ObjectivesUnstructured 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.MethodsDatabases 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.ResultsNineteen 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 Naïve Bayes being the best performing ML classifiers.ConclusionNLP 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.
Background The use of health information technology (IT) is rapidly increasing to support improvements in the delivery of care. Although health IT is delivering huge benefits, new technology can also introduce unique risks. Despite these risks, evidence on the preventability and effects of health IT failures on patients is scarce. In our study we therefore sought to evaluate the preventability and effects of health IT failures by examining patient safety incidents in England and Wales.Methods We designed our study as a retrospective analysis of 10 years of incident reporting in England and Wales. We used text mining with the words "computer", "system", "workstation", and "network" to explore free-text incident descriptors to identify incidents related to health IT failures following a previously described approach. We then applied an n-gram model of searching to identify contiguous sequences of words and provide spatial context. We examined incident details, recorded harm, and preventability. Standard descriptive statistics were applied. Degree of harm was identified according to standardised definitions and preventability was assessed by two independent reviewers. FindingsWe identified 2627 incidents related to health IT failures. 2557 (97%) of 2627 incidents were assessed for harm (70 incidents were excluded). 2106 (82%) of 2557 health IT failures caused no harm to patients, 331 (13%) caused low harm, 102 (4%) caused moderate harm, 14 (1%) caused severe harm, and four (<1%) contributed to the death of a patient. 1964 (75%) of 2627 incidents were deemed to be preventable.Interpretation Health IT is fundamental to the delivery of high-quality care, yet there is a poor understanding of the effects of IT failures on patient safety and whether they can be prevented. Failures are complex and involve interlinked aspects of technology, people, and the environment. Health IT failures are undoubtedly a potential source of substantial harm, but they are likely to be under-reported. Worryingly, three-quarters of IT failures are potentially preventable. There is a need to see health IT as a fundamental tenet of patient safety, develop better methods for capturing the effects of IT failures on patients, and adopt simple measures to reduce their probability and mitigate their risk.
With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.
The study aims to conduct a systematic review to characterise the spread and use of the concept of ‘disruptive innovation’ within the healthcare sector. We aim to categorise references to the concept over time, across geographical regions and across prespecified healthcare domains. From this, we further aim to critique and challenge the sector-specific use of the concept. PubMed, Medline, Embase, Global Health, PsycINFO, Maternity and Infant Care, and Health Management Information Consortium were searched from inception to August 2019 for references pertaining to disruptive innovations within the healthcare industry. The heterogeneity of the articles precluded a meta-analysis, and neither quality scoring of articles nor risk of bias analyses were required. 245 articles that detailed perceived disruptive innovations within the health sector were identified. The disruptive innovations were categorised into seven domains: basic science (19.2%), device (12.2%), diagnostics (4.9%), digital health (21.6%), education (5.3%), processes (17.6%) and technique (19.2%). The term has been used with increasing frequency annually and is predominantly cited in North American (78.4%) and European (15.2%) articles. The five most cited disruptive innovations in healthcare are ‘omics’ technologies, mobile health applications, telemedicine, health informatics and retail clinics. The concept ‘disruptive innovation’ has diffused into the healthcare industry. However, its use remains inconsistent and the recognition of disruption is obscured by other types of innovation. The current definition does not accommodate for prospective scouting of disruptive innovations, a likely hindrance to policy makers. Redefining disruptive innovation within the healthcare sector is therefore crucial for prospectively identifying cost-effective innovations.
This article reflects on the changing nature of health information access and the transition of focus from electronic health records (EHRs) to personal health records (PHRs) along with the challenges and need for alignment of national initiatives for EHR and PHR in the National Health Service (NHS) of the UK. The importance of implementing integrated EHRs as a route to enhance the quality of health delivery has been increasingly understood. EHRs, however, carry several limitations that include major fragmentation through multiple providers and protocols throughout the NHS. Questions over ownership and control of data further complicate the potential for fully utilising records. Analysing the previous initiatives and the current landscape, we identify that adopting a patient health record system can empower patients and allow better harmonisation of clinical data at a national level. We propose regional PHR ‘hubs’ to provide a universal interface that integrates digital health data at a regional level with further integration at a national level. We propose that these PHR hubs will reduce the complexity of connections, decrease governance challenges and interoperability issues while also providing a safe platform for high-quality scalable and sustainable digital solutions, including artificial intelligence across the UK NHS, serving as an exemplar for other countries which wish to realise the full value of healthcare records.
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