Stanford Health Policy is a joint effort of the Freeman Spogli Institute for International Studies and the Stanford School of Medicine
FSI’s researchers assess health and medicine through the lenses of economics, nutrition and politics. They’re studying and influencing public health policies of local and national governments and the roles that corporations and nongovernmental organizations play in providing health care around the world. Scholars look at how governance affects citizens’ health, how children’s health care access affects the aging process and how to improve children’s health in Guatemala and rural China. They want to know what it will take for people to cook more safely and breathe more easily in developing countries.
FSI professors investigate how lifestyles affect health. What good does gardening do for older Americans? What are the benefits of eating organic food or growing genetically modified rice in China? They study cost-effectiveness by examining programs like those aimed at preventing the spread of tuberculosis in Russian prisons. Policies that impact obesity and undernutrition are examined; as are the public health implications of limiting salt in processed foods and the role of smoking among men who work in Chinese factories. FSI health research looks at sweeping domestic policies like the Affordable Care Act and the role of foreign aid in affecting the price of HIV drugs in Africa.
ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.
Background: Positron emission tomography (PET) with 18-fluorodeoxyglucose (FDG) is a potentially useful but expensive test to diagnose solitary pulmonary nodules.
Objective: To evaluate the cost-effectiveness of strategies for pulmonary nodule diagnosis and to specifically compare strategies that did and did not include FDG-PET.
Design: Decision model.
Data Sources: Accuracy and complications of diagnostic tests were estimated by using meta-analysis and literature review. Modeled survival was based on data from a large tumor registry. Cost estimates were derived from Medicare reimbursement and other sources.
Target Population: All adult patients with a new, noncalcified pulmonary nodule seen on chest radiograph.
Time Horizon: Patient lifetime.
Perspective: Societal.
Intervention: 40 clinically plausible combinations of 5 diagnostic interventions, including computed tomography, FDG-PET, transthoracic needle biopsy, surgery, and watchful waiting.
Outcome Measures: Costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios.
Results of Base-Case Analysis: The cost-effectiveness of strategies depended critically on the pretest probability of malignancy. For patients with low pretest probability (26%), strategies that used FDG-PET selectively when computed tomography results were possibly malignant cost as little as $20 000 per QALY gained. For patients with high pretest probability (79%), strategies that used FDG-PET selectively when computed tomography results were benign cost as little as $16 000 per QALY gained. For patients with intermediate pretest probability (55%), FDG-PET strategies cost more than $220 000 per QALY gained because they were more costly but only marginally more effective than computed tomography-based strategies.
Results of Sensitivity Analysis: The choice of strategy also depended on the risk for surgical complications, the probability of nondiagnostic needle biopsy, the sensitivity of computed tomography, and patient preferences for time spent in watchful waiting. In probabilistic sensitivity analysis, FDG-PET strategies were cost saving or cost less than $100 000 per QALY gained in 76.7%, 24.4%, and 99.9% of computer simulations for patients with low, intermediate, and high pretest probability, respectively.
Conclusions: FDG-PET should be used selectively when pretest probability and computed tomography findings are discordant or in patients with intermediate pretest probability who are at high risk for surgical complications. In most other circumstances, computed tomography-based strategies result in similar quality-adjusted life-years and lower costs.