Background: Patient and public involvement (PPI) continues to develop as a central policy agenda in health care. The patient voice is seen as relevant, informative and can drive service improvement. However, critical exploration of PPI's role within monitoring and informing medical performance processes remains limited.
Objective: To explore and evaluate the contribution of PPI in medical performance processes to understand its extent, purpose and process.
Search Strategy: The electronic databases PubMed, PsycINFO and Google Scholar were systematically searched for studies published between 2004 and 2018.
Inclusion Criteria: Studies involving doctors and patients and all forms of patient input (eg, patient feedback) associated with medical performance were included.
Data Extraction And Synthesis: Using an inductive approach to analysis and synthesis, a coding framework was developed which was structured around three key themes: issues that shape PPI in medical performance processes; mechanisms for PPI; and the potential impacts of PPI on medical performance processes.
Main Results: From 4772 studies, 48 articles (from 10 countries) met the inclusion criteria. Findings suggest that the extent of PPI in medical performance processes globally is highly variable and is primarily achieved through providing patient feedback or complaints. The emerging evidence suggests that PPI can encourage improvements in the quality of patient care, enable professional development and promote professionalism.
Discussion And Conclusions: Developing more innovative methods of PPI beyond patient feedback and complaints may help revolutionize the practice of PPI into a collaborative partnership, facilitating the development of proactive relationships between the medical profession, patients and the public.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433319 | PMC |
| http://dx.doi.org/10.1111/hex.12852 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Association of childhood maltreatment and adverse lifetime experiences with post-injury psychopathology: evidence from the China Severe Trauma Cohort.
BMC Med
January 2025
Med-X Center for Informatics, Sichuan University, Chengdu, China.
Background: Adverse life experiences have been associated with increased susceptibilities to psychopathology in later life. However, their impact on psychological responses following physical trauma remains largely unexplored.
Methods: Based on the China Severe Trauma Cohort, we conducted a cohort study of 2937 patients who were admitted to the Trauma Medical Center of West China Hospital between June 2020 and August 2023.
Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.
BMC Med Inform Decis Mak
January 2025
Department of Biomedical Engineering, National Defense Medical Center, Taiwan, No.161, Sec.6, Minchiuan E. Rd., Neihu Dist, Taipei, 11490, Taiwan.
Background: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.
View Article and Find Full Text PDFMachine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.
Alzheimers Res Ther
January 2025
Department of Neurology, Faculty of Medicine, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu, Oita, 879-5593, Japan.
Background: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatment. This study developed machine learning models to classify positron emission tomography (PET) Aβ-positivity in participants with preclinical and prodromal AD using data accessible to primary care physicians.
View Article and Find Full Text PDFHealth professionals' willingness to share responsibility and strengthen interprofessional collaboration: a cross-sectional survey.
BMC Med Educ
January 2025
Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland.
Background: Doctors' unwillingness to share responsibility acts as a major barrier to interprofessional collaboration (IPC). Educating both doctors and allied health professionals in taking on or relinquishing responsibility could enhance IPC. Yet there is no evidence that these educational efforts increase IPC willingness.
View Article and Find Full Text PDFDeveloping a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.
J Transl Med
January 2025
Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, No 134 Dongjie Street, Gulou District, Fuzhou, Fujian, 350001, People's Republic of China.
Objectives: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients.
Methods: This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings.
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!