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Researchers can use patient health data to compare treatments. But these data may include information, like names or social security numbers, that could identify patients. Researchers use different methods to remove such information and protect patients' privacy. Some methods work well to protect privacy but may make data less useful for research. Other methods don't protect privacy well enough.

Current methods for protecting privacy don't work well when:

• The number of patients in the data set is smaller than the number of data fields, such as patient traits or health conditions, and data are updated many times
• Patients' health and treatments are measured at more than one point in time
• Data are displayed as a graph to better capture some types of content

In this study, the research team created three new methods. The team wanted to see if the new methods better protect patient privacy but also make sure data remain useful for research.

To access the methods and software, please visit the AIMS Group at Emory University.

Many healthcare systems use electronic health records. Researchers use data from these records in their studies. Some records have missing or incorrect data. When this happens, people might not be able to trust a study's results. The research team wanted to:

• Create guidance to judge whether data that a study used were high quality
• Find new ways to display the quality of data
• Learn why researchers don't always report the quality of data that they used in studies

To access the methods and software, please visit the DQCODE-A-Thon GitHub.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Electronic healthcare data are useful for this type of research. These data come from medical records and insurance claims. The data include information about how well patients respond to treatments. But many things--not just treatments--affect whether a patient's health improves.

How well a patient responds to a treatment may depend on the patient's age or what medicines the patient takes. It could also depend on what other health problems a patient has and how severe those problems are. Or a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to determine whether changes in a patient's health result from a certain treatment or something else.

Different statistical methods help researchers account for the various things that can affect treatment results. But researchers don't know which methods work best. This study compared several methods. The team looked at how well the methods worked to predict patients' responses to treatment, taking into account their personal situations and health. The team then created a computer program to help researchers use the methods.

To access the methods and software, please visit the Hdps GitHub and TargetedLearning GitHub.

In a stepped-wedge cluster randomized trial, or SW-CRT, researchers compare new treatments to standard treatments in groups of patients, such as patients at different clinics, to look at the treatments' effectiveness. They assign groups by chance to switch from the standard to new treatment at different time points until all groups have received the new treatment. The different time points to switch treatments are called steps.

SW-CRTs take time and resources. If researchers know they can't collect data on all groups and all steps in a SW-CRT, they can plan to use an incomplete SW-CRT design. In incomplete SW-CRTs, researchers plan the study knowing that some clinics or steps will have missing data. But researchers need better guidance for planning incomplete SW-CRTs that still get accurate results.

Also, current methods for planning how many patients and groups should take part in SW-CRTs don't work well for large studies. They also don't work well with certain types of outcomes, like yes or no outcomes; outcomes that have counts, like number of hospital visits; or continuous outcomes, like a score from 0 to 100.

In this study, the research team developed and tested new methods to design and analyze SW-CRTs with different patterns of planned missing data, large data sets, and different types of outcomes.

Medical devices, such as pacemakers or stents, can help diagnose, treat, or prevent health problems. Companies that make medical devices label them with unique device identifiers, or UDIs. UDIs contain data about a device, such as the make, model, and expiration date. Healthcare providers can scan UDIs when they use the devices and record UDI data in patients' health records.

Right now, UDI data can only be accessed by the health systems that use the devices. Having the UDI data in insurance claim forms, instead of only in patients' health records, would mean that researchers could look at data over time and across health systems. They could then use these data to help monitor devices for safety or study questions like how well devices are working.

In this study, the research team created ways to send UDI data from health systems to insurance claims forms.

For research studies, researchers can use data about patients' health and treatments from electronic health records, or EHRs. They may also collect self-reported data directly from patients. But a patient's EHR and self-reported data may not always agree. For example, differences may exist between the medicines that patients report taking and the medicines listed in their EHRs. Researchers don't know which of these two data sources is the most accurate.

In this project, the research team looked at EHR and self-reported data to learn which data source was more accurate.

Comparative effectiveness research compares two or more treatments to see which one works best for which patients. Information from health insurance claims could be useful for this type of research. These claims include data on how well patients respond to treatments. But many things--not just treatments--affect whether patients' health improves.

How well patients respond to treatments could depend on patients' ages or medicines they take. It could also depend on how many health problems a patient has and how severe the problems are. Also, a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to figure out whether changes in patients' health result from treatment or something else.

Comparing treatments is hard in small studies with only a few patients. When there are few patients in a study, researchers can study only a few events. An event is an outcome related to the health problem or treatment researchers are studying. When there are few events and many things that could affect treatment results, it is hard to figure out what causes changes in patients' health. To address this problem, researchers use different statistical methods to account for all the things that could affect treatment results. But researchers don't know which methods might work best in studies with few events. In this study, the research team compared several methods to see which ones worked best.

Data from healthcare systems, patients and communities, and health plans can support health research. Two types of data sources are

• Patient-powered research networks, or PPRNs. In PPRNs, patients, families, caregivers, and community members share health data with the network. They work closely with researchers to plan and conduct research.
• Health plan research networks, or HPRNs. In HPRNs, networks of health plans have access to health claims data from members for research.

By linking patient records across PPRNs and HPRNs, researchers may be able to do more robust research. To link records, researchers use computer programs to connect the records of people in a PPRN with their claims data in an HPRN. Current methods to link records require use of personal information, such as names and dates of birth. But patients may not want to share this information.

In this project, the research team developed methods for linking data from PPRNs and HPRNs without using patients' personal information.

Clinical trials study the effects of medical treatments, like how safe they are and how well they work. But most clinical trials don't get all the data they need from patients. Patients may not answer all questions on a survey, or they may drop out of a study after it has started. The missing data can affect researchers' ability to detect the effects of treatments.

To address the problem of missing data, researchers can make different guesses based on why and how data are missing. Then they can look at results for each guess. If results based on different guesses are similar, researchers can have more confidence that the study results are accurate. In this study, the research team created new methods to do these tests and developed software that runs these tests.

To access the sensitivity analysis methods and software, please visit the MissingDataMatters website.

Data collected from interviews and group discussions, called qualitative data, can help researchers understand people's experiences, values, and cultures. But large amounts of qualitative data can be hard to show in a way that's easy for people to understand.

In this study, the research team created charts called ethnoarrays. These charts use color coding to show individual stories and overall patterns in qualitative data. The team wanted to learn whether ethnoarrays were useful and easy to understand.