The King´s Fund is a charity thar seeks to understand how the health system in England can be improved. Using the insight, we help to shape policy, transform services and bring about behaviour change.

The King´s Fund

Analysis of companies’ digital maturity by hesitant fuzzy linguistic MCDM methods

Background Digital health in hospital settings is viewed as a panacea for achieving the “quadruple aim” of health care, yet the outcomes have been largely inconclusive. To optimize digital health outcomes, a strategic approach is necessary, requiring digital maturity assessments. However, current approaches to assessing digital maturity have been largely insufficient, with uncertainty surrounding the dimensions to assess. Objective The aim of this study was to identify the current dimensions used to assess the digital maturity of hospitals. Methods A systematic literature review was conducted of peer-reviewed literature (published before December 2020) investigating maturity models used to assess the digital maturity of hospitals. A total of 29 relevant articles were retrieved, representing 27 distinct maturity models. The articles were inductively analyzed, and the maturity model dimensions were extracted and consolidated into a maturity model framework. Results The consolidated maturity model framework consisted of 7 dimensions: strategy; information technology capability; interoperability; governance and management; patient-centered care; people, skills, and behavior; and data analytics. These 7 dimensions can be evaluated based on 24 respective indicators. Conclusions The maturity model framework developed for this study can be used to assess digital maturity and identify areas for improvement.

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Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review

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.

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

Abstract Patients’ experiences of their diagnosis, condition, and treatment (including the impact on their lives), and their experiences surrounding expectations of care, are becoming increasingly important in shaping healthcare systems that meet the evolving needs and priorities of different patient communities over time; this is an ongoing goal of all healthcare stakeholders. Current approaches that capture patient experiences with data are fragmented, resulting in duplication of effort, numerous requests for information, and increased patient burden. Application of patient experience data to inform healthcare decisions is still emerging and there remains an opportunity to align diverse stakeholders on the value of these data to strengthen healthcare systems. Given the collective value of understanding patient experiences across multiple stakeholder groups, we propose a more aligned approach to the collection of patient experience data. This approach is built on the principle that the patients’ experiences are the starting point, and not just something to be considered at the end of the process. It must also be based on meaningful patient engagement, where patients are collaborators and decision makers at each step, thereby ensuring their needs and priorities are accurately reflected. The resulting data and evidence should be made available for all stakeholders, to inform their decision making and healthcare strategies in ways that meet patient priorities. We call for multi-stakeholder collaboration that will deliver healthcare systems and interventions that are better centered around and tailored to patient experiences, and that will help address patients’ unmet needs. 

https://link.springer.com/content/pdf/10.1007/s43441-022-00432-x.pdf

Building from Patient Experiences to Deliver Patient-Focused Healthcare Systems in Collaboration with Patients: A Call to Action

Objectives Unstructured 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. Methods Databases 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. Results Nineteen 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 Naive Bayes being the best performing ML classifiers. Conclusion NLP 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.

https://informatics.bmj.com/content/bmjhci/28/1/e100262.full.pdf

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

Background: One of the essential elements of a strategic approach to improving patients’ experience is to measure and report on patients’ experiences in real time. Real-time feedback (RTF) is increasingly being collected using digital technology; however, there are several factors that may influence the success of the digital system. Objective: The aim of this review was to evaluate the digital maturity and patient acceptability of real-time patient experience feedback systems. Methods: We systematically searched the following databases to identify papers that used digital systems to collect RTF: The Cochrane Library, Global Health, Health Management Information Consortium, Medical Literature Analysis and Retrieval System Online, EMBASE, PsycINFO, Web of Science, and CINAHL. In addition, Google Scholar and gray literature were utilized. Studies were assessed on their digital maturity using a Digital Maturity Framework on the basis of the following 4 domains: capacity/resource, usage, interoperability, and impact. A total score of 4 indicated the highest level of digital maturity. Results: RTF was collected primarily using touchscreens, tablets, and Web-based platforms. Implementation of digital systems showed acceptable response rates and generally positive views from patients and staff. Patient demographics according to RTF responses varied. An overrepresentation existed in females with a white predominance and in patients aged ≥65 years. Of 13 eligible studies, none had digital systems that were deemed to be of the highest level of maturity. Three studies received a score of 3, 2, and 1, respectively. Four studies scored 0 points. While 7 studies demonstrated capacity/resource, 8 demonstrated impact. None of the studies demonstrated interoperability in their digital systems. Conclusions: Patients and staff alike are willing to engage in RTF delivered using digital technology, thereby disrupting previous paper-based feedback. However, a lack of emphasis on digital maturity may lead to ineffective RTF, thwarting improvement efforts. Therefore, given the potential benefits of RTF, health care services should ensure that their digital systems deliver across the digital maturity continuum.

Mustafa Khanbhai

Papers

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

Evaluating Digital Maturity and Patient Acceptability of Real-Time Patient Experience Feedback Systems: Systematic Review

Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care.

Enriching the value of patient experience feedback: interactive dashboard development using co-design and heuristic evaluation (Preprint)