The health care industry has grown exponentially since the Affordable Care Act went into effect in 2013.
It is also growing exponentially in terms of its reach, and it has now grown to include a vast array of survey data.
But one type of data that has been largely absent from the industry is cross-sectional, in which a person is surveyed by one company, often a biotechnology company, to gauge how the healthcare system is changing.
Cross-sectional surveys, which were initially designed to measure the health of a specific population, have become increasingly popular among researchers as they become more efficient and less costly to produce.
This is because, as a cross-sectional survey is much more accurate than a person-to-person survey, the cost of conducting a cross sectionally representative survey is lower, according to a study from Duke University.
The study found that cross-surveys are a much cheaper and more accurate way to study the health effects of new drugs than individual-to.
But it also said that researchers need to consider the biases that can arise in the way people are asked to answer questions.
The study found a lack of clear definitions of the word “cross-sectional,” making it difficult to understand what survey data actually does, and how that data can be used.
The authors of the Duke study, Matthew L. Zalewski, M.D., and Kevin R. Jaffe, M,C, PhD, of the Center for Health Systems Research at Duke University, found that people who answered a question on the survey often didn’t understand it, or were unclear about what the question meant.
They also found that questions that asked respondents to list the most important factor that led to a particular change in their health were often questions that were not asked by researchers and didn’t offer clear answers.
This lack of clarity could lead to inaccurate findings, which in turn could impact the research that follows, the authors wrote.
The cross-cultural nature of this survey can also contribute to bias and make it difficult for researchers to understand the validity of results, they said.
The cross-pollination of multiple sources has also made it easier for researchers and doctors to gather and analyze data on a variety of topics, from how people respond to new medications to what is the risk of certain diseases.
However, these sources can also lead to issues with sampling and cross-referencing.
The Duke study noted that many cross-disciplinary researchers use the same data sources to gather data on many different topics.
For example, it found that some cross-research studies on cancer are conducted using multiple data sources, but researchers can still obtain a clearer picture of how cancer is progressing through the body by asking about different cancer risk factors, like diet, smoking, or alcohol consumption.
In some cases, there may be multiple studies on a single topic that use the cross-curve method, which combines data from different sources to provide a more complete picture of an individual patient.
This method is commonly used by researchers who study diseases that are related to inflammation and are thought to be the cause of many diseases, such as diabetes and cardiovascular disease.
Researchers who use this method often do not include all of the cross curve studies that are included in the data, such data is often incomplete, and the cross sections can be skewed by using different people as controls, according a paper by L. Scott Coyle, Ph.
D. of the University of Arizona, published in the Journal of Health Economics.
The authors found that data that was collected by cross-checking the information in multiple cross-reference studies were often inaccurate.
The Duke study found this bias to be a major problem in cross-study studies, as researchers often had a hard time determining the accuracy of the results they were getting from different research projects.
Another common problem researchers encountered with cross-validation was a lack in the research literature about how people are able to identify specific data sources that are accurate.
In fact, it can be very difficult to identify cross-questionnaires that accurately identify a specific person, and this is why it is important for researchers not to use them, according the authors.
The report also highlighted the need to develop better methods to collect and interpret data from cross-sections.
The researchers found that a number of new technologies were being developed that are making it easier to gather cross-quality data, but also make it more difficult for scientists to make accurate predictions about individual samples.
For example, there is an increasing interest in collecting data from multiple locations, and in some cases a new approach is being developed to capture cross-questions using a new platform called the eSurvey.
However in some instances, this approach can be misleading because it does not capture cross points in cross sections, according.
For instance, some people may answer the survey incorrectly because they have a family member who is sick.
In this case, researchers need more accurate cross-factors