License to Heal: Understanding a Healthcare Platform Organization as a Multi-Level Surveillant Assemblage

Platform organizations bring renewed attention to power disparities and risks in the rise of surveillance capitalism. However; such critical accounts provide a partial understanding of the complexity of surveillance phenomena in such shifting socio-technical and digital environments.The findings from a netnographic investigation of a healthcare platform organization, PatientsLikeMe, unravel how platforms become the locus where multi-level flows of surveillance converge, thereby constituting what we identify as a surveillant assemblage. We develop a comprehensive approach for understanding how platforms constitute a dynamic crossroads of micro-, meso- and macro-surveillance phenomena within and beyond the online communities they create.This study highlights this surveillant assemblage\u27s emerging practices and potentially empowering outcomes that enable multi-stakeholder involvement in big data and knowledge generation in healthcare. Broader implications of multi-level surveillance in and through platforms are discussed

Patients’ online descriptions of their experiences as a measure of healthcare quality

Introduction Patients are describing their healthcare experiences online using rating websites. There has been substantial professional opposition to this, but the government in England has promoted the idea as a mechanism to improve healthcare quality. Little is known about the content and effect of healthcare rating and review sites. This thesis aims to look at comments left online and assess whether they might be a useful measure of healthcare quality. Method I used a variety of different approaches to examine patients’ comments and ratings about care online. I performed an examination of the comments left on the NHS Choices website, and analysed whether there was a relationship between the comments and traditional patient surveys or other measures of clinical quality. I used discrete choice experiments to look at the value patients place on online care reviews when making decisions about which hospital to go to. I used natural language processing techniques to explore the comments left in free text reviews. I analysed the tweets sent to NHS hospitals in England over a year to see if they contained useful information for understanding care quality. Results The analysis of ratings on NHS Choices demonstrates that reviews left online are largely positive. There are associations between online ratings and both traditional survey methods of patient experience and outcome measures. There is evidence of a selection bias in those who both read and contribute ratings online – with younger age groups and those with higher educational attainment more likely to use them. Discrete choice experiments suggest that people will use online ratings in their decisions about where to seek care, and the effect is similar to that of a recommendation by friends and family. I found that sentiment analysis techniques can be used classify free text comments left online into meaningful information that relates to data in the national patient surveys. However, the analysis of comments on Twitter found that only 11% of tweets were related to care quality. Conclusions Patients rating their care online may have a useful role as a measure of care quality. It has some drawbacks, not least the non-random group of people who leave their comments. However, it provides information that is complementary to current approaches to measuring quality and patient experiences, may be used by patients in their decision-making, and provides timely information for quality improvement. I hypothesise that it is possible to measure a ‘cloud of patient experience’ from all of the sources where patients describe their care online, including social media, and use this to make inferences about care quality. I find this idea has potential, but there are many technical and practical limitations that need to be overcome before it is useful.Open Acces

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Investigations into the patient voice: a multi-perspective analysis of inflammation

The patient is the expert of their medical journey and their experiences go largely unheard in clinical practice. Understanding the patient is important as bridging gaps in the medical domain enhances clinical knowledge, benefiting patient care in addition to improving quality of life. Valuable solutions to these problems lie at the intersection of Machine learning and sentiment analysis; through ontologies, semantic similarity, and clustering. In this thesis, I present challenges and solutions that explore patient quality of life pertaining to two inflammatory diseases: Uveitis and Inflammatory Bowel Disease, which are immune-mediated inflammatory diseases and often undifferentiated. This thesis explores how a patient’s condition and inflammation influences their voice and quality of life via sentiment analysis, clustering, and semantic characterisations. Methods With guidance from domain experts and a foundation derived from clinical consensus documents, I created an application ontology, Ocular Immune-Mediated Inflammatory Diseases Ontology (OcIMIDo), which was enhanced with patient-preferred terms curated from online forum conversations, using a semi-automated statistical approach - with application of annotating term-frequency and sentiment analysis. Semantic similarity was explored using a preexisting embedding model derived from clinical letters to train other models consisting of patient-generated texts for systematic comparison of the clinician and patient voice. In a final experimental chapter, blood markers were clustered and analysed with their corresponding quantitative quality of life outcomes using patients in the UK Biobank with Inflammatory Bowel Disease. Results OcIMIDo is the first of its kind in ophthalmology and sentiment analysis revealed that first posts were more negative compared to replies. Systematic comparisons of embedding models revealed frequent misspellings from clinicians; use of abbreviations from patients; and patient priorities - models performed better when the clinical domain was extended with equivalent-sized, patient-generated data. Clusters unveiled insight into the presence of inflammatory stress and the relationship with happiness and the presence of a maternal smoking history with a Crohn’s disease diagnosis. Summary Patient-preferred terms prove the patient voice provides meaningful text mining and fruitful sentiment analysis, revealing the role a forum plays on patients; semantic similarity highlighted potential novel disease associations and the patient lexicon; and clustering blood markers featured clusters presenting a relationship with sentiment. In summary, this deeper knowledge of quality of life biomarkers through the patient voice can benefit the clinical domain and patient outcomes as understanding the patient can improve the clinical-patient relationship and communication standards: all benefiting the diagnosis process, developing treatment plans, and shortening these intensive time hauls in clinical practice

Electronic Health Record Optimization for Cardiac Care

Electronic health record (EHR) systems have been studied for over 30 years, and despite the benefits of information technology in other knowledge domains, progress has been slow in healthcare. A growing body of evidence suggests that dissatisfaction with EHR systems was not simply due to resistance to adoption of new technology but also due to real concerns about the adverse impact of EHRs on the delivery of patient care. Solutions for EHR improvement require an approach that combines an understanding of technology adoption with the complexity of the social and technical elements of the US healthcare system. Several studies are presented to clarify and propose a new framework to study EHR-provider interaction. Four focus areas were defined - workflow, communication, medical decision-making and patient care. Using Human Computer Interaction best practices, an EHR usability framework was designed to include a realistic clinical scenario, a cognitive walkthrough, a standardized simulated patient actor, and a portable usability lab. Cardiologists, fellows and nurse practitioners were invited to participate in a simulation to use their institution’s EHR system for a routine cardiac visit. Using a mixed methods approach, differences in satisfaction and effectiveness were identified. Cardiologists were dissatisfied with EHR functionality, and were critical of the potential impact of the communication of incorrect information, while displaying the highest level of success in completing the tasks. Fellows were slightly less dissatisfied with their EHR interaction, and demonstrated a preference for tools to improve workflow and support decision-making, and showed less success in completing the tasks in the scenario. Nurse practitioners were also dissatisfied with their EHR interaction, and cited poor organization of data, yet demonstrated more success than fellows in successful completion of tasks. Study results indicate that requirements for EHR functionality differ by type of provider. Cardiologists, cardiology fellows, and nurse practitioners required different levels of granularity of patient data for use in medical decision-making, defined different targets for communication, sought different solutions to workflow which included distribution of data input, and requested technical solutions to ensure valid and relevant patient data. These findings provide a foundation for future work to optimize EHR functionality

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe