OVERVIEW

AGENDA

MATERIALS AND PRODUCTS / PRESENTATION SLIDES

HOW TO REGISTER

REGISTRATION FORM

HOW TO PARTICIPATE ON AUDIO CONFERENCE

Using Data to Tell the Safety Net Story

The Third in a Series of Three Free Web-assisted Audio Conferences for State and Local Health Officials

September 25, 2003

TRANSCRIPT

Cindy DiBiasi: Good afternoon. Welcome to Using Data To Tell The Safety Net Story. This is the final event in a series of web-assisted audio conferences on monitoring the healthcare safety net. These events are designed for state and local health officials. The series is co-sponsored by the U.S. Department of Health and Human Services Agency for the Healthcare Research and Quality or AHRQ, and the Health Resources and Administration or HRSA. My name is Cindy DiBiasi and I will be your moderator for today’s session.

In 2000, the Institute of Medicine released a report describing the healthcare safety net as “in tact, but endangered.” The safety net, as you know, is the nation’s system of providing healthcare to low income and other vulnerable populations. In particular, the report emphasizes the precarious financial situation of many institutions that provide care to Medicaid, uninsured and other vulnerable patients. It also examines the changing financial, economic and social environment in which these institutions operate. It looks at the highly-localized patchwork structure of the safety net.

One of the five key recommendations in the report focused on the need for data systems and measures to assess the performance of the safety net and health outcomes of vulnerable populations. In response, AHRQ and HRSA are leading a joint safety net monitoring initiative. This initiative involves a three-part strategy focusing on both safety net providers and the populations they serve. As a result, they resolved to create two data books that describe baseline information on a wide variety of local safety nets, develop a tool kit for state and local policymakers, planners and analysts to assist them in monitoring the status of their local safety nets, and identify the data elements that would be needed to successfully monitor the capacity and performance of local safety nets. All of the information related to the AHRQ and HRSA initiatives is available on AHRQ’s Web site at www.ahrq.gov/data/safetynet.

As we talk about the safety net, it is important to make sure there is a common understanding regarding what it encompasses. The healthcare safety net consists, as we said, of a wide variety of providers delivering care to low income and other vulnerable populations. These include the uninsured and those covered by Medicaid. Many of these providers have either a legal mandate or an explicit policy to provide services regardless of a patient’s ability to pay. Major safety net providers include public hospitals and community health centers as well as teaching and community hospitals, private physicians and other providers to deliver a substantial amount of care to these populations.

In the past two web-assisted audio conferences, we have examined the new data books and data collection strategies outlined in the safety net tool kit. Like the data book the tool kit is designed to help policy analysts and planners at the state and local levels assess the performance and needs of their local safety nets. Chapters included in this book are written by experts in the field covering a wide variety of topics.

Today we will examine three more chapters examining how to use data to tell the safety net story. These reports are included in the forthcoming book entitled Monitoring the Healthcare Safety Net, Book III, Tools for Monitoring the Healthcare Safety Net. The book will be available later this fall and will tell our listeners how to get this new book at the end of today’s discussion. But let me begin by introducing today’s panelists. In the studio with me I have Pete Bailey, chief of health and demographics for the South Carolina Budget and Control Board. Andrew Bazemore, assistant professor in the Department of Family Medicine at the University of Cincinnati, Christine Shannon, administrator in the Office of Health Planning and Medicaid for the New Hampshire Department of Health and Human Services. Joining us remotely is John Billings, director for the Center for Health and Public Services Research at New York University’s Wagner School of Public Service. Also here with us here in the studio is Robin Weinick. Robin is the senior research scientist and senior advisor on safety nets and low-income populations at AHRQ. As the lead on AHRQ’s Safety Net Monitoring Initiatives, she will stay with us today and join us during the question and answer session. Welcome everyone.

Before we begin our discussion, I would like to tell the audience a bit about the format of this web-assisted audio conference. First we will talk with our three panelists and then open up the lines to take your questions. We will give instructions on how to send your questions to us later on in the program.

In the meantime, if you experience any web-related technical difficulty during this event, please click the “help” function in your window to troubleshoot your web connection. If it appears the slides are not advancing, you may need to restart your browser and log on again. If you are on the phone, dial “*0” to be connected to technical assistance. Also, if you have difficulty with the audio stream or if you experience an uncomfortable lag time between the streamed audio and your slide presentation, we encourage you to access the audio via your phone. The number is 1-888-469-5316. This is the same number to call to ask questions when we get to the question and answer portion of the program. Because of the technical nature of today’s call, we highly recommend that you download slides from today’s event. You can do that by logging on to www.academyhealth.org/ahrq/ulp/safetynet.

Well now I think we are ready to tackle today’s topic, Using Data to Tell the Safety Net Story. Let’s begin with John Billings, director for the Center of Health and Public Service Research at New York University’s Wagner School of Public Service. John, was not only a primary collaborator in developing the two data books; he also wrote the safety net chapter entitled Using Administrative Data to Monitor, Access, Identify Disparities and Assess Performance of the Safety Net. John, exactly what are administrative data? Can you give us an example of that?

John Billings: Sure. Administrative data are computerized records. They are usually gathered for some administrative purpose, hence the clever title “administrative data.” They are used generally for bill paying, reimbursement, and record keeping. The interesting thing is, they typically have lots of information about individuals: demographics, age, sex, race, and ethnicity. Then a lot about what services have been utilized, how the money flows, how the money is being spent. Then other events like births or deaths. Some examples are, as I say, birth and death records, which are very interesting for looking at things like prenatal care. Then hospital discharge records that record admissions to hospitals and discharge from hospitals. Increasingly used are our emergency department records, which talk about or have evidence and information about visits in emergency departments. Then there is a long history of using Medicare and Medicaid claims files. These are the records that record the payments from the Medicare and Medicaid program. They also tell a lot about where the money goes and how services are utilized.

Cindy DiBiasi: What are some of the advantages and disadvantages of using this kind of data?

John Billings: Well, the main advantage is they are already there so you don’t have to go out and collect them. Another important advantage is they have already been computerized so you don’t have to be the one who has to put them on the computer and be responsible for all the errors. Now they can be also relatively inexpensive to get because they have already been gathered and it is just electronic data. Now that is not always true. Sometimes some places charge more than others. But they are also generally inexpensive to analyze because again they have already been computerized. They can often tell you something very interesting about what is going on in your community with respect to the safety net, where people are getting services, what kind of service they are getting and a little bit of something about the kind of barriers they may be experiencing.

Do you want me to talk a little bit about the disadvantages, of which there are some?

Cindy DiBiasi: Yes, yes, please.

John Billings: A disadvantage, caution is required in using administrative data. Remember, they have been gathered for something else. So they could be dirty. Some elements are often much better than others. A good test is usually if someone is going to go to jail if the data is bad, which is not most of the data elements, but sometimes that is true. For example, on hospital discharge data, the diagnostic fields are very important. That is the way people get paid and there are penalties for fraud if those fields are filled out inaccurately. On the other hand, the fields about race, ethnicity or the source of admission, other things that aren’t so important to payment are less and less likely to be accurate.

The second limit that people need to keep in mind when they use this kind of data is they seldom tell the whole story. You often raise as many new questions as you can answer the old questions. That is often a very useful process, but you would need to go into it understanding that you are not going to get the whole story from administrative data, but they often point you in the right direction.

Another problem can be, especially with something like emergency room data where there are only a few states that have that data available and not everyone is willing to share, which is required. If you are going to look at emergency room use in a community to understand how the safety net is working, you really pretty much need to go to all the hospitals in the community to cooperate. Not everyone is always willing to cooperate. They sometimes see it as private information.

Finally, even where you are getting it from the state, for a relatively low cost, you are going to be dealing with people who have other jobs to do and helping you get the data and getting it to you in a timely way isn’t always their first priority and so you need to recognize that there could be trouble in getting it. But once you get it, you are halfway there.

Cindy DiBiasi: Now how would you measure something like birth outcomes, for example?

John Billings: Well, birth records have been used by lots of people for many years to look at two or three things. One of the most important things, obviously, that has been looked at over time is infant mortality. Thank goodness that is becoming a rarer and rarer event, so people are looking more at things like did the mother get timely prenatal care? So the rates of later prenatal care. There is also interest in looking at things like low birth weight, which might tell you something about the kind of care the person got or some of the problems they may have in their lives. Also pre-term birth, which tells a similar story about access to care and the social and economic circumstances of the parents.

Now we have tried to look at some of this data in many different ways. We find it very useful to get to the lowest geographic area possible when looking at things like the rate of prenatal care. Then there are lots of ways to display it graphically, which are very intuitive to policymakers. One of the impressive things about using this kind of data is that policymakers get it. So if you show a chart that says, all right, on the Y-axis, is the rate of late or no prenatal care; on the x-axis is how poor the neighborhood is and each dot represents or each square represents a zip code in a city. It is very easy for a policymaker to see, for example, that low-income areas tend to have much higher rates of late or no prenatal care than high-income areas. They can see it in a glance and it conveys a complicated story very easily.

Then we often in many circumstances try to map the data so you could take the zip code-level data or county-level data and you can then map it to display in the community, which again helps policymakers identify the areas in the community that have the most severe problems. For example, in New York City we found that some of the neighborhoods where there were relatively small African-American populations, were some of the neighborhoods where the African-American mothers were getting the worst health outcomes, the worst prenatal care rates. So by mapping them out, you can really see a trend that you might otherwise miss.

Cindy DiBiasi: What about hospital discharge data? What can we learn about utilization of services by analyzing that data?

John Billings: People have been looking at that for years. With respect to the safety net, what is often used is something called Ambulatory Care-Sensitive Conditions or Preventable Hospitalization. These are things where timely and effective care outside of the hospital in the ambulatory care setting and help prevent the need for hospitalization. Chronic conditions like asthma or diabetes or congestive heart disease, acute conditions like ear, nose and throat infections, cellulitis, pneumonia and then totally preventable things where if you got for example, an immunization you won’t get the illness. These are things where if you got timely and effective care, the rate of hospital admissions from the area or for the population should be much lower. So people have used these sorts of things to again, look within a community to try to understand which areas of town are having some of the worst health outcomes or have the biggest potential barriers to getting timely care and which populations are having similar problems. So the example, we have looked at Baltimore at the zip code-level and saw again much higher rates of these preventable hospitalizations among children in low-income areas compared to children in high-income areas.

Another interesting thing to do is to look at different populations separately. So if you look at children you may see one pattern. Then if you look at adults, typically you see a somewhat different pattern. That is not necessarily surprising and can help illustrate something important to policymakers and that is that we have invested a lot of money in improving access to children whether it is through the health centers sponsored by HRSA or through a stanchion of insurance company coverage through Medicaid and CHIP programs. What you see when you analyze that is disparities between rich and poor neighborhoods are generally much smaller for children than they are for adults. That can be an enormously useful thing for policymakers because we seldom have good news stories to tell on health and there is a good news story. We invested money and we saw some good health outcomes. But also helps people understand where the problems might be and where they need to focus their intervention.

Cindy DiBiasi: Now I know you also looked at ACS admissions in Atlanta. What did you find there?

John Billings: Well, as usual, we found big differences depending on what part of the community you lived in. We first did the work at the county level, which is something that is very appealing to people. We mapped that out and when we looked at Atlanta at the county level, looked at the MSA of Atlanta at the county level, you really didn’t see an enormous difference among counties. It really wasn’t providing much useful information. But if you get a little better resolution by going down to the zip code-level, which in most hospital discharge databases you can, you suddenly see some counties that on average look pretty good. Some parts of the county looked much worse than other parts of the county. Those are typically areas that have a lot of vulnerable populations, either lots of low-income patients or minority populations, immigrant populations. Again, by mapping it out it can be very useful to policymakers to both number one illustrate that there are disparities within a county or within a geographic area, but more importantly to help them target where you want to go and do something about it.

Cindy DiBiasi: What did you see when you looked at this in New York?

John Billings: In New York, we have been looking at this for almost 15 years, I am ashamed to admit. You find lots of interesting things. For example, there is obviously a very strong correlation between income and preventable hospitalization rates. That is the higher the hospitalization rate, generally the lower the income of the neighborhood. But that is not always the case. Some neighborhoods that are equally poor have incredibly different admission rates. For example, in one part of New York the admission rate for a low-income neighborhood is about three times that of another neighborhood in New York. That helps you understand that this isn’t just a poverty issue; it has something to do that is much more complex with the nature of the population being served, but also how well the healthcare delivery system is performing in that area. So by illustrating that you can see two zip codes that have similar demographic characteristics, but have dramatically different hospitalization rates, it can help you start thinking about how to make an intervention.

Cindy DiBiasi: John, tell us about the algorithm that you developed for using emergency data.

John Billings: Well, there is increasing interest in what is going on in the emergency room. Hospitals have to take patients who show up in the emergency room by federal law. Therefore, the emergency room is being increasingly recognized to be the safety net for the safety net. So looking at the pattern of utilization of patients who come into the emergency room, we thought it might be a useful way of understanding something again about the nature of the barriers to care and the community and where those barriers might be. So we together with others, developed an algorithm that tries to classify emergency room use into four categories: non-emergent care. These are things where you don’t need to be seen today. A sore throat is pretty typical of that. Emergent meaning you need to be seen today, but it could be primary care treatable meaning you don’t need to be in the emergency room. That might be a child, an infant with a 102-degree fever. Well, it would be appropriate to see the physician today, but you don’t need to rush to the emergency room. You can go to your own doctor. Then there is a whole series of things where you are in the right place if you go to the emergency room. If you are having chest pain, please go to the emergency room. But there are other things where we want you to go to the emergency room that if we had seen you earlier in the episode maybe we could have prevented it from becoming so acute. You needed to go. In a classic example, that might be a diabetes chronic-acute attack, that raises your blood sugar level and suddenly you need attention. If we treated (unclear) you had been able to go to the doctor earlier in the week, you won’t show up with ketoacidosis in the emergency room on Thursday. So again, it has been a tool that we then could look at a zip code level and look at, compare one neighborhood to another. We did that in Baltimore and in several other cities where we could again see a very strong association between emergency room use for things that are preventable and avoidable and the income of the neighborhood. Again, the poor in the neighborhood had much higher rates of emergency room visit rates for things that are preventable or avoidable.

We have also again mapped it out periodically. For example, in Austin, Texas for one of the CAP programs that HRSA sponsored, they wanted to look within their community to try to identify where some of the biggest barriers were and where people were using emergency rooms that maybe they could prevent that and so they were able to apply this algorithm and map it out and show which parts of town had the highest rate.

Cindy DiBiasi: What is that line going through Austin?

John Billings: That lovely red checkered line? Well, when you are doing maps, even for people who have lived there their entire life they get disoriented. So we found it very useful to put geographic markers on the map to help orient people. That is the highway that goes through the middle of Austin so people know which side of the highway they live on. They know which county they live in and so, they may not know the boundaries of their zip code, but once you put some identifiers on there they can help orient themselves. So that is useful to policymakers and to the press and other people who might be looking at this sort of stuff.

Cindy DiBiasi: So based on your experience working with this kind of data, any words of caution that you would like to share with us?

John Billings: Yes. This is something where you have got to be very cautious as you are using it. As I said earlier, the date could be dirty and it is important that you do some tests to make sure you are looking at what you really think you are looking at. Then if a number is way high or way low, it is usually something wrong with the data. Now that is not always the case because disparities in health outcomes can be astoundingly enormous. But my first reaction is always I did something wrong or there is something wrong with the data and so it is important to go check it out before you announce to the world that there is a problem or not a problem.

Then as I said earlier, you can’t expect often from this data is the final answer. What you can expect is the targeting of the next set of questions or focusing of future work to try to figure out what to do next.

The final thing I would say, a final caution, is to avoid easy explanations. When we started looking at these preventable hospitalizations, the first explanation was well, there are not enough doctors. We need to get more doctors in the community. In New York we invested an enormous amount of money in expanding primary care capacity. Whereas that was pretty important, there were lots of other things that might have explained why people were not getting timely and effective care. They didn’t know when to come, they didn’t know where to go and they didn’t like where they were going. So it wasn’t necessarily just that there wasn’t a doctor there. It can be a much more complex issue about the social dynamics of the family, but also the performance of the safety net in the community.

Cindy DiBiasi: John, thanks. We will come back to you during the question and answer period so hang on with us.

I will turn now to Andrew Bazemore, the assistant professor in the Department of Family Medicine at the University of Cincinnati. Andrew is the co-author of the safety net tool kit chapter entitled Mapping Tools for Monitoring the Safety Net. Andrew, let me start with the Geographic Information System, the GIS. What is that?

Andrew Bazemore: Cindy, in the broadest sense, a geographic information system is a tool that is capable of linking together any data as long as it has a geographic location or address attached together with map features. In recent years, what this has really meant is large software programs, particularly as we have seen computer data handling and graphics ability increase. The software programs can bring together large data sets, such as the ones John is talking about, that then allow us to collect, to retrieve at will, to transform and display spatial data from the real world. So really what GIS allows us to do is to analyze and transform complex data from many, many sources together into maps that as John as already demonstrated, will illustrate problems effortlessly for experts and non-experts alike.

Cindy DiBiasi: You were mentioning that it integrates any existing data that has a location or an address. What sort of data can be used for mapping in healthcare?

Andrew Bazemore: Well, John has given us a great lead-in here. Basically any number of administrative and claims data can be put together. For example, in the safety net, the community health centers are required to collect for the federal government, data from every single patient visit into a uniform data set. So we can take their UDS data, their patient billing records. We can put that together with population data such as from the U.S. Census. We can add in if we want insurance or claims data. We can put, as John mentioned, highway data, transportation, roads or bus lines. We could put on top of that city planning data such as from the waters and utilities division. If we wanted to, we could overlay satellite or mapping data and finally as mentioned, there are many, many sources of data from local, regional and state health departments.

Cindy DiBiasi: And how is that going to be helpful and how can safety net clinics use this technology?

Andrew Bazemore: Well, I would say the possibilities are fairly endless. I will give you a few examples. For one, if we took the community health centers uniform data sets data and we put it together for them, we can actually show exactly what service areas their clinic serves, by individual clinic. On top of that, if we overlay population data from the census, we can allow these same clinics to look at their market penetration rates, all the way down to areas that are only a few blocks wide. Now, as you probably know, the community health center networks are required to attempt to serve at least, medically-underserved areas as defined by the federal government. When we overlaid the maps of these medically-underserved areas, we could allow these community health centers to see whether or not they are succeeding in this process. Particularly, we then breakdown the medically-underserved area to look for target areas or regions where they can better serve either the entire population or a particular at-risk subset, say by age, race or gender.

Now all of these functions really come together to allow a clinic to better provide community-oriented primary care. By that I mean instead of just focusing on the patients walking into the clinic doors, they can really look at the needs of the community and the communities that these patients represent.

Ultimately, this would be best done if we were actually able to put this on the web. If were able to let the users, the clinicians and the leaders to actually go with their questions that come up on a day-to-day basis and withdraw their data so long as it was securely stored at a web-based site.

Cindy DiBiasi: Let’s look at some of the areas that you have mapped.

Andrew Bazemore: Moving on, you will see in this first map, Boone County, Missouri. The black boundary lines you see actually represent U.S. Census tracts. Now the dead center of Boone County is Columbia, a medium-sized city and also the county seat. The map on the left shows you census tracts, those being a small division done by the Census to break down population groups. These were the tracts, in white, that were federally designated as medically-underserved areas for that county. As we just said, these were the areas that the community health centers, of which there are two in Boone County, are supposed to target when they apply for their grant funding. However, when we took the actual clinic data and we turned it into maps, as you see on the right, the new white areas are their actual service area. What you will see is the two don’t exactly match. We could take this one of two ways, but Boone County’s community health centers wisely looked at this discrepancy and didn’t see service failure. They saw opportunity. So they were able to return and say we need to actually expand our services. We need to go out into the county and provide more service than we did before.

Now another example in the next slide is a clinician in Baltimore, I was working with the largest network of community health centers called Baltimore Medical Systems. What we did was take their four largest centers and try to map their service areas, which you see in green, blue, red and yellow here. Then on top of that, you are going to see some brown and some pink-shaded areas. These are the tracts where the health service areas actually overlap. Now this was particularly helpful to the administrators of the Baltimore Medical Systems clinic and recognizing for the first time that their own clinics actually had overlapping service areas. We actually had two clinicians pull us aside and say, “I’m glad you took some heat off of us. We were hearing that our inability to fill our clinic schedule was due to loss to competition.” Well, their competition it turns out was probably within their own network.

We look at the next slide. Similar to Boone County, Missouri, this map is going to show you the medically-underserved area census tracts, in pink, which were supposed to be served by one of the Baltimore Medical Systems clinics. What you see with all these concentric overlay circles are quarter-mile intervals moving out from this clinic. The big, blue circles are at one mile and two miles respectively out from the clinic. We were asked early on in the mapping process, it is kind of a sample question, could we evaluate a pending decision by the clinic to move itself approximately two miles outward from its current location? They said we are well within what the federal government has told us that this is our medically-underserved area if we go to the upper right hand corner of this map. We said absolutely. But if we then look at the next slide, the bottom left hand corner of this new map shows us the actually placement locations from the previous year for that clinic. The clinic told us they were particularly concerned about pediatric patients and African-American patients. So we actually took the target population and mapped that. What you will see is an enormous cluster of these patients sitting in the bottom left hand corner, not the upper right hand corner of the map. Essentially, our data along with another of the maps that I don’t have here to show you allowed us to help them change their minds about moving to save a few dollars on leasing. We feel that through this mapping really gave them a large benefit.

Cindy DiBiasi: Are there any obstacles to using and interpreting these maps that make it more difficult for some of these community health centers to replicate what you have been doing?

Andrew Bazemore: Well John wisely said whenever you use data you have to be very careful in the way that you interpret it. For example, as we have found in moving from Boone County to Baltimore, it is very challenging to find the ideal service area or the ideal penetration rate for a community health center. There are a myriad of safety net providers from the emergency rooms to clinics to hospital outpatient settings that all would lay claim to a certain group of targeted population. It is very difficult again to say this is the group that we should go after. However, again, ideally we bring together all sorts of data from the safety net and merge it in maps. Theoretically, we could actually map an entire urban region and show again the gaps and places of over-utilization with an entire urban area such as Baltimore.

The second big gap, of course, or the second big obstacle is the cost and the technical expertise required to form this level of mapping at the individual clinic level. Again, this is where web-based mapping would come in and allow us not to need information technology expertise or expensive software for each clinic but actually take advantage of the economies of scale that a large, large group of clinics such the CHC’s or other safety net providers can give us.

Cindy DiBiasi: Let’s talk also about the need for accurate interpretation because it would seem that interpretation is really what this is all about.

Andrew Bazemore: Absolutely and of course we started with some pilot efforts to do this mapping and we needed to take it back to the clinic and see could they actually use this? Would they find this information helpful? So we conducted a series of informal focus groups and key informant interviews with the leaders of our clinic network and almost uniformly to a man found positive responses. Just about every leader or clinician had a new question that they could drum up for us to ask and answer using mapping.

Throughout this series, they really felt like the most important uses of the maps early on would be for strategic planning and resource allocations as well as possible expansion of their network.

As you mentioned, the most important piece is to pair what we do in maps and what we see in the data, as John mentioned, was knowledge of the existing community. So we used their information and found that they said these maps were very easy to interpret, particularly since they knew the roads, the clinics, the individual patients in the communities they served very well. They found that what this allowed them to do was take otherwise useless data or data they weren’t using, bring it together and bring it to life and really get to know their communities much better.

Over the course of that day, we generated a long list of questions, which we continue to work on and I should also note that the static mass that you are seeing in our presentation here today were really deemed least useful by the clinics. They said having a dynamic mapping source, having something that they can interact with, take layers on and off of, such as pull population data from or add another water or sewage line to, really allowed them to answer their questions in real time, which really reinforced our notion that a web-based interface, particularly broadly used, would be the most useful way of mapping the safety net.

Cindy DiBiasi: Andrew, we will be back with some questions for you. But let’s now talk to Pete Bailey from the South Carolina Budget and Control Board. Pete is the author of the chapter entitled Integrated State Data Systems. South Carolina does have an integrated data system. Pete, what types of data are included in that system?

Pete Bailey: Before I answer that question, I would sort of like to mention a quotation that I think is very useful. Someone once said that good health is what happens when everything else is OK.

So you will see in our integrated data system that we are covering more than health; that we are trying to cover the whole human services side so that you will see that we have private sector-type data in terms of hospitalizations, emergency room visits, outpatient surgery and home health visits. These are the actual bills. We have the Medicaid eligibility and claims, the state employee/teachers health plan eligibility and claims and vital records. A good portion of the free clinic database in South Carolina. Then, as related to state agencies, we covered the social services side. That is like (unclear), food stamps, abuse and neglect and foster children. The mental health and alcohol/drug side, that is like hospitalizations, all mental health center visits. We cover the public education side. For example, school readiness, achievement scores, and exit exams. Other health agencies like the health department, disabilities and special needs and voc rehab. Then we cover crash files from public safety and the criminal justice system in terms of juvenile justice and arrest data like from South Carolina law enforcement. So you can see that we have tried to cover a full range of human services from health, physical and mental to social services to education to criminal justice to the private sector health data.

Cindy DiBiasi: How do you link individual records from the different systems?

Pete Bailey: I think that is a very important question because the date that I just mentioned to you, believe it or not, we have the ability to link and track people across this whole system. That means we do get identifiers, but we use these identifiers to create a unique tracking number so that with the identifiers, the tracking number that we would create for you, and by the way, this is a randomized number. It is not a code that you can break. That tracking number would stay with you whether you came through the emergency room or the social services department. What we do is use that tracking number to link the statistics from the different integrated systems so that at no time do personal identifiers end up with the statistical data.

Cindy DiBiasi: So what added value does an integrated system provide over individual administrative data sets?

Pete Bailey: I think the most interesting example to cover that is children with special healthcare needs. When we pulled this system together and we began working with children with special healthcare needs in the Department of Health and Environmental Control, they basically knew about children’s rehabilitative services, which they ran, and Baby Net. That covered around 13,000 people. Once we began working with them and they gave us all the ICD9 codes of children with special healthcare needs, then we were able to sweep through like the Medicaid data system, all the hospitalizations, and all the emergency rooms. Once we swept through, you can see that we ended up in the 300,000’s in terms of children with special healthcare needs. The great thing about this is once you have this cohort or this sub-group of population, you can do this for a large number of areas. I am just using children with special healthcare needs as an example. But once you are able to do that, then you can link that cohort like children with special healthcare needs over to social services and say what is their abuse and neglect rate of children with special healthcare needs? Or you could link it to juvenile justice systems to see if by chance they are ending up more in the juvenile justice system because of misunderstanding or link it to school. By the way, we link children with special healthcare needs over to the school data and found that they were not doing near as well as children without special healthcare needs.

Then the fact that Medicaid is such a large piece of this cohort. In the Medicaid system you know everything about what happens to an individual. So you can get down to physician rates. You can get down to hospitalizations, whole mental side so that there are enormous advantages to being able to pull a large cohort together and then jaunt to different areas like social services or law enforcement.

Cindy DiBiasi: If you would, give us an example, Pete, of the analysis from the integrated data system and how those results were used.

Pete Bailey: The most interesting example I think that I could use is how we ended up linking the Medicaid data system to the educational data side. We began working on the educational side. They wanted a school report card. To do a school report card they wanted to use a poverty indicator. The only poverty indicator they had from the educational side was students on free and reduced lunch. They felt that high school kids would not take free and reduced lunch because of embarrassment and so they were not pleased with that as an indicator for poverty. So we suggested why not, let’s link and unduplicate free and reduced lunch and Medicaid because our Medicaid included children’s health insurance program, SCHIP. Well, when we did that it turned out to be 54% of the children in public schools in South Carolina. But by being able to do that, for the first time then we could see how the Medicaid children do in school or how (unclear) in general. So the chart that you are seeing is the duplicated free and reduced lunch and Medicaid. You can see you have four categories, below basic to basic to proficient and advanced. You can see these enormous numbers in terms of the below basic and the basic for the poor. The ramifications of this has to be that that means the poor are not going to be able to go to college. They are not going to end up in the professional jobs. So this sort of opened up an enormous door. Our governor at the time, virtually all you could hear out of him was education, education, and education. That is across the country. But once were able to link health with education, he began saying health and education, not just education.

Since then we have several projects that are really interesting. We have a school district that is looking at four high schools that are pretty differing high schools. We are pulling the kids that are Medicaid kids so that they can look in their high school, and one high school was two-thirds Medicaid, they can look at their doctor visits. They can look at the emergency room visits, their hospitalizations, even pharmaceuticals. The things that we see, for example, in this particular high school, their number one visit to physicians was for pregnancies. The other thing that we saw so much of is mental illness and mental conditions that we were somewhat caught about. So this type of linkage has tremendous potential.

Cindy DiBiasi: The same in the safety net population. An integrated system such as your could really be helpful in addressing problems.

Pete Bailey: The first time that I actually, I didn’t know a lot about it. I went to a conference on safety net and they kept talking about it and I thought, who is the safety net population? Someone said, well, it is basically the Medicaid, the (unclear), the food stamps and the uninsured. I thought, well gosh, we can just swing through our populations and identify that population. So you will see that we did that and it was like shocking because see, what you are seeing here is that South Carolina, and this certainly doesn’t cover all the uninsured. It was those that have hit the system through emergency room, but you see that 24% of our population is safety net. Look at the non-white, which is heavily African-American, look at the 1-14, 74% in the safety net. So just being able to identify this cohort. When I mean identify that means with this tracking number now that we can put our arms around them, we can look at them in mental health. We can look at them in hospitalizations. We can look at them in vital records. That is a fantastic thing. So what it means is that you can take this safety net population, look at their health problems, look at their problems around birth, look at abuse and neglect, look at law enforcement issue. Just a wonderful thing.

Cindy DiBiasi: Was your integrated system useful in helping to locate and enroll children in SCHIP?

Pete Bailey: It was tremendously helpful there because we were able to run through the food stamps, tenant, which most were on Medicaid and the children that were using the emergency room that at that point in time were uninsured, link them over to the Medicaid eligibility to see if they still were not covered by SCHIP or Medicaid. But by being able to do that, then we used our DIS system to actually map, down to the Census block level and we then created density maps so that they had outreach workers going out and finding children that were uninsured. It was very useful to them. The other thing that we did is that we took the free and reduced lunch files from the educational side, link them over to the Medicaid eligibility side, to see how many of them were not on Medicaid, which gave us by school and school district the volume, potential volume of uninsured. Of course then the outreach people really concentrated on those schools. Then the great thing about it is that as you initiated programs, we could use the emergency room data and the children that were coming that were uninsured to test and see how well our programs, what we were trying to go out and do outreach and find them, how well they were working.

The really interesting thing about this effort is that between 1998 and 2001, now this is strictly looking at emergency room data. Children that had come in there without insurance. We cut that number by 38%. For some counties it was up as high as almost 70%. I think we broke our bank in terms of the Medicaid system, but it did show that you can go out and find these children and you can get them enrolled.

Cindy DiBiasi: It must also, being able to identify and track this many people in the safety net population, must help in dealing with disparities.

Pete Bailey: It really does because in almost any area you look, for example, let’s say under the Medicaid, under hospitalizations or mental health or whatever area you start to move in, that you are looking for disparities. Where you see disparities, you don’t know how much of it is due to for example, age, sex and poverty. Typically, you can control for age. Typically you can control for sex, but poverty is very difficult. But by having the safety net population, when you look to mental health, that means you know that subset of population that is poor. So you can control for poverty. So if you see disparities, like we did with education, when we were able to look at the poor and see the massive disparities, we thought well, we have known that there are disparities in the educational system in the percent of children below basic. We thought that the disparity between races was due to poverty. Once we had the poverty data, we could look at poverty and then race. It is not just these two disparities. We don’t know what it is due to. So being able to do that linkage really does tremendously help you understand disparities.

Cindy DiBiasi: Now it is one thing to figure out how to deal with the data, but how does a state that might be interested in developing an integrated system, work to the political and organizational issues that must surely arise in this?

Pete Bailey: One thing, it is very important, and I am not sure, as long as I have been working in the health system, that we fully understand this. That is that every state is different organizationally and that each state has to work through its political structure and personalities. They are very important. I can simply tell you certain things that we think have been really important to us. We are a neutral organization. That is, we do not offer any programmatic services. So there is no way that we are competitive with any agency that we are working with in terms of programs. So I think if you can possibly put your integrated data system in a neutral organization and the other thing about that neutral organization is absolutely they should have no power at all over data. As much as you have seen in terms of data systems that we have, we have no control over any of those data systems. What that means is that we have not upset the balance of power in South Carolina and that is really important to do. So you certainly have to be able to try this. We have had so many years that we have been able to do a good job, both in terms of agencies in the private sector. As I mentioned, you have got to be able to preserve that existing power structure and respect partner’s roles. Then of course you have got to have the appropriate controls of data.

I think that maybe it is appropriate to say that if we can do it in South Carolina, a real liberal state; they ought to be able to do it everywhere. What is most important here is not thinking about your organizational structure. Not thinking about use, but thinking about the people we believe we can help. Then concentrating on accessing the data, not improving your particular organizational structure.

Cindy DiBiasi: Good advice in so many areas. Thank you, Pete. We will come back to you during the question and answer. But in a moment, we are going to open up the discussion for questions from our listening audience. But first let’s go to Christine Shannon, who is the administrator in the Office of Health Planning and Medicaid for the New Hampshire Department of Health and Human Services. Christine, how would you use the data and tools that we talked about today?

Christine Shannon: Well, we have seen here today that there are different types of data that we can use to tell the safety net story. We can use this data alone or we can certainly can fit them together to answer questions and to solve problems. And answering questions and solving problems are critical for us in making our planning decisions, where do we target our resources? Do we do an overall campaign? Target the whole state or rather do we look at certain areas? Policy decisions, what the impact may have on clients if you decide to do something about services for clients, still maintain access. And also informing decision makers, not only within our state, but sometimes when we apply for grants, or community health centers are looking to expand their services, we can also inform decision makers there.

Cindy DiBiasi: Could you give us some specifics on how you might use it in your area?

Christine Shannon: The GIS mapping? Let me give you a few examples of how we have used GIS mapping and how we plan to use it. Basically, I think what GIS mapping says for me is there are so many things we don’t have pictures of. We are finding that GIS is a tremendously useful tool for assessing provider network adequacy. We have looked at our pharmacy participation and we are in the process of looking at dentists and physician participation in Medicaid to see how we could focus our Medicaid recruitment effort.

We have also looked at CHC coverage adequacy to determine whether or not where we might target resources for future expansion. We are also right now looking at Medicaid participation to give some answers to questions that our CHC’s have on needs in their communities. How many Medicaid participants we have got in the area where I deliver my services.

The next three slides that you are going to see illustrate these examples that I have just given you. The slide on Medicaid pharmacy providers, what you are seeing here is the northernmost county in New Hampshire, the Canadian border. Now from where I sit, you could have given me a list of participating pharmacies and the towns they were in. That would have been nice. But now when I see this spatial relationship here, it really gives me a good picture of access and tells me that I might make some decisions or we might make decisions that could impact access to Medicaid participants in being able to get their pharmaceuticals. Basically what I think this slide shows you is that the GIS mapping gives clarity that the tables and lists just can’t possibly give you.

The next picture here is a slide we did a couple of years ago. We did this slide to show a couple of things. One was we wanted to portray to policymakers just what our community health center coverage looked like in New Hampshire. Then we also wanted to see where we might have areas that were lacking access to community health center services.

The last thing that I mentioned, trying to answer some questions for our community health centers is we just started taking a look at this. We are looking at where the percentage of the population in New Hampshire that participates in Medicaid resides. Right now we just started doing this. We have a picture here that shows you where participants reside in terms of counties in New Hampshire. We are going to try and break that down a little further.

Cindy DiBiasi: What about the emergency department data? How have you been able to use that? I am particularly interested in the ED algorithm that John Billings shared with us today.

Christine Shannon: Well, the ED algorithm is a good example of why participating in sessions like this are most useful. First of all, we used the administrative data to take a look at the emergency department use. We use our Medicaid claims and we also used hospital discharge data. We set out initially looking at our outpatient usage for Medicaid clients and we zeroed in on emergency departments. We examined our Medicaid-funded emergency visits to see how Medicaid clients compared to non-Medicaid clients in terms of emergency department use. One of the things that John Billings and others have raised here today is you look at a piece of this information and it raises more questions. It has actually been kind of fun to start exploring the questions that this has raised.

We looked at the time of day that emergency department visits occur and sort of blew some conceptions that I had that everybody goes at night, which we found wasn’t true. Over 60% of both Medicaid and non-Medicaid emergency department use was happening during the hours of 6 am and 6 pm.

We also wanted to look at the reasons people go to the emergency room. We found that for non-Medicaid as well as non-Medicaid the reasons are pretty much the same. However, our Medicaid clients are using emergency departments at two to three times the rate of those with other types of payment. We also found that our Medicaid clients are using the emergency room more often for common illnesses versus injury.

Cindy DiBiasi: So once you get that date, then how does that inform decisions from policymakers and decision makers?

Christine Shannon: Well, what we are looking at is now we could use this information in assessing our Medicaid physician provider network. As I mentioned before, we are also going to use it for our dental network. We are going to try to identify gaps in services and also steps in access. What we are going to do is we are going to take the easy data, the administrative data that I just talked about, and then do some GIS mapping. I have been told by my staff that today’s Geo-Access software is going to permit us to assess physician networks in terms of availability and by distance and capacity. I think that is very important as we look at; we are a state that no longer has a Medicaid managed care product. We are going to be looking at how to maintain access for our Medicaid clients to physician services. This is going to be a first step for us to take a look at our physician network adequacy.

Cindy DiBiasi: Interesting. Christine, we will be back with you in a moment.

In a moment, we are going to open up the discussion for questions from our listening audience. There are two ways you can send in your questions. We encourage you to ask your question by phone. If you are already listening via the phone, press “*1” to indicate that you have a question. If you are listening through your computer and want to call in with questions, just dial 1-888-469-5316 and then press “*1”. While asking your question on the air, please do not use a speakerphone to ask your question. If you are listening to the audio through your computer, please turn down your computer volume after speaking with the operator. There is a significant time delay between the web and telephone audio.

If you want to send a question via the Internet, simply click on the button marked “Q&A” on the event window on your computer screen. Then type in your question and click the “Send” button. One important thing, if you prefer not to use your name when you communicate with us, that is fine. But we would like to know what state you are from and the name of your department or organization. So please provide those details regardless of the way in which you transmit your question.

As you are formulating your questions or queuing up on the phone lines, I would like to say a few words about our sponsors. The mission of AHRQ is to support and conduct health services research designed to improve the outcomes and quality of healthcare, reduce its cost, address patient safety and medical errors and broaden access to effective services.

AHRQ’s User Liaison Program serves as a bridge between researchers and state and local policymakers. ULP not only brings research-based information to policymakers so that you are better informed, but we also take your questions back to AHRQ researchers so they are aware of priorities at the state and local levels. Hundreds of state and local officials participate in ULP workshops each year. The audio conference is being co-sponsored by the Center for Health Services Financing and Managed Care and the Department of Health, Resources and Services Administration or HRSA. HRSA is the Department of Health and Human Services access agency. It assures the availability of quality healthcare to low-income, uninsured, isolated, vulnerable and special needs populations.

Its mission is to improve and expand access to quality healthcare for all Americans. I would like to take a quick moment to thank Rhoda Abrams, the director of HRSA’s Center for Health Services Financing and Managed Care. She has been instrumental in helping to develop and produce these safety net products.

We would appreciate any feedback you have on this web-assisted audio conference and at the end of today’s broadcast, a brief evaluation form will appear on your screen. There are easy to follow instructions on how to fill it out. Please be sure to take the time to complete this form. For those of you who have been listening by telephone only and not using your computer, we ask that you stay on the line. The operator will ask you to respond to the same evaluation questions using your telephone keypad.

Your comments on this audio conference will provide us with a valuable tool in planning future events that better suit your needs. Also if you please, email your comments to the AHRQ User Liaison Program at ulp@ahrq.gov. Now let’s go to some questions from the audience. This one is for John Billings from Jessica McCann. She wants to know, “Do local health departments collect the data on hospital admissions for ambulatory care-sensitive conditions? I have never seen these type of data before.

John Billings: Well, there are about 35 or 36 states that have statewide data systems with hospital discharge data in them. I forgot, what state was she from?

Cindy DiBiasi: She was from, it doesn’t say, actually.

John Billings: Oh. Well, I don’t know whether her state does or doesn’t. Then there are a handful of states where the local hospital association has that sort of data. They have it to help hospitals understand the patterns of utilization and market share. So you can often get it from the state hospital association. But it is typically not a local department of health. It is typically a statewide department of health or data authority. In each state, of course, it is a different place.

Cindy DiBiasi: OK. On the phone from Nevada we have Barry who is on the line. Hello? Hello, is Barry there? Seems like we may have lost him. I will try to get him back in a little bit. Here is a question for Pete Bailey from Dee Hunsaker. “Can you present data from the integrated system at local levels such as the county level or census tract level?”

Pete Bailey: Yes, all of the data that we get, of course we have got county codes, zip codes. But also with the addresses we have the ability to run through address match software to go down to anywhere from longitude-latitude all the way up to block groups, census tracts. We are still way behind in that but in some ways we have made a lot of progress on the; of course, it requires good address match. For example, I know that when we have just done working on Medicaid and I think we ended up with around a 75% match. So a lot of addresses are just not good. Our address match system is really good because we work it through the E911 system. But we can go down, and the interesting thing which we have not done this and I hope more states will move into this arena, is once you have done this type of (unclear) coding, you can go down to a house in senate districts or even congressional districts, and once we start doing our data at that level I think we can begin to put some of our elected officials on the spot.

John Billings It may also be useful sometimes if you can geo-code addresses to get below the zip code level. Many densely populated or urban areas, zip code, which seems like a small area is actually quite a large area. That can be big differences from one part of the zip code to another. So listening to Pete suggests that you can also do that at the zip code level where you have got that sort of situation.

Cindy DiBiasi: Robin?

Robin Weinick: I just wanted to echo what John was saying that depending on what kind of area you are in, a zip code may not even be a continuous area. They are designed for postal service delivery routes rather than for the convenience of researchers and policymakers and planners. So it is really worth paying attention to that. Pete?

Pete Bailey: I think that your best area, as those of you who know census well, know that bought groups are as far as you can go down in terms of socio-economic data. So by looking at block groups, you can tell how homogeneous your area is in terms of poverty or education. But in the way you can get over it, I don’t know if people have done this. We have done a lot of this is the way you can get around small numbers is to take your block groups and group them into several groupings, like three groupings. So your low socioeconomic, your middle and your high. Then when you put your data together, you have got larger numbers and you can really see the effects of poverty or education.

Cindy DiBiasi: We are going to go back to the phone and try to get Barry back from Nevada. Are you there? Hello?

Barry: Hello, I’m here.

Cindy DiBiasi: Hi. Do you have a question?

Barry: Yes. When they started this reference to using administrative records, specifically diagnostic codes tied to payment and how those were usually valid because they were tied to possible truth fraud issues. What I wanted to point out is that those are, I figured them as often quite invalid specifically because they are tied to payment. My background is in mental health and there is any number of times where I have seen a person admitted for one diagnosis as opposed to another because you could only get paid for the admitting diagnosis.

Then a more recent one that folks may be aware of is Zyban, the antidepressant that works for smoking cessation. The insurance plan that we have here at least, if it is prescribed for major depression, it is covered by an insurance plan, but if it is prescribed for smoking cessation it is not. As a result, I rather suspect that if you were to look at administrative records, you would think that we had had a major epidemic of major depression amongst people trying to quit smoking. This is one example of the sort of things that can happen.

John Billings I think that is a good illustration of caution is in order whenever you do this sort of thing. First of all, I think it is fair to say that since DRGs have come into play where people really being are being paid based on the diagnostic code, there have been lots of good studies suggesting that the accuracy of the coding, apart from gaming, which is real, has gotten much better. Gaming is what the gentleman is suggesting where you are trying to get a higher payment level or a different payment level or you are trying to disguise something. That is a real problem.

I think in the mental health area it is a particular problem. We have always struggled with trying to understand those codes where there is a lot of slippage here, there and everywhere. So for those sort of things, extra caution seems to be required.

Cindy DiBiasi: OK. A question from Kathleen DeMinor. I’d ask also everybody who is writing in with emails if they could please let us know what state they are from. That may be helpful to the panelists.

Her question is, “If you don’t have the resources to do the in-depth examination of emergency room medical records using the four-category algorithm developed by John Billings, is there any way to make use of the data simply by identifying certain typical diagnoses that should be primary care treatable such as otitis media?

John Billings: Sure, you could do that. What you should know is that this ED algorithm is incredibly easy to use. AHRQ has helped and HRSA has helped make it available at no cost to anybody. It is on a Web site that we can let you know about where you can get it. But it really doesn’t take an enormous amount of effort to run the algorithm. You can run it in Access, which is a pretty common program. You can run it SPSS and you can run it in SAS. So it is not terribly difficult. Having said all that, there is nothing wrong with saying, all right, in our community what we are really interested in is otitis media. What you are going to find, interestingly, is otitis media is pretty high up there on the utilization level, but the winners on emergency room use in most communities are things like sore throats, fever of unknown origin, abdominal pain, common cold, that sort of thing. So you have a lot to choose from. What I would suggest is doing a quick frequency distribution of the top 50 diagnoses and take a look at them. It is usually pretty eye opening because they are usually not serious emergencies; that is not to say there are not important emergencies that go on in emergency rooms, but there is a lot of primary care going on in that setting. You will see that by a simple frequency distribution of the ICD-9 code.

Robin Weinick: I wanted to second what John said. If you have to look at the date to find information on otitis media, you are going to be looking at the same data using the algorithm, which is very, very easy to use and it is available free of charge on AHRQ’s Web site at www.ahrq.gov/data/safetynet. We will give you that address at the end of the broadcast as well.

Cindy DiBiasi: I would like to go to the phone where Renata from Georgia is on the phone. Renata?

Renata: Yes. Hello. I am going to direct this question to the two panelists who were speaking about the CHCs. Most of the community health centers came online in the 70s and the 80s, at least the majority of them that are still out there right now. When they were initially brought on board, many of them were placed in what by policy was called the medically-underserved areas. Many of them have found that while they are in a particular location now, many of their patients come from way further out than the specific area that they are located in and has been deemed as a medically-underserved area. It is has been traditionally very, very difficult to work with HRSA to get them to understand that community health centers, where their patients are really coming from are not necessarily the area that these community health centers are located in. What, if any, changes do they know may be coming about or even just from working with the CHCs on these particular projects, might be coming about from HRSA that will allow for a different kind of definition of a medically-underserved area?
Medicaid patients are all over the place right now. They don’t necessarily just reside in one small area like patients used to when there was no transportation available and you really couldn’t get out to other areas to see a doctor. That is not the case anymore. People can go anywhere they want and a lot of the CHCs are really stuck in these little communities where their patients really don’t come from. I guess my question again is just what is happening at the HRSA level to maybe redefine how NUAs are, the definition of an MUA?

Cindy DiBiasi: Andrew?

Andrew Bazemore: Well you know, I can’t really speak to changes in definitions of NUAs so much as taking the data that you have and try to demonstrate the very things that you have just mentioned. Namely that you have patients that come from outside your NUA and I think HRSA and anyone else is well aware of that. The maps that we have used were really outstanding as evidence going to all sorts of levels of policy, both funding agencies, granting agencies for outreach projects and interventions, etc., just to show the very thing you mentioned. Which is, service areas do extend beyond NUAs. They are bigger than that. They often are patchwork-like quilts rather than a central bulls-eye shaped target region. Again, I think focusing less on whether or not you exactly fit your NUA, if anything this mapping shows you, you often don’t exactly fit it. It is less important than trying to use the data that you have, discover where your patients are coming from, where you can find areas for targeting your outreach for better access and better utilization. Again, I found that with the group that I worked with, they were using the information to take it to local officials. To take it to non-profit organizations that had grant funding for for instance breast health outreach. So we helped their outreach through grants that allowed them to go to this group and actually say we are going to target individuals outside of our NUA who are not getting adequate access to breast health services. With this map, we are going to be able to find within the population the most densely penetrated census lot groups with the target population of interest.

Renata: OK, all right. Thank you very much.

Cindy DiBiasi: Thank you. A question for Christine. “Is the New Hampshire emergency department setting published and how can we get copies of those?

Christine Shannon: It is not published. This is definitely a work in progress. They are right in the middle of this right now. So much so that when I participated in the planning for this webcast, I learned about the NYU ED algorithm and we have, we are just in the process of running our data on that. But if the caller has my contact information, which is available on the Web site, I would love to talk and contact me within the next few months as we work through this and we will be able to give you more information.

Cindy DiBiasi: OK. A question from Colorado. “What information accuracy issues exist in gathering data regarding addresses? Is it considered self-reported data and what if addresses are a qualifying element in getting access to care? Does it become less reliable? Andrew?

Andrew Bazemore: That is an excellent question. It certainly come up in the very first stages of what we call geocoding, which is taking, for instance, the uniform data set records and entering them into a mapping software, whatever their software of choice is. In that geocoding process, the very first step is running it through the system of a geocode to see what your match rate is. The match rate being the number of addresses that are considered valid. Other concerns of course are P.O. boxes, which can be far, far away from the actual user’s home address. Mismatches, we find that when we look at geocoded data that for instance sometimes we were plopping users down in the middle of the Inner Harbor in Baltimore, where they obviously did not live. We did however find, this is in the limited pilot data that was used, that in just about every case, within the first or second pass we were able to get the match rate well above 80%, which is the index standard in the industry as a good match.

As far as cleaning up and extensive cleaning it up, this is going to vary center to center. I certainly found that working with the largest network in Baltimore meant we had much cleaner data and much better IT support. But I have also worked with one of the smaller centers in Cincinnati, where they really didn’t have any IT support. We have found that still, because of the federal requirements and because of the needs to turn in the patient visit records, that they had pretty good data and pretty good address matches for the data. If anything, what we are doing is forcing groups that are using our mapping software to go back when they see patients in the future and reinvent themselves as good data collectors, so the future maps will be of even better quality than the ones we have.

Cindy DiBiasi: Andrew, should you be using the patient’s address or the hospital’s or clinic’s address?

Andrew Bazemore: The patient’s address for?

Christine Shannon: This is also a question for John Billings. For example, when you are looking at things like visit rates for clinics and that sort of thing. Sometimes I think you are interested in the patient’s address but sometimes you are interested in the hospital’s address. John, I am particularly thinking about issues where you are analyzing, for example, ambulatory care center’s admissions to hospitals?

John Billings: In most circumstances, I think you are very interested in where the patient is living. Because what you are trying to do is understand what is available and what the barriers are for that patient and where they are living. So I think in most circumstances, you are very interested in that.

Of course we had a case where, I won’t name the state, but it is right across the Hudson River from New York, that one hospital geocoded all the patients in their hospital to the hospital’s particular zip code. So the admission rates for that zip code were awfully high that year.

Pete Bailey: One thing I wanted to say about the whole idea of quality of addresses is that we really do need to move forward with putting our address-match system into edit capabilities on the front-end when data is collected so that when someone comes in an address, at that point you can run it through your address match and see if it is hitting or if it is a bad address because if you don’t get quality data there, you really get yourself in trouble. As far as the codes, what you should do, of course, is get your geocodes on both. You can do patient origin, you can look at distance and all of that. So you always want to try to cover both if possible.

Cindy DiBiasi: A question on GIS technology. This caller says, “GIS technology is extremely powerful and perfect for analyzing the local healthcare safety net. My concern is that the safety net providers who could use it the most are probably least likely to have the expertise or resources to do so. Any ideas?”

Andrew Baseman: I would agree completely, with everything you said there. This is why it is going to be a long process, as everything else is healthcare seems to be a few years behind the rest of the industry, to actually get this to be used by safety net providers, particularly by CHC providers. We have found with providers that actually used our mapping, that they have again been uniformly positive in their response, have already come back to us with multiple questions. But I still feel like the quantum leap won’t be made until we can take this to a web-based interface where someone without the technological expertise or the ability to purchase software or IT programming can access their data real time, when they want it, when they have the questions.

Cindy DiBiasi: Andrew, a follow-up question from Erin Grace. She wants to know, “Are there any specific GIS packages you would recommend and how much do they cost?”

Andrew Bazemore: On the air, I probably will not recommend any one package, but I can discuss a few packages. ESRI is certainly the largest maker of geographic information systems software products ranging from ArcView products where you could actually interact and create maps to ARCIMS, which I mentioned is a web-based server. Maptitude is I know the provider for software packages that cost slightly less. Then probably of greatest interest, specifically given the last question, would be free software. Using EpiInfo and MapInfo, which I believe is still downloadable for free off of the CDC Web site, would be a recommendation for one who has cost containment in mind.

Cindy DiBiasi: OK. A question from Arizona. Howard is on the air. Hello?

Howard: Thank you. I just want to ask any of the panel members, have they looked at any of the tribal areas in terms of either administrative-type data or mapping?

John Billings: In that data books that have been produced, the data comes at the county level so the extent to which the tribal areas have boundaries that are contiguous with counties, or at least get a sense of that. You will get some information, but there hasn’t been an effort to date, that I am aware of, by us anyway, that is targeting particularly the very specific boundaries of a tribal area. Again, the point being made earlier about geocoding, it is possible to do that by building up census tracts by zip code.

Cindy DiBiasi: Pete, a question about how you are able to integrate date from various sources and not have problems with health information privacy provisions under HIPAA, the Health Insurance Portability and Accountability Act?

Pete Bailey: I think our main advantage is that we have state statutes or requirements for data, which really does help you in terms of HIPAA. Working with research partners that come in and use data, they are forced to go through an IRV and then for other organizations where we are receiving data, we use the business associate agreement. If you need to get into further detail about the whole HIPAA side, I have a privacy officer that I have called back on and if you will call me I can put you in touch with her. I think that basically, we still are able to do everything that we want to do without having problems with HIPAA. They may make it a little more difficult, but we are OK with it.

Cindy DiBiasi: A question from Colorado for John. The person who is emailing says, “I am concerned about the aggregating of unit data like ER utilization. Would the mapping of utilization be distorted by a large volume of simple visits? Should it be charged all the way to admissions?

John Billings: Well, it depends on what you are trying to look at. If you are trying to look at where the most money is coming from, then you are going to want to look at that. If you are trying to look at people problems, then which people are having the biggest problem? Sometimes these common cold and sore throat problems are not high-cost items. So looking at the rate at which people are coming to the emergency room for these things on a per-capita basis, can be very, very powerful. So I think, as I say, it depends on what you are looking at. We are looking at access barriers. We have tried to focus not on the money, because that raises, money is in the back of everybody’s mind, but we try to focus on the outcomes and things that happen when you don’t get timely and effective care, which no one argues very much with. So sometimes it makes sense to stay away from money; sometimes it makes sense to focus exactly on money. If we have mapped out how much it costs to medicate a patient in New York City at the zip code level. I can tell you, it is an enormous disparity from one zip code to another. So just about anything you look at you can find disparities.

Cindy DiBiasi: A question from Antonio Laci. He wants to know, “Are there any GIS models that have been created through web access?”

Andrew Baseman: Well, Antonio, I am not aware of any specifically for the community health centers. We are working on this in the pilot stages in Cincinnati trying to work with two local community health centers to work again using this web-based software to design a system whereby they can do queries, live time or real time, but we did not find, at least in the literature, any evidence that anybody has done this before. Now when you get into the larger models, I would probably defer that question to Pete or someone else who has integrated components like this into GIR data into GIS.

Peter Bailey: And I do not know of any either. I do have people if you call our office, I am sure I have got more specialized people in that area that would probably know that.

Cindy DiBiasi: OK, Pete, now twice you have offered to have people call in. You’d better give your phone number.

Pete Bailey: OK. It is 803-898-9941 and don’t give up on me. I am not at my desk a lot. Leave me a message and I will get back to you.

Cindy DiBiasi: Andrew, when you first presented your set of maps to the clinic leaders in Baltimore, how were they received? Did they buy into the idea of analytic mapping?

Andrew Bazemore: They really did. Not only the clinic leaders, but once we trickled down from the CEO and the CMO level, we found particularly that some of the clinicians, the director of pediatrics, the leader of the outreach initiative, had particular uses for this. We are working, as I mentioned, to help augment breast health and African-American women over 40 with their outreach coordinator. They have a wealth of outreach funding through grants, that they are trying to actually grow and six outreach team members to work with. They are trying to take this from heading out to churches in random areas in what they thought was their service area by clinic, to actually very specifically geographically-directed outreach, door to door, and densely penetrated areas with a target population of interest.

Cindy DiBiasi: So they were surprised, in part at least, by what they discovered from the mapping.

Andrew Bazemore: Both surprised at what they discovered. Pleased to learn it and have come up with a number of ideas out of our mapping presentation.

Cindy DiBiasi: You were talking too, Christina, about some of the surprises and the misconceptions that arose from this. We are constantly learning from, as long as you are in the healthcare industry, I guess you know what the trends are and everything and then you start to do some of this data discovery and find out that it isn’t turning out that way.

Christina Shannon: I think you tended to put your own experience on it. So but going back to the emergency department example, finding out that that greatest ER use was during the day. I would think well, when would I go to an emergency. I would go when I couldn’t get to my primary care provider. But it doesn’t seem to be the case with our providers. If you went with your gut, like I did and others did that it was during the evenings that we had to work on, you could set up a whole outreach effort or programming effort and find out that you had not at all done anything to alleviate the problems.

Cindy DiBiasi: I thank you all for joining us today. Some very interesting thoughts and things that you are working on. I would like to go around the room and to New York and get some final thoughts from our presenters. Andrew, why don’t we start with you?

Andrew Bazemore: Sure. I guess my final thoughts would be simply that the data is available, particularly for those of you working at the clinic level. Population data is easy to come by and working with the geographic information systems, I found it easier than I expected. I think it will get even better, particularly as we get web access in the future.

Cindy DiBiasi: Pete?

Pete Bailey: Well, I think I should again put in a plug for a wide range of integrated data systems cutting across more than just health. I certainly want to emphasize my agreement about how important the GIS system is and I think when you put these two together, you are almost unstoppable in terms of the analytical capabilities.

Cindy DiBiasi: Christine?

Christine Shannon: I would like to second what Pete just said about putting the two tools together. Also, warn the participants in this call that you get answers to your questions, but they give you more questions and that you need to be careful. When you get excited about an answer, to realize that it is going to lead you down another path. If you think that that was the final answer, you could be in error and it might lead you down a path you don’t want to go.

Cindy DiBiasi: John?

John Billings: I think I would echo all of that. I think number one, be cautious. Number two, don’t be intimidated by this. This is not rocket science. It is not that hard. I think people find it a very intuitive way of understanding problems. It could be very useful to policymakers in helping them get off the dime.

Cindy DiBiasi: Robin?

Robin Weinick: We have put these tools together for you to use as well as the data books. So not only of course would we like to make them as available as possible to you and Cindy will tell you in just a minute how to get them and how to reach me so that you can ask additional questions. Please let us know how else we can help you in your efforts to monitor your local safety net. The more information we have from you about your needs, the better we can do at meeting them.

Cindy DiBiasi: Thank you all for joining us this afternoon. If you have any unanswered questions, please send an email to ulp@ahrq.gov. Depending on the number of questions, we will try to answer you directly. We also encourage you to send us any researchable questions that you are facing at the state or local level for AHRQ’s consideration as the agency plans its future research priorities.

As we wind down, let me mention that a number of products from the audio conference will be available at a later date. An audio streamed archive of today’s call, a written transcript, and all the presenter’s slides, including those used in the question and answer session, will be posted to the AHRQ Web site. PowerPoint and text versions of the slides are now available at www.academyhealth.org/ahrq/ulp/safetynet. An audio tape of this event will be available for purchase in several weeks’ time. The cost for a set of three audiotapes from this series will be $10. To order a copy, call the AHRQ Publications Clearinghouse at 1-800-358-9295. Ask for AHRQ03-AV12A. It is entitled Monitoring the Healthcare Safety Net.

Also remember that information related to the AHRQ and HRSA initiatives to monitor the healthcare safety net is available on the web. Copies of electronic versions of the tool kit chapters will be put up on the safety net Web site, www.ahrq.gov/data/safetynet. They will be put up as they become available. Right now, five of the nine chapters are already online.

John Billing’s software to analyze inpatient hospital and emergency department data will also be available on the site.

Hard copies of Book III entitled Tools for Monitoring the Healthcare Safety Net are not available yet. To request a copy to be mailed to you when they become available, please send an email to sasenet@ahrq.gov. The data books introduced on Tuesday’s web-assisted audio conference are now available. To request a copy of the data books, please send an email to the Clearinghouse at ahrqpubs@ahrg.gov or you can call 1-800-358-9295 and ask for publications ahrq03-0025 and ahrq03-0026.

Please mark your calendars for the next ULP web-assisted audio conference. On October 21st, we will be holding the fourth event in our five-part bioterrorism series. The fourth call is entitled The Role of Information Communication Technology and Monitoring Surveillance Systems in Bioterrorism and Preparedness. We hope you will join us on October 21st from 2-3:30 PM Eastern daylight time. For more information on how to register, you can contact the ULP mailbox, ulp@ahrq.gov.

Finally, before you log off, don’t forget to take a few minutes to fill out the brief evaluation form that will appear on your screen at the end of the broadcast. Easy to follow instructions are included. For those of you who have been listening by phone only and not using your computer, please stay on the line. The operator will ask you to respond to the same evaluations questions by using the keypad on your telephone. You may also email your comments to us at ulp@ahrq.gov. Thanks for joining us and have a nice day.

Back to Top