Clinical data warehouses, initially directed towards clinical research or financial analyses, are evolving to support quality improvement efforts, and must now address the quality improvement life ...cycle. In addition, data that are needed for quality improvement often do not reside in a single database, requiring easier methods to query data across multiple disparate sources. We created a virtual data warehouse at NewYork Presbyterian Hospital that allowed us to bring together data from several source systems throughout the organization. We also created a framework to match the maturity of a data request in the quality improvement life cycle to proper tools needed for each request. As projects progress in the Define, Measure, Analyze, Improve, Control stages of quality improvement, there is a proper matching of resources the data needs at each step. We describe the analysis and design creating a robust model for applying clinical data warehousing to quality improvement.
Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost, technology, and appropriate data availability. In this study, two methods for semi-automated review of text ...records within the Veterans Administration database are utilized to identify AEs related to the placement of central venous catheters (CVCs): a Natural Language Processing program and a phrase-matching algorithm. A sample of manually reviewed records were then compared to the results of both methods to assess sensitivity and specificity. The phrase-matching algorithm was found to be a sensitive but relatively non-specific method, whereas a natural language processing system was significantly more specific but less sensitive. Positive predictive values for each method estimated the CVC-associated AE rate at this institution to be 6.4 and 6.2%, respectively. Using both methods together results in acceptable sensitivity and specificity (72.0 and 80.1%, respectively). All methods including manual chart review are limited by incomplete or inaccurate clinician documentation. A secondary finding was related to the completeness of administrative data (ICD-9 and CPT codes) used to identify intensive care unit patients in whom a CVC was placed. Administrative data identified less than 11% of patients who had a CVC placed. This suggests that other methods, including automated methods such as phrase matching, may be more sensitive than administrative data in identifying patients with devices. Considerable potential exists for the use of such methods for the identification of patients at risk, AE surveillance, and prevention of AEs through decision support technologies.
To determine potential predictors of sustainability among community-based organizations that are implementing health information technology (HIT) with health information exchange, in a state with ...significant funding of such organizations.
A longitudinal cohort study of community-based organizations funded through the first phase of the $440 million Healthcare Efficiency and Affordability Law for New Yorkers program.
We administered a baseline telephone survey in January and February 2007, using a novel instrument with open-ended questions, and collected follow-up data from the New York State Department of Health regarding subsequent funding awarded in March 2008. We used logistic regression to determine associations between 18 organizational characteristics and subsequent funding.
All 26 organizations (100%) responded. Having the alliance led by a health information organization (odds ratio OR 11.4, P = .01) and having performed a community-based needs assessment (OR 5.1, P = .08) increased the unadjusted odds of subsequent funding. Having the intervention target the long-term care setting (OR 0.14, P = .03) decreased the unadjusted odds of subsequent funding. In the multivariate model, having the alliance led by a health information organization, rather than a healthcare organization, increased the odds of subsequent funding (adjusted OR 6.4; 95% confidence interval 0.8, 52.6; P = .08).
Results from this longitudinal study suggest that both health information organizations and healthcare organizations are needed for sustainable HIT transformation.
The scarcity of cost-effective patient identification methods represents a significant barrier to clinical research. Research recruitment alerts have been designed to facilitate physician referrals ...but limited support is available to clinical researchers. We conducted a retrospective data analysis to evaluate the efficacy of a real-time patient identification alert delivered to clinical research coordinators recruiting for a clinical prospective cohort study. Data from log analysis and informal interviews with coordinators were triangulated. Over a 12-month period, 11,295 were screened electronically, 1,449 were interviewed, and 282 were enrolled. The enrollment rates for the alert and two other conventional methods were 4.65%, 2.01%, and 1.34% respectively. A taxonomy of eligibility status was proposed to precisely categorize research patients. Practical ineligibility factors were identified and their correlation with age and gender were analyzed. We conclude that the automatic prescreening alert improves screening efficiency and is an effective aid to clinical research coordinators.
Introduction: To address the electronic health data fragmentation that is a methodological limitation of comparative effectiveness research (CER), the Washington Heights Inwood Informatics ...Infrastructure for Comparative Effectiveness Research (WICER) project is creating a patient-centered research data warehouse (RDW) by linking electronic clinical data (ECD) from New York Presbyterian Hospitals clinical data warehouse with ECD from ambulatory care, long-term care, and home health settings and the WICER community health survey (CHS). The purposes of the research were to identify areas of overlap between the WICER CHS and two other surveys that include health behavior data (the Behavioral Risk Factor Surveillance System (BRFSS) Survey and the New York City Community Health Survey (NYC CHS)) and to identify gaps in the current WICER RDW that have the potential to affect patient-centered CER.
Methods: We compared items across the three surveys at the item and conceptual levels. We also compared WICER RDW (ECD and WICER CHS), BRFSS, and NYC CHS to the County Health Ranking framework.
Results: We found that 22 percent of WICER items were exact matches with BRFSS and that there were no exact matches between WICER CHS and NYC CHS items not also contained in BRFSS.
Conclusions: The results suggest that BRFSS and, to a lesser extent, NYC CHS have the potential to serve as population comparisons for WICER CHS for some health behavior related data and thus may be particularly useful for considering the generalizability of CER study findings. Except for one measure related to health behavior (motor vehicle crash deaths), the WICER RDWs comprehensive coverage supports the mortality, morbidity, and clinical care measures specified in the County Health Ranking framework but is deficient in terms of some socioeconomic factors and descriptions of the physical environment as captured in BRFSS. Linkage of these data in the WICER RDW through geocoding can potentially facilitate patient-centered CER that integrates important socioeconomic and physical environment influences on health outcomes. The research methods and findings may be relevant to others interested in either integrating health behavior data into RDWs to support patient-centered CER or conducting population-level comparisons.
Next-generation tablets (iPads and Android tablets) may potentially improve the collection and management of clinical research data. The widespread adoption of tablets, coupled with decreased ...software and hardware costs, has led to increased consideration of tablets for primary research data collection. When using tablets for the Washington Heights/Inwood Infrastructure for Comparative Effectiveness Research (WICER) project, we found that the devices give rise to inherent security issues associated with the potential use of cloud-based data storage approaches. This paper identifies and describes major security considerations for primary data collection with tablets; proposes a set of architectural strategies for implementing data collection forms with tablet computers; and discusses the security, cost, and workflow of each strategy. The paper briefly reviews the strategies with respect to their implementation for three primary data collection activities for the WICER project.
Development of evidence‐based practice requires practice‐based evidence, which can be acquired through analysis of real‐world data from electronic health records (EHRs). The EHR contains volumes of ...information about patients—physical measurements, diagnoses, exposures, and markers of health behavior—that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real‐world data into reliable real‐world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real‐world data into high‐quality, fit‐for‐purpose analytical data sets used to generate real‐world evidence.
Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National ...COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (P value < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio aOR, 1.48; 95% confidence interval CI, 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.
To investigate temporal trends and outcomes associated with early antibiotic prescribing in patients hospitalized with COVID-19.
Retrospective propensity-matched cohort study using the National COVID ...Cohort Collaborative (N3C) database.
Sixty-six health systems throughout the United States that were contributing to the N3C database. Centers that had fewer than 500 admissions in their dataset were excluded.
Patients hospitalized with COVID-19 were included. Patients were defined to have early antibiotic use if they received at least 3 calendar days of intravenous antibiotics within the first 5 days of admission.
None.
Of 322,867 qualifying first hospitalizations, 43,089 patients received early empiric antibiotics. Antibiotic use declined across all centers in the data collection period, from March 2020 (23%) to June 2022 (9.6%). Average rates of early empiric antibiotic use (EEAU) also varied significantly between centers (deviance explained 7.33% vs 20.0%, p < 0.001). Antibiotic use decreased slightly by day 2 of hospitalization and was significantly reduced by day 5. Mechanical ventilation before day 2 (odds ratio OR 3.57; 95% CI, 3.42-3.72), extracorporeal membrane oxygenation before day 2 (OR 2.14; 95% CI, 1.75-2.61), and early vasopressor use (OR 1.85; 95% CI, 1.78-1.93) but not region of residence was associated with EEAU. After propensity matching, EEAU was associated with an increased risk for in-hospital mortality (OR 1.27; 95% CI, 1.23-1.33), prolonged mechanical ventilation (OR 1.65; 95% CI, 1.50-1.82), late broad-spectrum antibiotic exposure (OR 3.24; 95% CI, 2.99-3.52), and late Clostridium difficile infection (OR 1.60; 95% CI, 1.37-1.87).
Although treatment of COVID-19 patients with empiric antibiotics has declined during the pandemic, the frequency of use remains high. There is significant inter-center variation in antibiotic prescribing practices and evidence of potential harm. Our findings are hypothesis-generating and future work should prospectively compare outcomes and adverse events.
Collected to support clinical decisions and processes, clinical data may be subject to validity issues when used for research. The objective of this study is to examine methods and issues in ...summarizing and evaluating the accuracy of clinical data as compared to primary research data. We hypothesized that research survey data on a patient cohort could serve as a reference standard for uncovering potential biases in clinical data. We compared the summary statistics between clinical and research datasets. Seven clinical variables, i.e., height, weight, gender, ethnicity, systolic and diastolic blood pressure, and diabetes status, were included in the study. Our results show that the clinical data and research data had similar summary statistical profiles, but there are detectable differences in definitions and measurements for individual variables such as height, diastolic blood pressure, and diabetes status. We discuss the implications of these results and confirm the important considerations for using research data to verify clinical data accuracy.
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