Insurees’ preferences in hospital choice—A population-based study

In Germany, the patient himself makes the choice for or against a health service provider. Hospital comparison websites offer him possibilities to inform himself before choosing. However, it remains unclear, how health care consumers use those websites, and there is little information about how preferences in hospital choice differ interpersonally.We conducted a Discrete-Choice-Experiment (DCE) on hospital choice with 1500 randomly selected participants (age 40–70) in three different German cities selecting four attributes for hospital vignettes. The analysis of the study draws on multilevel mixed effects logit regression analyses with the dependent variables: “chance to select a hospital” and “choice confidence”. Subsequently, we performed a Latent-Class-Analysis to uncover consumer segments with distinct preferences.590 of the questionnaires were evaluable. All four attributes of the hospital vignettes have a significant impact on hospital choice. The attribute “complication rate” exerts the highest impact on consumers’ decisions and reported choice confidence. Latent-Class-Analysis results in one dominant consumer segment that considered the complication rate the most important decision criterion.Using DCE, we were able to show that the complication rate is an important trusted criterion in hospital choice to a large group of consumers. Our study supports current governmental efforts in Germany to concentrate the provision of specialized health care services. We suggest further national and cross-national research on the topic.     

Transitioning Technologies: Pathfinders of the Future

Medical libraries have a history of providing quality health information. Consumers who search the Internet on their own often find out-of-date, biased, or invalid information. A strong library web presence is the best approach to connecting consumers to reliable, online resources. However, hospital web development teams focus on strategic initiatives and are not always accommodating to the web page needs of medical librarians. Utilizing LibGuides and aligning the content of web pages with hospital approved sites, librarians at a large academic medical center successfully curated modern pathfinders that are attractive, easily updated, and acceptable to hospital leadership. This article details the creation, content selection, and usage of the pathfinder guides.     

Consumer Health Education Center: An Overview of Library Services

This paper provides a brief review of the development and history of the Community Health Education Center (CHEC), a library for patients and their family members over its past 14 years. Over time, CHEC management recognized new patient needs and evolved to meet the needs of its users through a variety of value-added services. This article discusses current services that CHEC offers including reference, business services, and health and wellness programs. Libraries planning to provide consumer health information services to patients and their family members can learn valuable lessons from the history, development, challenges, and services of CHEC.     

Use of Social Media for Consumer Health Outreach in a Solo Hospital Library

Using social media in hospital libraries is an excellent tool for reaching and engaging existing or potential users. However, small or solo hospital librarians do not always have enough staff or time to incorporate these resources into their menu of services. Hospital librarians should consider the potential benefits of using the library as a trusted social media resource for the community and can customize outreach without sacrificing staff time, straining the budget, or battling the firewall. Having the hospital library offer health information to the community through social media has many potential benefits for improving health knowledge and health literacy.     

MELLO: Medical lifelog ontology for data terms from self-tracking and lifelog devices

ObjectiveThe increasing use of health self-tracking devices is making the integration of heterogeneous data and shared decision-making more challenging. Computational analysis of lifelog data has been hampered by the lack of semantic and syntactic consistency among lifelog terms and related ontologies. Medical lifelog ontology (MELLO) was developed by identifying lifelog concepts and relationships between concepts, and it provides clear definitions by following ontology development methods. MELLO aims to support the classification and semantic mapping of lifelog data from diverse health self-tracking devices.MethodsMELLO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of lifelog data, (2) identifying lifelog concepts, (3) assigning relationships among MELLO concepts, (4) developing MELLO properties (e.g., synonyms, preferred terms, and definitions) for each MELLO concept, and (5) evaluating representative layers of the ontology content. An evaluation was performed by classifying 11 devices into 3 classes by subjects, and performing pairwise comparisons of lifelog terms among 5 devices in each class as measured using the Jaccard similarity index.ResultsMELLO represents a comprehensive knowledge base of 1998 lifelog concepts, with 4996 synonyms for 1211 (61%) concepts and 1395 definitions for 926 (46%) concepts. The MELLO Browser and MELLO Mapper provide convenient access and annotating non-standard proprietary terms with MELLO (http://mello.snubi.org/). MELLO covers 88.1% of lifelog terms from 11 health self-tracking devices and uses simple string matching to match semantically similar terms provided by various devices that are not yet integrated. The results from the comparisons of Jaccard similarities between simple string matching and MELLO matching revealed increases of 2.5, 2.2, and 5.7 folds for physical activity,body measure, and sleep classes, respectively.ConclusionsMELLO is the first ontology for representing health-related lifelog data with rich contents including definitions, synonyms, and semantic relationships. MELLO fills the semantic gap between heterogeneous lifelog terms that are generated by diverse health self-tracking devices. The unified representation of lifelog terms facilitated by MELLO can help describe an individual's lifestyle and environmental factors, which can be included with user-generated data for clinical research and thereby enhance data integration and sharing.     

我国2市/县6所医院门急诊产品伤害监测结果的描述性分析

目的了解我国产品伤害的特点,探索在我国建立以医院为基础的产品伤害监测系统模式,为今后开展产品伤害预防控制工作提供基础资料和科学依据。方法选取两个全国伤害监测系统监测点作为试点,在试点地区6所监测医院由护士或医生填写统一制定的《产品伤害监测报告卡》,收集伤害就诊患者的伤害及相关产品信息。结果共上报有效伤害病例数39 573例,其中与产品相关的伤害25 102例（63.43%）。涉及人次数居前三位的产品依次为道路交通工具（25.52%）、公共场所设施（23.45%）和家庭用品（21.82%）。产品相关伤害中,男性占67.05%,主要发生在25~44岁（42.86%）、20~24岁（16.51%）和45~64岁（11.95%）。主要发生原因为跌倒/坠落（31.36%）、道路交通伤害（28.12%）和钝器伤（20.08%）;主要伤害部位为上肢（33.34%）、头部（24.16%）和下肢（23.59%）;主要伤害性质是挫伤/擦伤（41.94%）、锐器伤/咬伤/开放伤（41.13%）和扭伤/拉伤（10.48%）;以非故意伤害（94.15%）、轻度伤害（84.04%）和接受治疗后回家（87.00%）为主。结论男性和25~44岁青壮年是产品相关伤害的主要影响人群,道路交通工具、公共场所设施和家庭用品是伤害涉及的主要产品类型,应针对上述特点进一步挖掘信息,针对性地采取有效的预防措施,为制定产品伤害管理政策、策略提供基础信息。以医院为基础的产品伤害监测模式收集产品伤害基础信息在我国可行。     

Computer-assisted update of a consumer health vocabulary through mining of social network data

Background

Consumer health vocabularies (CHVs) have been developed to aid consumer health informatics applications. This purpose is best served if the vocabulary evolves with consumers’ language.

Objective

Our objective was to create a computer assisted update (CAU) system that works with live corpora to identify new candidate terms for inclusion in the open access and collaborative (OAC) CHV.

Methods

The CAU system consisted of three main parts: a Web crawler and an HTML parser, a candidate term filter that utilizes natural language processing tools including term recognition methods, and a human review interface. In evaluation, the CAU system was applied to the health-related social network website PatientsLikeMe.com. The system’s utility was assessed by comparing the candidate term list it generated to a list of valid terms hand extracted from the text of the crawled webpages.

Results

The CAU system identified 88,994 unique terms 1- to 7-grams (“n-grams” are n consecutive words within a sentence) in 300 crawled PatientsLikeMe.com webpages. The manual review of the crawled webpages identified 651 valid terms not yet included in the OAC CHV or the Unified Medical Language System (UMLS) Metathesaurus, a collection of vocabularies amalgamated to form an ontology of medical terms, (ie, 1 valid term per 136.7 candidate n-grams). The term filter selected 774 candidate terms, of which 237 were valid terms, that is, 1 valid term among every 3 or 4 candidates reviewed.

Conclusion

The CAU system is effective for generating a list of candidate terms for human review during CHV development.