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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.

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.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. One type of research study is a randomized controlled trial, or an RCT. In an RCT, the research team assigns patients to a treatment by chance.

Other types of studies use information from health records and registries. Registries store data about patients with a specific health problem. They often include information on how each patient responds to a treatment. Because researchers don't assign treatments by chance in such studies, differences in how patients respond to a treatment may be from the treatment or something else, such as a patient's age or the severity of their illness. In studies using registries and health records, researchers apply statistical approaches, called causal inference methods, to estimate how treatments work. At the same time, they look at other things that could affect results, like a patient's age.

Researchers can choose among many different causal inference methods. But they may have a hard time knowing which methods to use or how to use complex methods correctly. In this study, the research team made an interactive online guide for researchers. The guide, called CERBOT, helps researchers design studies and select these methods.

Researchers often combine data from different sources, such as insurance claims and health records, to get a better picture of patients' health and use of health care. Researchers use unique identifiers, like Social Security numbers, to connect patient records and make them more complete. But sometimes this approach doesn't work well, especially when records don't have much personal information. Having limited personal data can lead to errors when linking records.

In this study, the research team created new methods to link data sets with limited personal information. Then they compared the new methods with existing ones. They also applied the new methods with real patient data.

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 better for certain patients. Researchers often use data from patients' electronic health records to compare different treatments. This study addresses some problems that can arise from this practice. In some long-term research studies, researchers use data collected when patients in the studies see their doctors. Regularly scheduled doctor visits, called well visits, include yearly checkups or periodic blood pressure checks. Other doctor visits, called sick visits, occur when a patient feels sick or needs special care.

Well and sick visits can produce different types of health record data. In addition, test results at sick visits may be different from results at well visits. Using data from sick visits may inappropriately influence, or bias, a study's results. Also, patients may go to the doctor more often when they have symptoms or chronic health problems. Researchers may then collect more data from these patients than they collect from the healthier patients. Unequal amounts of data per patient make it harder to compare treatment results.

For this study, the research team created three tests to find if data from sick visits lead to bias in a study's findings. The team also compared standard and newer statistical methods for analyzing data that include sick visits. Researchers designed the newer methods to reduce bias from data obtained at sick visits. With less biased results, doctors can be more certain about which treatment worked better for certain patients.

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 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.

Comparative effectiveness research, or CER, compares two or more treatments. In some CER studies, researchers use patient data from electronic health records, or EHRs, to compare treatments. But patient traits like age may affect doctors' and patients' choice of treatments, which can bias results. Using EHR systems to identify eligible patients and assign them to treatments by chance could improve results of CER studies that use EHR data.

In this study, the research team explored the views of patients, clinic staff, and clinicians, such as doctors or nurses, on doing CER studies in clinics. The team also tested software with a widely used EHR system. The software finds patients who qualify for a study. During a clinic visit, the software prompts doctors to invite patients to take part in the study. If patients agree, the software assigns patients by chance to a treatment.

Electronic health records, or EHRs, have data on patient traits, health problems, and treatments. Researchers can use EHR data to study how treatments work or which patient traits affect health outcomes. But EHR data can have errors.

The best way to get accurate EHR data is to closely review patients' original records. But reviewing all patient records isn't possible when many patients are in a study. In such cases, researchers can review and correct records for a few patients and use the revised records to adjust data for all patients. But existing methods for using revised records don't address some kinds of errors, such as errors that are related. For example, errors in a treatment starting date can lead to mistakes in the data on length of treatment.

In this project, the research team created and tested new methods to improve the accuracy of EHR data. The new methods corrected records from some patients. Then the team used the corrections to address related errors for all patients.

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

Health registries record data about patients with a specific health problem. These data may include age, weight, blood pressure, health problems, medical test results, and treatments received. But data in some patient records may be missing. For example, some patients may not report their weight or all of their health problems.

Research studies can use data from health registries to learn how well treatments work. But missing data can lead to incorrect results. To address the problem, researchers often exclude patient records with missing data from their studies. But doing this can also lead to incorrect results. The fewer records that researchers use, the greater the chance for incorrect results.

Missing data also lead to another problem: it is harder for researchers to find patient traits that could affect diagnosis and treatment. For example, patients who are overweight may get heart disease. But if data are missing, it is hard for researchers to be sure that trait could affect diagnosis and treatment of heart disease.

In this study, the research team developed new statistical methods to fill in missing data in large studies. The team also developed methods to use when data are missing to help find patient traits that could affect diagnosis and treatment.

To access the methods, software, and R package, please visit the Long Research Group website.

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.

Electronic health records, or EHRs, have information about a patient's health such as test results, diagnoses, and treatments. EHRs also have clinical notes that doctors and patients can use to track goals and decisions.

Clinical notes may be useful for research or to help improve care. But it's hard to get information from these notes across large groups of patients. The notes may use different ways to describe the same thing. For example, high blood pressure may be called hypertension. Also, the notes may use abbreviations or have spelling mistakes.

In this project, the research team designed and built a search engine to make EHR notes easier to search and use for patient care and research.

Clinical notes in electronic health records, or EHRs, may contain information that can help researchers study and compare treatments. But it takes researchers a lot of time to find information in EHR notes.

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 in EHR notes, some sentences may contain more than one topic. Also, EHR notes may discuss a single topic over many sentences. In these cases, current NLP methods don't work well to find complete and accurate information about a specific topic.

In this study, the research team developed and tested new NLP methods to identify topics from EHR notes.

Researchers can use data on patient traits such as age, health problems, and treatment preferences, to create personalized treatment rules, or PTRs. PTRs provide doctors with guidance on how to treat patients' health problems based on their traits. But PTRs based on a single data source may not apply to all patients. For example, if researchers create a PTR using data from older people with heart failure, it may not apply to younger people with heart failure.

To avoid this problem, researchers can create PTRs by combining data from many sources. PTRs based on many data sources can help guide treatment for patients with different traits.

In this study, the research team created and tested a new method for creating PTRs using data from multiple sources.