DOES THE MESSAGE MATTER? ENHANCING PATIENT ADHERENCE THROUGH PERSUASIVE MESSAGES

To improve health and reduce costs, we need to encourage patients to make better healthcare decisions. Many informatics interventions are aimed at improving health outcomes by influencing patient behavior. However, we know little about how the content of a message in these interventions can influence a health-related decision. In this research we formulate a conceptual model to help explain and guide the design of “persuasive messages”, those which can change and influence patient behavior. We apply the conceptual model to design persuasive appointment reminder messages using humancentered design principles. Finally, we empirically test our hypotheses in a randomized controlled trial in order to determine the effectiveness of persuasive appointment reminders to reduce the number of missed appointments in a sample of 1016 subjects in a community health center. The results of the study confirm that reminder messages are effective in reducing missed appointment compared with no reminders (p=0.028). Further, reminder messages that incorporate heuristic cues such as authority, commitment, liking, and scarcity are more effective than reminder messages without such cues (p=0.006). However, the addition of systematic arguments or reasons for attending appointments have no effect on appointment adherence (p=0.646). The results of this research suggest that the content of reminder messages may be an important factor in helping to reduce missed appointments

Guided Self-help Teletherapy for Behavioural Difficulties in Children with Epilepsy

Behavioural difficulties impact greatly upon quality of life for children with chronic illness and their families but are often not identified or adequately treated, possibly due to the separation of physical and mental health services. This case study describes the content and outcomes of guided self-help teletherapy for behavioural difficulties in a child with epilepsy and complex needs using an evidence-based behavioural parenting protocol delivered within a paediatric hospital setting. Behavioural difficulties and progress towards the family’s self-identified goals were monitored at each session. Validated measures of mental health and quality of life in children were completed before and after intervention and satisfaction was measured at the end of treatment. Measures demonstrated clear progress towards the family’s goals and reduction in weekly ratings of behavioural difficulties. This case demonstrates that a guided self-help teletherapy approach delivered from within the paediatric setting may be one way of meeting unmet need

Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.Comment: 10 pages, 7 figures, 4 table

The association between income inequality and adult mental health at the subnational level—a systematic review

Purpose A systematic review was undertaken to determine whether research supports: (i) an association between income inequality and adult mental health when measured at the subnational level, and if so, (ii) in a way that supports the Income Inequality Hypothesis (i.e. between higher inequality and poorer mental health) or the Mixed Neighbourhood Hypothesis (higher inequality and better mental health). Methods Systematic searches of PsycINFO, Medline and Web of Science databases were undertaken from database inception to September 2020. Included studies appeared in English-language, peer-reviewed journals and incorporated measure/s of objective income inequality and adult mental illness. Papers were excluded if they focused on highly specialised population samples. Study quality was assessed using a custom-developed tool and data synthesised using the vote-count method. Results Forty-two studies met criteria for inclusion representing nearly eight million participants and more than 110,000 geographical units. Of these, 54.76% supported the Income Inequality Hypothesis and 11.9% supported the Mixed Neighbourhood Hypothesis. This held for highest quality studies and after controlling for absolute deprivation. The results were consistent across mental health conditions, size of geographical units, and held for low/middle and high income countries. Conclusions A number of limitations in the literature were identified, including a lack of appropriate (multi-level) analyses and modelling of relevant confounders (deprivation) in many studies. Nonetheless, the findings suggest that area-level income inequality is associated with poorer mental health, and provides support for the introduction of social, economic and public health policies that ameliorate the deleterious effects of income inequality. Clinical registration number PROSPERO 2020 CRD42020181507

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.Comment: 2023 AMIA Annual Symposium, see https://amia.org/education-events/amia-2023-annual-symposiu

Lipid-lowering with inclisiran: areal-world single-centre experience

Objective The reduction in circulating low-density lipoprotein cholesterol (LDL-c) is the primary aim of lipid-lowering therapies as a method of atherosclerotic cardiovascular disease risk reduction. Inclisiran is a new and potent lipid-lowering drug that is shown to be effective in reducing LDL-c in randomised controlled trials, however, real-world data of its use are not yet known. We sought to analyse the early effects of this drug in a tertiary centre lipid and cardiovascular risk clinic. Methods We performed a retrospective analysis of the first 80 patients who received a single dose of inclisiran at our lipid clinic between 1 December 2021 and 1 September 2022. Data were collected using electronic healthcare records. Baseline blood tests were taken prior to start of treatment and were repeated at 2 months follow-up. Data on adverse events were also recorded. Results At 2 months after treatment initiation, mean baseline LDL-c fell from 3.5±1.1 mmol/L by 48.6% to 1.8±1.0 mmol/L and total cholesterol from 5.7±1.3 mmol/L by 33.3% to 3.8±1.1 mmol/L (both p<0.0001). Mean high-density lipoprotein-c rose by 7.7% to 1.4±0.4 mmol/L (p=0.02) and median triglycerides fell by 31.3% to 1.1 mmol/L (IQR 0.9–2) (p=0.001). Adverse events (injection site reaction, fatigue and headache) were recorded in three patients and all had self-resolved by time of follow-up. Conclusion Inclisiran use in line with National Institute for Health and Care Excellence guidelines led to significant lowering of LDL-c at 2 months, with efficacy similar to that reported in trials with good tolerability

Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression

This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods. Two complexity levels of RE methods were used to extract and generate the training data. The SpaCy package and RoBERTa transformer models were used to build the NER model and evaluate its performance with the manual-labeled gold standards. The comparison of the RE methods with the gold standard showed that as the complexity increased in the RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER models demonstrated excellent predictions, with the simple RE method showing 0.84-0.92 in the evaluation metrics, and the advanced and combined RE method demonstrating 0.95-0.99 in the evaluation. This study provided an example of the benefit of combining NER methods and NLP models in extracting target information from free-text to structured data and fulfilling the need for missing diagnoses from unstructured notes.Comment: IEEE ICHI 2023, see https://ieeeichi.github.io/ICHI2023/program.htm

ADEA‐ADEE Shaping the Future of Dental Education III

The central purpose of scientific research and emerging dental health technologies is to improve care for patients and achieve health equity. The Impact of Scientific Technologies and Discoveries on Oral Health Globally workshop conducted joint American Dental Education Association (ADEA) and the Association for Dental Education in Europe (ADEE) 2019 conference, Shaping the Future of Dental Education III, highlighted innovative technologies and scientific discoveries to support personalized dental care in an academic and clinical setting. The 2019 workshop built upon the new ideas and way forward identified in the 2017 ADEE‐ADEA joint American Dental Education Association (ADEA) and the Association for Dental Education in Europe (ADEE) 2019 conference, Shaping the Future of Dental Education II held in London. During the most recent workshop the approach was to explore the “Teaching Clinic of the Future”. Participants applied ideas proposed by keynote speakers, Dr. Walji and Dr. Vervoorn to educational models (Logic Model) in an ideal dental education setting. It is only through this continuous improvement of our use of scientific and technological advances that dental education will be able to convey to students the cognitive skills required to continually adapt to the changes that will affect them and consequently their patients throughout their career. This workshop was a valuable experience for highlighting opportunities and challenges for all stakeholders when aiming to incorporate new technologies to facilitate patient care and students’ education.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153630/1/jdd12027.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153630/2/jdd12027_am.pd

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions