Journal of hospital medicine最新文献

Background: Despite nationally endorsed treatment guidelines and stewardship programs, variation and deviation from evidence-based antibiotic prescribing occur, contributing to inappropriate use and medication-related adverse events. Measures of antibiotic prescribing variability can aid in quantifying this problem but are not adequate.

Objective: The objective of this study is to develop a standardized metric to quantify antibiotic prescribing variability (diversity) within and across children's hospitals, and to examine its association with outcomes.

Methods: We performed a cross-sectional study of empiric antibiotic exposure among children hospitalized during 2017-2019 with one of 15 common pediatric infections using the Pediatric Health Information System database. Encounters for children with complex chronic conditions, transfers in, and birth hospitalizations were excluded. Using the Shannon-Weiner entropy index, we quantified antibiotic diversity for each infection type using the d-measure of diversity. Generalized linear mixed-effects models were used to examine the association between hospital-level antibiotic diversity and risk-adjusted length of stay and costs.

Results: A total of 79,515 hospitalizations for common pediatric infections were included. Antibiotic diversity varied within and across hospitals. Infections with low mean antibiotic diversity included appendicitis (mean diversity [mDiv] = 4.9, SD = 2.5) and deep neck space infections (mDiv = 5.9, SD = 1.9). Infections with high mean antibiotic diversity included pneumonia (mDiv = 23.4, SD = 5.6) and septicemia/bacteremia (mDiv = 28.5, SD = 12.1). There was no statistically significant association between hospital-level antibiotic diversity and risk-adjusted LOS or costs.

Conclusions: We developed and applied a novel metric to quantify diversity in antibiotic prescribing that permits comparisons across hospitals and can be leveraged to identify high-priority areas for local and national stewardship interventions.

Background: Stigma within the healthcare environment limits access to treatment for opioid use disorder (OUD), even as OUD results in significant morbidity and mortality. Language in clinical documentation affects patient experience and future care through the transmission of stigma or positive regard. With the passage of the 21st Century Cures Act, patients have full access to their medical records online.

Objectives: The objective of our study was to understand providers' use of stigmatizing and affirming language in the electronic health record (EHR) for OUD patients with long hospital stays.

Methods: We selected patients with a first-time referral to the Duke University Hospital OUD consult service who met diagnostic criteria for OUD with a hospital stay ≥28 days from July 2019 to February 2022. Two reviewers independently evaluated each admission and discharge note for stigmatizing or affirming language and the group met weekly to validate coding reliability.

Results: Forty-eight patients (96 notes) met our inclusion criteria. We identified 434 occurrences of stigmatizing and 47 occurrences of affirming language. One-third (34%) of stigmatizing language appeared in system-generated fields (drop-down categories and diagnosis codes) and the rest was authored by providers.

Conclusions: Stigmatizing language was present in both provider- and system-generated language and was nine times more frequent than affirming language in the medical records of hospitalized patients with OUD. While provider education may reduce stigmatizing language, institutional level changes to the EHR and International Classification of Disease codes are necessary to decrease stigmatizing language within medical records.

Background: Hospital at Home (HaH) programs are used throughout the United States and are beneficial in both providing patients care in environments most comfortable to them and freeing up inpatient beds. Better informing patients about HaH programs, while promoting shared decision-making (SDM), should be prioritized by health systems. SDM apps may promote increased patient agency and understanding of complex HaH care decisions. We previously developed, usability tested, and refined a HaH SDM app.

Objectives: To evaluate the utility of SDM apps in assisting pneumonia patients with HaH admission.

Methods: Usability surveys (N = 16) and semistructured interviews with patients (N = 9) and nurse navigators (N = 3) were utilized to evaluate our app in assisting pneumonia patients as they contemplated HaH admission. Recruitment occurred at three hospitals in the southeastern United States. Surveys were analyzed consistent with their validated measures, while interviews were analyzed using inductive coding methodologies.

Results: Patients supported receiving HaH information via an app, with many noting that presenting content via multiple modalities (e.g., videos, pictures, text) was helpful and that the app assisted their care decision. App-guided inquiries into patients' care preferences helped patients visualize their priorities and promoted feelings of agency, while providing important information to care teams. Participants found visuals effective at conveying program details, for example, HaH's in-home setup, which may assist with health literacy challenges. Potential barriers included the need to expand app accessibility for vision impaired and non-English speaking patients.

Conclusions: SDM apps may better inform patients' HaH care decisions, allowing patients self-directed access to information and engagement with visual content, which may address challenges related to health literacy and navigating complex, time-sensitive decisions.

Background: Hospital readmission rates are used for quality and pay-for-performance initiatives. To identify readmissions from administrative data, two commonly employed methods are focusing either on unplanned readmissions (used by the Centers for Medicare & Medicaid Services, CMS) or potentially avoidable readmissions (used by commercial vendors such as SQLape or 3 M). However, it is not known which of these methods has higher criterion validity and can more accurately identify actually avoidable readmissions.

Objectives: A manual record review based on data from seven hospitals was used to compare the validity of the methods by CMS and SQLape.

Methods: Seven independent reviewers reviewed 738 single inpatient stays. The sensitivity, specificity, positive predictive value (PPV), and F1 score were examined to characterize the ability of an original CMS method, an adapted version of the CMS method, and the SQLape method to identify unplanned, potentially avoidable, and actually avoidable readmissions.

Results: Both versions of the CMS method had greater sensitivity (92/86% vs. 62%) and a higher PPV (84/91% vs. 71%) than the SQLape method, in terms of identifying their outcomes of interest (unplanned vs. potentially avoidable readmissions, respectively). To distinguish actually avoidable readmissions, the two versions of the CMS method again displayed higher sensitivity (90/85% vs. 66%), although the PPV did not differ significantly between the different methods.

Conclusions: Thus, the CMS method has both higher criterion validity and greater sensitivity for identifying actually avoidable readmissions, compared with the SQLape method. Consequently, the CMS method should primarily be used for quality initiatives.

Background: Text messaging has emerged as a popular strategy to engage patients after hospital discharge. Little is known about how patients use these programs and what types of needs are addressed through this approach.

Objective: The goal of this study was to describe the types and timing of postdischarge needs identified during a 30-day automated texting program.

Methods: The program ran from January to August 2021 at a primary care practice in Philadelphia. In this mixed-methods study, two reviewers conducted a directed content analysis of patient needs expressed during the program, categorizing them along a well-known transitional care framework. We describe the frequency of need categories and their timing relative to discharge.

Results: A total of 405 individuals were enrolled; the mean (SD) age was 62.7 (16.2); 64.2% were female; 47.4% were Black; and 49.9% had Medicare insurance. Of this population, 178 (44.0%) expressed at least one need during the 30-day program. The most frequent needs addressed were related to symptoms (26.8%), coordinating follow-up care (20.4%), and medication issues (15.7%). The mean (SD) number of days from discharge to need was 10.8 (7.9); there were no significant differences in timing based on need category.

Conclusions: The needs identified via an automated texting program were concentrated in three areas relevant to primary care practice and within nursing scope of practice. This program can serve as a model for health systems looking to support transitions through an operationally efficient approach, and the findings of this analysis can inform future iterations of this type of program.