Sanius Health shares its insights on its acute NHS Provider Patient Experience Trust League Table and the value of AI-captured social media listening in improving care experiences.
We are in election season – where patient care and experiences remain a key challenge for any new administration. Sanius Health’s recent release of the Mental Health (MH) provider AI-powered social media data insights was well received by NHS leadership colleagues. As a company, Sanius therefore agreed to bring forward our schedule for a long-awaited Acute provider release.
Sanius looked at 50,000 data points extracted from a multitude of public social media posts, with the data showing that 35% of providers have a negative feedback proportion of over 25% – patient experience scores showing levels of sensitivity and variation outside of more structured measures, such as the Friends and Family Test.
Critically, the value of AI-supported social media listening (SML) is that provides the unedited experience of many patients and families while receiving care as opposed to after, where they may have forgotten the true experience of the service.
A core goal Sanius moves forward is to ensure that the company continues to build on the strong foundation laid by tools like the FFT, all while embracing emerging innovative approaches. Leveraging technologies such as advanced analytics, machine learning, and real-time feedback systems like SML can offer deeper, more actionable insights. These new methodologies provide a much more comprehensive and precise understanding of patient experiences.
Indeed, by integrating cutting-edge technologies with patient feedback mechanisms, Sanius can advance beyond the limitations of traditional methods and foster a more dynamic, responsive healthcare system that better meets the needs of patients and providers alike.
Public Perceptions and Their Role in Reshaping NHS Pathways
For those of you unfamiliar with SML or Sanius Health’s previous work in targeted disease and mental health SML extraction, the core concept is this: through AI-driven methodologies, data is extracted against specific keywords from various online platforms and analysed for the core themes and sentiments expressed by posters. Natural Language Processing (NLP) algorithms allows for context and sentiment analysis on thousands of responses to be performed within a proprietary AI platform.
As Sanius and others have shown, this approach is one that lends itself to a multitude of use cases and sources of information. Indeed, alongside the company’s more detailed trust-level analysis, Sanius was also interested in exploring public and service user views on the NHS at a wider level. To do this, Sanius scraped comments from an online health-focused journal, with several key themes emerging from reader feedback on recent publications.
From both a patient experiences perspective and a more general NHS operational, transformation, and flow perspective, these comments highlighted key themes within healthcare pathways. For patients, key areas of challenge were linked to the quality of care, potentially underlined by ongoing capacity constraints at the frontline. This seemed to be compounded by service inefficiency, specifically around the discharge process and follow-up, as well as poor patient-staff interactions with noted communication failures.
At a broader level, many comments were focused on employee experience and satisfaction – 75% of responses displaying a negative sentiment – as well as staffing levels or compensation (71% negative), financial management or funding allocation (68% negative), and the leadership team (61% negative). Moreover, issues were identified regarding ongoing systemic changes and strategic planning (59% negative), with underlying technology that could support data management and data flow (58% negative) also highlighted as critical challenges.
Transforming the Healthcare Landscape Across Service Types
Sanius’s insights have already been hard at work pinpointing critical areas needing improvement for NHS colleagues, from systemic capacity constraints to inefficiencies across pathways, and with a particular focus on discharge and follow-up. Building upon prior work across other NHS pathways, the company has uncovered some key themes that strike a particular chord with service users. Critically, these SML analyses offer actionable insights for NHS teams seeking new ways to guide decision-making and planning – enhancing services, patient experiences, and quality of care through novel insights into what’s happening on the ground.
By focusing on the most impactful areas for service users, Sanius aims to support its partner trusts in transforming the acute health landscape. Leveraging patient voices and advanced AI/ML technologies, Sanius Health is uncovering the underlying factors affecting patient care and identifying solutions to address them. The company is constantly enriching the depth and breadth of it SML databank and insights, and for those interested in learning more about Sanius Health’s work and its implications for acute health services, please reach out here or through [email protected].
