Systematic review showed measures of individual burden of osteoarthritis poorly capture the patient experience

2013 ◽  
Vol 66 (8) ◽  
pp. 826-837 ◽  
Author(s):  
Lucy Busija ◽  
Richard H. Osborne ◽  
Carol Roberts ◽  
Rachelle Buchbinder
Keyword(s):  
Systematic Review ◽  
Patient Experience

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

Examining the Business Case for Patient Experience: A Systematic Review

2021 ◽  
Vol 66 (3) ◽  
pp. 200-224
Author(s):  
Denise D. Quigley ◽  
Kerry Reynolds ◽  
Stephanie Dellva ◽  
Rebecca Anhang Price
Keyword(s):  
Systematic Review ◽  
Patient Experience ◽  
Business Case

scholarly journals A Systematic Literature Review of Empirical Studies on Decentralized Nursing Stations

2018 ◽  
Vol 12 (1) ◽  
pp. 44-68 ◽  
Author(s):  
Lindsey Fay ◽  
Hui Cai ◽  
Kevin Real
Keyword(s):  
Systematic Review ◽  
Patient Care ◽  
Patient Experience ◽  
Negative Impact ◽  
Evaluation Framework ◽  
Positive Trend ◽  
Review Of Literature ◽  
Related Factors ◽  
Healthcare Design ◽  
The Past

Objectives: The objective of this systematic review of literature was to critically evaluate peer-reviewed evidence regarding the effectiveness of decentralized nurse stations (DNSs). Background: The DNS has become an important topic in healthcare design research and practice over the past decade with aims of improving staff efficiency and patient experience. Research has shown to be inconclusive, with studies reporting an assortment of mixed findings. Method: A systematic review of literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses search process of electronic databases, citation tracking, and manual searches of references. All authors evaluated the studies independently. Studies included were empirical, peer-reviewed investigations of DNS in hospitals over the past 15 years. Each study was evaluated using an accepted healthcare design evaluation framework. Results: Over 200 studies were identified. After exclusions, 21 studies published since 2003 were available for full evaluation. Key findings from this review include (a) there is a positive trend toward patient experience in units with DNS, (b) nursing teamwork was perceived to decline in units with DNS, (c) methodological issues may be responsible for the mixed and inconsistent findings, and (d) there is no consistent categorization of nurse station typology or standard definition for DNS. Conclusions: Based on the evaluation framework, DNS are supportive of the patient experience yet have a negative impact on nursing teamwork. Higher quality studies are needed to classify specific typologies of DNS and account for elements such as patient care models, communication, visibility, and other patient care–related factors.

scholarly journals Racial and socioeconomic disparities in patient experience of clinician empathy: a protocol for systematic review and meta-analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e034247 ◽  
Author(s):  
Brian W Roberts ◽  
Christian J Trzeciak ◽  
Nitin K Puri ◽  
Anthony J Mazzarelli ◽  
Stephen Trzeciak
Keyword(s):  
Systematic Review ◽  
Patient Experience ◽  
Meta Analysis ◽  
Socioeconomic Disparities ◽  
Future Research ◽  
Individual Patient Level ◽  
Patient Reported ◽  
Original Manuscript ◽  
Methodological Guidelines ◽  
Care Measure

IntroductionClinician empathy is a vital component of high-quality healthcare. Healthcare disparities may reflect a societal lack of empathy for disadvantaged persons in general, and recent research suggests that socioeconomic disparities exist in patient satisfaction with clinicians. However, it is currently unclear if there are disparities in patient experience of empathy from clinicians. Our objective is to systematically analyse the scientific literature to test the hypothesis that racial and socioeconomic status (SES) disparities exist in patient-reported experience of clinician empathy.Methods and analysisIn accordance with published methodological guidelines for conducting a systematic review, we will analyse studies reporting patient assessment of clinician empathy using the Consultation and Relational Empathy (CARE) measure, which to date is the most commonly used and well-validated methodology in clinical research for measuring clinician empathy from the patient’s perspective. We will use a standardised data collection template and assess study quality (risk of bias) using the Newcastle-Ottawa Scale. We will abstract data for the CARE measure stratified by race and SES, and we will contact the corresponding authors to obtain stratified data by race/SES if not reported in the original manuscript. Where appropriate, we will pool the data and perform quantitative meta-analysis to test if non-white (compared to white) patients and low SES (compared to high SES) patients report lower scores for clinician empathy.Ethics and disseminationNo individual patient-level data will be collected and thus the proposed systematic review does not require ethical approval. This systematic review will test if racial and SES differences exist in patient experience of clinician empathy, and will inform future research to help promote healthcare equity.PROSPERO registration numberCRD42019142809.

scholarly journals A systematic review of dimensions evaluating patient experience in chronic illness

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Bastien Forestier ◽  
Emmanuelle Anthoine ◽  
Ziad Reguiai ◽  
Cécile Fohrer ◽  
Myriam Blanchin
Keyword(s):  
Systematic Review ◽  
Chronic Illness ◽  
Patient Experience

scholarly journals ‘They do not care how much you know until they know how much you care’: a qualitative meta-synthesis of patient experience in the emergency department

2019 ◽  
Vol 36 (6) ◽  
pp. 355-363 ◽  
Author(s):  
Blair Graham ◽  
Ruth Endacott ◽  
Jason E Smith ◽  
Jos M Latour
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Lived Experience ◽  
Conceptual Model ◽  
Patient Experience ◽  
Meta Analysis ◽  
Clinical Effectiveness ◽  
Qualitative Studies ◽  
Care Providers ◽  
Qualitative Approaches

BackgroundPatient experience is positively associated with both clinical effectiveness and patient safety and should be a priority for emergency care providers. While both quantitative and qualitative approaches can be used to evaluate patient experience in the emergency department (ED), the latter is well aligned to develop a detailed understanding of features influencing the lived experience of ED patients. This study aimed to systematically review the literature of qualitative studies to identify determinants of adult patient experience in the ED.MethodsA Preferred Reporting Items for Systematic review and Meta-Analysis compliant systematic review was conducted using PubMed, CINAHL, EMBASE, BNI and bibliography searches to identify qualitative studies exploring patient experiences in ED published in English between 1997 and 2018. Quality assessment was conducted using the Critical Appraisal Skills Programme checklist. Descriptive text and quotations relating to patient experience were extracted from included studies and a meta-synthesis conducted using thematic analysis.ResultsA total of 625 records were screened from which 40 studies underwent full review and 22 were included. Results were coded by two researchers (BG and JML). Meta-synthesis identified 198 discrete units of analysis which were clustered around five analytical themes. These were based on the perceived ‘needs’ of patients visiting the ED and were defined as communication, emotional, competent care, physical/environmental and waiting needs. Findings were translated into a conceptual model for optimising patient experience in the ED.ConclusionThis meta-synthesis provides a framework for understanding the determinants of patient experience in the ED. The resulting conceptual model and recommendations may have the potential to directly inform practice and improve the patient experience.

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