The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.

Variation in Laboratory Test Naming Conventions in EHRs Within and Between Hospitals: A Nationwide Longitudinal Study.

BACKGROUND: Electronic health records provide clinically rich data for research and quality improvement work. However, the data are often unstructured text, may be inconsistently recorded and extracted into centralized databases, making them difficult to use for research. OBJECTIVES: We sought to quantify the variation in how key laboratory measures are recorded in the Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) across hospitals and over time. We included 6 laboratory tests commonly drawn within the first 24 hours of hospital admission (albumin, bilirubin, creatinine, hemoglobin, sodium, white blood cell count) from fiscal years 2005-2015. RESULTS: We assessed laboratory test capture for 5,454,411 acute hospital admissions at 121 sites across the VA. The mapping of standardized laboratory nomenclature (Logical Observation Identifiers Names and Codes, LOINCs) to test results in CDW varied within hospital by laboratory test. The relationship between LOINCs and laboratory test names improved over time; by FY2015, 109 (95.6%) hospitals had >90% of the 6 laboratory tests mapped to an appropriate LOINC. All fields used to classify test results are provided in an Appendix (Supplemental Digital Content 1, http://links.lww.com/MLR/B635). CONCLUSIONS: The use of electronic health record data for research requires assessing data consistency and quality. Using laboratory test results requires the use of both unstructured text fields and the identification of appropriate LOINCs. When using data from multiple facilities, the results should be carefully examined by facility and over time to maximize the capture of data fields.

Comprehensive risk factor evaluation of postoperative delirium following major surgery: clinical data warehouse analysis.

BACKGROUND: Postoperative delirium (POD) in older adults is a very serious complication. Due to the complexity of too many risk factors (RFs), an overall assessment of RFs may be needed. The aim of this study was to evaluate comprehensively the RFs of POD regardless of the organ undergoing operation, efficiently incorporating the concept of comprehensive big data using a smart clinical data warehouse (CDW). METHODS: We reviewed the electronic medical data of inpatients aged 65 years or older who underwent major surgery between January 2010 and June 2016 at Hallym University Sacred Heart Hospital. The following six major operation types were selected: cardiac, stomach, colorectal, hip, knee, and spine. Clinical features, laboratory findings, perioperative variables, and medication history were compared between patients without POD and with POD. RESULTS: Six hundred eighty-six of 3634 patients (18.9%) developed POD. In multivariate logistic regression analysis, common, independent RFs of POD were as follows (descending order of odds ratio): operation type ([hip] OR 8.858, 95%CI 3.432-22.863; p = 0.000; [knee] OR 7.492, 95%CI 2.739-20.487; p = 0.000; [spine] OR 6.919, 95%CI 2.687-17.815; p = 0.000; [colorectal] OR 2.037, 95%CI 0.784-5.291; p = 0.144; [stomach] OR 1.500, 95%CI 0.532-4.230; p = 0.443; [cardiac] reference), parkinsonism (OR 2.945, 95%CI 1.564-5.547; p = 0.001), intensive care unit stay (OR 1.675, 95%CI 1.354-2.072; p = 0.000), stroke history (OR 1.591, 95%CI 1.112-2.276; p = 0.011), use of hypnotics and sedatives (OR 1.307, 95%CI 1.072-1.594; p = 0.008), higher creatinine (OR 1.107, 95%CI 1.004-1.219; p = 0.040), lower hematocrit (OR 0.910, 95%CI 0.836-0.991; p = 0.031), older age (OR 1.053, 95%CI 1.037-1.069; p = 0.000), and lower body mass index (OR 0.967, 95%CI 0.942-0.993; p = 0.013). The use of analgesics (OR 0.644, 95%CI 0.467-0.887; p = 0.007) and antihistamines/antiallergics (OR 0.764, 95%CI 0.622-0.937; p = 0.010) were risk-reducing factors. Operation type with the highest odds ratio for POD was orthopedic surgery. CONCLUSIONS: Big data analytics could be applied to evaluate RFs in electronic medical records. We identified common RFs of POD, regardless of operation type. Big data analytics may be helpful for the comprehensive understanding of POD RFs, which can help physicians develop a general plan to prevent POD.

ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories.

In the last decades, the amount of medical imaging studies and associated metadata has been rapidly increasing. Despite being mostly used for supporting medical diagnosis and treatment, many recent initiatives claim the use of medical imaging studies in clinical research scenarios but also to improve the business practices of medical institutions. However, the continuous production of medical imaging studies coupled with the tremendous amount of associated data, makes the real-time analysis of medical imaging repositories difficult using conventional tools and methodologies. Those archives contain not only the image data itself but also a wide range of valuable metadata describing all the stakeholders involved in the examination. The exploration of such technologies will increase the efficiency and quality of medical practice. In major centers, it represents a big data scenario where Business Intelligence (BI) and Data Analytics (DA) are rare and implemented through data warehousing approaches. This article proposes an Extract, Transform, Load (ETL) framework for medical imaging repositories able to feed, in real-time, a developed BI (Business Intelligence) application. The solution was designed to provide the necessary environment for leading research on top of live institutional repositories without requesting the creation of a data warehouse. It features an extensible dashboard with customizable charts and reports, with an intuitive web-based interface that empowers the usage of novel data mining techniques, namely, a variety of data cleansing tools, filters, and clustering functions. Therefore, the user is not required to master the programming skills commonly needed for data analysts and scientists, such as Python and R.

A standardized glucose-insulin-potassium infusion protocol in surgical patients: Use of real clinical data from a clinical data warehouse.

AIMS: We evaluated the clinical usefulness of a new unified glucose-insulin-potassium (GIK) regimen in a general surgical department. METHODS: Surgical patients treated under the previous diverse GIK regimens (September 2016 to August 2017) and the new unified GIK regimen (September 2017 to August 2018) were identified in records of the Clinical Data Warehouse of Seoul National University Bundang Hospital. Serial and area under the curve (AUC) glucose levels, and percentages of time within the target glucose levels were compared in propensity score matched patients in the diverse GIK regimen and in the unified GIK regimen (n = 227 in each group). RESULTS: The AUC of glucose at 6 h and 12 h was lower under the unified GIK regimen than the diverse GIK regimen. The percentage of target glucose levels was higher in the unified GIK regimen compared to the diverse GIK regimen (81.5% vs. 75.0%, P = 0.026), but the occurrence of hypoglycaemia did not differ significantly between groups. CONCLUSIONS: The unified GIK regimen was more effective than the diverse GIK regimen for glycaemic control and did not increase the number of patients developing hypoglycaemia. This validated written GIK regimen can be safely used in a general surgical department.

Leveraging Digital Data to Inform and Improve Quality Cancer Care.

BACKGROUND: Efficient capture of routine clinical care and patient outcomes is needed at a population-level, as is evidence on important treatment-related side effects and their effect on well-being and clinical outcomes. The increasing availability of electronic health records (EHR) offers new opportunities to generate population-level patient-centered evidence on oncologic care that can better guide treatment decisions and patient-valued care. METHODS: This study includes patients seeking care at an academic medical center, 2008 to 2018. Digital data sources are combined to address missingness, inaccuracy, and noise common to EHR data. Clinical concepts were identified and extracted from EHR unstructured data using natural language processing (NLP) and machine/deep learning techniques. All models are trained, tested, and validated on independent data samples using standard metrics. RESULTS: We provide use cases for using EHR data to assess guideline adherence and quality measurements among patients with cancer. Pretreatment assessment was evaluated by guideline adherence and quality metrics for cancer staging metrics. Our studies in perioperative quality focused on medications administered and guideline adherence. Patient outcomes included treatment-related side effects and patient-reported outcomes. CONCLUSIONS: Advanced technologies applied to EHRs present opportunities to advance population-level quality assessment, to learn from routinely collected clinical data for personalized treatment guidelines, and to augment epidemiologic and population health studies. The effective use of digital data can inform patient-valued care, quality initiatives, and policy guidelines. IMPACT: A comprehensive set of health data analyzed with advanced technologies results in a unique resource that facilitates wide-ranging, innovative, and impactful research on prostate cancer. This work demonstrates new ways to use the EHRs and technology to advance epidemiologic studies and benefit oncologic care.See all articles in this CEBP Focus section, "Modernizing Population Science."

Catalyzing traffic safety advancements via data linkage: Development of the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse.

Objective: Our objective is to describe the development of the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse, a unique and comprehensive data source that integrates various state-level administrative databases in New Jersey to enable the field of traffic safety to address critical, high-priority research questions.Methods: We have obtained full identifiable data from the following statewide administrative databases for the state of New Jersey: (1) driver licensing database; (2) Administration Office of the Courts data on traffic-related citations; (3) police-reported crash database; (4) birth certificate data; (5) death certificate data; and (6) hospital discharge data as well as (7) childhood electronic records from New Jersey residents who were patients of the Children's Hospital of Philadelphia pediatric health care network and (8) census tract-level indicators. We undertook an iterative process to develop a linkage algorithm in LinkSolv 9.0 software using records for individuals born in select birth years (1987 and 1988) and subsequently execute the linkage for the entire study period (2004-2017). Several metrics were used to evaluate the quality of the linkage process.Results: We identified a total of 62,685,619 records and 19,247,363 distinct individuals; 10,352,998 of these individuals had more than one record brought together during the linkage process. Our evaluation of this linkage suggests that the linkage was of high quality.Conclusions: The resulting NJ-SHO data warehouse will be one of the most comprehensive and rich traffic safety data warehouses to date. The warehouse has already been utilized for numerous studies and will be fully primed to support a host of rigorous studies, both in and beyond the field of traffic safety.