1
|
Tse MP, Dhalla I, Nayyar D. Google star ratings of Canadian hospitals: a nationwide cross-sectional analysis. BMJ Open Qual 2024; 13:e002713. [PMID: 39038856 DOI: 10.1136/bmjoq-2023-002713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 06/29/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Data on patients' self-reported hospital experience can help guide quality improvement. Traditional patient survey programmes are resource intensive, and results are not always publicly accessible. Unsolicited online hospital reviews are an alternative data source; however, the nature of online reviews for Canadian hospitals is unknown. METHODS We conducted a nationwide cross-sectional study of Canadian acute care hospitals with more than 10 Google Reviews during the 2018-2019 fiscal year. We characterised the volume and distribution of Google Reviews of Canadian hospitals, and assessed their correlation with hospital characteristics (teaching status, size, occupancy rate, length of stay, resource utilisation) and Canadian Patient Experience Survey on Inpatient Care (CPES-IC) scores. RESULTS 167 out of 523 (31.9%) acute care hospitals in Canada met the inclusion criteria. Among included hospitals, there was a total of 10 395 Google Reviews and a median of 35 reviews per hospital. The mean Google Star Rating for included hospitals was 2.85 out of 5, with a range of 1.36-4.57. Teaching hospitals had significantly higher mean Google Star Ratings compared with non-teaching hospitals (3.16 vs 2.81, p <0.01). There was a weak, positive correlation between hospitals' Google Star Ratings and CPES-IC 'Overall Hospital Experience' scores (p =0.04), but no significant correlation between Google Star Ratings and other hospital characteristics or subcategories of CPES-IC scores. INTERPRETATION There is significant interhospital variation in patients' self-reported care experiences at Canadian acute care hospitals. Online reviews can serve as a readily accessible source of real-time data for hospitals to monitor and improve the patient experience.
Collapse
Affiliation(s)
| | - Irfan Dhalla
- Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Dhruv Nayyar
- Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Measuring Patient Experience and Patient Satisfaction—How Are We Doing It and Why Does It Matter? A Comparison of European and U.S. American Approaches. Healthcare (Basel) 2023; 11:healthcare11060797. [PMID: 36981454 PMCID: PMC10048416 DOI: 10.3390/healthcare11060797] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/12/2023] [Accepted: 02/28/2023] [Indexed: 03/12/2023] Open
Abstract
(1) Background: Patients’ experiences and satisfaction with their treatment are becoming increasingly important in the context of quality assurance, but the measurement of these parameters is accompanied by several disadvantages such as poor cross-country comparability and methodological problems. The aim of this review is to describe and summarize the process of measuring, publishing, and utilizing patient experience and satisfaction data in countries with highly developed healthcare systems in Europe (Germany, Sweden, Finland, Norway, the United Kingdom) and the USA to identify possible approaches for improvement. (2) Methods: Articles published between 2000 and 2021 that address the topics described were identified. Furthermore, patient feedback in social media and the influence of sociodemographic and hospital characteristics on patient satisfaction and experience were evaluated. (3) Results: The literature reveals that all countries perform well in collecting patient satisfaction and experience data and making them publicly available. However, due to the use of various different questionnaires, comparability of the results is difficult, and consequences drawn from these data remain largely unclear. (4) Conclusions: Surveying patient experience and satisfaction with more unified as well as regularly updated questionnaires would be helpful to eliminate some of the described problems. Additionally, social media platforms must be considered as an increasingly important source to expand the range of patient feedback.
Collapse
|
3
|
Zitek T, Bui J, Day C, Ecoff S, Patel B. A cross-sectional analysis of Yelp and Google reviews of hospitals in the United States. J Am Coll Emerg Physicians Open 2023; 4:e12913. [PMID: 36852191 PMCID: PMC9960977 DOI: 10.1002/emp2.12913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
Abstract
Objective Patient satisfaction is now an important metric in emergency medicine, but the means by which satisfaction is assessed is evolving. We sought to examine hospital ratings on Google and Yelp as compared to those on Medicare's Care Compare (CC) and to determine if certain hospital characteristics are associated with crowdsourced ratings. Methods We performed a cross-sectional analysis of hospital ratings on Google and Yelp as compared to those on CC using data collected between July 8 and August 2, 2021. For each hospital, we recorded the CC ratings, Yelp ratings, Google ratings, and each hospital's characteristics. Using multivariable linear regression, we assessed for associations between hospital characteristics and crowdsourced ratings. We calculated Spearman's correlation coefficients for CC ratings versus crowdsourced ratings. Results Among 3000 analyzed hospitals, the median hospital ratings on Yelp and Google were 2.5 stars (interquartile ratio [IQR], 2-3) and 3 stars (IQR, 2.7-3.5), respectively. The median number of Yelp and Google reviews per hospital was 13 and 150, respectively. The correlation coefficients for Yelp and Google ratings with CC's overall star ratings were 0.19 and 0.20, respectively. For Yelp and Google ratings with CC's patient survey ratings, correlation coefficients were 0.26 and 0.22, respectively. On multivariable analysis, critical access hospitals had 0.22 (95% confidence interval [CI], 0.14-0.30) more Google stars and hospitals in the West had 0.12 (95% CI, 0.05-0.18) more Google stars than references standard hospitals. Conclusion Patients use Google more frequently than Yelp to review hospitals. Median UnS hospital ratings on Yelp and Google are 2.5 and 3 stars, respectively. Crowdsourced reviews weakly correlate with CC ratings. Critical access hospitals and hospitals in the West have higher crowdsourced ratings.
Collapse
Affiliation(s)
- Tony Zitek
- Department of Emergency MedicineMount Sinai Medical CenterMiami BeachFloridaUSA,Herbert Wertheim College of Medicine at Florida International UniversityMiamiFloridaUSA
| | - Joseph Bui
- Herbert Wertheim College of Medicine at Florida International UniversityMiamiFloridaUSA
| | - Christopher Day
- Herbert Wertheim College of Medicine at Florida International UniversityMiamiFloridaUSA
| | - Sara Ecoff
- Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic MedicineFort LauderdaleFloridaUSA
| | - Brijesh Patel
- Department of Emergency MedicineMount Sinai Medical CenterMiami BeachFloridaUSA
| |
Collapse
|
4
|
Quality Measure Concepts for Inpatient Rehabilitation That Are Best Understood From the Patient's Perspective. Rehabil Nurs 2022; 47:210-219. [PMID: 36002927 DOI: 10.1097/rnj.0000000000000385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The aim of this study was to identify inpatient rehabilitation quality-of-care concepts that are best understood from the patient perspective. DESIGN We conducted 12 focus groups with 95 former patients, caregivers, and rehabilitation clinicians and asked them to describe high-quality inpatient rehabilitation care. METHODS We independently reviewed the focus group transcripts and then used an iterative process to identify the quality measure concepts identified by participants. RESULTS Based on participants' comments, we identified 18 quality measure concepts: respect and dignity, clinician communication with patient, clinician communication with family, organizational culture, clinician engagement with patient, clinician engagement with family, rehabilitation goals, staff expertise, responsiveness, patient safety, physical environment, care coordination, discharge planning, patient and family education, peer support, symptom management (pain, anxiety, fatigue, sadness), sleep, and functioning. CLINICAL RELEVANCE TO THE PRACTICE OF REHABILITATION NURSING Rehabilitation nurses should be aware of the quality-of-care issues that are important to patients and their caregivers. CONCLUSION Important patient-reported domains of quality of care include interpersonal relationships, patient and family engagement, care planning and delivery, access to support, and quality of life.
Collapse
|
5
|
Ramasubramanian H, Joshi S, Krishnan R. Wisdom of the Experts Versus Opinions of the Crowd in Hospital Quality Ratings: Analysis of Hospital Compare Star Ratings and Google Star Ratings. J Med Internet Res 2022; 24:e34030. [PMID: 35881418 PMCID: PMC9364164 DOI: 10.2196/34030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 05/08/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Popular web-based portals provide free and convenient access to user-generated hospital quality reviews. The Centers for Medicare & Medicaid Services (CMS) also publishes Hospital Compare Star Ratings (HCSR), a comprehensive expert rating of US hospital quality that aggregates multiple measures of quality. CMS revised the HCSR methods in 2021. It is important to analyze the degree to which web-based ratings reflect expert measures of hospital quality because easily accessible, crowdsourced hospital ratings influence consumers’ hospital choices. Objective This study aims to assess the association between web-based, Google hospital quality ratings that reflect the opinions of the crowd and HCSR representing the wisdom of the experts, as well as the changes in these associations following the 2021 revision of the CMS rating system. Methods We extracted Google star ratings using the Application Programming Interface in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from the CMS Hospital Compare website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by Hospital Compare. Fractional response models were used to estimate the association between Google star ratings and HCSR as well as individual components of quality (n=2619). Results The Google star ratings are statistically associated with HCSR (P<.001) after controlling for hospital-level effects; however, they are not associated with clinical components of HCSR that require medical expertise for evaluation such as safety of care (P=.30) or readmission (P=.52). The revised CMS rating system ameliorates previous partial inconsistencies in the association between Google star ratings and quality component scores of HCSR. Conclusions Crowdsourced Google star hospital ratings are informative regarding expert CMS overall hospital quality ratings and individual quality components that are easier for patients to evaluate. Improvements in hospital quality metrics that require expertise to assess, such as safety of care and readmission, may not lead to improved Google star ratings. Hospitals can benefit from using crowdsourced ratings as timely and easily available indicators of their quality performance while recognizing their limitations and biases.
Collapse
Affiliation(s)
- Hari Ramasubramanian
- Accounting Department, Frankfurt School of Finance and Management, Frankfurt am Main, Germany
| | - Satish Joshi
- College of Agriculture & Natural Resources, Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI, United States
| | - Ranjani Krishnan
- Accounting and Information Systems, Broad College of Business, Michigan State University, East Lansing, MI, United States
| |
Collapse
|
6
|
Derdzakyan N, Pourmand A, Shesser R, Ganguli S, Trevino J. The potential use of Google reviews to assess patient satisfaction in the emergency department. Am J Emerg Med 2021; 52:110-113. [PMID: 34920391 DOI: 10.1016/j.ajem.2021.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 10/19/2022] Open
Affiliation(s)
- Nicole Derdzakyan
- Department of Emergency Medicine, George Washington University School of Medicine & Sciences, 2120 L St NW, Suite 450, Washington, DC 20037, United States of America
| | - Ali Pourmand
- Department of Emergency Medicine, George Washington University School of Medicine & Sciences, 2120 L St NW, Suite 450, Washington, DC 20037, United States of America
| | - Robert Shesser
- Department of Emergency Medicine, George Washington University School of Medicine & Sciences, 2120 L St NW, Suite 450, Washington, DC 20037, United States of America
| | - Sangrag Ganguli
- Department of Emergency Medicine, George Washington University School of Medicine & Sciences, 2120 L St NW, Suite 450, Washington, DC 20037, United States of America
| | - Jesus Trevino
- Department of Emergency Medicine, George Washington University School of Medicine & Sciences, 2120 L St NW, Suite 450, Washington, DC 20037, United States of America.
| |
Collapse
|
7
|
Rahim AIA, Ibrahim MI, Chua SL, Musa KI. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare (Basel) 2021; 9:1679. [PMID: 34946405 PMCID: PMC8701188 DOI: 10.3390/healthcare9121679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 02/05/2023] Open
Abstract
While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital's Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.
Collapse
Affiliation(s)
- Afiq Izzudin A. Rahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Mohd Ismail Ibrahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Sook-Ling Chua
- Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| |
Collapse
|
8
|
Patient Satisfaction Determinants of Inpatient Healthcare. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111337. [PMID: 34769856 PMCID: PMC8582779 DOI: 10.3390/ijerph182111337] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/26/2021] [Accepted: 10/12/2021] [Indexed: 12/19/2022]
Abstract
The aim of the study was to analyse and evaluate the determinants influencing the overall satisfaction of patients with inpatient healthcare in the conditions of the Czech Republic. A total of the 1425 patients, who experienced hospitalisation and agreed to participate, were questioned in the study. A research questionnaire was used to obtain data on satisfaction with hospitalisation. The subject of the research consisted of the indicators related to the following factors: (i) satisfaction with the hospital, clinic, room and meals; (ii) satisfaction with medical staff-nurses, physician expertise and other staff; (iii) the quality of the treatment provided; (iv) satisfaction with leaving the hospital. The formulated statistical hypotheses were evaluated through structural equation modelling. The results of the analyses brought interesting findings. Satisfaction with medical staff is the most significant factor which has a positive effect on satisfaction with hospitalisation. Physician expertise (with trust and good communication skills) is more important for patients than satisfaction with nurses or other staff. The results obtained from the study represent valuable information for policymakers, regional healthcare plans, as well as for managers of hospitals.
Collapse
|
9
|
Rahim AIA, Ibrahim MI, Musa KI, Chua SL, Yaacob NM. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare (Basel) 2021; 9:1369. [PMID: 34683050 PMCID: PMC8544585 DOI: 10.3390/healthcare9101369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/27/2021] [Accepted: 10/12/2021] [Indexed: 02/05/2023] Open
Abstract
Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study's objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.
Collapse
Affiliation(s)
- Afiq Izzudin A. Rahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Mohd Ismail Ibrahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Sook-Ling Chua
- Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia;
| | - Najib Majdi Yaacob
- Unit of Biostatistics and Research Methodology, Health Campus, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia;
| |
Collapse
|
10
|
A. Rahim AI, Ibrahim MI, Musa KI, Chua SL, Yaacob NM. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9912. [PMID: 34574835 PMCID: PMC8466628 DOI: 10.3390/ijerph18189912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 02/05/2023]
Abstract
Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals' Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.
Collapse
Affiliation(s)
- Afiq Izzudin A. Rahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Mohd Ismail Ibrahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Sook-Ling Chua
- Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia;
| | - Najib Majdi Yaacob
- Units of Biostatistics and Research Methodology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia;
| |
Collapse
|
11
|
A. Rahim AI, Ibrahim MI, Musa KI, Chua SL. Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147454. [PMID: 34299905 PMCID: PMC8306730 DOI: 10.3390/ijerph18147454] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 02/05/2023]
Abstract
Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time.
Collapse
Affiliation(s)
- Afiq Izzudin A. Rahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Mohd Ismail Ibrahim
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
- Correspondence: ; Tel.: +60-97676621; Fax: +60-97653370
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia; (A.I.A.R.); (K.I.M.)
| | - Sook-Ling Chua
- Faculty of Computing and Informatics, Persiaran Multimedia, Multimedia University, Cyberjaya 63100, Selangor, Malaysia;
| |
Collapse
|
12
|
Suárez JL, García S, Herrera F. Ordinal regression with explainable distance metric learning based on ordered sequences. Mach Learn 2021. [DOI: 10.1007/s10994-021-06010-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|