What factors affect patients' recall of general practitioners' advice?

<p>Abstract</p> <p>Background</p> <p>In order for patients to adhere to advice, provided by family doctors, they must be able to recall it afterwards. However, several studies have shown that most patients do not fully understand or memorize it. The aim of this study was to determine the influence of demographic characteristics, education, amount of given advice and the time between consultations on recalled advice.</p> <p>Methods</p> <p>A prospective survey, lasting 30 months, was conducted in an urban family practice in Slovenia. Logistic regression analysis was used to identify the risk factors for poorer recall.</p> <p>Results</p> <p>250 patients (87.7% response rate) received at least one and up to four pieces of advice (2.4 ± 0.8). A follow-up consultation took place at 47.4 ± 35.2 days. The determinants of better recall were high school (OR 0.4, 95% CI 0.15-0.99, p = 0.049) and college education (OR 0.3, 95% CI 0.10-1.00, p = 0.050), while worse recall was determined by number of given instructions three or four (OR 26.1, 95% CI 3.15-215.24, p = 0.002; OR 56.8, 95% CI 5.91-546.12, p < 0.001, respectively) and re-test interval: 15-30 days (OR 3.3, 95% CI 1.06-10.13, p = 0.040), 31-60 days (OR 3.2, 95% CI 1.28-8.07, p = 0.013) and more than 60 days (OR 2.5, 95% CI 1.05-6.02, p = 0.038).</p> <p>Conclusions</p> <p>Education was an important determinant factor and warrants further study. Patients should be given no more than one or two instructions in a consultation. When more is needed, the follow-up should be within the next 14 days, and would be of a greater benefit to higher educated patients.</p

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

Development and Practical Use of a Medical Vocabulary-Thesaurus-Dictionary for Patient Empowerment

Health empowerment can be obtained through an informative and educational intervention to increase one's ability to think critically and act autonomously. Medical texts are usually written by professionals and can be difficulty understood by non-experts who do not have the same skills and vocabularies. Thus, it would be desirable to have an online medical vocabulary-thesaurus-dictionary that can help a non-expert to easily find the consumer equivalent of medical (technical) terms and additional consumer information. To this end, we have developed an online multilingual medical vocabulary-thesaurus-dictionary by interconnecting different online sources, i.e., medical vocabularies to create a list of technical terms, consumer health vocabularies (CHVs) for translating technical terms into their consumer equivalents and consumer dictionaries for finding explanations of the terms. In addition, we have built an online editor that allows to add new medical terms (with the related consumer information) and modify existing consumer terms and definitions. Furthermore, we have built some practical applications, on top of the medical vocabulary-thesaurus-dictionary, in order to facilitate the empowerment of patients or non-experts in general. The applications are located at the data, information and knowledge levels of the \u2018knowledge pyramid\u2019 that, in our case, contains the empowerment at the top leve