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HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

ABSTRACT: BACKGROUND:There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics. METHODS:We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab. RESULTS:Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models. CONCLUSION:Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

SUBMITTER: Ehwerhemuepha L

PROVIDER: S-EPMC7304122 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

Ehwerhemuepha Louis L Gasperino Gary G Bischoff Nathaniel N Taraman Sharief S Chang Anthony A Feaster William W

BMC medical informatics and decision making 20200619 1

<h4>Background</h4>There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics.<h4>Methods</h4>We utilized the architecture of the modern predictive ...[more]

PMID: 32560653

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Project description:OBJECTIVE:To use patient-level clinical variables to develop and validate a parsimonious model to predict hospital readmissions from home healthcare (HHC) in Medicare fee-for-service beneficiaries. DESIGN:Retrospective analysis using multivariable logistic regression and gradient boosting machine (GBM) learning to develop and validate a predictive model. SETTING/PARTICIPANTS/MEAUREMENTS:A 5% national sample of patients, aged 65?years or older, with Medicare fee-for-service who received skilled HHC services within 5 days of hospital discharge in 2012 (n = 43 407). Multiple data sets were merged, including Medicare Outcome and Assessment Information Set, Home Health Claims, Medicare Provider Analysis and Review, and Master Beneficiary Summary Files, to extract patient-level variables from the first HHC visit after discharge and measure 30-day readmission outcomes. RESULTS:Among 43 407 patients with inpatient hospitalizations followed by HHC, 14.7% were readmitted within 30?days. Of the 53 candidate variables, seven remained in the final model as individually predictive of outcome: Elixhauser comorbidity index, index hospital length of stay, urinary catheter presence, patient status (ie, fragile health with high risk of complications or serious progressive condition), two or more hospitalizations in prior year, pressure injury risk or presence, and surgical wound presence. Of interest, surgical wounds, either from a total hip or total knee arthroplasty procedure or another surgical procedure, were associated with fewer readmissions. The optimism-corrected c-statistics for the full model and parsimonious model were 0.67 and 0.66, respectively, indicating fair discrimination. The Brier score for both models was 0.120, indicating good calibration. The GBM model identified similar predictive variables. CONCLUSION:Variables available to HHC clinicians at the first postdischarge HHC visit can predict readmission risk and inform care plans in HHC. Future analyses incorporating measures of social determinants of health, such as housing instability or social support, have the potential to enhance prediction of this outcome. J Am Geriatr Soc 67:2505-2510, 2019.

Project description:BACKGROUND:Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE:This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS:The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS:The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS:To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

Dataset Information

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

Publications

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

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