Digital Pharmacovigilance: the medwatcher system for monitoring adverse events through automated processing of internet social media and crowdsourcing

Thesis (Ph.D.)--Boston UniversityHalf of Americans take a prescription drug, medical devices are in broad use, and population coverage for many vaccines is over 90%. Nearly all medical products carry risk of adverse events (AEs), sometimes severe. However, pre- approval trials use small populations and exclude participants by specific criteria, making them insufficient to determine the risks of a product as used in the population. Existing post-marketing reporting systems are critical, but suffer from underreporting. Meanwhile, recent years have seen an explosion in adoption of Internet services and smartphones. MedWatcher is a new system that harnesses emerging technologies for pharmacovigilance in the general population. MedWatcher consists of two components, a text-processing module, MedWatcher Social, and a crowdsourcing module, MedWatcher Personal. With the natural language processing component, we acquire public data from the Internet, apply classification algorithms, and extract AE signals. With the crowdsourcing application, we provide software allowing consumers to submit AE reports directly. Our MedWatcher Social algorithm for identifying symptoms performs with 77% precision and 88% recall on a sample of Twitter posts. Our machine learning algorithm for identifying AE-related posts performs with 68% precision and 89% recall on a labeled Twitter corpus. For zolpidem tartrate, certolizumab pegol, and dimethyl fumarate, we compared AE profiles from Twitter with reports from the FDA spontaneous reporting system. We find some concordance (Spearman's rho= 0.85, 0.77, 0.82, respectively, for symptoms at MedDRA System Organ Class level). Where the sources differ, milder effects are overrepresented in Twitter. We also compared post-marketing profiles with trial results and found little concordance. MedWatcher Personal saw substantial user adoption, receiving 550 AE reports in a one-year period, including over 400 for one device, Essure. We categorized 400 Essure reports by symptom, compared them to 129 reports from the FDA spontaneous reporting system, and found high concordance (rho = 0.65) using MedDRA Preferred Term granularity. We also compared Essure Twitter posts with MedWatcher and FDA reports, and found rho= 0.25 and 0.31 respectively. MedWatcher represents a novel pharmacoepidemiology surveillance informatics system; our analysis is the first to compare AEs across social media, direct reporting, FDA spontaneous reports, and pre-approval trials

Leveraging FAERS and Big Data Analytics with Machine Learning for Advanced Healthcare Solutions

This research study explores the potential of leveraging the FDA Adverse Event Reporting System (FAERS), combined with big data analytics and machine learning techniques, to enhance healthcare solutions. FAERS serves as a comprehensive database maintained by the U.S. Food and Drug Administration (FDA), encompassing reports of adverse events, medication errors, and product quality issues associated with diverse drugs and therapeutic interventions.By harnessing the power of big data analytics applied to the vast information within FAERS, healthcare professionals and researchers gain valuable insights into drug safety, discover potential adverse reactions, and uncover patterns that may not have been discernible through traditional methods. Particularly, machine learning plays a pivotal role in processing and analyzing this extensive dataset, enabling the extraction of meaningful patterns and prediction of adverse events.The findings of this study demonstrate various ways in which FAERS, big data analytics, and machine learning can be leveraged to provide advanced healthcare solutions. Machine learning algorithms trained on FAERS data can effectively identify early signals of adverse events associated with specific drugs or treatments, allowing for prompt detection and appropriate actions.Big data analytics applied to FAERS data facilitate pharmacovigilance and drug safety monitoring. Machine learning models automatically classify and analyze adverse event reports, efficiently flagging potential safety concerns and identifying emerging trends.The integration of FAERS data with big data analytics and machine learning enables signal detection and causality assessment. This approach aids in the identification of signals that suggest a causal relationship between drugs and adverse events, thereby enhancing the assessment of drug safety.By analyzing FAERS data in conjunction with patient-specific information, machine learning models can assist in identifying patient subgroups that are more susceptible to adverse events. This information is instrumental in personalizing treatment plans and optimizing medication choices, ultimately leading to improved patient outcomes.The combination of FAERS data with other biomedical information offers insights into potential new uses or indications for existing drugs. Machine learning algorithms analyze the integrated data, identifying patterns and making predictions about the efficacy and safety of repurposing existing drugs for new applications.The implementation of FAERS, big data analytics, and machine learning in advanced healthcare solutions necessitates meticulous consideration of data privacy, security, and ethical implications. Safeguarding patient privacy and ensuring responsible data use through anonymization techniques and appropriate data governance are paramount.The integration of FAERS, big data analytics, and machine learning holds immense potential in advancing healthcare solutions, enhancing patient safety, and optimizing medical interventions. The findings of this study demonstrate the multifaceted benefits that can be derived from leveraging these technologies, paving the way for a more efficient and effective healthcare ecosystem

Protecting Public Health: Unique Identification of Facilities in Adverse Event Reporting

The purpose of this project was to provide the United States Food & Drug Administration with an evaluation of the possible use of unique facility identifiers within the drug adverse event reporting system. Through interviews, research, and database demonstrations, we identified current shortcomings in the availability of facility information and proposed a future state which would expedite investigations of public health signals. A gap analysis was then utilized to develop proposed policy, informatics, and process changes to improve FDA

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Pharmacovigilance is a clinically oriented discipline, which may guide appropriate drug use through a balanced assessment of drug safety. Although much has been done in recent years, efforts are needed to expand the border of pharmacovigilance. We have provided insight into the FDA_Adverse Events Reporting Systems (FDA_AERS), a worldwide publicly available pharmacovigilance archive, to exemplify how to address major methodological issues. We believe that fostering discussion among researchers will increase transparency and facilitate definition of the most reliable approaches. By virtue of its large population coverage and free availability, the FDA_AERS has the potential to pave the way to a new way of looking to signal detection in PhV. Our key messages are: (1) before applying statistical tools (i.e., Data Mining Approaches - DMAs) to pharmacovigilance database for signal detection, all aspects related to data quality should be considered (e.g., drug mapping, missing data and duplicates); (2) at present, the choice of a given DMA mostly relies on local habits, expertise and attitude and there is room for improvement in this area; (3) DMA performance may be highly situation dependent; (4) over-reliance on these methods may have deleterious consequences, especially with the so-called "designated medical events", for which a case-by-case analysis is mandatory and complements disproportionality; and (5) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous biases and confounders that may influence performance and incremental utility of DMAs

Information Technology: FDA Needs to Fully Implement Key Management Practices to Lessen Modernization Risks

A letter report issued by the Government Accountability Office with an abstract that begins "While FDA has taken several important steps toward modernizing its IT environment, much remains to be done. FDA reported spending about $400 million for IT investments in fiscal year 2011; however, the agency currently lacks a comprehensive IT inventory that identifies and provides key information about the systems it uses and is developing. Office of Management and Budget (OMB) and GAO guidance call for federal agencies to maintain such an inventory in order to monitor and manage their IT investments. This inventory should include information on each system, such as costs, functionality or purpose, and status. However, FDA does not have such a comprehensive list of its systems. Instead, the agency points to budget documents required by OMB, which included information on 44 IT investments for fiscal year 2011. The agency also provided a partial list of 21 mission-critical systems and modernization initiatives. Nonetheless, agency officials acknowledged that these documents do not identify all FDAs systems or the complete costs, purpose, or status of each system. Until the agency has a complete and comprehensive inventory, it will lack critical information needed to effectively assess its IT portfolio.

Using the theory of planned behavior to predict Texas pharmacists' intention to report serious adverse drug events

textThe purpose of this dissertation was to use the theory of planned behavior (TPB) to predict Texas pharmacists’ intention to report serious adverse drug effects (ADEs) to the Food and Drug Administration (FDA). The study explored the utility of the TPB model constructs (attitude [A], subjective norm [SN], perceived behavioral control [PBC]), as well as past reporting behavior (PRB), and perceived moral obligation (PMO) to predict pharmacists’ intention to report serious ADEs to the FDA. The study also determined if the pharmacists’ A, SN and PBC were related to practice characteristics and demographic factors. A survey was developed based on two focus group interviews, pretested and mailed to 1,500 Texas practicing pharmacists. An overall response rate of 26.4 percent was obtained (n = 377 pharmacists). Overall, pharmacists intended to report serious ADEs, had a favorable attitude towards reporting, were somewhat influenced by social norms regarding reporting and perceived themselves to have some control over reporting serious ADEs to the FDA. For direct measures, A and SN were significant predictors of intention to report serious ADEs, but PBC was not. The TPB constructs together accounted for 34.0 percent of the variance in intention to report serious ADEs to the FDA. Using indirect measures, A, SN and PBC were significant predictors of intention and together accounted for 28.8 percent of the variance in intention to report serious ADEs. PRB and PMO improved the explanatory power of the regression models (direct and indirect measures) over and above the TPB constructs. Unlike most other practice characteristics and demographic factors examined, knowledge was significantly related with the TPB constructs. In summary, A, SN, PBC (indirect measures), PRB, and PMO influence the formation of pharmacists’ intention to report serious ADEs. The TPB has utility in predicting ADE reporting behavior. Pharmacy educators should explore pharmacists’ attitudes, beliefs, and expectations of important others in designing educational programs. Strategies to help pharmacists report more serious ADEs should focus on altering their perception of social pressure towards reporting and addressing the barriers towards ADE reporting (e.g., lack of knowledge).Pharmac

Evidence and Extrapolation: Mechanisms for Regulating Off-Label Uses of Drugs and Devices

A recurring, foundational issue for evidence-based regulation is deciding whether to extend governmental approval from an existing use with sufficient current evidence of safety and efficacy to a novel use for which such evidence is currently lacking. This extrapolation issue arises in the medicines context when an approved drug or device that is already being marketed is being considered (1) for new conditions (such as off-label diagnostic categories), (2) for new patients (such as new subpopulations), (3) for new dosages or durations, or (4) as the basis for approving a related drug or device (such as a generic or biosimilar drug). Although the logic of preapproval testing and the precautionary principle—first, do no harm—would counsel in favor of prohibiting extrapolation approvals until after traditional safety and efficacy evidence exists, such delays would unreasonably sacrifice beneficial uses. The harm of accessing unsafe products must be balanced against the harm of restricting access to effective products. In fact, the Food and Drug Administration\u27s (FDA\u27s) current regulations in many ways reject the precautionary principle because they largely permit individual physicians to prescribe medications for off-label uses before any testing tailored to those uses has been done. The FDA\u27s approach empowers physicians, but overshoots the mark by allowing enduring use of drugs and devices with insubstantial support of safety and efficacy. This Article instead proposes a more dynamic and evolving evidence-based regime that charts a course between the Scylla and Charybdis of the overly conservative precautionary principle on one hand, and the overly liberal FDA regime on the other. Our approach calls for improvements in reporting, testing, and enforcement regulations to provide a more layered and nuanced system of regulatory incentives. First, we propose a more thoroughgoing reporting of off-label use (via the disclosure of diagnostic codes and detailing data) in manufacturers\u27 annual reports to the FDA, in the adverse event reports to the FDA, in Medicare/Medicaid reimbursement requests, and, for a subset of FDA-designated drugs, in prescriptions themselves. Second, we would substantially expand the agency\u27s utilization of postmarket testing, and we provide a novel framework for evaluating the need for postmarket testing. Finally, our approach calls for a tiered labeling system that would allow regulators and courts to draw finer reimbursement and liability distinctions among various drug uses, and would provide the agency both the regulatory teeth and the flexibility it presently lacks. Together, these reforms would improve the role of the FDA in the informational marketplace underlying physicians\u27 prescribing decisions. This evolutionary extrapolation framework could also be applied to other contexts

Using electronic health records to support clinical trials: a report on stakeholder engagement for EHR4CR

Background. The conduct of clinical trials is increasingly challenging due to greater complexity and governance requirements as well as difficulties with recruitment and retention. Electronic Health Records for Clinical Research (EHR4CR) aims at improving the conduct of trials by using existing routinely collected data, but little is known about stakeholder views on data availability, information governance, and acceptable working practices. Methods. Senior figures in healthcare organisations across Europe were provided with a description of the project and structured interviews were subsequently conducted to elicit their views. Results. 37 structured interviewees in Germany, UK, Switzerland, and France indicated strong support for the proposed EHR4CR platform. All interviewees reported that using the platform for assessing feasibility would enhance the conduct of clinical trials and the majority also felt it would reduce workloads. Interviewees felt the platform could enhance trial recruitment and adverse event reporting but also felt it could raise either ethical or information governance concerns in their country. Conclusions. There was clear support for EHR4CR and a belief that it could reduce workloads and improve the conduct and quality of trials. However data security, privacy, and information governance issues would need to be carefully managed in the development of the platform