Qian Li, PhDResearch Assistant Professor, Health Outcomes and Biomedical Informatics, University of Florida

Towards more reliable clinical evidence.

Advancing biomedical research through real-world data, AI, and rigorous study design.

I study how real-world data and AI can improve the way clinical studies are designed, measured, conducted and evaluated. I trained in biostatistics and biomedical informatics, and most of my work happens before any model is fit: deciding who is eligible, what is measured and over which window, and whether the data can see it at all.

Research

Three directions, each at a different stage, share one concern: whether the evidence a clinical study produces can be trusted given how its data were defined and collected.

Depression care as a set of possible pathsSix stages from symptoms to outcomes. A dashed path returns from engagement to recognition after relapse, and any stage can end in leaving observed care.Symptomsemerge or worsenRecognitionscreening or visitDiagnosisrecorded, or notTreatment starttherapy or drugEngagementkept up, or notOutcomessymptoms, functionrelapse, returnmay leave careAny step can be skipped, repeated or left

Conceptual illustration

Depression care is a set of paths, and each data source sees part of it

Mental Health Care and Outcomes

Question
How do adolescents and new mothers with depression move through care, and how can routine data measure that path and where it leads?
Status
In development

With collaborators in mental health research, I am planning studies of adolescent and maternal and postpartum depression that follow recognition, treatment start, engagement and outcomes in real-world data, and testing whether language models can recover the symptoms and outcomes that only clinical notes record.

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RequestGLP-1 receptor agonists and glycemic control

Identify adults with type 2 diabetes2 who started a GLP-1 receptor agonist1 between 2019 and 2023. Exclude anyone with prior insulin use3. Baseline HbA1c is the most recent value on record5; the outcome is HbA1c six months later6. Also include the adolescent endocrinology cohort4 so the sample is large enough.

  1. Missing decision
  2. Ambiguous definition
  3. Undefined window
  4. Internal contradiction
  5. Potential error
  6. Observability

Synthetic example

Reviewing a data request before any data are pulled

AI-Assisted Research Design and Data Quality

Question
Can an AI assistant find the missing decisions, unclear definitions and contradictions in a real-world data request before anyone builds the dataset?
Status
Proposal in development

I am developing a University of Florida seed-funding proposal to test whether an AI copilot can review researchers' data requests as well as expert reviewers and structured templates do. The idea is at an early stage and there are no results yet.

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A one-time count versus recruitment opportunity over timeSynthetic curves over 24 months. A one-time database count is flat. Patients already in the record who remain eligible decline over time, while newly eligible patients accumulate, so the true opportunity first falls below and later rises above the one-time count. A database that is refreshed with a delay shows fewer patients than are really available; the shaded band is the difference.0612182401×Months after the trial opensEligible patients, relative to the countRecruitment opportunityOne-time query countWhat the data showExisting patients still eligibleA one-time count versus recruitment opportunity over time0612182401×Months after the trial opensRecruitment opportunityOne-time query countWhat the data showExisting patientsNot yet visible in the data

The shaded band is the part of the opportunity a delayed data refresh cannot yet see.

Synthetic example

From a one-time count to recruitment opportunity over time

Clinical Trial Design and Feasibility

Question
How many patients could a trial actually enroll, and when, once eligibility over time, new patients and the age of the data are taken into account?
Status
Proposal submitted

A UF-FSU CTSA pilot proposal, From Population to Participant: Calibrating Real-World Data for Trial Feasibility and Accrual, has been submitted. It would turn one-time database counts into time-varying estimates of recruitment opportunity and check them against real accrual.

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Selected foundations

Published work the current directions grow from.

Foundations in detail All 27 publications

Published

  1. 2023Early prediction of Alzheimer's disease and related dementias using real‐world electronic health recordsAlzheimer's & Dementia
  2. 2019Assessing the Validity of a a priori Patient-Trial Generalizability Score using Real-world Data from a Large Clinical Data Research Network: A Colorectal Cancer Clinical Trial Case StudyAMIA Annual Symposium Proceedings
  3. 2020Using Real-World Data to Rationalize Clinical Trials Eligibility Criteria Design: A Case Study of Alzheimer's Disease TrialsAMIA Annual Symposium Proceedings
  4. 2019A study of deep learning methods for de-identification of clinical notes in cross-institute settingsBMC Medical Informatics and Decision Making

Beyond research

Life is a separate notebook for things outside the research program: software and tools, projects at home, food, photographs and travel.

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