Why Not the Best? Results From the National Scorecard on U.S. Health System Performance, 2011

Assesses the U.S. healthcare system's average performance in 2007-09 as measured by forty-two indicators of health outcomes, quality, access, efficiency, and equity compared with the 2006 and 2008 scorecards and with domestic and international benchmarks

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

Probing herb induced liver injury (HILI) using population data and computational phyto-analysis

Despite the ‘natural’ origin of herbal medicines, they can cause adverse effects such as hepatotoxicity, which has led to regulatory action including market withdrawal. This study aimed to explore herb-hepatotoxicity associations and to identify a common pharmacophore among the phytoconstituents of implicated herbs. Data from the United States adverse event reporting system (AERS) (2004-2011) were analysed retrospectively for herb-hepatotoxicity associations using disproportionality analysis. Chemical constituents in identified hepatotoxic herbs were examined for a common pharmacophore and validated against a set of known hepatotoxic and non-hepatotoxic compounds. Significant herb-liver injury associations (p<0.05) were found for 15 herbs including kava, valerian and black cohosh. Analysis of specific adverse reaction groupings revealed new information: HILI with immune features was significantly associated with seven herbs including kava, evening primrose and valerian. Pharmacophore analysis revealed a 3-point hypothesis with 1 hydrogen bond acceptor, 1 hydrogen bond donor and 1 hydrophobic group that gained a high survival score, high selectivity and high specificity relative to other hypotheses. This hypothesis may be a clue to a common toxicity pathway shared by these herbs. Further research is required to investigate whether a causal relationship exists between the implicated herbs and liver injury and to identify the toxicity mechanisms

Bisindolylmaleimide IX: a Novel Anti-SARS-CoV2 Agent Targeting Viral Main Protease 3CLpro Demonstrated by Virtual Screening Pipeline and In-Vitro Validation Assays

SARS-CoV-2, the virus that causes COVID-19 consists of several enzymes with essential functions within its proteome. Here, we focused on repurposing approved and investigational drugs/compounds. We targeted seven proteins with enzymatic activities known to be essential at different stages of the viral cycle including PLpro, 3CLpro, RdRP, Helicase, ExoN, NendoU, and 2′-O-MT. For virtual screening, energy minimization of a crystal structure of the modeled protein was carried out using the Protein Preparation Wizard (Schrodinger LLC 2020-1). Following active site selection based on data mining and COACH predictions, we performed a high-throughput virtual screen of drugs and investigational molecules (n = 5903). The screening was performed against viral targets using three sequential docking modes (i.e., HTVS, SP, and XP). Virtual screening identified ∼290 potential inhibitors based on the criteria of energy, docking parameters, ligand, and binding site strain and score. Drugs specific to each target protein were further analyzed for binding free energy perturbation by molecular mechanics (prime MM-GBSA) and pruning the hits to the top 32 candidates. The top lead from each target pool was further subjected to molecular dynamics simulation using the Desmond module. The resulting top eight hits were tested for their SARS-CoV-2 anti-viral activity in-vitro. Among these, a known inhibitor of protein kinase C isoforms, Bisindolylmaleimide IX (BIM IX), was found to be a potent inhibitor of SARS-CoV-2. Further, target validation through enzymatic assays confirmed 3CLpro to be the target. This is the first study that has showcased BIM IX as a COVID-19 inhibitor thereby validating our pipeline

The value of community pharmacy incident reporting in optimising the safety and quality use of medicines

Medication safety has emerged as a healthcare priority with the launch of the World Health Organization’s third global patient safety challenge. Understanding the complex interplay between human and system factors that potentiate medication incidents can illuminate improvement opportunities in organisational safeguards and safe medication practices. This thesis aimed to develop, implement, and evaluate systematic incident reporting system (IRS) to identify, characterise and address risks to medication safety and quality use of medicines (QUM) in primary care. The study was conducted in 30-community pharmacies in Sydney, Australia, through a confidential and anonymous IRS called QUMwatch. The study used the Advanced Incident Management System (AIMS) taxonomy, which is a hierarchical classification system based on error theory. Analysis of 1,013 incident reports collected over 30 months, identified medication incidents (MIs) that affected patients over 65 years old, the prescribing stage, and medicines acting on the cardiovascular and nervous systems. Human, task, and organisational factors contributed to MIs, particularly healthcare providers' cognitive errors, communication problems, poor risk management, and safety culture. Factors that facilitated error recovery included individual attributes, appropriate intervention, effective communication, and the use of standardised protocols. Remedial actions included changes in care plans, dosages, reviews of medicines, and medicine cessation. The study evaluated the QUMwatch program's tools and methods using a mixed-methods approach and found that 16 out of 20 variables on the data collection form had over 90% complete data, and data consistency was high. Anonymity was the preferred method of reporting. The stimulatory package significantly raised the reporting rate from a baseline average of 32.4 to 77.3 reports/month (p < .001). The AIMS taxonomy for MIs had substantial validity for high-order medication processes for the Australian community pharmacy context. The study demonstrated the feasibility of a well-designed IRS in community pharmacy to identify MIs and to generate safety lessons and recommendations

COMBINING HUMAN FACTORS AND DATA SCIENCE METHODS TO EVALUATE THE USE OF FREE TEXT COMMUNICATION ORDERS IN ELECTRONIC HEALTH RECORDS

Medication errors are a leading cause of death in the United States. Electronic Health Records (EHR) along with Computerized Provider Order Entry (CPOE) are considered promising ways to reduce these errors. However, EHR systems have not eliminated medication errors. Moreover, in some cases they have facilitated errors due to issues such as poor usability and negative effects on clinical workflows. The use of unexpected free text within a CPOE system can serve as a marker that the system does not adequately support clinical workflow. Prior studies have looked at the use of free text within medication orders, but the inclusion of medication related information in communication for non-medication orders (CNMOs), a type of free text order, has not been adequately studied. This mixed-methods study identified the prevalence, nature and reasons for the inclusion of medication related information in CNMOs using a large sample of CNMOs placed at a mid-Atlantic hospital system in 2017, and via interviews with physicians. The study found that more than 42% of CNMOs contain medication related information. Moreover, the use of CNMOs varied significantly across provider types, hospital locations, patient settings and other factors. The study found 10 themes that might cause providers to adopt such workarounds, including missing functionality and poor usability. The viii study also identified several general challenges in communicating medication information in the EHR, and potential solutions to mitigate these challenges. This dissertation also demonstrates how natural language processing could be used to identify medication related CNMOs