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Rationale: The Sequential Organ Failure Assessment (SOFA) tool is a commonly used measure of illness severity. Calculation of the respiratory subscore of SOFA is frequently limited by missing arterial oxygen pressure (PaO2) data. Although missing PaO2 data are commonly replaced with normal values, the performance of different methods of substituting PaO2 for SOFA calculation is unclear. Objectives: The study objective was to compare the performance of different substitution strategies for missing PaO2 data for SOFA score calculation. Methods: This retrospective cohort study was performed using the Weill Cornell Critical Care Database for Advanced Research from a tertiary care hospital in the United States. All adult patients admitted to an intensive care unit (ICU) from 2011 to 2019 with an available respiratory SOFA score were included. We analyzed the availability of the PaO2/fraction of inspired oxygen (FiO2) ratio on the first day of ICU admission. In those without a PaO2/FiO2 ratio available, the ratio of oxygen saturation as measured by pulse oximetry to FiO2 was used to calculate a respiratory SOFA subscore according to four methods (linear substitution [Rice], nonlinear substitution [Severinghaus], modified respiratory SOFA, and multiple imputation by chained equations [MICE]) as well as the missing-as-normal technique. We then compared how well the different total SOFA scores discriminated in-hospital mortality. We performed several subgroup and sensitivity analyses. Results: We identified 35,260 unique visits, of which 9,172 included predominant respiratory failure. PaO2 data were available for 14,939 (47%). The area under the receiver operating characteristic curve for each substitution technique for discriminating in-hospital mortality was higher than that for the missing-as-normal technique (0.78 [0.77-0.79]) in all analyses (modified, 0.80 [0.79-0.81]; Rice, 0.80 [0.79-0.81]; Severinghaus, 0.80 [0.79-0.81]; and MICE, 0.80 [0.79-0.81]) (P < 0.01). Each substitution method had a higher accuracy for discriminating in-hospital mortality (MICE, 0.67; Rice, 0.67; modified, 0.66; and Severinghaus, 0.66) than the missing-as-normal technique. Model calibration for in-hospital mortality was less precise for the missing-as-normal technique than for the other substitution techniques at the lower range of SOFA and among the subgroups. Conclusions: Using physiologic and statistical substitution methods improved the total SOFA score's ability to discriminate mortality compared with the missing-as-normal technique. Treating missing data as normal may result in underreporting the severity of illness compared with using substitution. The simplicity of a direct oxygen saturation as measured by pulse oximetry/FiO2 ratio-modified SOFA technique makes it an attractive choice for electronic health record-based research. This knowledge can inform comparisons of severity of illness across studies that used different techniques. [ABSTRACT FROM AUTHOR]

PURPOSE: Typically stored as unstructured notes, surgical pathology reports contain data elements valuable to cancer research that require labor-intensive manual extraction. Although studies have described natural language processing (NLP) of surgical pathology reports to automate information extraction, efforts have focused on specific cancer subtypes rather than across multiple oncologic domains. To address this gap, we developed and evaluated an NLP method to extract tumor staging and diagnosis information across multiple cancer subtypes. METHODS: The NLP pipeline was implemented on an open-source framework called Leo. We used a total of 555,681 surgical pathology reports of 329,076 patients to develop the pipeline and evaluated our approach on subsets of reports from patients with breast, prostate, colorectal, and randomly selected cancer subtypes. RESULTS: Averaged across all four cancer subtypes, the NLP pipeline achieved an accuracy of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.89 for T staging, 0.90 for N staging, and 0.97 for M staging. It achieved an F1 score of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.88 for T staging, 0.90 for N staging, and 0.24 for M staging. CONCLUSION: The NLP pipeline was developed to extract tumor staging and diagnosis information across multiple cancer subtypes to support the research enterprise in our institution. Although it was not possible to demonstrate generalizability of our NLP pipeline to other institutions, other institutions may find value in adopting a similar NLP approach—and reusing code available at GitHub—to support the oncology research enterprise with elements extracted from surgical pathology reports. An NLP method to demonstrate extracting TNM staging in a large medical center with a broad range of oncology patients [ABSTRACT FROM AUTHOR]

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants' experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools. We strive to ensure that our project is patient-centered; nurtures collaboration among all stakeholders; develops scalable solutions facilitating growth and connections; chooses simple, elegant solutions wherever possible; and explores ways to streamline the administrative and regulatory approval process across sites. [ABSTRACT FROM AUTHOR]

Objective To determine characteristics and effects of nurse dosing over-rides of a clinical decision support system (CDSS) for intensive insulin therapy (IIT) in critical care units. Design Retrospective analysis of patient database records and ethnographic study of nurses using IIT CDSS. Measurements The authors determined the frequency, direction„greater than recommended (GTR) and less than recommended (LTR)„ and magnitude of overrides, and then compared recommended and over-ride doses' blood glucose (BG) variability and insulin resistance, two measures of HT CDSS associated with mortality. The authors hypothesized that rates of hypoglycemia and hyperglycemia would be greater for recommended than over-ride doses. Finally, the authors observed and interviewed nurse users. Results 5.1% (9075) of 179452 IIT CDSS doses were over-rides. 83.4% of over-ride doses were LTR, and 45.5% of these were ≥50% lower than recommended. In contrast, 78.9% of GTR doses were ≤25% higher than recommended. When recommended doses were administered, the rate of hypoglycemia was higher than the rate for GTR (p=0.257) and LTR (p=0.033) doses. When recommended doses were administered, the rate of hyperglycemia was lower than the rate for GTR (p=0.003) and LTR (p<0.001) doses. Estimates of patients' insulin requirements were higher for LTR doses than recommended and GTR doses. Nurses reported trusting NT CDSS overall but appeared concerned about recommendations when administering LTR doses. Conclusion When over-riding HT CDSS recommendations, nurses overwhelmingly administered LTR doses, which emphasized prevention of hypoglycemia but interfered with hyperglycemia control, especially when BG was >150 mg/dl. Nurses appeared to consider the amount of a recommended insulin dose, not a patient's trend of insulin resistance, when administering LTR doses overall. Over-rides affected HT CDSS protocol performance. [ABSTRACT FROM AUTHOR]

The article discusses the influence of organization in intensive insulin therapy (ITT) implementation. It also explores the role of organizational structure necessary for IIT. It states that health care organizations have become concerned on the implementations of IIT. It notes that organizational influence has played a role in the IIT adoption and the mechanisms of institutional isomorphism. It indicates that the mechanisms are analytical and may overlap in practice but offer an important perspective regarding the causes and consequences of organizational change.

Informaticians increasingly view clinical information systems as asynchronous communication systems instead of data processing tools. Outside of health care, popular web technologies like blogs, wikis, and discussion forums have proven to be platforms for effective asynchronous communication. These popular technologies have implications for improving the coordination of clinical care and social support. In order to appropriately evaluate these webbased tools for use in clinical information systems, it will be essential for the informatics community to formally identify the distinguishing attributes of these communication methodologies. The authors propose seven interpersonal and informational attributes to compare and contrast the purposes of blogs, wikis, and discussion forums. This attribute-based approach to analyzing emerging web technologies will lead to a better understanding of the design choices involved in web-based information systems. Two case studies demonstrate how informatics researchers and developers can consider these attributes in the design and evaluation of clinical information systems. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes. [ABSTRACT FROM AUTHOR]