Patient perceptions of electronic medical records: physician satisfaction, portability, security and quality of care

Sibona, Christopher; Walczak, Steven; Brickey, Jon; Parthasarathy, Madhavan

doi:10.1504/ijhtm.2011.037221

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Relations between patients and hospitals have traditionally been random, i.e., people used to visit healthcare facilities only when they were ill, and stopped visiting them as soon as they got recovered. Today, the emphasis is replaced by the preventive measures in order to prevent illness and keep good health at old age [9]. This is so-called "Medicine 4 P": Predictive, Personalized, Preemptive and Participatory [10].…”

Section: Electronic Healthmentioning

confidence: 99%

Security of Personal Medical Data for the Redis Concept

Muradova¹

2018

JPIT

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Section: Electronic Healthmentioning

confidence: 99%

Security of Personal Medical Data for the Redis Concept

Muradova¹

2018

JPIT

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“…In tandem with this unprecedented growth of digital data, techniques for data mining have gained popularity in a wide variety of domains. Incentives from government, such as the American Recovery and Reinvestment Act of 2009, have prompted many healthcare providers to adopt EHR systems at a much faster scale in last few years (Sibona et al, 2011). While the healthcare industry has benefited from information sharing and data mining, patients are increasingly concerned about invasion of their privacy by these practices.…”

Section: Introductionmentioning

confidence: 99%

Developing privacy solutions for sharing and analysing healthcare data

Motiwalla

2013

IJBIS

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The extensive use of electronic health data has increased privacy concerns. While most healthcare organizations are conscientious in protecting their data in their databases, very few organizations take enough precautions to protect data that is shared with third party organizations. Recently the regulatory environment has tightened the laws to enforce privacy protection. The goal of this research is to explore the application of data masking solutions for protecting patient privacy when data is shared with external organizations for research, analysis and other similar purposes. Specifically, this research project develops a system that protects data without removing sensitive attributes. Our application allows high quality data analysis with the masked data. Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are altered to prevent privacy disclosure. A pilot evaluation study on large real-world healthcare data shows the effectiveness of our solution in privacy protection.

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“…The 111th Congress has set aside substantial resources for effectiveness research-1. These incentives have prompted many healthcare providers to make heavy economic investments for adopting electronic health care systems which can become unavailable in the near future to clinical investigators [9]. Similarly, in Europe, medical research would soon be interrupted without access to individual data [10], thus posing a significant risk to economic investments in scientific infrastructures, registries, cohort studies and biologic banks.…”

mentioning

confidence: 99%