Information technology (IT) has entered and transformed the world of health care and clinical medicine in which the work of doctors and the care of patients proceed with higher quality, efficiency and lower cost. In 2020 post covid this has become one of the core challenge. It is also no secret that IT has merged into clinical safety practice and sparks the creation of worldwide pharmacovigilance systems for safety signal detection. The IT transformative force and health IT adoption have fundamentally changed the conduct of clinical research, practice of medicine, and medicinal safety monitoring.
Industry wide challenges in in systematically analyzing and interpreting voluntarily submitted data involving multiple drugs, medical conditions, and events per report, without the benefit of a research protocol, randomization, and a control group of persons taking the placebo. Other difficulties include chronic under-reporting, occasional publicity-driven and litigation-driven episodes of over-reporting and misreporting, incomplete and missing information, and inconsistencies and changes over time in reporting and naming/coding practices. In addition, there is considerable uncertainty regarding the quality and completeness of the information contained in each data field, including dosage, formulation type, timing of exposure, and length of exposure and follow-up and in estimating the corresponding product exposure and background rate of adverse events. The extraction of useful information from this database presents multiple challenges, including managing, storing, querying, and analyzing such a large amount of data, and resolving event and drug dictionary problems and data miscoding. There is a need for analytical methods that are capable of systematically screening this database to identify potential serious adverse events of concern in such a noisy background that properly balance the concerns for excessive signalling and accounting for background noise.
Next challenge will be determining rules to trigger an alert, when to consider a signal likely enough to be real to warrant follow-up, and when a signal needs to be elevated to represent a potential safety risk. If data mining analysis was performed on data for millions of people taking thousands of drugs, statistic significance could emerge as data on a drug–event relationship accumulate, even after adjustment for repeated testing. Such P value-driven thresholds could result from the size of the population and the strength of the supposed association. Taking account of multiple covariates such as severity of adverse events, whether a safe alternative treatment is available, or how much benefit the drug provides will likely cut down the list to prioritize focused follow-up. Methodology to calculate the information component (IC) value for drug-event combinations for drugs belonging to the anatomic therapeutic chemical (ATC) classes of the cardiovascular system, musculoskeletal system, and nervous system where only the suspected drug was considered, and also where both concomitant and suspected drugs were considered using data from the Swedish Drug Information System and reported that the proportion of “type C” reactions signalled when considering both concomitant and suspected drugs as compared with suspected drugs only. Conversely, taking action prematurely on the basis of inadequate data could result in unnecessary confusion and harmful discontinuations of useful treatments. We cannot know now what inputs will be optimal for each decision analysis. But stating such inputs transparently up front will help to clarify the decision-making process of regulators who will have to act on these signals. It will also facilitate the communication of decisions, by enabling regulators to frame recommendations or actions in terms of prestated assumptions about acceptable risks for a given product. If such tools are applied well, the system will be able to provide early notice of adverse drug effects that have previously taken years to discover. It seems that there is a fine balance of judgment on public warnings on possible hazards. Caution needs to be exercised to issue public announcement on unreal hazards. An excessively high threshold for warnings would keep real risks hidden too long, but an excessively low threshold could undermine public trust in clinical products, the surveillance system, and the entire medical world. Proper implementation of the pharmacovigilance technology solution will require expertise in intelligibly communicating information about risks in relation to benefits to clinicians and patients alike.
Challenge area also lies in clinical process re-engineering to ensure modern pharmacovigilance technology systems are configured, tailored, and implemented in the context of addressing safety process improvements and organizational needs to support daily clinical safety operations. In the past four decades from the thalidomide tragedy to the recent drug recalls, companies have used pharmacovigilance methods to identify rare, easily identified safety problems. During the same four decades, we have seen the growth of a fragmented clinical or health care system that lacks a unifying infrastructure. As a result, this system operates primarily in reaction to rather than in anticipation of major pharmaceutical safety events. As drug consumption has increased and the public has grown to expect greater drug safety, the traditional reactive approach has proven largely incapable of addressing both shifts in public expectations and regulatory and media scrutiny. This reality has revealed improvement areas involved in patient safety operations: organizational alignment, operations management, data management, and risk management. Table 2 summarizes key functional activities and recommended best practices under the specified four areas to enable realization of the capability of pharmacovigilance systems in an adaptive operations framework.