Dynamic Generation of a Table of Contents with Consumer-Friendly Labels

Consumers increasingly look to the Internet for health information, but available resources are too difficult for the majority to understand. Interactive tables of contents (TOC) can help consumers access health information by providing an easy to understand structure. Using natural language processing and the Unified Medical Language System (UMLS), we have automatically generated TOCs for consumer health information. The TOC are categorized according to consumer-friendly labels for the UMLS semantic types and semantic groups. Categorizing phrases by semantic types is significantly more correct and relevant. Greater correctness and relevance was achieved with documents that are difficult to read than with those at an easier reading level. Pruning TOCs to use categories that consumers favor further increases relevancy and correctness while reducing structural complexity

Personalising patient Internet searching using electronic patient records

The research reported in this thesis addresses a patient's information requirements when searching the Internet for health information. A patient's lack of information about his/her health condition and its care is officially acknowledged and traditional patient information sources do not address today's patient information needs. Internet health information resources have become the foremost health information platform. However, patient Internet searching is currently manual, uncustomised and hindered by health information vocabulary and quality challenges. Patient access to quality Internet health information is currently ensured through national health gateways, medical search engines, third-party accredited search engines and charity health websites. However, such resources are generic, i.e. do not cater for a patient particular information needs. In this study, we propose personalising patient Internet searching by enabling a patient's access to their Electronic Patient Records (EPRs) and using this EPR data in Internet information searching. The feasibility of patient access to EPRs has recently been promoted by national health information programmes. Very recently, in the literature, there are reports about pilot studies on personal Health Record (PHR) systems that offer a patient online access to their medical records and related health information. However, the extensive literature searching shows no reports about patient-personalised search engines, within the reported PHR prototypes, that utilise a patient's own data to personalise the search features for a patient especially with regard to health information vocabulary needs. The thesis presents a novel approach to personalising patient information searching based on linking EPR data with relevant Internet Information resources, integrating medical and lay perspectives in a diagnosis vocabulary that distinguishes between medical and lay information needs, and accommodating a variable perspective on online information quality. To demonstrate our research work, we have implemented a prototype online patient personal health information system, known as the Patient Health Base (PHB) that offers a patient a Summary Medical Record (SMR) and a Personal Internet Search (PerlS) service. PerlS addresses patient Internet search challenges identified in the project. Evaluation of PerlS's approach to improving a patient's medical Internet searching demonstrated improvements in terms of search capabilities, focusing techniques and results. This research explored a new direction for patient Internet searching and foresees a great potential for further customising Internet information searching for patients, families and the public as a whole