Dataset Information

Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.

ABSTRACT:

Background

The American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers' clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.

Methods

The Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008-2020 were used.

Results

Comprehensive clinical data for 67,617 patients across six tumor types: 28,954 with non-small-cell lung cancer, 2,540 with small-cell lung cancer, 30,035 with head and neck cancer, 4,950 with esophageal cancer, 966 with thymic cancer, and 172 with mesothelioma were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.

Conclusions

This landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.

SUBMITTER: Jung HA

PROVIDER: S-EPMC8577969 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.

Jung Hyun Ae HA Jeong Oksoon O Chang Dong Kyung DK Park Sehhoon S Sun Jong-Mu JM Lee Se-Hoon SH Ahn Jin Seok JS Ahn Myung-Ju MJ Park Keunchil K

Translational lung cancer research 20211001 10

<h4>Background</h4>The American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but ...[more]

PMID: 34858777

Similar Datasets

Project description:BackgroundResearch addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients.MethodsThe warehouse is based on a National Institutes of Research-funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries.ResultsWe describe the two institutions' administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request.ConclusionThe resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.

Project description:BackgroundHealth care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.ObjectiveThe aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.MethodsWe describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.ResultsThe majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.ConclusionsSyndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

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.

Project description:BackgroundThe ubiquitous use of mobile phones in sending and receiving text messages has become a norm for young people. Undeniably, text messaging has become a new and important communication medium not only in the social realm but in education as well. The aim of this study is to evaluate the effectiveness of using text messaging as a means to collect data for a medical research project.A cross sectional study was carried out during a double blind, randomized controlled trial to assess the efficacy and safety of a probiotic in the management of Irritable Bowel Syndrome (IBS). The study aim was to assess the response rate of weekly symptom reports via Short Message Service (SMS). The subjects were undergraduates in a private medical university in Malaysia. They were identified through a previous university wide study as suffering from IBS based on Rome III criteria. The subjects were randomly assigned to either the treatment arm receiving a daily probiotic, or the placebo arm. They were required to score their symptoms using eight-item-questionnaires at baseline, and thereafter weekly, for a total of 8 weeks. All subjects were given the choice to communicate their symptom scores by text messaging via mobile phones or by email. SMS text messages were sent to remind trial subjects to attend face-to-face visits and to complete a paper based 34-item-questionnaires on IBS quality of life assessment at baseline and at end of 8 weeks.FindingsThe response rate of weekly symptom scores via Short Message Service (SMS) from a total of 38 subjects was 100%. Through the study, 342 reports were submitted: 33.3% of these were received on the due date without reminder, 60.0% one day after the deadline, after a single reminder, 6.1% 2-3 days after the deadline, after 2-3 reminders and 0.6% 5 days after the deadline, after SMS, phone reminder and face-to-face encounter. All SMS symptom reports, whether on time or late, were complete. With the help of SMS reminder, all trial subjects completed the paper based IBS quality of life assessment at baseline and at end of study.ConclusionsThis study found using text messaging via mobile phone an excellent instrument for collecting weekly symptom reports in response to trial medication, reminding trial subjects to attend face to face visits and completing more complex paper based evaluation. The 100% response rate of weekly symptom reports was facilitated by using simple number codes for SMS submission.Trial registrationNot appropriate.

Project description:BackgroundTotal hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support. The study aimed to demonstrate the feasibility of automated cohorting of patients with a first arthroplasty in two hospital data warehouses (HDW) in France.MethodsThe study included adult patients with an arthroplasty between 2010 and 2019 identified by 2 data sources: hospital discharge and pharmacy. Selection was based on the health insurance thesaurus of IMDs in the pharmacy database: 1,523 distinct IMD references for primary THSKA. In the hospital discharge database, 22 distinct procedures for native joint replacement allowing a matching between IMD and surgical procedure of each patient selected. A program to automate information extraction was implemented in the 1st hospital data warehouse using natural language processing (NLP) on pharmacy labels, then it was then applied to the 2nd hospital.ResultsThe e-cohort was built with a first arthroplasty for THKSA performed in 7,587 patients with a mean age of 67.4 years, and a sex ratio of 0.75. The cohort involved 4,113 hip, 2,630 knee and 844 shoulder surgical patients. Obesity, cardio-vascular diseases and hypertension were the most frequent medical conditions.DiscussionThe implementation of an e-cohort for material surveillance will be easily workable over HDWs France wild. Using NLP as no international IMD mapping exists to study IMD, our approach aims to close the gap between conventional epidemiological cohorting tools and bigdata approach.ConclusionThis pilot study demonstrated the feasibility of an e-cohort of orthopaedic devices using clinical data warehouses. The IMD and patient features could be studied with intra-hospital follow-up and will help analysing the infectious and unsealing complications.

Dataset Information

Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.

Background

Methods

Results

Conclusions

Publications

Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets