Dataset Information

Defining Levels of US Hospitals' Pediatric Capabilities.

ABSTRACT:

Importance

Classifying hospitals across a wide range of pediatric capabilities, including medical, surgical, and specialty services, would improve understanding of access and outcomes.

Objective

To develop a classification system for hospitals' pediatric capabilities.

Design, setting, and participants

This cross-sectional study included data from 2019 on all acute care hospitals with emergency departments in 10 US states that treated at least 1 child per day. Statistical analysis was performed from September 2023 to February 2024.

Exposure

Pediatric hospital capability level, defined using latent class analysis. The latent class model parameters were the presence or absence of 26 functional capabilities, which ranged from performing laceration repairs to performing organ transplants. A simplified approach to categorization was derived and externally validated by comparing each hospital's latent class model classification with its simplified classification using data from 3 additional states.

Main outcomes and measures

Health care utilization and structural characteristics, including inpatient beds, pediatric intensive care unit (PICU) beds, and referral rates (proportion of patients transferred among patients unable to be discharged).

Results

Using data from 1061 hospitals (716 metropolitan [67.5%]) with a median of 2934 pediatric ED encounters per year (IQR, 1367-5996), the latent class model revealed 4 pediatric levels, with a median confidence of hospital assignment to level of 100% (IQR, 99%-100%). Of 26 functional capabilities, level 1 hospitals had a median of 24 capabilities (IQR, 21-25), level 2 hospitals had a median of 13 (IQR, 11-15), level 3 hospitals had a median of 8 (IQR, 6-9), and level 4 hospitals had a median of 3 (IQR, 2-3). Pediatric level 1 hospitals had a median of 66 inpatient beds (IQR, 42-86), level 2 hospitals had a median of 16 (IQR, 9-22), level 3 hospitals had a median of 0 (IQR, 0-6), and level 4 hospitals had a median of 0 (IQR, 0-0) (P < .001). Level 1 hospitals had a median of 19 PICU beds (IQR, 10-28), level 2 hospitals had a median of 0 (IQR, 0-5), level 3 hospitals had a median of 0 (IQR, 0-0), and level 4 hospitals had a median of 0 (IQR, 0-0) (P < .001). Level 1 hospitals had a median referral rate of 1% (IQR, 1%-3%), level 2 hospitals had a median of 25% (IQR, 9%-45%), level 3 hospitals had a median of 70% (IQR, 52%-84%), and level 4 hospitals had a median of 100% (IQR, 98%-100%) (P < .001).

Conclusions and relevance

In this cross-sectional study of hospitals from 10 US states, a system to classify hospitals' pediatric capabilities in 4 levels was developed and was associated with structural and health care utilization characteristics. This system can be used to understand and track national pediatric acute care access and outcomes.

SUBMITTER: Michelson KA

PROVIDER: S-EPMC11250363 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Defining Levels of US Hospitals' Pediatric Capabilities.

Michelson Kenneth A KA Alpern Elizabeth R ER Remick Katherine E KE Cash Rebecca E RE Kemal Samaa S Wolk Courtney Benjamin CB Camargo Carlos A CA Samuels-Kalow Margaret E ME

JAMA network open 20240701 7

<h4>Importance</h4>Classifying hospitals across a wide range of pediatric capabilities, including medical, surgical, and specialty services, would improve understanding of access and outcomes.<h4>Objective</h4>To develop a classification system for hospitals' pediatric capabilities.<h4>Design, setting, and participants</h4>This cross-sectional study included data from 2019 on all acute care hospitals with emergency departments in 10 US states that treated at least 1 child per day. Statistical an ...[more]

PMID: 39008298

Similar Datasets

Project description:IntroductionHigh-performing primary care is recognised as the foundation of an effective and efficient healthcare system. Many medical graduates report they are not prepared for independent practice. To date, no research has been conducted to identify the key capabilities and competencies of high-performing family medicine graduates in Canada. This pilot project aims to identify the capabilities and competencies of high-performing early-career family physicians in Ontario, Canada, and explore opportunities for enhancing learning, teaching and assessment within family medicine residency programmes.Methods and analysisEmploying a mixed-methods explanatory sequential study design, this research will use a theory-driven Professional Capability Framework, previously validated in studies across nine professions, to guide the investigation. The first (quantitative) phase involves surveying ~50 high-performing early-career family physicians identified as high performing by educators, colleagues and leaders. The objective of the survey is to identify the key competencies and personal, interpersonal and cognitive capabilities of high-performing family physicians. The second (qualitative) phase involves conducting workshops with stakeholders, including educators, professional associations, regulators and colleges, to test the veracity of the results. Quantitative data will be analysed using descriptive statistics, and qualitative data will be analysed using Braun and Clarke's thematic analysis. The first and second phases will identify the key capabilities and competencies required to confidently adapt to the independent practice of comprehensive family medicine and inform fit-for-purpose educational strategies for teaching, learning and assessment.Ethics and disseminationThe study is approved by the University of Toronto's Health Sciences Research Ethics Board (#41799). Research findings will be discussed with professional bodies, educators responsible for family medicine curricula and universities. Study findings will also be disseminated through academic conferences and academic publications in peer-reviewed journals. Project summaries and infographics will be developed and disseminated to key stakeholders.

Project description:AbstractHealth information technology (IT) is often proposed as a solution to fragmentation of care, and has been hypothesized to reduce readmission risk through better information flow. However, there are numerous distinct health IT capabilities, and it is unclear which, if any, are associated with lower readmission risk.To identify the specific health IT capabilities adopted by hospitals that are associated with hospital-level risk-standardized readmission rates (RSRRs) through path analyses using structural equation modeling.This STROBE-compliant retrospective cross-sectional study included non-federal U.S. acute care hospitals, based on their adoption of specific types of health IT capabilities self-reported in a 2013 American Hospital Association IT survey as independent variables. The outcome measure included the 2014 RSRRs reported on Hospital Compare website.A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis, and corroborated by confirmatory factor analysis. Subsequent path analysis using Structural equation modeling revealed that a one-point increase in the hospital adoption of patient engagement capability latent scores (median path coefficient ß = -0.086; 95% Confidence Interval, -0.162 to -0.008), including functionalities like direct access to the electronic health records, would generally lead to a decrease in RSRRs by 0.086%. However, computerized hospital discharge and information exchange capabilities with other inpatient and outpatient providers were not associated with readmission rates.These findings suggest that improving patient access to and use of their electronic health records may be helpful in improving hospital performance on readmission; however, computerized hospital discharge and information exchange among clinicians did not seem as beneficial - perhaps because of the quality or timeliness of information transmitted. Future research should use more recent data to study, not just adoption of health IT capabilities, but also whether their usage is associated with lower readmission risk. Understanding which capabilities impact readmission risk can help policymakers and clinical stakeholders better focus their scarce resources as they invest in health IT to improve care delivery.

Project description:BackgroundAs the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.MethodsThis scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.ResultsOf 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).ConclusionsThis review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

Project description:To compare the prevalence, resource utilization, and mortality for pediatric severe sepsis identified using two established identification strategies.Observational cohort study from 2004 to 2012.Forty-four pediatric hospitals contributing data to the Pediatric Health Information Systems database.Children 18 years old or younger.We identified patients with severe sepsis or septic shock by using two International Classification of Diseases, 9th edition, Clinical Modification-based coding strategies: 1) combinations of International Classification of Diseases, 9th edition, Clinical Modification codes for infection plus organ dysfunction (combination code cohort); 2) International Classification of Diseases, 9th edition, Clinical Modification codes for severe sepsis and septic shock (sepsis code cohort). Outcomes included prevalence of severe sepsis, as well as hospital and ICU length of stay, and mortality. Outcomes were compared between the two cohorts examining aggregate differences over the study period and trends over time. The combination code cohort identified 176,124 hospitalizations (3.1% of all hospitalizations), whereas the sepsis code cohort identified 25,236 hospitalizations (0.45%), a seven-fold difference. Between 2004 and 2012, the prevalence of sepsis increased from 3.7% to 4.4% using the combination code cohort and from 0.4% to 0.7% using the sepsis code cohort (p < 0.001 for trend in each cohort). Length of stay (hospital and ICU) and costs decreased in both cohorts over the study period (p < 0.001). Overall, hospital mortality was higher in the sepsis code cohort than the combination code cohort (21.2% [95% CI, 20.7-21.8] vs 8.2% [95% CI, 8.0-8.3]). Over the 9-year study period, there was an absolute reduction in mortality of 10.9% (p < 0.001) in the sepsis code cohort and 3.8% (p < 0.001) in the combination code cohort.Prevalence of pediatric severe sepsis increased in the studied U.S. children's hospitals over the past 9 years, whereas resource utilization and mortality decreased. Epidemiologic estimates of pediatric severe sepsis varied up to seven-fold depending on the strategy used for case ascertainment.

Project description:BackgroundExpert opinion and professional society statements have called for multi-tier care systems for the management of cardiogenic shock (CS). However, little is known about how to pragmatically define centers with different levels of care (LOC) for CS.MethodsEleven of 23 hospitals within our healthcare system sharing a common electronic health record were classified as different LOC according to their highest mechanical circulatory support (MCS) capabilities: Level 1 (L-1)-durable left ventricular assist device, Level 1A (L-1A)-extracorporeal membrane oxygenation, Level 2 (L-2)-intra-aortic balloon pump and percutaneous ventricular assist device; and Level 3 (L-3)-no MCS. All adult patients treated for CS (International Classification of Diseases, ICD-10 code R57.0) between 2016 and 2022 were included. Etiologies of CS were identified using associated diagnostic codes. Management strategies and outcomes across LOC were compared.ResultsHigher LOC centers had higher volumes: L-1 (n = 1): 2,831 patients, L-1A (n = 4): 3,452, L-2 (n = 1): 340, and L-3 (n = 5): 780. Emergency room admissions were more common in lower LOC (96% at L-3 vs. 46% L-1; p < 0.001), while hospital transfers were predominant at higher LOC (40% at L-1 vs. 2.7% at L-3; p < 0.001). Men comprised 61% of the cohort. Patients were younger in the higher LOC [69 (60-78) years at L-1 vs. 77 (67-85) years at L-3; p < 0.001]. Patients with acute myocardial infarction (AMI)-CS and acute heart failure (AHF)-CS were concentrated in higher LOC centers while other etiologies of CS were more common in L-2 and L-3 (p < 0.001). Cardiac arrest on admission was more prevalent in lower LOC centers (L-1: 2.8% vs. L-3: 12.1%; p < 0.001). Patients with AMI-CS received more percutaneous coronary intervention in lower LOC (51% L-2 vs. 29% L-1; p < 0.01) but more coronary arterial bypass graft surgery at higher LOC (L-1: 42% vs. L-1A: 23%; p < 0.001). MCS use was consistent across levels for AMI-CS but was more frequent in higher LOC for AHF-CS patients (L-1: 28% vs. L-2: 10%; p < 0.001). Despite increasing in-hospital mortality with decreasing LOC, no significant difference was seen after multivariable adjustment.ConclusionThis is the first report describing a pragmatic classification of LOC for CS which, based on MCS capabilities, can discriminate between centers with distinct demographics, practice patterns, and outcomes. This classification may serve as the basis for future research and the creation of CS systems of care.

Dataset Information

Defining Levels of US Hospitals' Pediatric Capabilities.

Importance

Objective

Design, setting, and participants

Exposure

Main outcomes and measures

Results

Conclusions and relevance

Publications

Defining Levels of US Hospitals' Pediatric Capabilities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets