Measuring patient-perceived quality of care in US hospitals using Twitter

BACKGROUND: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. DESIGN: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. KEY RESULTS: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). CONCLUSIONS: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

COPOS: Corpus de Opiniones de Pacientes en Español. Aplicación de Técnicas de Análisis de Sentimientos

Every day more users are interested in the opinion that other patients have about a physician or about health topics in general. According to a study in 2015, 62% of Spanish people access the Internet in order to be informed about topics related to health. This paper is focused on Spanish Sentiment Analysis in the medical domain. Although Sentiment Analysis has been studied for different domains, health issues have hardly been examined in Opinion Mining and even less with Spanish comments or opinions. Thus we have generated a corpus by crawling the website Masquemedicos with Spanish opinions about medical entities written by patients. We present this new resource, called COPOS (Corpus Of Patient Opinions in Spanish). To the best of our knowledge, this is the first attempt to deal with Spanish opinions written by patients about medical attention. In order to demonstrate the validity of the corpus presented, we have also carried out different experiments with the main methodologies applied in polarity classification (Semantic Orientation and Machine Learning). The results obtained encourage us to continue analysing and researching Opinion Mining in the medical domain.Cada día son más los usuarios interesados en la opinión que otros pacientes tienen sobre un médico o sobre temas de salud en general. De acuerdo con un estudio de 2015, el 62% de la población española consulta información en Internet acerca de temas relacionados con la salud. Este trabajo está centrado en el Análisis de Sentimientos en español aplicado al dominio médico. Aunque el Análisis de Sentimientos ha sido estudiado en diferentes dominios, el dominio de la salud apenas ha sido investigado, especialmente en opiniones escritas en español. Por ello, hemos generado un corpus en español con opiniones de pacientes sobre médicos a partir de la extracción de las mismas del portal web Masquemedicos. Este corpus ha sido denominado COPOS (Corpus Of Patient Opinions in Spanish - Corpus de Opiniones de Pacientes en Español). Hasta donde sabemos, es la primera vez que se intenta trabajar con opiniones en español sobre atención médica escritas por pacientes. Para demostrar la validez de este recurso, hemos realizado diferentes experimentos con las principales metodologías aplicadas en la tarea de clasificación de polaridad (Orientación Semántica y Aprendizaje Automático). Los resultados obtenidos nos animan a seguir investigando en el Análisis de Sentimientos en este dominio.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), REDES project (TIN2015-65136-C2-1-R) from the Spanish Government and by a Grant from the Ministerio de Educación Cultura y Deporte (MECD - scholarship FPU014/00983)

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Deciphering Medical Errors: What Matters for Patients on Social Media

This study investigates medical errors, germane to patient safety, from the patient’s perspective. We analyzed social media data, Twitter posts, about patients’ perspective on their medical experiences, which have been rarely translated into a systemic and rigorous research result. Employing a combined-research method, the qualitative content analysis and the analytical automatic categorization of text data, we analyzed 1,806 tweet entries during four and half years, from December 2017 to June 2022. We identified the categories and consequences of medical errors, critical from the patient’s perspective. The common medical errors include ignorance, misdiagnosis, negligence, and medication errors. The manifested consequences of medical errors include medical complications, death, and paralyzed/disabled. The study emphasizes the importance of patient’s experience in complementing other error reporting systems and mechanisms, that have been utilized by healthcare professionals for establishing more meaningful recommendations for reducing medical errors

Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry

Data analysis of social media posting continues to offer a huge variety of information about the health situation faced by an individual. Social networking or social media websites provide us a wealth of information generated by users in a variety of domains, that generated information are unstructured and unlabeled and are not captured in an exceedingly systematic manner, as info generated is not humanly possible to process due to its size. One traditional way of collecting patients experience is by conducting surveys and questionnaires, as these methods ask fixed questions and are expensive to administer. In this paper, a patient feedback platform (PFP) using free text sentiment analysis is developed to computationally identify and categorize the polarity expressed in a piece of text. Six machine learning latest algorithms have been used as key evaluation for evaluating accuracy of the developed (PFP) model. Results achieved have shown 88 % accuracy on the basis of which it is recommended that developed (PFP) patient feedback platform could be used to improve E-health care services indeed