Research
The research behind the approach.
Doogooda’s scientific foundation brings together causal inference, healthcare economics, patient choice, and epidemiologic modelling. These methods address different questions: what changed after an intervention, what can be attributed to it, and how alternative choices might unfold.
This foundation informs our analytical services. For each question, we assess which methods fit the population, setting, available evidence, and assumptions the work can support.
Patient choice and resource allocation
Access depends on the options people can actually use.
A hospital’s location is one part of an access question. The relevant services, coverage, timing, clinical needs, and available alternatives also matter. Patient-choice models can help examine how people may respond when those alternatives change.
Observed use reflects choices made under existing conditions. It does not, on its own, reveal unrestricted preferences or the care people needed but could not obtain. We assess which influences the data and model can distinguish and make the remaining assumptions explicit.
Doctoral research on patient choice and hospital funding
In her doctoral research, Lina developed a model connecting patient hospital choice, distance, and care quality to hospital funding decisions under a budget constraint. The work examined how allocation choices could change patient welfare within the model’s assumptions.
Read the doctoral research — Chapter 2 →This is a research foundation for framing new analyses. A new application requires evidence and assumptions suited to the population and services in question. The original model did not incorporate every access barrier, including variation in waiting time.
The choice process depends on the service. Planned cancer treatment and emergency care may involve different referral, scheduling, transport, and clinical constraints. We assess whether a choice model fits the question and which other methods and evidence are needed.
The public example
What the synthetic example demonstrates.
Our illustrative analyses demonstrate service and coverage eligibility, capacity-constrained allocation, and comparison of resource options. Patient-response estimation is a separate methodological step and is assessed for each question.
Different questions need different evidence.
What changed?
Observation
Describe what changed in the available data, without attributing the change to a particular action on that basis alone.
What would a different choice be expected to achieve?
Model-based comparison
Compare what different actions could achieve under explicit inputs, constraints, and modelling assumptions.
Which part of the observed change is attributable to the choice?
Causal evaluation
Estimate the effects attributable to an action when the evidence, comparison design, and assumptions support that interpretation.
An original target, a historical baseline, and a causal counterfactual serve different purposes. Meeting a target does not establish what caused the result. An effect estimated in one setting also does not automatically predict the effect of a new choice elsewhere.
Causal inference
Causal inference asks a different question.
A before-and-after change does not, by itself, establish an intervention’s effect. Causal evaluation asks how outcomes differ from what would likely have happened without the defined intervention.
Our founder’s research examines organizational and policy changes in healthcare. In a scoped analysis, that perspective helps define the intervention and affected population, assess potential comparisons, and make the assumptions behind any causal interpretation explicit.
When a credible causal comparison is not possible, that is a limit of the analysis—not a reason to relabel a trend as an effect.
Research areas.
Causal inference
Study the effects of defined organizational or policy changes using an appropriate comparison, with explicit assumptions and uncertainty.
Healthcare economics and operations
Examine resource use, capacity, timing, costs, and trade-offs between operating choices.
Patient choice and allocation
Examine how access, quality, and available alternatives shape choices and the allocation of healthcare resources.
Epidemiologic and disease modelling
Represent relevant clinical and population pathways, including transmission or disease progression where the question calls for them.
Evidence from healthcare use and operating context.
Our hospital-system research has used Medicare claims alongside hospital operations, quality, and local-market data. The inputs for a new engagement depend on its question and population. They may concern services, implementation timing, activity, resources, costs, or relevant outcomes.
Historical research access does not imply current commercial access. Coverage, permitted use, and the suitability of comparison data are assessed for each application.
Selected research
A selection related to the company’s questions, not a complete list. Publication status is stated per item.
Coauthored publication, Management Science, 2023
The Impact of Vertical Integration on Physician Behavior and Healthcare Delivery: Evidence from Gastroenterology Practices
Coauthored research examining how physician integration changes care processes, resource use, and outcomes. It informs the questions and mechanisms we consider; it does not validate the current demonstration or every integration setting.
First-author publication, Health Services Research, 2019
Changes in screening colonoscopy following Medicare reimbursement and cost-sharing changes
Lina’s first-author research examined screening colonoscopy after Medicare reimbursement and cost-sharing changes, and how the way care is classified in claims affects the estimated result.
Unpublished manuscript and related public working paper
The Spillover Effects of Hospital Closures on the Efficiency and Quality of Other Hospitals
Spillover effects of hospital closures on other hospitals.
Unpublished manuscript
The Allocation of Funds in healthcare: Investment Decisions under Access-Quality Trade-Off
The general structure of patient choice and investment allocation under an access–quality trade-off, distinct from disease-specific estimates.
First-author publication, PLOS ONE, 2015
Cost-Effectiveness Analysis of Brief and Expanded Evidence-Based Risk Reduction Interventions for HIV-Infected People Who Inject Drugs in the United States
Transmission and economic evaluation.
Coauthored publication, Medicine, 2019
The cost-effectiveness of medication, laser trabeculoplasty, and trabeculectomy for treatment of open-angle glaucoma in South Korea
A treatment comparison using a Markov model.
Coauthored publication, PLOS ONE, 2018
The cost-effectiveness of systematic screening for age-related macular degeneration in South Korea
A screening evaluation.
From research to carefully scoped analysis.
Research provides methods and evidence that may inform a new question. Each application still requires an assessment of its setting, inputs, assumptions, and analytical limits.
Our own analytical tools support the team’s work. We are developing more connected workflows around traceable inputs, documented calculations, and reviewed outputs. The cancer-care and emergency-care sections describe possible analysis scopes, and the GI example is a separate synthetic calculation. These examples do not establish performance in a customer setting.
A publication, executable research code, a synthetic example, and evidence from a customer application establish different things. Their evidentiary roles are kept separate.