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Social Media Feedback as a Mirror of Patient Experience in Healthcare
Management
Mariatul Liza Meor Gheda
1
, Chiu Qian Fei
2
, Nor Aslina Abd Jalil
3
, Nurul Fairuz Buang
4
, Sharifah
Rosfashida Syed Abd Latif
5
1,3,4,5
Faculty of Technology and Applied Sciences, Open University Malaysia, Malaysia
2
Halliburton Completions Tool (S) Pte Ltd, 11 Tuas South Ave 12, Singapore 637131
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100148
Received: 15 November 2025; Accepted: 22 November 2025; Published: 03 December 2025
ABSTRACT
Digital connectivity has become a central channel for patients to share their experiences with healthcare services,
particularly through platforms such as Facebook, Google Reviews, and X. These online spaces offer fast, visible
feedback that mirrors long-standing patient-experience concerns, especially around communication, empathy,
waiting times, and staff responsiveness. This study examines digital patient feedback within Malaysian
healthcare by analysing sentiment patterns from 2,000 social media posts and exploring how healthcare staff
interpret and respond to such feedback. A convergent mixed-methods design was used, combining sentiment
analysis with 28 semi-structured interviews involving administrators, project managers, frontline staff, and
customer-service personnel. The sentiment analysis showed that patient care and communication attracted the
highest share of positive posts, with favourable narratives highlighting empathy and professionalism. Staff
responsiveness recorded the most negative sentiment, driven by delays and difficulties obtaining assistance.
Monthly trends and statistical tests confirmed that sentiment varied across time, reflecting operational pressures
and seasonal workload patterns. The interviews revealed that while digital feedback is valued for its immediacy,
organisations struggle with high volumes of unstructured comments, limited analytic tools, inconsistent review
processes, and uncertainty about who holds responsibility for acting on online narratives. Participants noted that
acknowledging feedback strengthens trust, while ignoring concerns undermines confidence and discourages
future engagement. Challenges included resource constraints, manual workflows, skills gaps, and cultural
resistance to adopting digital practices. Together, the findings show that social media provides meaningful
insight into patient expectations and service bottlenecks, but its value depends on organisational readiness,
structured processes, and a culture that supports the use of patient voice in decision-making. The study highlights
the relevance of integrating digital feedback into routine quality-improvement and project-management systems
to strengthen healthcare responsiveness in Malaysia.
Keywords: social media feedback, healthcare management, patient satisfaction, service quality, digital
engagement.
INTRODUCTION
Digital connectivity has reshaped communication between patients and healthcare providers. Platforms such as
Facebook, Google Reviews, X, and Instagram have become open forums where individuals share experiences,
expectations, and concerns. As noted by Lagu et al. (2015), these public review spaces now function alongside
surveys and complaint systems, creating a faster and more visible feedback channel that managers monitor
closely for quality and reputational signals. Verhoef et al. (2014) similarly observed that this shift places social
media within mainstream feedback systems rather than on the periphery.
From the patient perspective, online activity reflects a wider shift towards digital health engagement. Social
media is frequently used to gather information, compare services, and express concerns in near real time, as
reported by Chen and Wang (2021) and Farsi et al. (2021). Studies of hospital reviews show that patients tend
to focus on communication, waiting times, staff conduct, and reliability instead of technical clinical details, a
pattern highlighted by Chakraborty and Church (2021) and Seltzer et al. (2022). Later studies linking online
sentiment with conventional patient-experience indicators, including Auyappan and Coffin (2023) and Rahim et
al. (2021a), reinforce the validity of these digital narratives.
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Organisational research continues to emphasise the value of patient feedback in shaping improvement efforts.
Berger et al. (2020) argued that when narratives are systematically analysed, they can inform changes to care
pathways and communication practices, while Baines et al. (2021) and Lloyd et al. (2023) noted that positive
comments strengthen morale and clarify what matters to patients. Social media expands this environment by
placing feedback in a public arena, prompting questions about how organisations interpret and incorporate digital
commentary within existing governance and project-management processes, a concern raised by Walsh et al.
(2022).
Reviews of social media use in healthcare indicate that these platforms bring both benefits and challenges. Online
communities can support patient engagement and highlight emerging issues, as shown by Anawade et al. (2024),
while Ukoha and Stranieri (2019) observed that organisations still struggle with selection bias, unstructured text,
and reputational risk. Concerns about how to convert online posts into meaningful insights are common in
settings where workflows and resources are limited, a pattern Walsh et al. (2022) documented across several
health systems. Together, these studies position social media feedback within broader debates on engagement,
communication, and risk.
Malaysia provides a relevant setting for examining these dynamics. Research on public hospitals shows that
Facebook reviews reflect service quality and patient satisfaction trends, with dimensions such as responsiveness
and empathy appearing prominently in online comments (Rahim et al., 2021a, 2021b). Yet, as both Lagu et al.
(2015) and Walsh et al. (2022) observed, relatively few studies examine how managers and staff in Malaysian
facilities interpret and respond to these digital insights. Addressing this gap, the present study focuses on two
aims: identifying sentiment patterns in social media posts and exploring how healthcare administrators, project
managers, and frontline staff make sense of digital feedback when engaging with patients and improving service
delivery.
LITERATURE REVIEW
Social media as a feedback channel in healthcare
Early work on digital rating and review sites positioned social media as an emerging, supplementary source of
information about healthcare quality, rather than a core data stream (Verhoef et al., 2014; Lagu et al., 2015).
Since then, studies have repeatedly shown that patients use platforms such as Facebook and specialist review
sites to share detailed experiences, but much of this work remains descriptive, mapping what is posted rather
than examining how organisations act on it (Chakraborty & Church, 2021; Zunic et al., 2019).
Chen and Wang (2021) and Farsi et al. (2021) both reported extensive use of social media for health-related
purposes, yet their reviews group together a wide range of activities such as information seeking, peer support,
and organisational feedback. By merging these different purposes, the distinction between patient-experience
commentary and other types of online interaction becomes blurred, which limits the ability to determine how
feedback posts specifically inform managerial decisions. More focused examinations, including Auyappan and
Coffin’s (2023) work on digital reviews in healthcare, recognise that online narratives influence patient choice
and institutional reputation. Even so, these studies stop short of offering a clear operational model that explains
how healthcare managers should interpret, prioritise, and incorporate digital feedback into project planning or
service improvement processes.
In the Malaysian context, Rahim et al. (2021a, 2021b) advanced the field by demonstrating that Facebook
reviews correlate with patient satisfaction and SERVQUAL dimensions in public hospitals. While valuable,
these studies still treat reviews as an external measurement instrument, rather than tracing how such information
is translated into specific operational responses, governance changes, or project-management decisions. This
gap between “measurement” and “management” is one of the key areas that the present study addresses.
Patient experience, satisfaction, and the nature of online feedback
Across multiple settings, patient-experience research highlights that what patients choose to share online centres
more on interpersonal and process-related issues than on technical clinical quality (Berger et al., 2020;
Chakraborty & Church, 2021). Positive reviews typically emphasise empathy, clear explanations, and respectful
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treatment, whereas negative comments cluster around waiting times, poor communication, confusing processes,
and perceived indifference (Chakraborty & Church, 2021; Seltzer et al., 2022). These patterns are repeatedly
confirmed in both traditional feedback systems and social-media-based studies, suggesting that online platforms
amplify but do not fundamentally change the core concerns of patients (Berger et al., 2020; Lloyd et al., 2023).
Yet the literature tends to romanticise the “voice of the patient” without adequately addressing whose voices are
actually heard. Lloyd et al. (2023) pointed out that positive feedback is often under-analysed in formal quality-
improvement work, despite its strong emotional and motivational value for staff, while Murray et al. (2024)
argued that patient-reported experiences on social media remain unevenly distributed, with over-representation
of digitally engaged groups. Walsh et al. (2022) raised similar concerns regarding selection bias and digital
divides, but they stopped short of proposing concrete strategies for combining digital feedback with more
representative survey or complaints data.
Within Malaysia, Rahim et al. (2021a, 2021b) showed that social media ratings and sentiment align with
SERVQUAL-based assessments of responsiveness, reliability, and assurance. Even so, their cross-sectional
designs mean it is not possible to see whether incorporating online feedback leads to measurable improvements
over time. Han et al. (2023) examined government-led patient feedback initiatives and highlighted how formal
mechanisms can support continuous monitoring of experience and service quality, yet they did not integrate
social media into these systems. The present study, by directly aligning social-media comments with themes
well established in patient-experience literature and with reported organisational responses, responds to this gap
in longitudinal and action-oriented evidence.
Organisational use of online feedback and stakeholder dynamics
Several studies have explored how health organisations use patient feedback in both traditional and digital forms
to support service improvement, yet consistent challenges remain. Berger et al. (2020) demonstrated that patient
comments can lead to meaningful enhancements when they are translated into specific interventions. At the same
time, their work highlighted that many hospitals gather far more feedback than they are able to process or apply
in practice. Evidence from Baines et al. (2021) adds further complexity. Their case study of an acute hospital
placed under special measures showed that incorporating online feedback required significant effort to define
responsibilities, establish clear workflows, and determine who should oversee the interpretation of digital
comments. Staff were frequently unsure whether social media posts would be considered credible by leadership,
illustrating ongoing uncertainty about the status and influence of digital narratives within formal decision-
making structures.
Walsh et al. (2022) extended this organisational view through a scoping review of stakeholder experiences with
social media in health-service design and quality improvement. They reported enthusiasm for the immediacy
and visibility of digital feedback but noted concerns about reputational risk, hostility in online environments,
and the emotional burden of reading unfiltered criticism. Khan et al. (2022) similarly presented social media as
a source of social support and satisfaction for patients, yet their conceptual framework did not fully engage with
the organisational risks and workload implications raised by Baines et al. (2021) and Walsh et al. (2022).
From a strategic perspective, Ukoha and Stranieri (2019) proposed criteria for measuring the value of social
media in healthcare settings, suggesting that platforms should be evaluated against outcomes such as improved
efficiency, quality, and engagement. Their narrative review, however, remained at a conceptual level, and did
not embed these criteria within concrete project-management processes or stakeholder structures. This leaves a
disconnect between theoretical stakeholder models and the daily realities of managers confronted with high
volumes of unstructured digital commentary.
Positive feedback as a resource for organisational learning remains underutilised. Lloyd et al. (2023) and Gallan
et al. (2022, in other sectors) emphasised that positive narratives can strengthen staff morale and clarify what
“good care” looks like from a patient perspective, yet much of the social-media literature continues to prioritise
complaint detection or sentiment polarity (negative vs positive) over more nuanced, asset-based interpretations.
The present study explicitly explores both positive and negative digital sentiment and links these to decisions on
staffing, communication protocols, and service redesign.
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Analytical approaches to social media data in healthcare
The analytical strand of the literature has grown rapidly. Zunic et al. (2019) reviewed sentiment analysis in health
and well-being and concluded that automated methods are valuable for high-volume monitoring but often
oversimplify complex narratives when reduced to polarity labels. Fu et al. (2023), in a scoping review of methods
for analysing health-related social media content, echoed this concern and recommended combining quantitative
text mining with qualitative interpretation to preserve contextual meaning.
Murray et al. (2024) proposed a structured design–acquire–process–model–analyse–visualise framework for
patient-experience data derived from social media, arguing that without such systematic pipelines, organisations
risk ad hoc or selective reading of online comments. Jeong et al. (2022) demonstrated the potential of aspect-
based sentiment analysis to identify nuanced usability issues in healthcare technologies, yet their work was
technology-centric and did not extend into organisational change or project management. Similarly, Anawade et
al. (2024) mapped social media and online communities in healthcare from a broad technological perspective,
but their review focused on opportunities rather than governance or operational integration.
Malaysia-focused studies have begun to use machine learning and text mining on Facebook reviews (Rahim et
al., 2021a, 2021b), illustrating the technical feasibility of sentiment classification and quality-dimension
extraction in local languages and contexts. Even so, these studies remain largely diagnostic. They identify what
patients are saying, but provide limited insight into how these analytic outputs are converted into structured
action plans, responsibilities, or performance indicators at the level of project and service management.
This methodological literature rarely addresses resource constraints and skills gaps in public healthcare systems.
Implementing sophisticated analytics demands not only tools but also data-governance frameworks, analytic
capacity, and cross-functional collaboration between IT teams, clinicians, and managers (Fu et al., 2023; Murray
et al., 2024). The evidence base says comparatively little about these organisational prerequisites, especially in
developing-country contexts.
Reputation, risk, and governance of digital feedback
Social media feedback operates at the intersection of patient experience and organisational reputation. Auyappan
and Coffin (2023) described how online reviews influence perceptions of quality and trust, and Farsi et al. (2021)
warned that highly visible negative posts can escalate rapidly and damage institutional credibility. Lagu et al.
(2015) even framed social media as a potential hospital quality-improvement tool, arguing that ignoring digital
narratives may constitute a missed opportunity for early issue detection. Yet they, too, acknowledged the danger
of reacting impulsively to isolated, high-profile posts rather than to systematically analysed trends.
Walsh et al. (2022) and Ukoha and Stranieri (2019) stressed ethical and governance challenges, including
privacy, anonymity, and the handling of potentially defamatory or misleading information. In practice, many
organisations respond to these concerns by limiting formal engagement with social media, which can
inadvertently widen the gap between patients’ public narratives and internal improvement processes. Rahim et
al. (2021a) showed that Malaysian hospitals can leverage Facebook data in ways that align with accreditation
and quality programmes, yet they did not examine how data-protection regulations or internal risk-management
frameworks are operationalised in doing so.
Stakeholder-focused models, such as those discussed by Khan et al. (2022) and Lloyd et al. (2023), argue that
meaningful engagement requires seeing social media feedback as more than reputational risk. Instead, it should
be treated as one component of a broader learning system that balances transparency, staff wellbeing, and patient
voice. Current literature offers limited concrete guidance on how to design such systems in resource-constrained
settings.
METHODOLOGY
The methodological approach was designed to address the gaps highlighted in the literature, particularly the need
to move beyond descriptive analyses of online feedback and towards an integrated understanding of both what
patients say and how organisations respond (Baines et al., 2021; Walsh et al., 2022). To achieve this, the study
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employed a mixed-methods design that combines large-scale sentiment analysis of social media posts with
qualitative interviews involving healthcare administrators and frontline staff. This approach aligns with
methodological recommendations from recent reviews that advocate pairing computational techniques with
contextual qualitative insights to avoid oversimplification of narratives (Fu et al., 2023; Zunic et al., 2019).
Research Design
A convergent mixed-methods strategy was adopted. Quantitative sentiment analysis provided a macro-level view
of stakeholder perceptions on social media, while qualitative interviews supported deeper exploration of
organisational interpretations and decision-making processes. This structure is consistent with the approaches
suggested by Murray et al. (2024), who emphasised the importance of linking analytic outputs to real-world
managerial contexts through qualitative validation. Combining these methods also responds to calls by Rahim
et al. (2021a, 2021b) for more integrated approaches that connect Facebook review patterns with internal
operational practices in Malaysian hospitals.
Data Sources and Sampling
Social Media Dataset
A dataset of 2,000 social media posts was extracted from Facebook, Google Reviews, and X (formerly Twitter)
pages associated with selected Malaysian healthcare facilities. These platforms were chosen based on evidence
that they are the most active and widely used channels for patient feedback in Malaysia (Rahim et al., 2021a;
Farsi et al., 2021). Reviews spanning a 12-month period were collected to capture seasonal variations and avoid
snapshot bias, addressing concerns raised by Lloyd et al. (2023) and Verhoef et al. (2014) about the volatility of
online comments.
Data collection followed ethical guidelines discussed by Ukoha and Stranieri (2019), ensuring that only publicly
accessible comments were included and that identifying details were removed during preprocessing.
Interview Participants
A total of 28 semi-structured interviews were conducted with healthcare administrators, project managers,
nurses, medical officers, and customer-service personnel. This stakeholder sampling reflects the multi-actor
nature of feedback interpretation described by Walsh et al. (2022) and Khan et al. (2022). Purposive sampling
ensured representation from both clinical and non-clinical domains, supporting triangulation of organisational
practices, as recommended by Berger et al. (2020).
Data Collection Procedures
Extraction and Preparation of Online Feedback
Social media comments were extracted using automated scraping tools compliant with platform policies.
Consistent with methodological guidance by Fu et al. (2023), data preprocessing included removal of duplicates,
filtering of spam-like content, anonymisation, and language standardisation for multilingual posts commonly
encountered in Malaysian contexts.
Sentiment polarity (positive, negative, neutral) and thematic clusters were identified using a combination of
lexicon-based and machine-learning models, an approach aligned with the mixed analytic methods
recommended by Jeong et al. (2022) and Zunic et al. (2019).
Interview Protocol
Interviews were guided by a flexible, semi-structured protocol that allowed participants to elaborate on their
experiences interpreting online feedback, responding to negative reviews, and integrating digital sentiment into
decisions on workflows, staffing, and communication. This design mirrors the exploratory approaches used in
organisational studies by Baines et al. (2021) and Lloyd et al. (2023). Each interview lasted 45–60 minutes and
was audio-recorded with consent.
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Data Analysis
Sentiment Analysis
Sentiment classification used a hybrid model consisting of a Malay–English lexicon and supervised machine-
learning classifiers trained on manually labelled data. This choice aligns with the methodological strengths
highlighted by Rahim et al. (2021a) in their Malaysian Facebook-review analysis and supports handling mixed-
language posts typical of Malaysian social-media usage.
Beyond polarity, topic modelling was performed using Latent Dirichlet Allocation (LDA) to identify recurrent
themes such as waiting times, communication quality, staff behaviour, and administrative efficiency. These
analytic layers respond directly to critiques raised by Zunic et al. (2019) that polarity alone provides insufficient
granularity for healthcare insights.
Qualitative Thematic Analysis
Interview transcripts were examined using reflexive thematic analysis, which allowed meanings to develop from
the data while remaining sensitive to professional context. The coding process was refined through repeated
comparison across different participant groups, an approach consistent with recommendations by Berger et al.
(2020) and supported by Walsh et al. (2022) in their work on organisational responses to feedback. Although the
themes were generated inductively, they were later organised into broad analytical categories that reflected how
participants interpreted sentiment, described barriers to using digital feedback, explained routine organisational
practices, and assessed the value of online comments. These categories align with patterns previously observed
in studies of healthcare feedback systems, including the work of Baines et al. (2021) and Khan et al. (2022), who
similarly noted the importance of understanding both practical constraints and staff perceptions when analysing
how organisations respond to patient narratives.
Triangulation between sentiment-analysis outputs and interview findings enabled identification of convergence
and divergence between patient narratives and organisational response patterns, addressing a gap noted by
Murray et al. (2024).
Ethical Considerations
As recommended by Farsi et al. (2021) and Ukoha and Stranieri (2019), ethical measures included
anonymisation of social media content, avoidance of private messages, and secure storage of interview data.
Institutional permission was obtained from participating healthcare organisations, and informed consent was
secured from all interviewees. Special attention was paid to compliance with Malaysia’s Personal Data
Protection Act (PDPA), reflecting concerns highlighted in local digital-feedback studies (Rahim et al., 2021a).
RESULT AND DISCUSSION
Table 4.1: Trends in Sentiments Over Time, showing the monthly distribution of positive, neutral, and negative
posts across 12 months.
Theme
Positive
Posts
Positive
(%)
Neutral
Posts
Neutral(%)
Negative
Posts
Negative
(%)
Total
Posts
Patient Care
240
59.60%
81
20.10%
82
20.30%
403
Service Quality
185
44.00%
122
29.00%
113
26.90%
420
Communication
210
49.10%
92
21.50%
126
29.40%
428
Staff Responsiveness
165
36.30%
112
24.60%
178
39.10%
455
Overall Satisfaction
140
47.60%
67
22.80%
87
29.60%
294
Overall %
940
47.30%
474
23.60%
586
29.10%
2000
Table 4.1 shows the sentiment breakdown shows clear variation across themes, with communication, patient
care, and service quality receiving the highest share of positive posts. Positive sentiment is strongest in patient-
care narratives (59.6%), echoing earlier work showing that empathy and clinical attentiveness often drive
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favourable online feedback (Berger et al., 2020; Lloyd et al., 2023). In contrast, staff responsiveness records the
highest proportion of negative posts (39.1%), consistent with findings that delays and difficulty obtaining help
are common triggers for dissatisfaction on social platforms (Chakraborty & Church, 2021; Rahim et al., 2021a).
Mixed sentiment in service quality and communication reflects the complexity of administrative and workflow
issues that patients frequently comment on in digital settings (Seltzer et al., 2022). Across all themes, negative
posts form nearly a third of total comments, supporting earlier claims that social media tends to amplify strong
reactions, particularly around operational challenges (Zunic et al., 2019; Walsh et al., 2022).
Figure 4.1: Monthly Sentiment Distribution. This line graph showing the monthly distribution of positive,
neutral, and negative posts across 12 months.
Figure 4.1 illustrates the monthly sentiment trends strengthen the statistical evidence generated by the Chi-
Square (χ² = 61.11, p = 2.82 × 10⁻¹⁰) and ANOVA tests (F = 10.16, p = 0.0026), confirming that sentiment
patterns vary systematically across themes and time. Positive sentiment remained consistently high each
month—ranging from 72 to 86 posts—suggesting stable appreciation of clinical care, professionalism, and
supportive interactions, findings that mirror international patterns of digital feedback (Berger et al., 2020; Lloyd
et al., 2023). Neutral sentiment fluctuated between 30 and 48 posts, with noticeable increases in April,
September, and November, likely reflecting administrative adjustments or communication gaps during high-
demand periods. Negative sentiment exhibited sharper peaks in January, April, June, and September, consistent
with the literature indicating that dissatisfaction intensifies during seasonal workload pressures and when
responsiveness decreases (Chakraborty & Church, 2021; Rahim et al., 2021a).
The combined monthly and statistical results demonstrate that stakeholder sentiment is sensitive to operational
cycles, suggesting the value of continuous digital-feedback monitoring for early detection of service bottlenecks
and shifts in patient expectations (Murray et al., 2024).
Professional Experience and Diversity of Perspectives
Participants reported 3–20 years of experience (M = 9.54), giving a balanced mix of long-term institutional
insight and newer operational views. This range strengthens interpretation of how feedback systems operate, as
experienced staff recognise structural gaps and newer staff often highlight digital expectations and workflow
issues. Similar patterns are noted in studies of patient-feedback integration, where mixed experience improves
depth and accuracy of analysis (Berger et al., 2020; Lloyd et al., 2023; Chakraborty & Church, 2021). The variety
of perspectives ensured that both traditional and emerging challenges in feedback management were captured.
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An even representation of males (50%) and females (50%) supported a balanced evaluation of communication,
service quality, and feedback processes. Research notes that gender influences approaches to communication
and service assessment in healthcare, contributing to diverse interpretations of patient feedback (Seltzer et al.,
2022; Queen et al., 2021; Farsi et al., 2021). The equal split helped avoid bias in understanding how digital
feedback is reviewed and acted on across roles.
Participants came from general hospitals (57%) and private hospitals (43%), enabling comparison of practices
across both environments. General hospitals tend to manage higher service volumes, which can slow feedback
processing, while private hospitals often have quicker cycles and stronger emphasis on user experience (Rahim
et al., 2021a; Baines et al., 2021; Khan et al., 2022). Including both settings strengthened the relevance of insights
across Malaysia’s healthcare landscape.
Feedback Processing Dynamics
Participants described difficulties managing large volumes of digital feedback and inconsistent processes across
departments. P1 stated, “The volume of feedback is overwhelming, and we don’t have the tools to make sense
of it efficiently.” These concerns echo studies showing that unstructured online data strains manual processes
and delays analysis (Zunic et al., 2019; Fu et al., 2023; Khanbhai et al., 2021).
Some organisations are exploring machine-learning or dashboard systems. P5 shared, “AI tools are promising
but require significant training and adaptation.” Tool complexity, limited skills, and resource gaps often slow
adoption. These challenges match findings that digital-feedback analytics require adequate system support and
workforce readiness (Jeong et al., 2022; Walash et al., 2022; Murray et al., 2024).
Key barriers raised included resource shortages, staff workload, and inconsistent data formats. These hinder
timely action on feedback and limit the potential of social media insights in guiding service changes. Despite
these difficulties, several participants described emerging strategies that help their organisations cope with digital
feedback. Some hospitals had created small cross-functional review groups that met weekly to triage online
comments, assign owners, and track completion of actions. Others used simple tagging systems or shared
spreadsheets to group posts by theme, for example waiting time, cleanliness, and communication, and to link
each cluster to an existing quality project or patient-safety indicator. These pragmatic approaches match calls in
the literature for structured workflows that turn digital narratives into specific tasks within improvement
programmes (Baines et al., 2021; Murray et al., 2024).
Impact on Stakeholder Satisfaction
Participants highlighted that timely acknowledgement of feedback strengthens trust and engagement. P9 stated,
“Acknowledging even minor suggestions makes patients valued.” This aligns with work showing that active
response to digital feedback promotes trust and engagement (Gallan et al., 2022; Chakraborty & Church, 2021;
Montgomery et al., 2022).
Participants also warned about the consequences of unaddressed feedback. P8 noted, “When feedback is ignored,
it damages trust and discourages future participation.” Research supports this, demonstrating that lack of follow-
up weakens stakeholder confidence and reduces willingness to contribute reviews (Montgomery et al., 2022;
Verhoef et al., 2014).
Stakeholder satisfaction increases when feedback is linked to visible improvements, clear communication, and
consistent follow-up. These aspects align with digital patient-experience evidence across multiple healthcare
settings.
Challenges in Implementation
Participants identified resource constraints, workflow limitations, and cultural resistance as key obstacles. P11
shared, “Without proper tools and training, feedback analysis is a daunting task.” Operational limitations such
as staff shortages and manual processes restrict effective feedback use. Studies in digital feedback integration
report similar challenges (Walsh et al., 2022; Anawade et al., 2024; Berger et al., 2020).
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Resistance to adopting new practices was frequently mentioned. P17 stated, “Resistance to change among staff
makes implementation of new practices difficult.” This matches prior work noting that cultural barriers can stall
digital transition in healthcare feedback systems (Lagu et al., 2015; Baines et al., 2021).
Balancing clinical priorities with patient feedback was another concern. Organisations often struggle to
incorporate suggestions while meeting safety standards and operational demands. Participants suggested several
ways to move past these barriers. These included appointing a named “digital feedback lead” in each unit,
integrating summaries of online reviews into existing quality or patient-safety meetings, and setting simple
response standards such as acknowledging all reviews within a set number of days. Staff felt that these steps
would make responsibilities clearer and reduce the sense that social media comments sit outside routine
governance structures, echoing the need for normalisation of digital feedback processes described by Baines et
al. (2021) and Walsh et al. (2022).
DISCUSSION
The study set out to understand digital patient feedback in Malaysian healthcare by examining social media
sentiment patterns and exploring how staff interpret and apply such feedback in daily practice. The sentiment
analysis showed that online narratives consistently highlighted patient care and communication as areas that
attracted the most favourable views, and this reflects long-standing evidence that empathy, clarity, and respectful
interaction remain central to patient experience, as noted by Berger et al. (2020), Lloyd et al. (2023), and related
Malaysian studies such as Rahim et al. (2021b). In contrast, comments linked to staff responsiveness contained
a higher share of negative reactions, and this pattern aligns with international findings that delays, limited
assistance, and perceived inattentiveness tend to trigger dissatisfaction in digital feedback, as reported by
Chakraborty and Church (2021) and Seltzer et al. (2022). The statistical tests reinforced these distinctions by
showing significant variation across themes and months, suggesting that online sentiment moves in line with
operational pressures, seasonal patient load, and administrative adjustments, an observation consistent with
Murray et al. (2024). Interviews with administrators, managers, and frontline personnel provided deeper insight
into these trends. Participants recognised that digital feedback gives a timely indication of patient concerns, yet
they described difficulties in managing large volumes of unstructured comments, uneven processes for reviewing
feedback, and uncertainty about who should take responsibility for acting on it. These experiences reflect
challenges documented by Zunic et al. (2019), Fu et al. (2023), and Baines et al. (2021), who reported similar
issues in health systems trying to use digital narratives for improvement. Staff also emphasised that
acknowledging comments, even those that seem minor, strengthens trust and encourages continued engagement,
a point supported by Gallan et al. (2022) and Montgomery et al. (2022). At the same time, participants expressed
concern that ignoring online feedback weakens confidence in the organisation, an issue raised earlier by Walsh
et al. (2022). Overall, the combined findings show that social media provides clear insight into what matters to
patients, yet its usefulness depends heavily on organisational readiness, consistent review routines, and a culture
that values patient voice. The findings indicate that integration of digital feedback into wider quality-
improvement and project-management systems remains uneven. A minority of sites had started to align social
media themes with existing tools such as incident reporting, accreditation indicators, and patient-experience
dashboards. In these settings, online comments were discussed in regular quality meetings, linked to Plan–Do–
Study–Act cycles, and used to prioritise small-scale projects on communication, signage, or staffing. Other
organisations treated digital reviews as a parallel activity, managed mainly for reputational reasons and seldom
connected to formal improvement work. This pattern mirrors earlier concerns that social media data often sit at
the margins of governance structures (Lagu et al., 2015; Walsh et al., 2022) and highlights the need for clearer
pathways that connect digital sentiment to routine performance monitoring and project decisions in Malaysian
hospitals.
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