Background: Healthcare purchasers have created financial incentives for primary care practices to achieve medical home recognition. Little is known about how changes in practice structure vary across practices or relate to medical home recognition.
Objective: We aimed to characterize patterns of structural change among primary care practices participating in a statewide medical home pilot.
Design: We surveyed practices at baseline and year 3 of the pilot, measured associations between changes in structural capabilities and National Committee for Quality Assurance (NCQA) medical home recognition levels, and used latent class analysis to identify distinct classes of structural transformation.
Participants: Eighty-one practices that completed surveys at baseline and year 3 participated in the study.
Main Measures: Study measures included overall structural capability score (mean of 69 capabilities); eight structural subscale scores; and NCQA recognition levels.
Results: Practices achieving higher year-3 NCQA recognition levels had higher overall structural capability scores at baseline (Level 1: 28.4 % of surveyed capabilities, Level 2: 40.9 %, Level 3: 48.7 %; p value = 0.001). We found no association between NCQA recognition level and change in structural capability scores (Level 1: 33.2 % increase, Level 2: 30.8 %, Level 3: 33.7 %; p value = 0.88). There were four classes of practice transformation: 27 % of practices underwent "minimal" transformation (changing little on any scale); 20 % underwent "provider-facing" transformation (adopting electronic health records, patient registries, and care reminders); 26 % underwent "patient-facing" transformation (adopting shared systems for communicating with patients, care managers, referral to community resources, and after-hours care); and 26 % underwent "broad" transformation (highest or second-highest levels of transformation on each subscale).
Conclusions And Relevance: In a large, state-based medical home pilot, multiple types of practice transformation could be distinguished, and higher levels of medical home recognition were associated with practices' capabilities at baseline, rather than transformation over time. By identifying and explicitly incentivizing the most effective types of transformation, program designers may improve the effectiveness of medical home interventions.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441668 | PMC |
http://dx.doi.org/10.1007/s11606-014-3176-3 | DOI Listing |
Dig Dis Sci
January 2025
Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, Tainan, 70401, Taiwan.
Aim: Sarcopenic obesity (SO) is associated with adverse outcomes in diseased patients. This study aimed to examine the prevalence and risks associated with SO, with a focus on the impact of SO on cardiovascular risk in patients with MASLD.
Materials And Methods: In this cross-sectional study, patients with MASLD were prospectively enrolled.
Zhongguo Dang Dai Er Ke Za Zhi
January 2025
Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology/Hubei Key Laboratory of Pediatric Genetic Metabolic and Endocrine Rare Diseases, Wuhan 430030, China.
Objectives: To study the clinical manifestations and genetic characteristics of children with maturity-onset diabetes of the young type 2 (MODY2), aiming to enhance the recognition of MODY2 in clinical practice.
Methods: A retrospective analysis was conducted on the clinical data of 13 children diagnosed with MODY2 at the Department of Pediatrics of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology from August 2017 to July 2023.
Results: All 13 MODY2 children had a positive family history of diabetes and were found to have mild fasting hyperglycemia [(6.
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFJ Neuroeng Rehabil
January 2025
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Vita Stråket 12, Floor 4, 41346, Gothenburg, Sweden.
Background: Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke.
Methods: In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week.
J Eat Disord
January 2025
GGZ Rivierduinen Eating Disorders Ursula, Sandifortdreef 19, 2333 AK, Leiden, The Netherlands.
Introduction: Overvaluation of shape and weight is a critical component in understanding and diagnosing eating disorders. While the transdiagnostic model states that overvaluation of shape and weight is the core pathology of all eating disorders, this concept is not a criterion for binge-eating disorder. The lack of recognition of overvaluation of shape and weight may lead to overlooking, and consequently failure to address this construct during treatment.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!