When a hospital wants to connect with community providers but runs into resistance, what’s a CIO to do? For Cletis Earle, the answer is a “road show.” By that, Earle is referring to the organization’s efforts to visit physicians, educate them about the local RHIO, and give them to nudge – and support – they need to climb on board. In this interview, Earle talks about St. Luke’s “localized HIE strategy,” his strong focus on security and data loss prevention, and the challenges in planning when possible mergers are looming. He also talks about the range of innovation happening at his organization, from population health alerts to adding bus routes to help transport patients between facilities.
- Consistent voice with community docs – “We all say the same thing.”
- Patient alerts — “They’re starting to see the benefits.”
- Building excitement around data analytics
- Hudson Valley’s core initiative
- Getting innovative with bus routes
- Breaking down silos
- “It’s trying to figure out, what can we do next?”
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It’s a win-win-win scenario because there are multiple parties that benefit from this — particularly the patient, because now they’re getting access to information and that information is being shared as near real time as possible.
If the doctor is alerted as soon as that patient hits the hospital, they now can come in and start to take care of them. You’re talking about maximizing efficiency but minimizing turnaround time, which is extremely important.
Millions and millions of dollars are being spent in the city, but there are a lack of measures that show success.
We’re getting people together to say, ‘okay, now we understand that statistical correlation — how do we work with programs to actually do that?’ We’re going to try to test out scenarios. We’re going to do some regressive analysis.
Now that we have everybody at the table and we have these protected methodologies of connecting, we’re now able to share information and then look at positive outcomes as far as big data.
Gamble: I would think having that access to you and being able to talk to you in person is something that goes a long way with them, rather than having a middle person.
Earle: We have a great team. I work with my CEO, the executive director of our RHIO. We sit down and strategize as far as the process, and then wherever possible, we basically all say the same thing. As long as we all say the same thing and they’re hearing it straight from the horse’s mouth, they start to say, ‘Okay, the story’s not changing. This must be the direction. Let’s move forward with it.’ And I’ve had a significant amount of cooperation.
And by the way, it goes both ways. And the reason I say that is we actually have used some of these relationships, particularly for Meaningful Use and the interconnectivity of Meaningful Use, to make sure that we can communicate with the 10 percent population — that initiative making sure it’s outside of your EMR. We’ve used that, those relationships to connect so that we can achieve these things. It’s a win-win-win scenario because there are multiple parties that benefit from this — particularly the patient, because now they’re getting access to information and that information is being shared as near real time as possible.
One of the other things we’ve done with our community physicians which has been very beneficial is we make sure that we send out alerts any time a patient of theirs is admitted or discharged from the facility. We started this about 2 and a half years ago when I got here. We did the alerts. We made sure they understood that if their patient is here, we’ll send that information to them securely, in a HIPAA-compliant way. They started to get that information and they’d see the benefit. They’d see the benefit of having real-time information.
As soon as that patient presents to our ED or as soon as that patient is discharged, they’re getting notified. And they say, ‘Okay, now that I’m notified, what’s the next step? How else can I benefit?’ The patient benefits because now if the doctor is alerted as soon as that patient hits the hospital, they now can come in if they have a very good relationship with that patient and start to take care of them. You’re talking about maximizing efficiency but minimizing turnaround time, which is extremely important. Again, the next phase now is health integration so that when they leave, all of that information comes to their system and it’s integrated.
Gamble: The benefits there are obvious just as far as being able to offer better quality care because they have the information that they need, at the time that they need it.
Gamble: That sounds like a good example of one of the things you’re doing in terms of data analytics. I wanted to talk a little bit more about what you’re doing there and what are your plans going forward. We have all this data; the big question is, how do we use it better?
Earle: That’s something I’m pretty excited about. We’ve done a lot of work to put our business analytics/intelligence platform in place and create a warehouse, and we’ve had some exciting results doing that. And things are still preliminary; we’re not talking about a mature process right now. It’s about a year in the making, but we’ve put a data warehouse in place and we’ve actually decided to work with other agencies.
I stood step back up for a second. There are about three things we’re working on in the Hudson Valley region, which is about seven counties. One program that we’re working on that’s very exciting is the core initiative. The core initiative is a pilot program set up by Governor Cuomo that selected two cities to basically get everybody at the table and be able to share information and get everybody at the table. What I mean by that is the city of Newburg was selected to actually have a good portion of the state agencies — whether they’re behavioral, DOH, transportation, as well as county agencies, the hospital, federally-qualified health centers — all of these people at the table, and we share what’s going on in the city.
When we identify a problem, we work collaboratively, and all of the red tape has now been broken down because I have a commissioner sitting at the table. Or I have a representative from the Governor’s Office or the Office of the State sitting there in front of me, and these are decision makers. We’re able to actually sit down and remediate the problem because what they’ve thought about — and what makes this a very exciting program — is that millions and millions of dollars are being spent in the city, but there are a lack of measures that show success. What they’ve decided to do is to get these niche programs in place to be able to measure success. By doing this, we, as an example, realized that we have a significant amount of readmissions for patients that should be going to their federally-qualified health centers. One of the reasons is that it was easier for those patients to get to the hospital because they didn’t have transportation to get them to the federally-qualified health center.
And so we are working with the Department of Transportation and all these different agencies to work out different transportation hubs throughout the city. One exciting take from this was we actually now have a bus line that starts at one campus — the Newburgh site — and it goes all the way to the Cornwall campus and all the sites between, and this is a cross-town route.
By working with state agencies and re-shifting resources that may be underutilized and putting them in the respective areas, this has been very beneficial to the community and the patients and the people of the cities. These are examples of having people breaking down silos and sitting at the table, working together collaboratively, to make things happen.
I also was able to work with SUNY-New Paltz Academic Center, as a result of core initiative, to work with them to share our hospital data. We were able to actually go down to the level of census block (which is pretty much a city block) to find out the correlation of our data as it applies to asthma and vacancy rates. We were able to see statistical correlation of information that any time there’s a vacant building, the likelihood of asthma is very high. Being able to determine that, now we’re moving and we’re getting the people together to say, ‘okay, now we understand that statistical correlation — how do we work with programs to actually do that?’ We’re going to try to test out scenarios. We’re going to do some regressive analysis. Because in the last few years we’ve been changing plots, so we’re going to actually see if there’s a continual correlation when property has been improved that the healthcare of asthma has actually reduced.
So these are the exciting things about big data and matching up information from whether they’re crime or weather patterns, or whether the asthma rates are associated to the wind direction. What didn’t happen before is organizations did not have the access to hospital information because of various barriers, but now that we have everybody at the table and we have these protected methodologies of connecting, we’re now able to share information and then look at positive outcomes as far as big data.
When you talk about analytics, this is extremely significant. We’ve done a matchup with analytics to show that there’s a high likelihood that any time the temperature goes above 82 degrees — and more importantly, which was more of a surprise, below 32 degrees — we saw the crime rate was spiked, and also that the AV rate in direct correlation. That’s amazing. We always think that when it gets hot people misbehave, but what we found in the inverse is that it’s happening when it gets cold as well.
These are some phenomenal things which you can do with data, and then it’s trying to figure out okay, what’s next? What can we do next? How can we work with people to actually have a better result as example of looking at data sets?
Gamble: That’s really interesting stuff and it’s particularly interesting to me that sometimes it’s something as seemingly simple as providing transportation or setting up a bus route that can lead to better outcomes. It’s very cool to see what big data can actually translate to and those are some really great examples.
Earle: Yeah. It’s population management. I wouldn’t say population health anymore; we’re talking about overall population management.