A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

A Learning Health System Approach to Hospital Quality Performance Benchmarking: The Composite Hospital Quality Index.

US hospital quality rankings and ratings use disparate methodologies and are weakly correlated. This causes confusion for patients and hospital quality staff. At the authors' institution, a Composite Hospital Quality Index (CHQI) was developed to combine hospital quality ratings. This approach is described and a calculator is shared here for other health systems to explore their performance. Among the US News and World Report Top 50 Hospitals, hospital-specific numeric summary scores were aggregated from the 2021 Centers for Medicare and Medicaid Services (CMS) Hospital Overall Star Rating, the Spring 2021 Leapfrog Safety Grade, and the April 2021 Hospital Consumer Assessment of Healthcare Providers and Systems Star Rating. The CHQI is the hospital-specific sum of the national percentile-rankings across these 3 ratings. In this example, mean (SD) percentiles were as follows: CMS Stars 74 (19), Hospital Consumer Assessment of Healthcare Providers and Systems 63 (19), Leapfrog 65 (24), with mean (SD) CHQI of 202 (49). The CHQI is used at the authors' institution to identify improvement opportunities and ensure that high-quality care is delivered across the health system.

Usage of EpiFinder clinical decision support in the assessment of epilepsy.

BACKGROUND: The diagnosis of epilepsy is at times elusive for both neurologists and nonneurologists, resulting in delays in diagnosis and therapy. The development of screening methods has been identified as a priority in response to this diagnostic and therapeutic gap. EpiFinder is a novel clinical decision support tool designed to enhance the process of information gathering and integration of patient/proxy respondent data. It is designed specifically to take key terms from a patient's history and incorporate them into a heuristic algorithm that dynamically produces differential diagnoses of epilepsy syndromes. OBJECTIVE: The objective of this study was to test the usability and diagnostic accuracy of the clinical decision support application EpiFinder in an adult population. METHODS: Fifty-seven patients were prospectively identified upon admission to the Epilepsy Monitoring Unit (EMU) for episode classification from January through June of 2017. Based on semiologic input, the application generates a list of epilepsy syndromes. The EpiFinder-generated diagnosis for each subject was compared to the final diagnosis obtained via continuous video electroencephalogram (cVEEG) monitoring. RESULTS: Fifty-three patients had habitual events recorded during their EMU stay. A diagnosis of epilepsy was confirmed (with cVEEG monitoring) in 26 patients while 27 patients were found to have a diagnosis other than epilepsy. The algorithm appropriately predicted differentiation between the presence of an epilepsy syndrome and an alternative diagnosis with 86.8% (46/53 participants) accuracy. EpiFinder correctly identified the presence of epilepsy with a sensitivity of 86.4% (95% confidence interval [CI]: 65.0-97.1) and specificity of 85.1% (95% CI: 70.2-96.4). CONCLUSION: The initial testing of the EpiFinder algorithm suggests possible utility in differentiating between an epilepsy syndrome and an alternative diagnosis in adult patients.

The influence of impression management scales on the Personality Assessment Inventory in the epilepsy monitoring unit.

The Somatic Complaints scale (SOM) and Conversion subscale (SOM-C) of the Personality Assessment Inventory perform best in classifying psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES); however, the impact of positive impression management (PIM) and negative impression management (NIM) scales on SOM and SOM-C classification has not been examined. We studied 187 patients from an epilepsy monitoring unit with confirmed PNES or ES. On SOM, the best cut score was 72.5 T when PIM was elevated and 69.5 T when there was no bias. On SOM-C, when PIM was elevated, the best cut score was 67.5 T and 76.5 T when there was no bias. Negative impression management elevations (n=9) were too infrequent to analyze separately. Despite similarities in classification accuracy, there were differences in sensitivity and specificity with and without PIM, impacting positive and negative predictive values. The presence of PIM bias generally increases positive predictive power of SOM and SOM-C but decreases negative predictive power.

Randomized comparison of the Personality Assessment Inventory and the Minnesota Multiphasic Personality Inventory-2 in the epilepsy monitoring unit.

The two most common personality measures used in evaluation of patients on epilepsy monitoring units (EMUs) are the Personality Assessment Inventory (PAI) and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Both have been evaluated separately for their ability to distinguish patients with epilepsy from patients with psychogenic events, but they have never been compared directly. The primary aim of this study was to provide comparison data in an EMU population between the PAI, MMPI-2, and the MMPI-2-RF (MMPI-2 Restructured Form). Results show that the PAI Somatic Complaints (SOM) scale and the Conversion subscale (SOM-C), with classification rates of 79%, outperform other indicators from the PAI and indicators from the MMPI-2 and the MMPI-2-RF. Given its other strengths combined with better diagnostic validity performance, the PAI may be the better personality assessment measure for use in distinguishing patients with epilepsy from those with psychogenic seizures in the EMU.