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Published online by Cambridge University Press: 30 December 2016
Electronic health record (EHR) systems hold great promise for improved patient care, but thus far they have not yet fulfilled their potential. The United States is not alone in facing EHR system implementation hurdles. In 2002, England's National Health Service launched an ambitious “National Programme for IT” designed to create a national EHR system for the United Kingdom. In 2011, the British government announced that the program was being dismantled because of delays and budget problems. Nevertheless, in 2013, the UK Health Secretary announced a new “paperless NHS” project whose target completion date is 2018. Likewise, in 2011 the Dutch Parliament rejected legislation to establish a national federated EHR system because of privacy concerns, but in 2013 the national information exchange became fully operational after these concerns were addressed. Indeed, the vast majority of medical practices in the United Kingdom, the Netherlands, the United States, and other developed countries use EHR systems, however imperfect.
Because contemporary EHR systems suffer from many shortcomings, the medical big data that they produce is also often flawed. Data-quality problems can compromise the value of databases for scientific research, quality assessment, public health, and other purposes.
Robert Wachter, author of The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age, imagines a future of optimally effective EHR systems. He describes them as follows:
Computerized decision support for clinicians will … be taken to a new level. While physicians will still be ultimately responsible for making a final diagnosis, the EHR will suggest possible diagnoses for the physician to consider, along with tests and treatments based on guidelines and literature that are a click or a voice command away. Color-coded digital dashboards will show at a glance whether all appropriate treatments have been given …
Big-data analytics will be constantly at work, mining the patient's database to assess the risk of deterioration (infection, falls, bedsores, and the like) before such risks become clinically obvious. These risk assessments will seamlessly link to the dashboards, suggesting changes in monitoring, staffing, or treatments when a patient's risk profile changes.
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