Health informatics support for outbreak management: How to respond without an electronic health record?

The seamless electronic health record is often hailed as the holy grail of health informatics. What is an electronic health record? This question is answered and consideration is given to the advantages and disadvantages of an electronic health record. The place of the electronic health record at the centre of a clinical information system is discussed. In expanding on the advantages several areas are covered including: analysis of data, accessibility and availability, and access control. Middleware technology and its place are discussed. Requirements for implementing a system and some of the issues that can arise in the field of women’s health are elucidated. Finally, in this exciting and fast moving field, future research is discussed.

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Toward an Electronic Health Record Leveraged to Learn from Every Complex Patient Encounter: Health Informatics Considerations with Pediatric Intestinal Rehabilitation as a Model

The Journal of Pediatrics ◽

10.1016/j.jpeds.2019.07.047 ◽

2019 ◽

Vol 215 ◽

pp. 257-263 ◽

Cited By ~ 1

Author(s):

Ethan A. Mezoff ◽

Peter C. Minneci ◽

Richard R. Hoyt ◽

Jeffrey M. Hoffman

Keyword(s):

Electronic Health Record ◽

Health Informatics ◽

Health Record ◽

Patient Encounter ◽

Complex Patient ◽

Intestinal Rehabilitation ◽

Electronic Health

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A tool to improve the efficiency and reproducibility of research using electronic health record databases

International Journal for Population Data Science ◽

10.23889/ijpds.v3i2.540 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Mohammad Al Sallakh ◽

Sarah Rodgers ◽

Ronan Lyons ◽

Aziz Sheikh ◽

Gwyneth Davies

Keyword(s):

Electronic Health Record ◽

Health Informatics ◽

Query Language ◽

Data Extraction ◽

Building Blocks ◽

Health Board ◽

Health Record ◽

Complex Data ◽

Versatile Tool ◽

Electronic Health

BackgroundInterrogation of electronic health record databases often involves time-consuming, manual, repetitive work in developing database queries. We developed a tool to automate this process. MethodsWe identified elementary approaches to query primary care data from the Secure Anonymised Information Linkage databank of Wales. We designed a web-based query builder that allows using combinations of these approaches as ‘building blocks’ to query complex variables. We created an R programme to automatically generate and execute the corresponding Structured Query Language queries. ResultsThe tool allows data extraction using combinations of the following methods: event count (e.g., asthma prescriptions); code/date of earliest/latest event; code/date/value of the event of maximum/minimum value; and frequency of temporally constrained events. Query intervals could be fixed, dynamic, or individualised. The tool integrates with a codeset repository. Data extraction procedures and codesets are saved on a web server as versioned, shareable, and citable objects. ConclusionThis versatile tool allows rapid and complex data extraction with minimal to no programming skills, reduces human errors, and improves research transparency and reproducibility. Funding/SupportHealth and Care Research Wales, ABMU Health Board, AUKCAR (AUK-AC-2012-01), Farr Institute of Health Informatics Research (MR/K006525/1-MR/K007017/1).

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