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Researchers often use surveys to learn about what patients prefer. The wording of survey questions may affect how patients answer.

In this study, the research team compared different ways of asking patients with type 2 diabetes questions in a national survey. The questions asked patients about managing their diabetes and the medicines they prefer. The team wanted to see how accurately the different ways of asking questions measured patients' preferences. The study looked at whether patients thought the different ways of asking questions:

• Were easy to understand and answer
• Led to answers that matched what patients really wanted

Patients with chronic health problems, such as diabetes, often need to change treatment plans over time to improve their health. To help with this process, doctors can monitor patients' health through follow-up clinic visits and lab tests. Doctors may also suggest changing a treatment plan in response to visits or lab test results. When a treatment plan changes in this way, it's called a dynamic treatment plan. In this study, the research team developed and tested new statistical methods to learn how dynamic treatment plans and choices about follow-up care affect patients' health. These methods use electronic health records, or EHRs. Using EHRs is helpful because they have data on

• What treatments patients have received over time
• How treatments have affected patients' health
• Follow-up information such as lab test results

But the data may differ for patients based on when and why they go to the doctor. These differences make it hard for researchers to accurately know the effect of dynamic treatment plans across many patients.

To access the methods and software, please visit the simcasual R Package.

Research that focuses on what's most important to patients can inform health decisions. Researchers use different methods to identify what's most important to patients.

In this study, the research team compared two methods for identifying what's most important to patients: one-on-one interviews and group concept mapping, or GCM. GCM is a three-round process that helps researchers get input from a group. In the first round, people brainstorm topics that are important to them. Next, people sort the topics into clusters based on similar ideas. Finally, researchers create a map to display and discuss the topics. Researchers can use the complete GCM process or the brainstorming round only.

The research team looked at one-on-one interviews versus GCM and compared the number of topics patients named and the amount of time and money required.

Comparative effectiveness research compares two or more treatments to see which one works best for which patients. But patient traits, such as age or income, may affect patients' treatment choices. These traits may also affect patients' responses to treatments. As a result, researchers may have trouble telling whether a patient's traits, the treatment, or a mix of the two affected how well a treatment worked.

Statistical methods called matching methods can help address this problem when researchers use patient data to compare the effects of treatments. Matching methods help researchers find data from patients who had similar traits such as age or race and received different treatments. Because the patients are similar except for the treatment they receive, the differences in patients' health can more likely be credited to the treatment. Existing methods work well for comparing up to two treatments. But they may not work with three or more treatments.

In this study, the research team created two new matching methods to compare the effects of three or more treatments. The team then analyzed the new methods under different conditions to see how well each worked."

Patient-reported outcome measures are surveys that ask patients how they feel and what activities they can do. These surveys ask about things such as how well people sleep and how much their pain interferes with daily life.

In this study, the research team wanted to learn if two clinics could gather patient-reported outcome measures during routine care visits, and if patients with type 2 diabetes could use the results to set goals for improving their health. The research team also wanted to learn if patients and clinic staff saw value in using these measures.

Clinical notes in electronic health records, or EHRs, can help researchers study treatments. For example, EHR notes may contain information about whether patients take their medicines as directed. But it takes researchers a lot of time to find this information.

Natural language processing, or NLP, methods can help researchers find information in EHR notes. With NLP, computer programs read and identify written language to make it easier to sort and study. But current NLP methods don't work well to find and label text about medicine use.

In this study, the research team created and tested a new NLP method to find and label EHR notes on patients' medicine use.

Researchers can use data from electronic health records, or EHRs, in studies that compare two or more treatments. In these studies, researchers need to identify all patients with the same phenotype. Phenotypes are a person's known traits, like height and weight, or known health problems, like diabetes. However, in EHR data, some data on patient traits or health problems may be missing for some patients.

Missing data in EHRs make it hard to correctly identify all patients with the same phenotype. It's even harder when data are missing due to a patient's health status. For example, patients with uncontrolled diabetes may need more lab tests than patients with controlled diabetes. As a result, researchers who are looking at lab tests may not identify patients with controlled diabetes as having diabetes.

In this project, the research team developed and tested a new statistical method that accounts for missing EHR data to estimate patient phenotypes.

To access the methods and software, please visit the bias_correction GitHub repository.