Validation and adaptation of the hospital consumer assessment of healthcare providers and systems in Arabic context: Evidence from Saudi Arabia

Improving patient experience by implementing an organisational culture model

A satisfactory patient care culture model can help improve most patients' quality of care in a hospital. This study aims to improve patients' experiences (PX) by implementing a culture model at King Abdul-Aziz Armed Forces Hospital in Dhahran, Saudi Arabia. To achieve the research aim, a set of interventions were implemented that included a patient and family advisory council, empathy training, recognition of the PX, leadership-patient interviews, PX champions and quality improvement. These interventions were further measured using the Hospital Consumer Assessment of Healthcare Providers and Systems survey in the inpatient, outpatient and emergency departments. The improvement project was conducted in 2020, focusing mainly on transforming the culture and launching activities targeting specific touchpoints identified as priority areas. After making these changes, the hospital saw improvements in all patient relationships, with an average score across all dimensions collectively increasing by more than 4%. The quality improvement project using the PX culture model approach demonstrated significant improvements. In addition, employee involvement in patient care has become a significant factor in improving the quality of care. The critical elements for improving the PX and culture included recognising staff and creating networks across the system through effective leadership, employee engagement and engagement of patients and their families.

Experiences of Palestinian patients with hospital services: a mixed-methods study

BACKGROUND

This study aimed at assessing patient experiences with hospital services and key factors associated with better experiences.

METHODS

The study design is cross-sectional supported by qualitative interviews. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) was used as data collection instrument. A convenience sample of 391 volunteers aged ≥18 years participated in this study. Qualitative interviews were conducted with patients and healthcare providers to further enrich and explain the quantitative results.

RESULTS

The average age of the sample was 41.34, SD (16.4), range (18-87). Females represented 61.9% of the whole sample. Almost 75% were from the West Bank and 25% from the Gaza Strip. The majority of respondents reported that doctors and nurses were respectful, listened to them and explained clearly to them always or most of the time. Only 29.4% of respondents were given written information about the symptoms they may have after discharge from the hospital. Factors that were independently associated with higher scores on the HCAHPS scale were; being females (coef: 0.87, 95% CI: 0.157 to 1.587, p=0.017), being healthy (coef: -1.58, 95% CI: -2.458 to -0.706, p=0.000), being with high financial status (coef: 1.51, 95% CI: 0.437 to 2.582, p=0.006), being from Gaza (coef: 1.45, 95% CI: 0.484 to 2.408, p=0.003) and who visited hospitals outside of Palestine (coef: 3.37, 95% CI: 1.812 to 4.934, p=0.000). Overcrowding, weak organisational and management processes, and inadequate supply of goods, medicines, and equipment were reported factors impeding quality services via in-depth interviews.

CONCLUSIONS

The overall hospital experiences of Palestinian patients were moderate but varied significantly based on patients' factors such as sex, health status, financial status and residency as well as by hospital type. Hospitals in Palestine should invest more in improving their services including communications with patients, the hospital environment and communication with patients.

Privatization, Corporatization, and Public-Private Partnership in the Kingdom of Saudi Arabia

There are different governmental reasons and technologies for seeking public-private partnerships throughout the developed countries. Significant motives have been to improve efficiency and risk distribution in comparison to traditional financing techniques and to lessen budget and borrowing limits. In this study, the movement toward privatization enhancing the efficiency of MoH's hospitals has been assessed using Data Envelopment Analysis (DEA) from 1979 to 2020. Moreover, the efficiencies of the individual hospital were estimated through the DEA model, which includes: scale efficiency (SE), pure technical efficiency (PTE), and overall technical efficiency (OTE). In addition, factor affecting such efficiencies was analyzed through Tobit regression. The VRS results suggest that the numbers of hospitals benefiting from the complete corporatized phase are greater than those benefiting from the last phase (and vice versa in the case of DRS). The variance between inefficient hospitals in the less corporatized phase (2000-2020) was more prominent than in the fully corporatized phase (1988-1999). In conclusion, fully corporatized hospitals (on average) achieve relatively better overall efficiency. It is recommended that additional corporatization might be stimulated by a standardized set of performance measures, which cover both the quality criteria and economic efficiency measurements from a healthcare perspective.

Inpatient Experience Surveys and Overall Hospital Rating: A Correlation Analysis for a Private Hospital in Central America

Objectives: To validate the use of a patient experience survey composed of the Spanish version of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey and of questions locally created in our institution, a private hospital in Panama. To identify which domains of our patient experience survey have the strongest association with the overall inpatient hospital experience rating. Methods: A retrospective analysis of 1619 electronic survey results of adult inpatient experience data was performed using exploratory factor and Pearson correlation analyses. The data was collected between March 2018 and December 2019 from a web-survey application, accessible through a link sent by email after hospital discharge. Results: Results from analyses showed that questions grouping occurred under the same domains as the HCAHPS English version (factor loading 0.456 to 0.918) and that a new domain named administrative processes (0.818 to 0.846) arose for the newly created questions. Cronbach's alpha values ranged from 0.32 to 0.85, with an α of0.65 for the new local questions. Correlation analyses were higher for the domains of communication from nurses (0.670) and administrative processes (0.618). Conclusions: Our investigation validates the use of our patient experience survey, composed of questions from the Spanish version of the HCAHPS survey and locally created ones. In our institution, communication from nurses is most correlated with the inpatient hospital experience rating. Hospital administrative processes were also strongly associated with the overall inpatient hospital rating, and we argue that these later aspects should be addressed to improve the patient experience.

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

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.

Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

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.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

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.

Development and Validation of Questionnaire to Assess Knowledge about Cervical Cancer among Women Aged 20 to 65 years in Oman

OBJECTIVE

This study aimed to develop and validate a questionnaire to assess awareness of cervical cancer, its risk factors, and methods of prevention among Arabic-speaking women aged 20 years and over.

METHODS

The study took place at primary healthcare institutions, Al Buraimi Governorate, Oman, between November 2018 to February 2019. In phase I, seventy items concerning cervical cancer and its prevention were generated through a literature review. In phase 2, the questionnaire was validated through calculating the content validity index (CVI) for both item level (I-CVI) and the scale level (S-CVI), in this phase a shortened English questionnaire of 55 items was formed, then rigorously translated to the Arabic language in phase III. The questionnaire was tested for reliability in two stages: A pilot and a large field test in phase IV.

RESULTS

A total of 55 out of 70 items formed the final version of the questionnaire. The final instrument had an S-CVI/Ave of 0.92. The questionnaire called the Knowledge in Cervical Cancer and Prevention Methods 55-items (KCCPM-55). The Cronbach alpha coefficient was 0.940 for the whole questionnaire, and ranged between 0.57 to 0.93 for each of the domains. Test-retest reliability was examined in a subsample of the total participants sample (r = 0.769, p < 0.001).

CONCLUSION

The KCCPM-55 has been successfully developed in the Arabic language and found to be a valid and reliable instrument for assessing the level of knowledge about cervical cancer and prevention methods among women aged 20 to 65 years in Oman.

Translation, Cross-Cultural Adaptation, and Validation of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) into the Malay Language

BACKGROUND

Patient feedback is an important tool in assessing health system quality. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) was developed in 2006 as a standardized instrument to assess patient perceptions in the United States of America. This study aimed to translate and validate the HCAHPS questionnaire into the Malay language in order to assess patient perceptions of health services in Malaysia.

METHODS

The original HCAPHS in English was translated into Malay based on the established guideline. The content validation involved an expert panel of 10 members, including patients. The face validation pilot testing of the HCAHPS-Malay version was conducted among 10 discharged patients. The exploratory factor analysis (EFA) used principal axis factor, and varimax rotation was established based on a cross-sectional study conducted among 200 discharged patients from Hospital Universiti Sains Malaysia (Hospital USM).

RESULTS

The overall content validity index was 0.87, and the universal face validity index was 0.82. From the EFA, the factor loading value ranged from 0.652 to 0.961 within nine domains. The internal consistency reliability with Cronbach's alpha was 0.844.

CONCLUSION

The HCAHPS-Malay is a reliable and valid tool to determine patients' perception of healthcare services among inpatients in Hospital USM based on the content and face validation result together with a good construct validity and excellent absolute reliability. Further testing on HCAHPS-Malay version in other settings in Malaysia needs to be done for cross-validation.