Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare

Preventing Opioid Misuse with Prescription Drug Monitoring Programs: A Framework for Evaluating the Success of State Public Health Laws

The United States is in the midst of a prescription opioid overdose and misuse epidemic. Although many factors have contributed to the escalation of prescription painkiller misuse, it parallels increases in the supply and prescribing of opioids. Prominent state-level regulatory interventions, such as the establishment of prescription drug monitoring programs (“PDMPs”), recognize prescribers as opioid gatekeepers. Prescribers, who are uniquely situated to distinguish between appropriate use and misuse of opioids, are a natural target for regulation. PDMPs also target patients who seek to obtain high volumes of prescription opioids for illicit purposes. PDMP policies are widespread but heterogeneous, largely uninformed by robust evidence or a systematic assessment of best practices. Whether these programs successfully reduce opioid misuse and overdoses remains unclear. As well, PDMPs present a number of legal and ethical challenges that, along with intervention effectiveness, warrant careful policymaker consideration going forward. This Article articulates and synthesizes for the first time key criteria intended to assist state regulators in dynamically evaluating and justifying PDMPs and other public health laws. The criteria focus on the legality of the policy, approaches to measure its effectiveness, and normative considerations that should be factored into good laws. Such a framework is crucial for policymakers given the complexities and magnitude of this public health challenge, the rich arsenal of policy options from which to choose, and the slow and uncertain progress in combating prescription painkiller misuse. Concluding recommendations include implementing PDMPs with the following features: timely and complete data, strong incentives for prescriber participation, user guidelines and education, integration into clinical work flow, and robust confidentiality and privacy protections. Ongoing evaluation of programs to identify features appropriate for retention and replication is also crucial if PDMPs are to fulfill their potential to curb prescription opioid overdose and misuse

Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data