Analysing and using subjective criteria to improve dental care recommendation systems

Online reviews and rating sites are shaping industries as the users rely on recommendations given by former consumers and sharing opinions on the web. Dentistry has also been impacted by dental patients' reviews. This paper classifies trust-related information for dental care recommendations onto 4 categories: context, relationship, reputation and subjective criteria. It discusses each category and describes how they help focussing on trust when matching patients and dentists in brief. The paper then focuses on subjective criteria and presents the results of a survey aimed at showing trustrelated information emerged from subjective characteristics. Traits of personalities are used as subjective characteristics of patients and that of dentists are derived from the online patients' reviews. 580 Australian patients were surveyed to determine what factors affect their decision to find the trusted dentist. Subjective characteristics of dentists such as dentists' qualities and experienced dentists are considered the most important factors after location and cost. The most preferred dentists' qualities by almost all types of personalities are experienced, professional and quality of service. When the patients are further classified based on levels of fear, their preferences for dentists' qualities changed. Subjective qualities of both patients and dentists are important factors to improve the matching capability for the dental care recommendation systems

Enhancing trust in dental care recommendation systems : using trust-enhanced information from social networks to improve the matching between patients and dentists

University of Technology Sydney. Faculty of Engineering and Information Technology.The recent growth in social media has impacted the way users are searching and sharing health information online. Crowdsources, such as review and rating websites, provide an outlet for consumers to share their opinions on healthcare professionals. Yet, faced with the enormity and diversity of information across multiple online sources, finding the right information can be a challenge for users, particularly when there is no consistency in the evaluation criteria across various sources. This difficulty is manifested when existing review and rating websites do not take patient information into consideration. Extremely biased views – positive or negative – are capable of skewing recommendations and thereby compounding the situation. This makes it important to filter trustworthy information from health social networks and dental crowdsources. In the case of dental care, the invasive nature of many dental treatments highlights the importance of selecting a suitable trustworthy provider for many patients, who may be anxious or reluctant to visit a new dentist. By analysing, from multiple perspectives, the trustworthiness of information available to patients, this study proposes a new trust-enhanced information model for dental care recommendation systems. In this model, dentists are profiled based on subjective information extracted from dental crowdsources. Subjective qualities are also used to profile patients. Currently, online social network data cannot be used for profiling purposes due to privacy and identification concerns. Instead, one of the popular personality tests, the DISC personality test, is used in this study. The importance and suitability of subjective qualities for recommendations is explored. Two matching algorithms are evaluated based on the responses to an online survey. When the patients are classified based on their levels of fear, preferred search methods and other attributes, their list of recommended dentists changes. The subjective characteristics of both patients and dentists are important factors which need to be incorporated to improve the matching capability of dental care recommendation systems. Including the subjective qualities of users could change the way that recommendations are provided in the future, especially in the health sector where the wrong information can lead to adverse impacts on health. Although patients’ discussions about their health are sensitive and private, they can benefit from more accurate recommendations in relation to health care providers

Improving the matching process of dental care recommendation systems by using subjective criteria for both patients and dentists

Nowadays, healthcare recommendation systems are matching health professionals with patients based on preferences such as location, type of treatments, price, availability or other information including their type of health insurance. In the health social network domain, subjective criteria such as attitude, personality and behaviour have not been considered for matching of patients and health professionals. In this research, we focus on dental care recommendation systems and we aim at introducing subjective criteria in the matching process. Patients are profiled in terms of attitudes, personalities and behaviours through a set of questionnaires, derived from the popular methods such as DISC (Dominant, Influencer, Steady, and Compliant) personality test. In addition, we use crowdsourcing to extract feedback from patients and to profile dentists according to their qualities (e.g.: Friendly, caring, rude, etc.). These qualities are then used in the matching process. A thorough investigation on how to improve the matching process of a patient's subjective profile with a dentist's qualities is done through online questionnaires and focus group. The research aims at deriving a dynamic set of matching rules to improve the process of recommendation that includes subjective aspects so that in the future, patients can be better matched with the 'right' dentist for them

Consultation skills in veterinary practice: exploring the links between consultation skills and key performance indicators.

This project was part of a larger research project aimed at researching and supporting the development of a new postgraduate qualification in general veterinary practice. It arose in the beginning from the establishment of an MSc group of 8 experienced veterinary general practitioners in 2001, who researched various aspects of the question ‘what are the postgraduate educational needs of the GP vet in the UK?’ After completing the original work, 5 of the group continued their research into their Doctorates, and this project was the one in the subject of consultation skills. The subject was very largely unexplored in the veterinary world, but extensive research had been done in the medical field. There were the challenges of formulating methods of collecting primary data in the veterinary field, and looking at the medical field to see if the methodologies were transferable. The choice of methodologies was strongly influenced by fear factors, in which the researcher had to make choices based on what methods of data collection the veterinary practitioners accepted. Research instruments were found and developed to explore aspects of the consultation. Key performance indicators were explored firstly separately, and then the research explored possible links between consultation skills and KPI’s. Significant links were found, which lead to a greater understanding of the importance of consultation skills and how they might be analysed in ways that had potential for veterinary surgeons to see ways of identifying their skills and improving them. The results were of high impact value

Rapid assessment of drinking water quality: a handbook for implementation

Water is a basic human right as recently re-clarified in General Comment 15 of the Committee on Economic, Social and Cultural Rights. The importance of water for health and development has been reflected in international policy initiatives since the International Decade for Water Supply and Sanitation Development (the 1980s). This was further emphasised by the Millennium Declaration Goal to halve the proportion of the World's population that lacks access to water supply and most recently the identification of water and sanitation as a highest priority issue by the Commission for Sustainable Development. Since the 1960s, WHO has periodically reported on the development of the access to safe drinking-water sources; this is now implemented as a joint programme of work with UNICEF through the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation. In recent reviews and evaluations the need for JMP to progressively address new and emerging priorities has been highlighted. As part of the ongoing plan of work of JMP, a major initiative to more comprehensively take account of water quality issues has been initiated. This handbook has been developed as a resource to support the systematic evaluation of drinking-water quality status at country and local levels. It is hoped that it will be useful to those interested in describing and understanding drinking-water quality status in the context of JMP at national/state levels and on a project basis

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Trembling thoughts:Oral Health, orofacial pain and dysfunction in Parkinson’s disease

The general aim of this thesis was to further our knowledge on the umbrella term “oral health” in patients with Parkinson’s Disease (PD), including oral hygiene, oral health and diseases (e.g., gingivitis, periodontitis, tooth decay, and tooth loss) and orofacial pain and dysfunction (e.g., temporomandibular disorders (TMD) pain, limited jaw movements, and bruxism). The thesis is divided into two parts: (i) oral health and diseases in PD patients (Chapters 2-5) and (ii) orofacial pain and dysfunction in PD patients (Chapters 6-9). Based on the outcomes of the studies included in this thesis, it can be concluded that oral health in its broadest sense is worse in PD patients than in healthy controls. When improving oral health care in this vulnerable patient group is deemed desirable by all stakeholders, we must acknowledge the difficulties experienced by the (oral) health practitioners working with these patients to establish a well-oiled interdisciplinary collaboration