The dual impact of AI on public health: Ethical risk of AI-generated case reports

Is it ethical to be a 'whistleblower' during COVID-19 pandemic? Ethical challenges confronted by health care workers in China.

In 1981, a profound crisis of authority was transforming the American health polity. Changing priorities between infectious and chronic diseases, communal and individual responsibilities for health, and comprehensive services and cost control created a fragmented health polity, leaderless and ill-equipped to address the AIDS epidemic. The American health polity may best serve the public interest when institutions within it do not accept fragmentation as the goal and the norm of health affairs.

Presidential Address: Our Voice in Action: Advancing Science, Care, and Our Profession

Patient Dumping: The Physician's Dilemma

Blissful Ignorance When Managing Pregnant Women With Syphilis and Nonreactive Nontreponemal Tests?

BackgroundPublic health reporting is the cornerstone of public health practices that inform prevention and control strategies. There is a need to leverage advances made in the past to implement an architecture that facilitates the timely and complete public health reporting of relevant case-related information that has previously not easily been available to the public health community. Electronic laboratory reporting (ELR) is a reliable method for reporting cases to public health authorities but contains very limited data. In an earlier pilot study, we designed the Public Health Automated Case Event Reporting (PACER) platform, which leverages existing ELR infrastructure as the trigger for creating an electronic case report. PACER is a FHIR (Fast Health Interoperability Resources)-based system that queries the electronic health record from where the laboratory test was requested to extract expanded additional information about a case.ObjectiveThis study aims to analyze the pilot implementation of a modified PACER system for electronic case reporting and describe how this FHIR-based, open-source, and interoperable system allows health systems to conduct public health reporting while maintaining the appropriate governance of the clinical data. MethodsELR to a simulated public health department was used as the trigger for a FHIR-based query. Predetermined queries were translated into Clinical Quality Language logics. Within the PACER environment, these Clinical Quality Language logical statements were managed and evaluated against the providers’ FHIR servers. These predetermined logics were filtered, and only data relevant to that episode of the condition were extracted and sent to simulated public health agencies as an electronic case report. Design and testing were conducted at the Georgia Tech Research Institute, and the pilot was deployed at the Medical University of South Carolina. We evaluated this architecture by examining the completeness of additional information in the electronic case report, such as patient demographics, medications, symptoms, and diagnoses. This additional information is crucial for understanding disease epidemiology, but existing electronic case reporting and ELR architectures do not report them. Therefore, we used the completeness of these data fields as the metrics for enriching electronic case reports.ResultsDuring the 8-week study period, we identified 117 positive test results for chlamydia. PACER successfully created an electronic case report for all 117 patients. PACER extracted demographics, medications, symptoms, and diagnoses from 99.1% (116/117), 72.6% (85/117), 70.9% (83/117), and 65% (76/117) of the cases, respectively.ConclusionsPACER deployed in conjunction with electronic laboratory reports can enhance public health case reporting with additional relevant data. The architecture is modular in design, thereby allowing it to be used for any reportable condition, including evolving outbreaks. PACER allows for the creation of an enhanced and more complete case report that contains relevant case information that helps us to better understand the epidemiology of a disease.

The COVID-19 pandemic of 2020 is a major global health challenge. At the time of writing, over 11.6 million people around the world had been registered as infected and 538,000 had died (Worldometers, 2020, accessed July 7, 2020). Public health responses to COVID-19 need to balance direct efforts to control the disease and its impact on health systems, infected people, and their families with the impacts from associated mitigating interventions. Such impacts include social isolation, school closure, health service disruption stemming from reconfiguring health systems, and diminished economic activity. The primary focus of both the United Nations (UN) and the World Health Organization (WHO) has been on addressing COVID-19 as a physical health crisis, but the need to strengthen mental health action, including suicide prevention, is increasingly recognized, as is the need for mental health research to be an integral part of the recovery plan (UN, 2020a). The impacts of the pandemic on physical and mental health will unfold differently over time and will vary depending on the duration and fluctuating intensity of the disease. Research is needed to help ensure that decision-making regarding all aspects of health, including mental health (Holmes et al., 2020), is informed by the best quality data at each stage of the pandemic. The pandemic poses a prolonged and unique challenge to public mental health, with major implications for suicide and suicide prevention (Gunnell et al., 2020; Reger, Stanley, & Joiner, 2020). A rise in suicide deaths in the wake of the pandemic is not inevitable. There is consensus, however, that the mitigation of risk will be contingent upon a proactive and effective response involving collaborative work between the state, NGOs, academia, and local governments and coordinated leadership across government ministries, including health, education, security, social services, welfare, and finance. Countries have responded in different ways to the pandemic, effectively creating a series of natural experiments. Thus, regions of the world affected later in the pandemic can draw on lessons from countries, such as China and Italy, affected in its early phase. Likewise, lessons learned early in the pandemic (e.g., on the impact of lockdown and physical distancing measures) can be used to inform responses to any future surges in the incidence of COVID-19. Although there are important parallels between countries in the course of the pandemic, some stressors, responses, and priorities are likely to differ between high- and low–middle-income countries and between cultures and regions. As COVID-19 appears to be disproportionately affecting Black, Asian, and minority ethnic communities, the response – and suicide prevention research carried out to inform the response – needs to be sufficiently granular and account for the complexity of risks in these groups (O'Connor et al., 2020). Throughout this editorial, when we refer to suicide and suicidal behavior, we mean to include both fatal and nonfatal suicidal behaviors and self-harm.

Applications of predictive modelling early in the COVID-19 epidemic

Infectious diseases can appear and spread rapidly. Timely information about disease patterns and trends allows public health agencies to quickly investigate and efficiently contain those diseases. But disease case reporting to public health has traditionally been paper-based, resulting in somewhat slow, burdensome processes. Fortunately, the expanding use of electronic health records and health information exchanges has created opportunities for more rapid, complete, and easily managed case reporting and investigation. To assess how this new service might impact the efficiency and quality of a public health agency's case investigations, we compared the timeliness of usual case investigation to that of case investigations based on case report forms that were partially pre-populated with electronic data. Between September 2013-March 2014, chlamydia disease report forms for certain clinics in Indianapolis were electronically pre-populated with clinical, lab and patient data available through the Indiana Health Information Exchange, then provided to the patient’s doctor. Doctors could then sign the form and deliver it to public health for investigation and population-level disease tracking. Methods: We utilized a novel matched case analysis of timeliness changes in receipt and processing of communicable disease report forms. Each Chlamydia cases reported with the pre-populated form were matched to cases reported in usual ways. We assessed the time from receipt of the case at the public health agency: 1) inclusion of the case into the public health surveillance system and 2) to close to case. A hierarchical random effects model was used to compare mean difference in each outcome between the target cases and the matched cases, with random intercepts for case. Twenty-one Chlamydia cases were reported to the public health agency using the pre-populated form. Sixteen of these pre-populated form cases were matched to at least one other case, with a mean of 23 matches per case. The mean Reporting Lag for the pre-populated form cases was 2.5 days, which was 2.7 days shorter than the mean Reporting Lag for the matched controls (p = <0.001). The mean time to close a pre-populated form case was 4.7 days, which was 0.2 days shorter than time to close for the matched controls (p = 0.792). Use of pre-populated forms significantly decreased the time it took for the local public health agency to begin documenting and closing chlamydia case investigations. Thoughtful use of electronic health data for case reporting may decrease the per-case workload of public health agencies, and improve the timeliness of information about the pattern and spread of disease.

In recent years, applications of big data-driven predictive analytics in public health programs have expanded, offering promises of greater efficiency and improved outcomes. This commentary considers the turn toward predictive modeling in US-based HIV public health initiatives. Through two case studies, we analyze emergent ethical problems and risks. We focus on potential harms related to (1) classifying people living with HIV in public health systems, (2) new ways of combining and sharing individuals’ health data that predictive approaches employ, and (3) how new applications of big data in public health challenge the underlying logics and regulatory paradigms that govern data re-uses and rights in public health practice. Drawing on critical technology scholarship, critical bioethics, and advocacy by organized networks of people living with HIV, we argue that stakeholders should enter into a new range of reform-oriented conversations about the regulatory frameworks, ethical norms, and best practices that govern re-uses of HIV public health data in the era of predictive public health interventions that target individuals.

ObjectivesIn this paper we discuss the main ethical challenges related to the conduct of medicine quality surveys and make suggestions on how to address them.MethodMost evidence‐based information regarding medicine quality derives from surveys. However, existing research ethical guidelines do not provide specific guidance for medicine quality surveys. Hence, those conducting surveys are often left wondering how to judge what counts as best practice. A list of the main ethical challenges in the design and conduct of surveys is presented.Results and conclusionsIt is vital that the design and conduct of medicine quality surveys uphold moral and ethical obligations and analyse the ethical implications and consequences of such work. These aspects include the impact on the local availability of and access to medicines; the confidentiality and privacy of the surveyors and the surveyed; questions as to whether outlet staff personnel should be told they are part of a survey; the need of ethical and regulatory approvals; and how the findings should be disseminated. Medicine quality surveys should ideally be conducted in partnership with the relevant national Medicine Regulatory Authorities. An international, but contextually sensitive, model of good ethical practice for such surveys is needed.

BackgroundDuring the COVID-19 pandemic, the public health workforce has experienced re-deployment from core functions such as health promotion, disease prevention, and health protection, to preventing and tracking the spread of COVID-19. With continued pandemic deployment coupled with the exacerbation of existing health disparities due to the pandemic, public health systems need to re-start the delivery of core public health programming alongside COVID-19 activities. The purpose of this scoping review was to identify strategies that support the re-integration of core public health programming alongside ongoing pandemic or emergency response.MethodsThe Joanna Briggs Institute methodology for scoping reviews was used to guide this study. A comprehensive search was conducted using: a) online databases, b) grey literature, c) content experts to identify additional references, and d) searching reference lists of pertinent studies. All references were screened by two team members. References were included that met the following criteria: a) involved public health organizations (local, regional, national, and international); b) provided descriptions of strategies to support adaptation or delivery of routine public health measures alongside disaster response; and c) quantitative, qualitative, or descriptive designs. No restrictions were placed on language, publication status, publication date, or outcomes. Data on study characteristics, intervention/strategy, and key findings were independently extracted by two team members. Emergent themes were established through independent inductive analysis by two team members.ResultsOf 44,087 records identified, 17 studies were included in the review. Study designs of included studies varied: descriptive (n = 8); qualitative (n = 4); mixed-methods (n = 2); cross-sectional (n = 1); case report (n = 1); single-group pretest/post-test design (n = 1). Included studies were from North America (n = 10), Africa (n = 4), and Asia (n = 3) and addressed various public health disasters including natural disasters (n = 9), infectious disease epidemics (n = 5), armed conflict (n = 2) and hazardous material disasters (n = 1). Five emergent themes were identified on strategies to support the re-integration of core public health services: a) community engagement, b) community assessment, c) collaborative partnerships and coordination, d) workforce capacity development and allocation, and e) funding/resource enhancement.ConclusionEmergent themes from this study can be used by public health organizations as a beginning understanding of strategies that can support the re-introduction of essential public health services and programs in COVID-19 recovery.

The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577-584. https://doi.org/10.2105/AJPH.2023.307225).

ObjectiveComparison of content in eCR and ELR cases reportingReview technical challenges and strategies for data managementIntroductionCommunicable disease reporting from providers can be a time-consuming process that results in delayed or incomplete reporting of infectious diseases, limiting public health's ability to respond quickly to prevent or control disease. The recent development of an HL7 standard for automated Electronic initial case reports (eICR) represents an important advancement for public health surveillance. The Illinois Department of Public Health (IDPH) participated in a pilot with the Public Health Informatics Institute and an Illinois-based provider group to accept eICR reports for Gonorrhea and Chlamydia.MethodsThe provider group working with their EHR vendor submitted a batch of CT and GC reports directly to IDPH in September 2017 according to the published eICR standard. A summary of the provider and PHII work has been presented previously in the STI eCR Learning Community. The eICR reports received from the provider were compared to case report data in the communicable disease surveillance system, I-NEDSS. Data was extracted from I-NEDSS that included race and ethnicity, timing of specimen collection, result, ELR submission surveillance action and treatment.ResultsIDPH received a batch of 89 files containing 77 unique persons, with 54 chlamydia (CT), 13 Gonorrhea (GC) and 10 co-infected case reports. The communicable disease surveillance system had captured 76 (98.7%) of the persons reported in the pilot. Among those, an Electronic Laboratory Report (ELR) was received for 72 (95%) cases, on average within 1 day of the lab report date. Data in I-NEDSS had a completion of 45% for race and ethnicity compared to 99% for race and 92% for ethnicity in the eICR files. Information on treatment in the surveillance system was reported for 18 (24%) cases compared to 67 (87%) cases.ConclusionsThis pilot was the first submission of real patient data submitted using the eICR standard to IDPH. Data was more complete from provider eICR reports for key demographic of race and ethnicity and treatment. A comparison with the current surveillance system showed near complete and timely case capture from ELR data. Integrated reporting of both ELR and eICR can produce a more complete case report through automated submissions and potentially reduce burden of data collection on health department communicable disease investigators. As public health reporting moves in this direction, public health agencies will have some substantial tasks to correctly ingest, map and interpret the increased amounts of information that are contained in the eICR. Further, the advantages of case reporting will be dependent on automated processes within the communicable disease system to merge data and apply business rules to automatically process completed case reports for high volume diseases, such as STIs. This work will continue as providers are ready to submit reports from different vendor products from a near real-time production environment.

Pathogen genomics, where whole genome sequencing technologies are used to produce complete genomic sequences of pathogens, is being increasingly used for infectious disease surveillance and outbreak response. Although proof-of-concept studies have highlighted the viability of using pathogen genomics in public health, few studies have investigated how end-users utilize pathogen genomics in public health. We describe a protocol for a study that aims to identify key factors that influence the use of pathogen genomics to inform public health responses against infectious diseases in Australia and New Zealand. We will use qualitative comparative analysis (QCA), a case-oriented methodology that systematically compares and analyses multiple cases (or 'units of analysis'), to identify multiple pathways leading to the use of pathogen genomics results in public health actions. As part of the process, we will develop a rubric to identify and define the use of pathogen genomics and individual factors affecting this process. Simultaneously, we will identify cases where pathogen genomics has been used in public health across Australia and New Zealand. Data for these cases will be collected from document review of publicly available and confidential documents and semi-structured interviews with technicians and end-users and summarized in a case report. These case reports will form the basis for scoring each case on the extent of the use of pathogen genomics data and the presence or absence of specific factors such as the ease of extracting essential information from pathogen genomics reports and perceptions toward pathogen genomics. Using the scores, cases will be analyzed using QCA techniques to identify pathways leading to the use of pathogen genomics data. These pathways will be interpreted alongside the cases to provide rich explanations of the use of pathogen genomics in public health. This study will improve our understanding of the key factors that facilitate or hinder the use of pathogen genomics to inform public health authorities and end-users. These findings may inform ways to enhance the use of pathogen genomics data in public health.