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  • A Comprehensive Review of Evolving Paradigms, Technologies, and Global Frameworks on Patient-Centric Pharmacovigilance and Digital Reporting Systems

  • 1Department of Pharmacy, Shanti Niketan College of Pharmacy, Mandi (HP)-India
    2Department of Pharmacy, LR Institute of Pharmacy, Jabli-Kyar, Solan HP-India 
     

Abstract

Background: Pharmacovigilance (PV) has historically been a healthcare professional (HCP) dominated discipline. The paradigm shift toward patient-centric PV acknowledges patients as primary stakeholders and active contributors to drug safety surveillance. Digital technologies are accelerating this transformation through mobile applications, web portals, social media mining, and artificial intelligence (AI)-enabled signal detection. Objectives: This review examines the evolution of patient-centric PV, evaluates digital reporting systems and their effectiveness, discusses global regulatory frameworks, and identifies challenges and future directions. Methods: A comprehensive narrative review was conducted using PubMed, Embase, WHO databases, and regulatory agency publications (2000–2025). Search terms included 'patient pharmacovigilance', 'adverse drug reaction reporting', 'digital pharmacovigilance', 'mHealth', and 'social media pharmacovigilance'. Results: Patient reports provide unique clinical insights not captured by HCP reports, particularly regarding quality-of-life impacts and non-serious adverse drug reactions (ADRs). Digital platforms have significantly improved reporting accessibility; however, challenges persist including data quality, health literacy barriers, privacy concerns, and the digital divide. Regulatory agencies globally are increasingly integrating patient-reported data into signal detection frameworks. Conclusion: Patient-centric PV, enabled by digital technologies, represents the future of drug safety monitoring. Collaborative frameworks engaging patients, HCPs, industry, and regulators are essential to harness the full potential of digitally enabled pharmacovigilance.

Keywords

Pharmacovigilance, Patient Reporting, Adverse Drug Reaction, Digital Health, mhealth, Drug Safety

Introduction

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Pharmacovigilance, as defined by the World Health Organization (WHO), encompasses the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine-related problem.1 Historically, ADR reporting has been primarily the domain of healthcare professionals physicians, pharmacists, and nurses who submitted individual case safety reports (ICSRs) to national regulatory authorities or marketing authorization holders. This model, while foundational, systematically excluded the most critical source of information: the patient. The thalidomide tragedy of the early 1960s catalysed the creation of formal pharmacovigilance systems globally, leading to the establishment of the WHO Programme for International Drug Monitoring in 1968.2 However, it was not until the 2000s that direct patient reporting was formally incorporated into national PV systems in the Netherlands, the United Kingdom, and the United States, recognizing that patients possess unique experiential data inaccessible to clinicians. Patient-centric pharmacovigilance reframes the patient not merely as a subject of drug surveillance but as an active partner in safety monitoring.3 Concurrently, the digital revolution manifested in smartphones, wearable technologies, electronic health records (EHRs), and social media has created unprecedented opportunities to collect real-world safety data at scale, with speed and granularity previously unimaginable. Despite these advances, the integration of patient-reported and digitally sourced data into mainstream PV remains incomplete. This review synthesizes the current evidence on patient-centric PV, digital reporting systems, regulatory responses, and the challenges that must be overcome to realize the full potential of this evolving paradigm.4,5

2. Evolution Of Pharmacovigilance Toward Patient-Centricity

2.1 Historical Context

The evolution of pharmacovigilance can be traced across distinct eras, from anecdotal case reports prior to regulatory oversight to the contemporary digitally integrated, patient-inclusive frameworks (Table 1). The introduction of mandatory spontaneous reporting systems following thalidomide established HCPs as the gatekeepers of drug safety data.2 The 1978 establishment of the Yellow Card scheme in the United Kingdom represented a landmark in systematic PV, followed by the creation of the FDA's MedWatch programme in 1993.6 These systems, however, were designed principally for HCPs, with patient participation remaining an afterthought for decades.

Table 1: Evolution of Pharmacovigilance Systems from HCP-Centric to Patient-Centric Paradigms

Era

Period

Key Development

Significance

Pre-Modern

Before 1960

Spontaneous case reports

Anecdotal HCP-driven reporting; no formal structures

Regulatory Genesis

1961–1968

Thalidomide tragedy; WHO Programme launched

Global regulatory awakening; mandatory ADR reporting frameworks established

Systematic PV

1970s–1990s

National pharmacovigilance centres; Yellow Card schemes

Structured data collection; HCP as primary reporters

Patient Inclusion

2000s

Direct patient reporting introduced in select countries

Expanded signal detection; first-hand ADR experiences captured

Digital PV

2010s

Social media mining, mobile apps, e-health platforms

Real-world data at scale; challenges in signal validation

Patient-Centric PV

2020–present

Integrated digital ecosystems, AI-driven analysis, co-creation with patients

Patient as active partner; enhanced safety signal quality and timeliness

2.2 Emergence of Direct Patient Reporting

The Netherlands was the first country to formally introduce direct patient reporting in 2003, with the Lareb centre piloting patient-accessible reporting channels.7 A landmark comparative study by van Grootheest et al. demonstrated that patient reports contained qualitatively different information from HCP reports, particularly regarding the impact of ADRs on daily functioning and quality of life, providing complementary rather than duplicative data. The UK MHRA extended the Yellow Card scheme to direct patient reporting in 2005, followed by expanded patient access in the United States through MedWatch.8 A systematic review by Harmark et al. confirmed that patients were more likely to report non-serious ADRs with significant quality-of-life impact, filling a critical surveillance gap in HCP-only systems.9

2.3 Patient-Reported Outcomes and Pharmacovigilance

The integration of patient-reported outcomes (PROs) with pharmacovigilance represents a significant conceptual advancement. PROs capture dimensions of drug effects fatigue, cognitive impairment, sexual dysfunction, and emotional disturbance that are systematically underreported by clinicians due to time constraints and reporting biases.10 Electronic PRO (ePRO) systems embedded in clinical trials and post-marketing studies have demonstrated improved ADR detection rates. The FDA's PRO guidance framework emphasizes that patient-reported symptoms should be incorporated into regulatory benefit-risk assessments, reflecting the growing institutional recognition of the patient perspective in drug safety.11

3. Digital Reporting Systems in Pharmacovigilance

3.1 Mobile Health Applications

The proliferation of smartphones has enabled the development of mobile health (mHealth) applications specifically designed for ADR reporting. The UK MHRA's Yellow Card app, launched in 2015, represents a gold standard for patient-facing PV applications, achieving a 24-fold increase in patient reports within two years of launch.12 The COVID-19 pandemic further demonstrated the power of app-based reporting when over 1 million vaccine-related reports were submitted via the Yellow Card app between December 2020 and mid-2021, providing real-time safety data that informed clinical practice guidelines within weeks. Key features of effective PV mobile applications include intuitive user interfaces, structured data entry fields, image upload capabilities for rashes and packaging, geolocation for detecting geographic ADR clusters, and push notification reminders for chronic medication users.13

3.2 Web-Based Portals and Electronic Reporting

Web-based reporting portals have progressively replaced paper-based reporting forms in most high-income countries. The FDA's MedWatch Online portal processes approximately 1.5 million adverse event reports annually, with an increasing proportion originating directly from consumers and patients.14 The EudraVigilance system manages the European Union's centralized ADR database, processing ICSRs from all 27member states under a standardized electronic format compliant with ICH E2B(R3) guidelines. The WHO's VigiBase, the world's largest repository of individual case safety reports containing over 30 million reports from more than 130 countries, now incorporates patient-reported data from national centres, enabling signal detection across pharmacological classes and patient populations at unprecedented scale.15

3.3 Social Media as a Pharmacovigilance Data Source

Social media platforms including Twitter/X, Facebook, patient forums, and health-specific communities like Patients Like Me have emerged as rich, unsolicited repositories of patient drug experiences.16 Natural language processing (NLP) and machine learning algorithms can extract ADR signals from these platforms with increasing accuracy. A landmark study by Freifeld et al. demonstrated that Twitter could detect safety signals up to 2 months earlier than FAERS for certain drug-ADR pairs.17 However, social media mining for PV faces significant methodological challenges. Posts frequently lack drug dosage, indication, and temporal relationship data essential for causality assessment. Signal amplification, bot-generated content, and anti-vaccine misinformation campaigns complicate algorithm performance.18 The EMA and FDA have both issued guidance acknowledging social media as a supplementary, rather than primary, data source requiring rigorous validation protocols before regulatory action.

3.4 Artificial Intelligence and Machine Learning in Digital PV

AI and machine learning are transforming pharmacovigilance operations across the signal detection pipeline. Deep learning models applied to FAERS data have demonstrated superior performance in identifying disproportionate reporting signals compared to traditional proportional reporting ratio (PRR) methods.19 Transformer-based NLP models (e.g., BioBERT, MedBERT) are being deployed to automate ICSR narrative processing, reducing manual coding time while improving MedDRA term accuracy. AI-enabled tools are also improving patient-facing reporting interfaces through chatbot-based ADR collection, automated follow-up queries for missing data, and real-time severity assessment algorithms that prioritise serious cases for regulatory review.20 These technologies hold particular promise for resource-limited regulatory environments where manual ICSR processing capacity is constrained.

Table 2: Overview of Key Digital Pharmacovigilance Platforms and Their Features

Platform/Tool

Type

Country/Scope

Key Features & Outcomes

UK Yellow Card App

Mobile application

United Kingdom

Direct patient/HCP reporting; real-time submission to MHRA; COVID-19 vaccine surveillance integration

FDA MedWatch Online

Web portal + mobile

United States

Patient-facing adverse event reporting; integration with FAERS; consumer-friendly interface

EudraVigilance

Centralised database

European Union

EU-wide ADR repository; public access portal; supports ICSR processing across member states

PatientsLikeMe

Online community platform

Global (US-based)

Patient self-reported symptom and treatment data; aggregated safety insights; real-world evidence generation

Twitter/X & Social Media

Social media mining

Global

NLP-based ADR signal detection; rapid pharmacovigilance surveillance; supplementary data source for regulators

VigiBase (WHO)

Global ICSR database

Worldwide (>130 nations)

Largest global ADR database; patient reports integrated; disproportionality analysis for signal detection

mHealth Apps (e.g., CareClinic, Ada)

Mobile health applications

Global

Continuous symptom tracking; push notification reminders; structured ADR data capture linked to EHR

4. Global Regulatory Frameworks For Patient-Centric Digital Pharmacovigilance

Regulatory agencies globally have progressively adapted their pharmacovigilance frameworks to accommodate both direct patient reporting and digitally sourced safety data (Table 3). The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) has developed E2B(R3) guidelines that standardize electronic transmission formats for ICSRs, facilitating cross-border data exchange.21

Table 3: Comparison of Global Regulatory Frameworks for Patient-Centric Digital Pharmacovigilance

Regulatory Body

Region

Patient Reporting Framework

Digital Initiatives

MHRA

United Kingdom

Yellow Card Scheme (patients since 2005)

Yellow Card app; COVID-19 vaccine real-time surveillance

FDA

United States

MedWatch (patients/consumers)

FAERS public dashboard; Sentinel System; FDA Adverse Event Reporting Portal

EMA

European Union

EudraVigilance (patient reports via national centres)

European Medicines Web Portal; Eudralink; ICH E2B(R3) electronic submissions

CDSCO

India

PvPI — Pharmacovigilance Programme of India (limited patient reporting)

Online ADR reporting portal; IPC national coordination; expanding digital reach

WHO-UMC

Global

VigiBase with patient-reported ICSRs from member nations

VigiAccess public portal; signal detection via VigiLyze; AI-assisted triage

The WHO's Good Pharmacovigilance Practices (GVP) guidelines, particularly Module VI (Management and Reporting of Adverse Reactions to Medicinal Products), were updated in 2017 to explicitly address patient reporting and encourage member states to develop patient-accessible reporting mechanisms.22 In India, the Pharmacovigilance Programme of India (PvPI) launched in 2010 under the aegis of the Central Drugs Standard Control Organisation (CDSCO) and coordinated by the Indian Pharmacopoeia Commission (IPC) has made significant strides but continues to face challenges in patient engagement and digital infrastructure, particularly in rural settings.23 The General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States create important legal frameworks governing the use of patient data in pharmacovigilance. These regulations, while protective of patient rights, can complicate the use of real-world digital data sources including social media, wearables, and EHRs for PV purposes, requiring careful balancing of public health interests and individual privacy.24

5. Challenges in Patient-Centric Digital Pharmacovigilance

Despite the promise of patient-centric digital PV, numerous challenges impede its full realization. These span clinical, technological, regulatory, and societal domains (Table 4).25

Table 4: Key Challenges in Patient-Centric Digital Pharmacovigilance and Proposed Solutions

Challenge

Impact on PV System

Proposed/Implemented Solutions

Underreporting by patients

Critical signal gaps; delayed identification of serious ADRs

Awareness campaigns, simplified reporting interfaces, mobile apps, education in patient leaflets

Data quality and completeness

Incomplete ICSRs reduce signal reliability; increased noise-to-signal ratio

Structured data entry forms, AI-assisted coding, follow-up prompts, minimum data set mandates

Health literacy barriers

Exclusion of vulnerable populations; reporting skewed toward educated users

Plain language forms, multilingual interfaces, patient advocacy co-design, pictogram-based reporting

Privacy and data security

Patient reluctance to report; regulatory compliance burden (GDPR/HIPAA)

Anonymisation, pseudonymisation, GDPR-compliant platforms, transparent data governance policies

Attribution and causality

Patient reports often lack clinical detail for confirmed causality assessment

Hybrid reporting (patient + HCP verification), structured causality tools (WHO-UMC scale), AI augmentation

Digital divide

Elderly, rural, and low-income populations underrepresented in digital reporting

Multi-channel reporting (phone hotlines, paper, digital), community health worker-assisted reporting

Duplicate and spurious reports

Database inflation; erroneous signal generation

Deduplication algorithms, blockchain-based unique identifiers, multi-source cross-validation

5.1 Underreporting and Reporting Fatigue

It is estimated that fewer than 10% of ADRs are formally reported in most high-income countries, with rates substantially lower in low- and middle-income countries (LMICs).26 Patient-facing digital tools have partially ameliorated this through improved accessibility; however, reporting fatigue particularly in chronic disease populations who experience frequent adverse effects remains a significant barrier. Longitudinal studies suggest that initial engagement with PV apps decreases markedly after the first three months of use.

5.2 Quality of Patient-Generated Data

Patient-reported ICSRs frequently contain incomplete information regarding drug dosage, indication, concomitant medications, and medical history elements critical for causality assessment. A comparative analysis of MHRA Yellow Card reports found that patient reports had lower data completeness scores than HCP reports across most mandatory data fields.27 However, proponents argue that even incomplete patient reports provide valuable signal-generating information that compensates for their structural limitations.

5.3 Digital Equity and the Digital Divide

Digital reporting tools disproportionately serve younger, more educated, and internet-connected populations, systematically excluding elderly patients, those with low health literacy, and communities in low-resource settings.28 Given that older adults are the most frequent consumers of polypharmacy and the most vulnerable to ADRs, their underrepresentation in digital PV datasets represents a critical public health blind spot requiring targeted multi-channel reporting strategies.

FUTURE DIRECTIONS

6.1 Wearable Technologies and Continuous Monitoring

Wearable devices smartwatches, biosensors, continuous glucose monitors, and implantable devices generate continuous physiological data streams that may enable passive, real-time ADR surveillance. Integration of wearable data with PV systems could automate detection of specific ADR signatures such as QT prolongation (via ECG monitoring), hypoglycaemia (via CGM), or anaphylaxis (via heart rate and galvanic skin response).29

6.2 Electronic Health Record Integration

Routine integration of EHR data with pharmacovigilance databases represents perhaps the most transformative opportunity in digital PV. Initiatives such as the FDA's Sentinel System, which links EHR data from over 100 million patients, have demonstrated the feasibility of active surveillance at population scale.30 The EU's DARWIN EU (Data Analysis and Real World Interrogation Network), launched in 2022, similarly leverages federated EHR databases for regulatory-grade real-world evidence generation including safety surveillance.

6.3 Patient Engagement and Co-Design

Emerging evidence suggests that patient co-design of PV systems involving patients in the development of reporting interfaces, data feedback mechanisms, and communication strategies substantially improves reporting rates and data quality.31 Patient advocacy organisations, disease-specific registries, and rare disease networks represent underutilised channels for building patient-centric PV infrastructure, particularly for conditions where formal reporting infrastructure remains weak.

6.4 Blockchain and Data Integrity

Blockchain technology has been proposed as a mechanism for ensuring data integrity, preventing duplicate reports, and maintaining auditable chains of evidence for patient-reported ICSRs.32 While proof-of-concept studies are promising, scalability, interoperability, and regulatory acceptance remain unresolved. The use of smart contracts to automate data sharing agreements between patients, HCPs, and regulatory authorities under transparent governance frameworks represents a longer-term but potentially transformative application.

CONCLUSION

Patient-centric pharmacovigilance, underpinned by digital technologies, represents a fundamental evolution in drug safety science. By recognizing patients as active partners rather than passive subjects of surveillance, the PV community gains access to qualitatively richer, more timely, and more representative safety data. Digital reporting systems from mobile applications to AI-powered signal detection and social media mining have substantially expanded the reach and responsiveness of pharmacovigilance infrastructure. However, the full potential of digital patient-centric PV remains unrealised. Persistent challenges in data quality, health literacy, digital equity, privacy protection, and regulatory harmonization must be systematically addressed through interdisciplinary collaboration among patients, clinicians, data scientists, industry stakeholders, and regulatory agencies. Particular attention must be paid to ensuring that digital advances do not exacerbate existing health inequities by marginalizing vulnerable patient populations.

Future pharmacovigilance systems must be co-designed with patients, underpinned by robust digital infrastructure, and governed by frameworks that balance the imperatives of patient privacy with the public health necessity of comprehensive drug safety monitoring. In this vision, every patient prescribed a medication becomes a potential contributor to the global safety knowledge base transforming pharmacovigilance from a reactive discipline into a proactive, patient-powered science.

REFERENCES

  1. World Health Organization. The importance of pharmacovigilance: safety monitoring of medicinal products. Geneva: WHO; 2002. Available from: https://www.who.int/medicines/areas/quality_safety/safety_efficacy/pharmvigi/en/
  2. McBride WG. Thalidomide and congenital abnormalities. Lancet. 1961;278(7216):1358.
  3. Blenkinsopp A, Wilkie P, Wang M, Routledge PA. Patient reporting of suspected adverse drug reactions: a review of published literature and international experience. Br J Clin Pharmacol. 2007;63(2):148-56.
  4. Bouvy JC, De Bruin ML, Koopmanschap MA. Epidemiology of adverse drug reactions in Europe: a review of recent observational studies. Drug Saf. 2015;38(5):437-53.
  5. Pacurariu AC, Coloma PM, van Haren A, Genov G, Sturkenboom MC, Straus SM. A description of signals during the first 18 months of the EMA pharmacovigilance risk assessment committee. Drug Saf. 2014;37(12):1059-66.
  6. Lindquist M. VigiBase, the WHO global ICSR database system: basic facts. Drug Inf J. 2008;42(5):409-19.
  7. van Grootheest K, Passier JL, van Puijenbroek E. Direct patient reporting of adverse drug reactions. Drug Saf. 2009;32(1):37-48.
  8. Herxheimer A, Crombholz M, Haag M, et al. Yellow card reporting by patients. Clin Med (Lond). 2010;10(3):285-6.
  9. Harmark L, Puijenbroek E, Straus S, van Grootheest K. Trends in the use of patient reporting in the Netherlands: more reports, better quality of information. Drug Saf. 2011;34(11):1015-24.
  10. Basch E. The missing voice of patients in drug-safety reporting. N Engl J Med. 2010;362(10):865-9.
  11. US Food and Drug Administration. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims. Rockville, MD: FDA; 2009.
  12. Medicines and Healthcare products Regulatory Agency. Yellow Card: guidance on adverse drug reactions. London: MHRA; 2022. Available from: https://yellowcard.mhra.gov.uk
  13. Ferrajolo C, Polimeni G, Scondotto G, et al. Drug-related hospital admissions for adverse events in children: an overview of systematic reviews and analysis of Italian data. Paediatr Drugs. 2013;15(3):177-85.
  14. US Food and Drug Administration. FDA Adverse Event Reporting System (FAERS) public dashboard. Silver Spring, MD: FDA; 2023. Available from: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
  15. Uppsala Monitoring Centre. VigiBase. Uppsala: WHO-UMC; 2024. Available from: https://www.who-umc.org/vigibase/vigibase
  16. Golder S, Norman G, Loke YK. Systematic review on the prevalence, frequency and comparative value of adverse events data in social media. Br J Clin Pharmacol. 2015;80(4):878-88.
  17. Freifeld CC, Brownstein JS, Menone CM, et al. Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf. 2014;37(5):343-50.
  18. Sarker A, Ginn R, Nikfarjam A, et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform. 2015; 54:202-12.
  19. Harpaz R, DuMouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012;91(6):1010-21.
  20. Trifirò G, Crisafulli S, Andersen M, et al. New multidisciplinary research strategies for adverse drug reactions: from genetic background to the use of artificial intelligence. Drug Saf. 2019;42(12):1351-64.
  21. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH harmonised guideline: electronic transmission of individual case safety reports E2B(R3). Geneva: ICH; 2016.
  22. European Medicines Agency. Guideline on good pharmacovigilance practices (GVP) Module VI – Collection, management and submission of reports of suspected adverse reactions to medicinal products (Rev 2). Amsterdam: EMA; 2017.
  23. Kalaiselvan V, Thota P, Singh GN. Pharmacovigilance Programme of India: recent developments and future perspectives. Indian J Pharmacol. 2016;48(6):624-8.
  24. Dankar FK, Ibrahim M. Protecting privacy in big data pharmacovigilance: challenges and perspectives. Drug Saf. 2019;42(2):181-90.
  25. Hazell L, Shakir SA. Under-reporting of adverse drug reactions: a systematic review. Drug Saf. 2006;29(5):385-96.
  26. Insani WN, Whittlesea C, Alwafi H, et al. Prevalence of adverse drug reactions in the primary care setting: a systematic review and meta-analysis. PLoS One. 2021;16(5): e0252161.
  27. Aagaard L, Nielsen LH, Hansen EH. Consumer reporting of adverse drug reactions: a retrospective analysis of the Danish adverse drug reaction database from 2004 to 2006. Drug Saf. 2009;32(11):1067-74.
  28. Ergör G, Ergör A, Baydur H. Digital divide in healthcare. Lancet Digit Health. 2021;3(4):e205-e206.
  29. Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Health. 2020;2(2):e85-93.
  30. US Food and Drug Administration. Sentinel initiative. Silver Spring, MD: FDA; 2023. Available from: https://www.sentinelinitiative.org
  31. Banerjee AK, Ingate S, Mayall S. Web-based patient information systems. Pharm Med. 2013;27(5):189-95.
  32. Omar IA, Debe M, Jayaraman R, Salah K, Omar M, Arshad J. Blockchain-based supply chain traceability for COVID-19 personal protection equipment. Comput Ind Eng. 2021; 167:107995.

Reference

  1. World Health Organization. The importance of pharmacovigilance: safety monitoring of medicinal products. Geneva: WHO; 2002. Available from: https://www.who.int/medicines/areas/quality_safety/safety_efficacy/pharmvigi/en/
  2. McBride WG. Thalidomide and congenital abnormalities. Lancet. 1961;278(7216):1358.
  3. Blenkinsopp A, Wilkie P, Wang M, Routledge PA. Patient reporting of suspected adverse drug reactions: a review of published literature and international experience. Br J Clin Pharmacol. 2007;63(2):148-56.
  4. Bouvy JC, De Bruin ML, Koopmanschap MA. Epidemiology of adverse drug reactions in Europe: a review of recent observational studies. Drug Saf. 2015;38(5):437-53.
  5. Pacurariu AC, Coloma PM, van Haren A, Genov G, Sturkenboom MC, Straus SM. A description of signals during the first 18 months of the EMA pharmacovigilance risk assessment committee. Drug Saf. 2014;37(12):1059-66.
  6. Lindquist M. VigiBase, the WHO global ICSR database system: basic facts. Drug Inf J. 2008;42(5):409-19.
  7. van Grootheest K, Passier JL, van Puijenbroek E. Direct patient reporting of adverse drug reactions. Drug Saf. 2009;32(1):37-48.
  8. Herxheimer A, Crombholz M, Haag M, et al. Yellow card reporting by patients. Clin Med (Lond). 2010;10(3):285-6.
  9. Harmark L, Puijenbroek E, Straus S, van Grootheest K. Trends in the use of patient reporting in the Netherlands: more reports, better quality of information. Drug Saf. 2011;34(11):1015-24.
  10. Basch E. The missing voice of patients in drug-safety reporting. N Engl J Med. 2010;362(10):865-9.
  11. US Food and Drug Administration. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims. Rockville, MD: FDA; 2009.
  12. Medicines and Healthcare products Regulatory Agency. Yellow Card: guidance on adverse drug reactions. London: MHRA; 2022. Available from: https://yellowcard.mhra.gov.uk
  13. Ferrajolo C, Polimeni G, Scondotto G, et al. Drug-related hospital admissions for adverse events in children: an overview of systematic reviews and analysis of Italian data. Paediatr Drugs. 2013;15(3):177-85.
  14. US Food and Drug Administration. FDA Adverse Event Reporting System (FAERS) public dashboard. Silver Spring, MD: FDA; 2023. Available from: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
  15. Uppsala Monitoring Centre. VigiBase. Uppsala: WHO-UMC; 2024. Available from: https://www.who-umc.org/vigibase/vigibase
  16. Golder S, Norman G, Loke YK. Systematic review on the prevalence, frequency and comparative value of adverse events data in social media. Br J Clin Pharmacol. 2015;80(4):878-88.
  17. Freifeld CC, Brownstein JS, Menone CM, et al. Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf. 2014;37(5):343-50.
  18. Sarker A, Ginn R, Nikfarjam A, et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform. 2015; 54:202-12.
  19. Harpaz R, DuMouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012;91(6):1010-21.
  20. Trifirò G, Crisafulli S, Andersen M, et al. New multidisciplinary research strategies for adverse drug reactions: from genetic background to the use of artificial intelligence. Drug Saf. 2019;42(12):1351-64.
  21. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH harmonised guideline: electronic transmission of individual case safety reports E2B(R3). Geneva: ICH; 2016.
  22. European Medicines Agency. Guideline on good pharmacovigilance practices (GVP) Module VI – Collection, management and submission of reports of suspected adverse reactions to medicinal products (Rev 2). Amsterdam: EMA; 2017.
  23. Kalaiselvan V, Thota P, Singh GN. Pharmacovigilance Programme of India: recent developments and future perspectives. Indian J Pharmacol. 2016;48(6):624-8.
  24. Dankar FK, Ibrahim M. Protecting privacy in big data pharmacovigilance: challenges and perspectives. Drug Saf. 2019;42(2):181-90.
  25. Hazell L, Shakir SA. Under-reporting of adverse drug reactions: a systematic review. Drug Saf. 2006;29(5):385-96.
  26. Insani WN, Whittlesea C, Alwafi H, et al. Prevalence of adverse drug reactions in the primary care setting: a systematic review and meta-analysis. PLoS One. 2021;16(5): e0252161.
  27. Aagaard L, Nielsen LH, Hansen EH. Consumer reporting of adverse drug reactions: a retrospective analysis of the Danish adverse drug reaction database from 2004 to 2006. Drug Saf. 2009;32(11):1067-74.
  28. Ergör G, Ergör A, Baydur H. Digital divide in healthcare. Lancet Digit Health. 2021;3(4):e205-e206.
  29. Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Health. 2020;2(2):e85-93.
  30. US Food and Drug Administration. Sentinel initiative. Silver Spring, MD: FDA; 2023. Available from: https://www.sentinelinitiative.org
  31. Banerjee AK, Ingate S, Mayall S. Web-based patient information systems. Pharm Med. 2013;27(5):189-95.
  32. Omar IA, Debe M, Jayaraman R, Salah K, Omar M, Arshad J. Blockchain-based supply chain traceability for COVID-19 personal protection equipment. Comput Ind Eng. 2021; 167:107995.

Photo
Deepak Prashar
Corresponding author

Department of Pharmacy, LR Institute of Pharmacy, Jabli-Kyar, Solan HP-India

Photo
Anu Sharma
Co-author

Department of Pharmacy, Shanti Niketan College of Pharmacy, Mandi (HP)-India

Photo
Priyanka Thakur
Co-author

Department of Pharmacy, LR Institute of Pharmacy, Jabli-Kyar, Solan HP-India

Photo
Kiran
Co-author

Department of Pharmacy, LR Institute of Pharmacy, Jabli-Kyar, Solan HP-India

Anu Sharma, Deepak Prashar*, Priyanka Thakur, Kiran, A Comprehensive Review of Evolving Paradigms, Technologies, and Global Frameworks on Patient-Centric Pharmacovigilance and Digital Reporting Systems, Int. J. Med. Pharm. Sci., 2026, 2 (5), 763-770. https://doi.org/10.5281/zenodo.20442808

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