About

Jacqueline Griffin is an Associate Professor in the Department of Mechanical and Industrial Engineering at Northeastern University College of Engineering. Her research focuses on developing new models and operations research methods to address decision-making complexities in health and humanitarian systems. She examines decision-making processes involving multiple competing objectives in real-world applications, emphasizing real-time dynamic decision making. Her work includes the development of methodologies and algorithms related to optimization, Markov decision processes, queueing systems, and stochastic programming. Dr. Griffin's research aims to improve healthcare resource allocation, model resiliency in complex systems, and optimize the design and management of outpatient health care clinics. She has contributed to projects such as designing information infrastructure for pharmaceutical supply chains, mitigating disruptions in critical supply chains, and improving patient flow in outpatient clinics. Her expertise also extends to simulation, data analytics, AI, and operations research, with a focus on resilient and sustainable service systems.

Research topics

• Computer Science
• Business
• Computer Security
• Artificial Intelligence
• Political Science
• Psychology
• Social psychology
• Risk analysis (engineering)
• Microeconomics
• Public relations
• Medicine
• Industrial organization
• Economics
• Law
• Management science
• Human–computer interaction
• Marketing
• Internet privacy
• Engineering

Selected publications

• Validating Simulated Agents with Pharmaceutical Supply Chain Game Data

  2025-12-07

  article
  PublisherDOI

• The Unreliability of <i>Estimated Release Dates</i> in Hospital Drug Shortage Management: A Case Study of Hospital Pharmacy Operations During the COVID-19 Pandemic

  medRxiv · 2025-07-11

  preprintOpen accessSenior author

  Abstract Drug shortages are prominent, persistent operational challenges that hospital pharmacies have been facing for years. During a drug shortage, hospital pharmacists must solve the problem of how best to invest resources to mitigate the effect of the drug shortage on patient health care. One piece of data they use to inform their decision-making is the estimate release date (ERD) of a drug, a point estimate given from the pharmaceutical manufacturer specifying when the next release of a drug (that is on shortage) will occur. Working with a hospital collaborator, we collected a novel set ERD and shipment data to analyze the the accuracy of this information and the impact on decision-making at hospitals. We show via statistical analysis that ERD information tends to be an inaccurate indicator of when the hospital should expect to receive more product and is subject to change randomly and at random intervals, adding additional complexity to managing drug shortages. We discuss managerial insights that stem from this analysis and lay a foundation for future research studying decision-making with unreliable ERD information.

  PublisherOA PDFDOI

• The unreliability of estimated release dates in hospital drug shortage management: A case study of hospital pharmacy operations during the COVID-19 pandemic

  PLoS ONE · 2025-10-13 · 1 citations

  articleOpen accessSenior authorCorresponding

  Drug shortages are prominent, persistent operational challenges that hospital pharmacies have been facing for years. During a drug shortage, hospital pharmacists must solve the problem of how best to invest resources to mitigate the effect of the drug shortage on patient health care. One piece of data they use to inform their decision-making is the estimated release date (ERD) of a drug, a lead time estimate given from the pharmaceutical manufacturer specifying when the next release of a drug (that is on shortage) will occur. Working with a hospital collaborator, we collected a novel set of ERD and shipment data to analyze the accuracy of this information and the impact on decision-making at hospitals. We show via statistical analysis that ERD information tends to be an inaccurate indicator of when the hospital should expect to receive more product and is subject to change unpredictably, adding additional complexity to managing drug shortages. We discuss managerial insights that stem from this analysis and lay a foundation for future research studying decision-making with unreliable lead time information.

  PublisherDOI

• A Two-Stage Simulation Framework for Evaluating AI Policy Recommendations: A Case Study of Covid-19

  2025-12-07

  article
  PublisherDOI

• Profiling the Dynamics of Trust &amp; Distrust in Social Media: A Survey Study

  2024-05-11 · 11 citations

  articleOpen access

  In the era of digital communication, misinformation on social media threatens the foundational trust in these platforms. While myriad measures have been implemented to counteract misinformation, the complex relationship between these interventions and the multifaceted dynamics of trust and distrust on social media remains underexplored. To bridge this gap, we surveyed 1,769 participants in the U.S. to gauge their trust and distrust in social media and examine their experiences with anti-misinformation features. Our research demonstrates how trust and distrust in social media are not simply two ends of a spectrum; but can also co-exist, enriching the theoretical understanding of these constructs. Furthermore, participants exhibited varying patterns of trust and distrust across demographic characteristics and platforms. Our results also show that current misinformation interventions helped heighten awareness of misinformation and bolstered trust in social media, but did not alleviate underlying distrust. We discuss theoretical and practical implications for future research.

  PublisherOA PDFDOI

• Adverse diagnostic events in hospitalised patients: a single-centre, retrospective cohort study

  BMJ Quality & Safety · 2024-10-01 · 8 citations

  article

  BACKGROUND: Adverse event surveillance approaches underestimate the prevalence of harmful diagnostic errors (DEs) related to hospital care. METHODS: We conducted a single-centre, retrospective cohort study of a stratified sample of patients hospitalised on general medicine using four criteria: transfer to intensive care unit (ICU), death within 90 days, complex clinical events, and none of the aforementioned high-risk criteria. Cases in higher-risk subgroups were over-sampled in predefined percentages. Each case was reviewed by two adjudicators trained to judge the likelihood of DE using the Safer Dx instrument; characterise harm, preventability and severity; and identify associated process failures using the Diagnostic Error Evaluation and Research Taxonomy modified for acute care. Cases with discrepancies or uncertainty about DE or impact were reviewed by an expert panel. We used descriptive statistics to report population estimates of harmful, preventable and severely harmful DEs by demographic variables based on the weighted sample, and characteristics of harmful DEs. Multivariable models were used to adjust association of process failures with harmful DEs. RESULTS: Of 9147 eligible cases, 675 were randomly sampled within each subgroup: 100% of ICU transfers, 38.5% of deaths within 90 days, 7% of cases with complex clinical events and 2.4% of cases without high-risk criteria. Based on the weighted sample, the population estimates of harmful, preventable and severely harmful DEs were 7.2% (95% CI 4.66 to 9.80), 6.1% (95% CI 3.79 to 8.50) and 1.1% (95% CI 0.55 to 1.68), respectively. Harmful DEs were frequently characterised as delays (61.9%). Severely harmful DEs were frequent in high-risk cases (55.1%). In multivariable models, process failures in assessment, diagnostic testing, subspecialty consultation, patient experience, and history were significantly associated with harmful DEs. CONCLUSIONS: We estimate that a harmful DE occurred in 1 of every 14 patients hospitalised on general medicine, the majority of which were preventable. Our findings underscore the need for novel approaches for adverse DE surveillance.

  PublisherDOI

• Patient–Clinician Diagnostic Concordance upon Hospital Admission

  Applied Clinical Informatics · 2024-08-01

  articleOpen access

  Abstract Objectives This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician. Methods Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance. Results A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 “R-code”) for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p &lt; 0.01), respectively. Conclusion About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 “R-code” entered as the principal problem and patient-reported lack of confidence may predict patient–clinician nonconcordance early during hospitalization via this approach.

  PublisherOA PDFDOI

• The role of hesitancy and infrastructure in the equity and efficiency of COVID-19 vaccine administration

  PLoS ONE · 2024-06-14 · 1 citations

  articleOpen accessSenior author

  After the first COVID-19 vaccines received emergency use authorization from the U.S. FDA in December 2020, U.S. states employed vaccine eligibility and administration plans (VEAPs) that determined when subgroups of residents would become eligible to receive the vaccine while the vaccine supply was still limited. During the implementation of these plans, public concern grew over whether the VEAPs and vaccine allocations from the federal government were resulting in an equitable and efficient vaccine distribution. In this study, we collected data on five states' VEAPs, federal vaccine allocations, vaccine administration, and vaccine hesitancy to assess the equity of vaccine access and vaccine administration efficiency that manifested during the campaign. Our results suggest that residents in states which opened eligibility to the vaccine sooner had more competition among residents to receive the vaccine than occurred in other states. Regardless of states' VEAPs, there was a consistent inefficiency in vaccine administration among all five states that could be attributed to both state and federal infrastructure deficits. A closer examination revealed a misalignment between federal vaccine allocations and the total eligible population in the states throughout the campaign, even when accounting for hesitancy. We conclude that in order to maximize the efficiency of future mass-vaccination campaigns, the federal and state governments should design adaptable allocation policies and eligibility plans that better match the true, real-time supply and demand for vaccines by accounting for vaccine hesitancy and manufacturing capacity. Further, we discuss the challenges of implementing such strategies.

  PublisherOA PDFDOI

• Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach

  JAMIA Open · 2023-04-06 · 10 citations

  articleOpen access

  Abstract Objective To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients. Materials and Methods Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers. Results Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team’s diagnosis (PDQ). Discussion A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE. Conclusions We identify challenges and offer lessons from our user-centered design process.

  PublisherOA PDFDOI

• Thought Bubbles: A Proxy into Players’ Mental Model Development

  2023-04-19 · 4 citations

  preprintOpen access

  Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players’ mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players’ mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people’s mental model development and influence their decisions.

  PublisherOA PDFDOI

Recent grants

• CRISP Type 1: Multi-agent Modeling Framework for Mitigating Distributed Disruptions in Critical Supply Chains

  NSF · $500k · 2016–2019

Frequent coauthors

• Pınar Keskinocak

  19 shared

• Cheryl Stokes

  Universidad del Noreste

  16 shared

• Nikki O’Hara

  Universidad del Noreste

  16 shared

• Atul Vats

  Institute of Medical Sciences

  16 shared

• Nurul Suhaimi

  Universiti Malaysia Pahang Al-Sultan Abdullah

  13 shared

• Stacy Marsella

  Universidad del Noreste

  13 shared

• Anuj K. Dalal

  Brigham and Women's Hospital

  12 shared

• Jeffrey L. Schnipper

  Harvard University

  11 shared

Awards & honors

• 2021 College of Engineering Faculty Fellow
• ARCS (Achievement Rewards for College Scientists) Foundation…
• National Defense Transportation Association Scholarship (Fal…
• Best Reviewer of 2010 Award