The comprehensive diagnostic study is suggested as a design to model the diagnostic process

ObjectivesThe classical diagnostic cross-sectional study has a focus on one disease only. Generalist clinicians, however, are confronted with a wide range of diagnoses. We propose the “comprehensive diagnostic study design” to evaluate diagnostic tests regarding more than one disease outcome.Study Design and SettingWe present the secondary analysis of a data set obtained from patients presenting with chest pain in primary care. Participating clinicians recorded 42 items of the history and physical examination. Diagnostic outcomes were reviewed by an independent panel after 6-month follow-up (n = 710 complete cases). We used Shannon entropy as a measure of uncertainty before and after testing. Four different analytical strategies modeling specific clinical ways of reasoning were evaluated.ResultsAlthough the “global entropy” strategy reduced entropy most, it is unlikely to be of clinical use because of its complexity. “Inductive” and “fixed-set” strategies turned out to be efficient requiring a small amount of data only. The “deductive” procedure resulted in the smallest reduction of entropy.ConclusionWe suggest that the comprehensive diagnostic study design is a feasible and valid option to improve our understanding of the diagnostic process. It is also promising as a justification for clinical recommendations.     

Extracting general practice data for timely vaccine coverage estimates: The England experience

In England, primary care providers use standardised coding systems to record health events such as vaccination as well as patient characteristics. This information can be automatically extracted to estimate coverage for vaccine programmes delivered through primary care, in the general population as well as in specific geographical, ethnic, age or clinical groups. This system provides timely vaccine coverage estimates as well as the flexibility to extract tailored data in order to directly inform a continuously evolving national vaccine programme. It is however limited by the quality and completeness of clinical coding in primary care. A centralised, individual-level register would however improve data quality, completeness and reliability and remains the gold standard.     

Utility-preserving privacy protection of textual healthcare documents

The adoption of ITs by medical organisations makes possible the compilation of large amounts of healthcare data, which are quite often needed to be released to third parties for research or business purposes. Many of this data are of sensitive nature, because they may include patient-related documents such as electronic healthcare records. In order to protect the privacy of individuals, several legislations on healthcare data management, which state the kind of information that should be protected, have been defined. Traditionally, to meet with current legislations, a manual redaction process is applied to patient-related documents in order to remove or black-out sensitive terms. This process is costly and time-consuming and has the undesired side effect of severely reducing the utility of the released content. Automatic methods available in the literature usually propose ad-hoc solutions that are limited to protect specific types of structured information (e.g. e-mail addresses, social security numbers, etc.); as a result, they are hardly applicable to the sensitive entities stated in current regulations that do not present those structural regularities (e.g. diseases, symptoms, treatments, etc.). To tackle these limitations, in this paper we propose an automatic sanitisation method for textual medical documents (e.g. electronic healthcare records) that is able to protect, regardless of their structure, sensitive entities (e.g. diseases) and also those semantically related terms (e.g. symptoms) that may disclose the former ones. Contrary to redaction schemes based on term removal, our approach improves the utility of the protected output by replacing sensitive terms with appropriate generalisations retrieved from several medical and general-purpose knowledge bases. Experiments conducted on highly sensitive documents and in coherency with current regulations on healthcare data privacy show promising results in terms of the practical privacy and utility of the protected output.     

利用儿童主索引解决预防接种管理信息共享问题的研究 与应用

将患者主索引的概念应用到儿童预防接种信息管理系统（CIIMS）中，创建儿童主索引，详细阐述其设计思想、在CIIMS中的应用模式以及区域平台之间的数据交换流程与实际应用场景，使儿童接种信息在全国范围实现共享。     

Brief communication: The Mid-South Clinical Data Research Network

The Mid-South Clinical Data Research Network (CDRN) encompasses three large health systems: (1) Vanderbilt Health System (VU) with electronic medical records for over 2 million patients, (2) the Vanderbilt Healthcare Affiliated Network (VHAN) which currently includes over 40 hospitals, hundreds of ambulatory practices, and over 3 million patients in the Mid-South, and (3) Greenway Medical Technologies, with access to 24 million patients nationally. Initial goals of the Mid-South CDRN include: (1) expansion of our VU data network to include the VHAN and Greenway systems, (2) developing data integration/interoperability across the three systems, (3) improving our current tools for extracting clinical data, (4) optimization of tools for collection of patient-reported data, and (5) expansion of clinical decision support. By 18 months, we anticipate our CDRN will robustly support projects in comparative effectiveness research, pragmatic clinical trials, and other key research areas and have the capacity to share data and health information technology tools nationally.     

Implementing partnership-driven clinical federated electronic health record data sharing networks

ObjectiveBuilding federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results.MethodsTwo federated data sharing pilot architectures developed to support network-based research associated with the University of Washington’s Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages.ResultsWe found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility.ConclusionAn iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.