AI Article Synopsis
- The study aimed to identify factors that predict how effective hospital accreditation is for improving care processes.
- It involved a comprehensive analysis of care processes at Danish hospitals over a 269-week period, focusing on various conditions like heart failure and breast cancer.
- Results showed varied impacts of accreditation based on the medical condition and type of care, with some areas showing consistent improvement while hospital characteristics did not significantly predict accreditation effectiveness.
Article Abstract
Objective: To identify predictors of the effectiveness of hospital accreditation on process performance measures.
Design: A multi-level, longitudinal, stepped-wedge, nationwide study.
Participants: All patients admitted for acute stroke, heart failure, ulcers, diabetes, breast cancer and lung cancer at Danish hospitals.
Intervention: The Danish Healthcare Quality Programme that was designed to create a framework for continuous quality improvement.
Main Outcome Measure(s): Changes in week-by-week trends of hospitals' process performance measures during the study period of 269 weeks prior to, during and post-accreditations. Process performance measures were based on 43 different processes of care obtained from national clinical quality registries. Analyses were stratified according to condition, type of care (i.e. treatment, diagnostics, secondary prevention and patient monitoring) and hospital characteristics (i.e. university affiliation, location, size, experience with accreditation and accreditation compliance).
Results: A total of 1 624 518 processes of care were included. The impact of accreditation differed across the conditions. During accreditation, heart failure and breast cancer showed less improvement than other disease areas. Across all conditions, diagnostic processes improved less rapidly than other types of processes. However, after stratifying the data by hospital characteristics, process performance measures improved more uniformly. In respect of the measures that had an unsatisfactory level of quality, the processes related to diabetes, diagnostics and patient monitoring all responded to accreditation and showed an increased improvement during the preparatory work.
Conclusion: Hospital characteristics were not found to be predictors for the effects of accreditation, whereas conditions and types of care to some extent predicted the effectiveness.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/intqhc/mzx052 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A novel LC-MS/MS assay for low concentrations of creatinine in sweat and saliva to validate biosensors for continuous monitoring of renal function.
J Chromatogr B Analyt Technol Biomed Life Sci
December 2024
Clinical Laboratory, Catharina Hospital Eindhoven, Eindhoven 5623 EJ, The Netherlands; Department of Biomedical Engineering, Chemical Biology, Eindhoven University of Technology, Groene Loper 3, Eindhoven 5612 AE, The Netherlands.
Monitoring of kidney function traditionally relies on plasma creatinine concentrations, necessitating invasive blood draws. Non-invasively obtainable biofluids, such as sweat and saliva, present a patient-friendly alternative with potential for continuous monitoring. This study focusses on developing and validating a novel Liquid Chromatography- tandem Mass Spectrometry (LC-MS/MS) assay as a reference test for measuring low creatinine concentrations in sweat and saliva.
View Article and Find Full Text PDFComparative study of Idesia polycarpa Maxim cake meal polysaccharides: Conventional versus innovative extraction methods and their impact on structural features, emulsifying, antiglycation, and hypoglycemic properties.
Food Chem
January 2025
College of Food Science and Engineering, Guiyang University, Guiyang, Guizhou 550005, PR China; Engineering Technology Research Center for Processing and Comprehensive Utilization of Idesia polycarpa, National Forestry and Grassland Administration of the People's Republic of China, Guiyang, Guizhou 550005, PR China. Electronic address:
Idesia polycarpa Maxim (IPM) cake meal, a major by-product of oil extraction, is often discarded in large quantities, resulting in considerable waste. This study explored the extraction of IPM polysaccharides (IPMPs) from cake meal using the innovative ultrasonic-assisted three-phase partitioning (UTPP) method, in comparison with conventional techniques, including acid, medium-temperature alkali, chelating agent, and enzyme extraction methods. The IPMP-UT prepared via UTPP method achieved superior extraction efficiency (10.
View Article and Find Full Text PDFUnpacking Performance Factors of Innovation Systems and Studying Germany's Attempt to Foster the Role of the Patient Through a Market Access Pathway for Digital Health Applications (DiGAs): Exploratory Mixed Methods Study.
J Med Internet Res
January 2025
Working Group for Data-Driven Innovation, Hamburg University of Technology, Hamburg, Germany.
Background: Health care innovation faces significant challenges, including system inertia and diverse stakeholders, making regulated market access pathways essential for facilitating the adoption of new technologies. The German Digital Healthcare Act, introduced in 2019, offers a model by enabling digital health applications (DiGAs) to be reimbursed by statutory health insurance, improving market access and patient empowerment. However, the factors influencing the success of these pathways in driving innovation remain unclear.
View Article and Find Full Text PDFAutonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFTwo-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study.
J Med Internet Res
January 2025
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!