Surain B. Roberts

Background: Acute severe ulcerative colitis (ASUC) is associated with significant morbidity. In patients with ulcerative colitis (UC), the estimated lifetime risk of developing severe colitis is 25%. Several gastrointestinal societies have provided recommendations on pathways of care for managing ASUC. The degree to which they are adhered to in different care settings remains unclear. Methods: We conducted a retrospective review using data from 7 acute-care hospitals collected through the general medicine inpatient initiative (GEMINI), a hospital research collaborative that collects administrative and clinical data from hospital information systems. We identified all patients with the most responsible inpatient discharge diagnosis of ulcerative colitis between April 2015 and December 2019. The primary outcome was the difference in hospital length of stay of patients admitted with ASUC based on hospital-type; community, academic, or inflammatory bowel disease (IBD)-focussed sites. Results: 765 eligible patients were identified between April 2015 and December 2019. The mean hospital length of stay was 9.21 days for the academic sites, 6.94 days for the community sites, and 8.03 for the IBD specialty centre (P = .094). Adverse events were uncommon overall. In our multiple logistic regression analysis, we identified that admission to an IBD-focussed centre compared to an academic centre, carried an odds ratio of 2.07 (95% CI, 1.16-3.78) for the outcome of inpatient-colectomy. Conclusions: The processes of care for patients with ASUC varied on the basis of the type of hospital they were admitted to, with the IBD specialty centre providing the most guideline adherent care. Low-cost interventions should be utilized to promote adherence to clinical practice recommendations.

Objectives: Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories. Materials and Methods: The GEMINI database collected data from 462 226 patient admissions across 32 hospitals from 2021 to 2023. We report data quality issues identified through semi-automated and manual data quality assessments completed during the data collection phase. We conducted a simulation experiment to evaluate the relationship between the number of records reviewed manually, the detection of true data errors (true positives) and the number of manual chart abstraction errors (false positives) that required unnecessary investigation. Results: The semi-automated data quality assessments identified 79 data quality issues requiring correction, of which 14 had a large impact, affecting at least 50% of records in the data. After resolving issues identified through semi-automated assessments, manual validation of 2676 patient encounters at 19 hospitals identified 4 new meaningful data errors (3 in transfusion data and 1 in physician identifiers), distributed across 4 hospitals. There were 365 manual chart abstraction errors, which required investigation by data analysts to identify as “false positives.” These errors increased linearly with the number of charts reviewed manually. Simulation results demonstrate that all 3 transfusion data errors were identified with 95% sensitivity after manual review of 5 records, whereas 18 records were needed for the physician’s table. Discussion and Conclusion: The GEMINI approach represents a scalable framework for data quality assessment and improvement in multisite EHR research databases. Manual data review is important but can be minimized to optimize the trade-off between true and false identification of data quality errors.

Objectives: Deidentification of personally identifiable information in free-text clinical data is fundamental to making these data broadly available for research. However, there exist gaps in the deidentification landscape with regard to the functionality and flexibility of extant tools, as well as suboptimal tradeoffs between deidentification accuracy and speed. To address these gaps and tradeoffs, we develop a new Python-based deidentification software, pyDeid. Materials and Methods: pyDeid uses a combination of regular expression-based rules, fixed exclusion lists and inclusion lists to deidentify free-text data. Additional configurations of pyDeid include optional named entity recognition and custom name lists. We measure its deidentification performance and speed on 700 admission notes from a Canadian hospital, the publicly available n2c2 benchmark dataset of American discharge notes, as well as a synthetic dataset of artificial intelligence (AI) generated admission notes. We also compare its performance with the Physionet De-identification Software and the popular open-source Philter tool. Results: Different configurations of pyDeid outperformed other tools on various metrics, with a “best” accuracy value of 0.988, best precision of 0.889, best recall of 0.950, and best F1 score of 0.904. All configurations of pyDeid were significantly faster than Philter and Physionet De-identification Software, with the fastest deidentification speed of 0.48 s per note. Discussion and Conclusions: pyDeid allows the flexibility to prioritize between performance and speed, as well as precision and recall, while addressing some of the gaps in functionality left by other tools. pyDeid is also generalizable to domains outside of clinical data and can be further customized for specific contexts or for particular workflows.

It is not known how disability, homelessness, or neighborhood marginalization influence risk-adjusted hospital performance measurement in a universal health care system. In this study, we evaluated the effect of including these equity-related factors in risk-adjustment models for in-hospital mortality, and 7- and 30-day readmission in 28 hospitals in Ontario, Canada. We compared risk adjustment with commonly used clinical factors to models that also included homelessness, disability, and neighborhood indices of marginalization. We evaluated models using historical data using internal-external cross-validation. We calculated risk-standardized outcome rates for each hospital in a recent reporting period using mixed-effects logistic regression. The cohort included 544 805 admissions. Adjustment for disability, homelessness, and neighborhood marginalization had little impact on discrimination or calibration of risk-adjustment models. However, the adjustment influenced comparative hospital performance on risk-standardized 30-day readmission rates, resulting in 5 hospitals being reclassified among below-average, average, and above-average groups. No hospital was reclassified for mortality and 7-day readmission. In a system with universally insured hospital services, adjustment for disability, homelessness, and neighborhood marginalization influenced estimates of hospital performance for 30-day readmission but not 7-day readmission or in-hospital mortality. These findings can inform researchers and policymakers as they consider when to adjust for these factors in hospital performance measurement.

Background Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research. Objective To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays. Design Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems. Participants Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022. Main Measures The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022. Key Results In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th–75th 0.848–0.876, range 0.816–0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th–75th 0.024–0.057, range 0.006–0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction. Conclusions An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

Background: Imaging procedures are commonly performed on hospitalized patients and waiting for these could increase length-of-stay. The study objective was to quantify delays for imaging procedures in General Internal Medicine and identify contributing patient, physician, and system factors. Methods: This was a retrospective cohort study of medical inpatients admitted to 5 hospitals in Toronto, Ontario (2010–2019), with at least one imaging procedure (CT, MRI, ultrasound, or peripherally-inserted central catheter [PICC] insertion). The primary outcome was time-to-test, and the secondary outcome was acute length-of-stay after test ordering. Results: The study cohort included 73,107 hospitalizations. Time-to-test was longest for MRI (median 22 hours) and shortest for CT (median 7 hours). The greatest contributors to time-to-test were system factors such as hospital site (up to 22 additional hours), location of test ordering (up to 10 additional hours), the timing of test ordering relative to admission (up to 13 additional hours), and ordering during weekends (up to 21 additional hours). Older patient age, having more comorbidities, and residence in a low-income neighborhood were also associated with testing delays. Each additional hour spent waiting for a test was associated with increased acute length-of-stay after test ordering, ranging from 0.4 additional hours for CT to 1.2 hours for MRI. Conclusions: The greatest contributors to testing delays relate to when and where a test was ordered. Wait times affect length-of-stay and the quality of patient care. Hospitals can apply our novel approach to explore opportunities to decrease testing delays locally.

Objective We simplified and evaluated the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin, which is difficult to standardize across clinical assays. Study Design and Setting Retrospective cohort study of adult general medical inpatients at 7 hospitals in Ontario, Canada. Results In 206,155 unique hospitalizations with 6.9% in-hospital mortality, the simplified KP method accurately predicted the risk of mortality. Bias-corrected c-statistics were 0.874 (95%CI 0.872-0.877) with troponin and 0.873 (95%CI 0.871-0.876) without troponin, and calibration was excellent for both approaches. Discrimination and calibration were similar with and without troponin for patients with heart failure and acute myocardial infarction. The Laboratory-based Acute Physiology Score (LAPS, a component of the KP method) predicted inpatient mortality on its own with and without troponin with bias-corrected c-statistics of 0.687 (95%CI 0.682-0.692) and 0.680 (95%CI 0.675-0.685), respectively. LAPS was well calibrated, except at very high scores. Conclusion A simplification of the KP method accurately predicted in-hospital mortality risk in an external general medicine cohort. Without troponin, and using common open-source tools, the KP method can be implemented for risk adjustment in a wider range of settings.