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Article ; Online: Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

Ashraf, Amir Reza / Mackey, Tim Ken / Fittler, András

JMIR public health and surveillance

2024 Volume 10, Page(s) e53086

Abstract: Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation ... ...

Abstract	Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. Methods: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Results: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.
MeSH term(s)	Humans ; Artificial Intelligence ; Controlled Substances ; Public Health ; Search Engine ; Databases, Factual
Chemical Substances	Controlled Substances
Language	English
Publishing date	2024-03-21
Publishing country	Canada
Document type	Journal Article
ISSN	2369-2960
ISSN (online)	2369-2960
DOI	10.2196/53086
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Tobacco Product Marketing Orders and Online Marketing and Sale of Unauthorized ENDS Products.

Le, Nicolette / McMann, Tiana J / Cui, Mandy / Cuomo, Raphael E / Yang, Joshua S / Mackey, Tim Ken

JAMA internal medicine

2023 Volume 183, Issue 10, Page(s) 1170–1172

Language	English
Publishing date	2023-09-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2699338-7
ISSN	2168-6114 ; 2168-6106
ISSN (online)	2168-6114
ISSN	2168-6106
DOI	10.1001/jamainternmed.2023.3101
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Article ; Online: State Health Care Reform: Waivers, Single-Payer, and the Need for Alternative Pathways.

Maroulis, James / Mackey, Tim Ken

Annals of internal medicine

2019 Volume 171, Issue 4, Page(s) 281–282

MeSH term(s)	Delivery of Health Care/legislation & jurisprudence ; Federal Government ; Health Care Reform/legislation & jurisprudence ; Humans ; Insurance, Health/legislation & jurisprudence ; Medicaid/legislation & jurisprudence ; Medicare/legislation & jurisprudence ; Patient Protection and Affordable Care Act ; State Government ; United States
Language	English
Publishing date	2019-07-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	336-0
ISSN	1539-3704 ; 0003-4819
ISSN (online)	1539-3704
ISSN	0003-4819
DOI	10.7326/M19-0509
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Article ; Online: An Interdisciplinary Review of Surgical Data Recording Technology Features and Legal Considerations.

Jue, Jessica / Shah, Neal A / Mackey, Tim Ken

Surgical innovation

2019 Volume 27, Issue 2, Page(s) 220–228

Abstract: Surgical data recording technology has great promise to generate patient safety and quality data that can be utilized to potentially reduce medical errors. Variations of these systems aim to improve surgical technique, develop better training simulation, ...

Abstract	Surgical data recording technology has great promise to generate patient safety and quality data that can be utilized to potentially reduce medical errors. Variations of these systems aim to improve surgical technique, develop better training simulation, and promote adverse event investigation similar to the aims of black box technology utilized in other industries. However, many unknowns remain for surgical data recording utilization in operating rooms and clinical settings in the United States. This includes the need to appropriately design systems so they collect meaningful and useful data that can be discussed by surgical team members in an open and safe environment to optimize clinical care processes. In order to better understand the clinical and regulatory environment for surgical data recording systems, we conducted an interdisciplinary review to identify key technology approaches, and assess legal and regulatory implications associated with this potentially disruptive technology. We found technology ranging from audio and visual data, to systems utilizing mobile applications, and kinematic data capture. The data collected present legal questions over ownership of information and privacy, along with regulatory issues at the federal and state levels. The benefits of these data should be balanced with the need to develop appropriate policies and regulations that protect the interests of both clinicians and patients in order to encourage further innovation and better realize the potential of surgical data recording technology to improve clinical decision making and patient safety outcomes.
MeSH term(s)	Documentation/methods ; Humans ; Iatrogenic Disease ; Medical Errors ; Operating Rooms/legislation & jurisprudence ; Operating Rooms/standards ; Patient Safety ; Surgical Procedures, Operative/adverse effects ; Surgical Procedures, Operative/legislation & jurisprudence ; Surgical Procedures, Operative/standards ; United States ; Video Recording
Language	English
Publishing date	2019-12-06
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2182571-3
ISSN	1553-3514 ; 1553-3506
ISSN (online)	1553-3514
ISSN	1553-3506
DOI	10.1177/1553350619891379
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Article ; Online: Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework.

Mackey, Tim Ken / Miyachi, Ken / Fung, Danny / Qian, Samson / Short, James

Journal of medical Internet research

2020 Volume 22, Issue 9, Page(s) e18623

Abstract: Background: An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then ...

Abstract	Background: An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse. Objective: The aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse. Methods: We developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation). Results: Our technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network. Conclusions: The proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.
MeSH term(s)	Algorithms ; Blockchain/standards ; Concept Formation/ethics ; Fraud/ethics ; Humans ; Medical Informatics/methods ; Medicare/standards ; United States
Language	English
Publishing date	2020-09-10
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/18623
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Emerging ethical issues in digital health information.

Solomonides, Anthony E / Mackey, Tim Ken

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees

2015 Volume 24, Issue 3, Page(s) 311–322

Abstract: The problems of poor or biased information and of misleading health and well-being advice on the Internet have been extensively documented. The recent decision by the Internet Corporation for Assigned Names and Numbers to authorize a large number of new ... ...

Abstract	The problems of poor or biased information and of misleading health and well-being advice on the Internet have been extensively documented. The recent decision by the Internet Corporation for Assigned Names and Numbers to authorize a large number of new generic, top-level domains, including some with a clear connection to health or healthcare, presents an opportunity to bring some order to this chaotic situation. In the case of the most general of these domains, ".health," experts advance a compelling argument in favor of some degree of content oversight and control. On the opposing side, advocates for an unrestricted and open Internet counter that this taken-for-granted principle is too valuable to be compromised, and that, once lost, it may never be recovered. We advance and provide evidence for a proposal to bridge the credibility gap in online health information by providing provenance information for websites in the .health domain.
MeSH term(s)	Consumer Health Information/ethics ; Health Information Exchange/ethics ; Humans ; Information Dissemination ; Internet/ethics ; Patient Education as Topic/ethics
Language	English
Publishing date	2015-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	1146581-5
ISSN	1469-2147 ; 0963-1801
ISSN (online)	1469-2147
ISSN	0963-1801
DOI	10.1017/S0963180114000632
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Article ; Online: Global reach of direct-to-consumer advertising using social media for illicit online drug sales.

Mackey, Tim Ken / Liang, Bryan A

Journal of medical Internet research

2013 Volume 15, Issue 5, Page(s) e105

Abstract: Background: Illicit or rogue Internet pharmacies are a recognized global public health threat that have been identified as utilizing various forms of online marketing and promotion, including social media.: Objective: To assess the accessibility of ... ...

Abstract	Background: Illicit or rogue Internet pharmacies are a recognized global public health threat that have been identified as utilizing various forms of online marketing and promotion, including social media. Objective: To assess the accessibility of creating illicit no prescription direct-to-consumer advertising (DTCA) online pharmacy social media marketing (eDTCA2.0) and evaluate its potential global reach. Methods: We identified the top 4 social media platforms allowing eDTCA2.0. After determining applicable platforms (ie, Facebook, Twitter, Google+, and MySpace), we created a fictitious advertisement advertising no prescription drugs online and posted it to the identified social media platforms. Each advertisement linked to a unique website URL that consisted of a site error page. Employing Web search analytics, we tracked the number of users visiting these sites and their location. We used commercially available Internet tools and services, including website hosting, domain registration, and website analytic services. Results: Illicit online pharmacy social media content for Facebook, Twitter, and MySpace remained accessible despite highly questionable and potentially illegal content. Fictitious advertisements promoting illicit sale of drugs generated aggregate unique user traffic of 2795 visits over a 10-month period. Further, traffic to our websites originated from a number of countries, including high-income and middle-income countries, and emerging markets. Conclusions: Our results indicate there are few barriers to entry for social media-based illicit online drug marketing. Further, illicit eDTCA2.0 has globalized outside US borders to other countries through unregulated Internet marketing.
MeSH term(s)	Advertising ; Commerce ; Community Participation ; Illicit Drugs ; Internationality ; Pharmaceutical Services, Online ; Social Media
Chemical Substances	Illicit Drugs
Language	English
Publishing date	2013-05-29
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/jmir.2610
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Combating Health Care Fraud and Abuse

Mackey, Tim Ken / Miyachi, Ken / Fung, Danny / Qian, Samson / Short, James

Journal of Medical Internet Research, Vol 22, Iss 9, p e

Conceptualization and Prototyping Study of a Blockchain Antifraud Framework

2020 Volume 18623

Abstract: BackgroundAn estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then ... ...

Abstract	BackgroundAn estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse. ObjectiveThe aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse. MethodsWe developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation). ResultsOur technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network. ConclusionsThe proposed framework and the initial prototype have the potential to improve the ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Subject code	360
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.

Xu, Qing / Nali, Matthew C / McMann, Tiana / Godinez, Hector / Li, Jiawei / He, Yifan / Cai, Mingxiang / Lee, Christine / Merenda, Christine / Araojo, Richardae / Mackey, Tim Ken

JMIR infodemiology

2022 Volume 2, Issue 1, Page(s) e35446

Abstract: Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand ...

Abstract	Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. Objective: This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods: The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention-related themes discussed by users. Results: We collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. Conclusions: Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.
Language	English
Publishing date	2022-04-28
Publishing country	Canada
Document type	Journal Article
ISSN	2564-1891
ISSN (online)	2564-1891
DOI	10.2196/35446
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram.

Mackey, Tim Ken / Li, Jiawei / Purushothaman, Vidya / Nali, Matthew / Shah, Neal / Bardier, Cortni / Cai, Mingxiang / Liang, Bryan

JMIR public health and surveillance

2020 Volume 6, Issue 3, Page(s) e20794

Abstract: Background: The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel "infodemic," including the online marketing and sale of unapproved, illegal, and ... ...

Abstract	Background: The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel "infodemic," including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable "cures." Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective: This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related health care products from Twitter and Instagram. Methods: This study is conducted in two phases beginning with the collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. Results: We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19-related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. Conclusions: Results from this study provide initial insight into one front of the "infodemic" fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.
MeSH term(s)	Big Data ; COVID-19 ; Commerce/legislation & jurisprudence ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Deep Learning ; Fraud/statistics & numerical data ; Humans ; Marketing/legislation & jurisprudence ; Natural Language Processing ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Social Media/statistics & numerical data ; United States/epidemiology
Keywords	covid19
Language	English
Publishing date	2020-08-25
Publishing country	Canada
Document type	Journal Article
ISSN	2369-2960
ISSN (online)	2369-2960
DOI	10.2196/20794
Database	MEDical Literature Analysis and Retrieval System OnLINE

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