The Emergence of National Electronic Health Record Architectures in the United States and Australia: Models, Costs, and Questions

By T. Gunter, Nicolas P. Terry. 2005

472 citations recorded by the source corpus.

Emerging electronic health record models present numerous challenges to health care systems, physicians, and regulators. This article provides explanation of some of the reasons driving the development of the electronic health record, describes two national electronic health record models (currently developing in the United States and Australia) and one distributed, personal model. The US and Australian models are contrasted in their different architectures (“pull” versus “push”) and their different approaches to patient autonomy, privacy, and confidentiality. The article also discusses some of the professional, practical, and legal challenges that health care providers potentially face both during and after electronic health record implementation.

Subfields: Health Information Management, Pharmacy, Public Health, Environmental and Occupational Health, Medical & Health AI, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Yong Chen
2. Sicong Che
3. Lifang He
4. Lichao Sun

In Medical & Health AI

1. Yong Chen
2. Sicong Che
3. Lifang He
4. Lichao Sun

Citations in the corpus

• Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “, electronic health records (EHRs) [22], claims, laboratory tests, imaging, and genomics) has been accumulated in medical institutions.”
• Using Blockchain for Electronic Health Records (2019, background): “A number of researchers have also identified that using blockchain technology in healthcarewould be a feasible solution [14]–[16].”
• Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. (2014, unknown): “Our operational definitions for considering a study “compliant” with the EHR were inclusive: from clinical data repository and health data repository (CDHR), to electronic medical---patient record (EMR and EPR), and EHR.(22)”
• Personal Health Records: A Systematic Literature Review (2017, unknown): “Health records are collections of patient health data, and the EHR is defined as a digital repository of the health status of patients [2-4].”
• From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis (2015, unknown): “…transformative approach to this problem would be to exploit the vast amount of information that is hidden in the Electronic Health Records (EHR) of patients to derive a CDSS. Adoption of EHR’s by health care systems was predicted to vastly improve the efficiency and quality of patient care [4].”