposeTo design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.MethodsDesign science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.ResultsThe analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.ImpactOur work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.ConclusionAugmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
| S-EPMC9705761 | biostudies-literature
Project description:We present new methodology based on Multi-Objective Markov Decision Processes for developing sequential decision support systems from data. Our approach uses sequential decision-making data to provide support that is useful to many different decision-makers, each with different, potentially time-varying preference. To accomplish this, we develop an extension of fitted-Q iteration for multiple objectives that computes policies for all scalarization functions, i.e. preference functions, simultaneously from continuous-state, finite-horizon data. We identify and address several conceptual and computational challenges along the way, and we introduce a new solution concept that is appropriate when different actions have similar expected outcomes. Finally, we demonstrate an application of our method using data from the Clinical Antipsychotic Trials of Intervention Effectiveness and show that our approach offers decision-makers increased choice by a larger class of optimal policies.
| S-EPMC5179144 | biostudies-literature
Project description:BackgroundScientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.ResultsSidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.ConclusionsSidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.
| S-EPMC3022632 | biostudies-literature
Project description:BackgroundSome patients involved in a road traffic collision (RTC) are physically entrapped and extrication is required to provide critical interventions. This can be performed either in an expedited way, or in a more controlled manner. In this study we aimed to derive a data-driven extrication algorithm intended to be used as a decision-support tool by on scene emergency service providers to decide on the optimal method of patient extrication from the vehicle.MethodsA retrospective observational study was performed of all trauma patients trapped after an RTC who were attended by a Helicopter Emergency Medical Service (HEMS) in the United Kingdom between March 2013 and December 2021. Variables were identified that were associated with the need for HEMS interventions (as a surrogate for the need for expedited extrication), based on which a practical extrication algorithm was devised.ResultsDuring the study period 12,931 patients were attended, of which 920 were physically trapped. Patients who scored an "A" on the AVPU score (n = 531) rarely required HEMS interventions (3%). Those who did were characterised by a shorter than average (29 vs. 37 min) 999/112 emergency call to HEMS on-scene arrival interval. A third of all patients responding to voice required HEMS interventions. Absence of a patent airway (OR 6.98 [1.74-28.03] p < .001) and the absence of palpable radial pulses (OR 9.99 [2.48-40.18] p < .001) were independently associated with the need for (one or more) HEMS interventions in this group. Patients only responding to pain and unresponsive patients almost invariably needed HEMS interventions post extrication (90% and 86% respectively). Based on these findings, a practical and easy to remember algorithm "APEX" was derived.ConclusionA simple, data-driven algorithm, remembered by the acronym "APEX", may help emergency service providers on scene to determine the preferred method of extrication for patients who are trapped after a road traffic collision. This has the potential to facilitate earlier recognition of a 'sick' critical patient trapped in an RTC, decrease entrapment and extrication time, and may contribute to an improved outcome for these patients.
| S-EPMC10696863 | biostudies-literature
Project description:An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
| S-EPMC7080550 | biostudies-literature
Project description:BackgroundIndividualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.ResultsIn the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.ConclusionsBy integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.
| S-EPMC7011438 | biostudies-literature
Project description:Plasmatic microRNA sequencing was conducted in specific matched subgroups of subjects (n = 53) with and without OSA using HTG EdgeSeq miRNA WTA Assay technology.
2024-04-01 | GSE229362 | GEO
Project description:Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
| S-EPMC4660926 | biostudies-literature
Project description:BackgroundAlthough microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework.MethodsWe propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted.ResultsFor the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis.ConclusionsFor both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.
| S-EPMC2684660 | biostudies-literature