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Free-text reports are used in health care to transfer information between working shifts and sites. This text, written by a physician, nurse, specialist, ward secretary or other healthcare worker, is full of jargon, idioms and shorthand that patients find difficult to understand. If patients are to be empowered to take an active role and make informed decisions in their health care, they need support for understanding these reports. This chapter discusses language technologies as a way to provide support for patients to better understand free-text reports with difficult clinical language. This includes expanding shorthand, replacing words with patient-centric terms, providing term definitions, hyperlinking to further information on patientfriendly and reliable sites on the internet , and personalizing medication advice and other content. To conclude, statistical evaluations and benchmarks in shared tasks give evidence of language technologies being successful in making text easier to understand and better personalized. Moreover, electronic health record s that both patients and clinicians use to read, write and share information are becoming more commonplace and provide a platform for language technologies to assist patients in reading free-text reports.

Discharge Summaries contains vocabulary that is difficult to understand for consumers. We used semantic annotation in SemLink to dynamically generate synonyms and hyperlinks to appropriate Internet resources for difficult terms in discharge summary text to make the text more comprehensible to consumers. This paper describes our semantic annotation approach and evaluates our automatic hyperlink generation algorithm in terms of success in locating web pages to hyperlink for difficult terms in the Clinical Management sections of a corpus of 200 discharge summary texts. The system achieved 95% success in hyperlinking topically relevant web resources to the difficult terms; 83% of the hyperlinks could be restricted to resources of reading grade-level 8 or less with no reduction in relevance. In the context of discharge summaries we find automated hyperlinking to provide a good level of performance in leveraging openly available resources to aid consumer interpretation of difficult terms.

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Background Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

eILI assists clinicians to report ILI cases to public health authorities within a stipulated time period and is associated with faster, more reliable and improved information transfer.

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