Peaks and Valleys in the Pursuit of Analytics

Ryan Black

Marco D. Huesch, MBBS, PhD, has deep thoughts on the role of analytics in healthcare. He also has an unbelievably deep well of experience to pull from.

Marco D. Huesch, MBBS, PhD, has deep thoughts on the role of analytics in healthcare. Many people have those, of course, but he pairs his with a very deep well of expertise. He is currently Vice Chair of Radiology Research and an Associate Professor of Radiology at Penn State University's Milton S. Hershey Medical Center. Before that, he taught Master’s classes in healthcare policy and economics at Duke and USC, and before that worked as a data consultant in the financial industry, which was preceded by nearly 8 years as a practicing physician.

Earlier this year, he and a colleague penned a compelling “Case for Data Scientists Inside Healthcare” for the New England Journal of Medicine’s Catalyst publication. He recently granted Healthcare Analytics News a long, thought-provoking interview, speaking of major systemic issues he sees in American healthcare’s adoption of analytics and their extensive upside for those that are able to make the leap. What follows is only a brief portion of our conversation.

Going off of the column…I wanted to ask you to briefly talk about the major things holding data analytics in healthcare back, before we dive into specifics.

I really do think it is a systemic issue in healthcare in general, that we are very slow to adopt techniques that other industries have adopted. That’s an issue that is beyond just healthcare analytics, but encompasses many aspects of modern management.

Just to give you an anecdote, I first studied activity-based costing in the mid ‘90’s as part of an MBA. Fast-forward two decades and you will be hard-pressed to find a hospital that uses activity-based costing, and in fact there’s been some studies that asked hospital CFOs if they knew what activity-based costing was and a low percentage knew what it is and very few had it in the hospital. When you think about the complexity of a hospital and the real need to understand your costs, not being able to do that sort of thing would just be an unbelievable failure in your management toolbox, and yet that’s something that’s tolerated. Despite all the investments in technology, enterprise resource planning and activity-based costing is just not something that has been implemented as a priority.

I’ll give you another…I was teaching healthcare strategy operations in an MBA program, and it wasn’t a popular program because the folks who were going off into life sciences were almost all going off into pharma and devices. Very few of them were going into hospitals, and that was because the income wasn’t there. If you’ve just done an MBA, you can’t really afford to work for less than $100,000, and those are the salaries that were being offered from hospitals, or lower from hospitals. Even big national chains.

On the other hand, hospitals do very well with training up Bachelor’s degrees, putting them in positions where they’re powerful deciders. You’ve got this sort of systemic, in my opinion, under-resourcing in management competencies, only one of which is analytics. Whatever you can say about the other weaknesses, however, applies in spades to analytics.

Do you think that the investments are what’s prohibitive, thinking of studies that show larger systems are better at embracing analytics methods than smaller ones?

I would put that down to a sense of spreading the cost across business units and franchises, even geography. That’s something that makes the perceived cost lower, whereas if you’re just a single hospital, you’re going to struggle to make that investment. It’s a very lumpy investment, you might need $750,000 a year to support one data scientist and a few analysts. I don’t want to knock the people that are balking at making that. But at the same time, the services [some companies] offer to a bulge-bracket hospital, 200-to-500 bed range, these guys are probably making a million a year off of some news services, some targeted emails, some consulting, some benchmarking…there’s money being spent in other areas on quasi-analytics, whereas there’s been very little thought as to why they’re doing this, why they’re getting generic information instead of in-housing that competence.

I have 3 data scientists that work for me, and none of them come from healthcare: they come from defense, banking, and the warehouse and transportation industry. What they’ve learned they can apply in healthcare. In a typical hospital, there’d be no one with consistently well-rounded analytics strengths, because it’s just not called for. If you’re a physician you’re too busy, if you’re a normal analyst you’re just doing retrospective business intelligence-type things, some Excel and some modest data handling and munging abilities would work fine.

I kind of think of it as a perfect storm: lack of history, lack of overall competences in the industry, lack of people with the skills, lack of means to purchase the skills, and then like we wrote in the article, just a very aggressive, repetitive sales pitch from a lot of the vendors who make a cogent case. ‘Down the road, they’re doing this, do you want to be left behind? Sign up for this generic implementation of…something.’ Whether they’re a third-party freestanding consultant or they work as an arm of an EMR vendor, there’s enough folks around there pounding the pavement to make it hard for an institution to take the risk on an untried group, a new hire, and all of those implementation issues.

So let’s step back and talk about the upside. In places you have seen where the investment has been made, and where it’s going well, what’s changing for those institutions?

There’s so many benefits, because every time you start taking data and doing analytics and changing what you do, you’re engaged in a cycle of learning. When you use analytics properly, you’re actually becoming a learning system. At Duke, the last time I looked at them they have like 6 layers, from CIO down to an assistant vice president who is responsible for disseminating service-level agreements among users and generators and data.

You have a hierarchy of people aligned with this mission of using data to reform strategy. You become a learning system, you set up a flywheel in which parts of the institution which were originally flying blind now fly with some data, and then appreciate it and want more and generate an increased desire to consume and generate more data. You have a very positive, virtuous circle going on in terms of your learning.

Then when you think of the impact on what we’re all here for, the patients, you have people who are admitted when it’s right for them to be admitted, discharged at the right time to a place that has the best possible outcomes, consistent with their means to pay and their insurance network, who are averted from needless readmissions and needless decompensations in their illnesses, who manage those illnesses better in innovative ways, who participate in their care better, who have lowered burden of illness. These are not science fiction, these are documented outcomes. You get hospitals that increase their ability to make money and raise their market share, increase their reputation and gain trust from potential partners.

I’m thinking of a place like Johns Hopkins University, where evidence-based medicine and the use of data is just such a key part of what they do that you see a very clear desire from other people to partner with them and learn from their approach and participate in the innovation platform that they may be running. Trial, treatment, devices and pharmaceuticals thrive in that particular place because it’s high-performing. One of the obvious uses of data sciences is to predict who needs training or who is going to leave, and can that be avoided. Those are things that affect quality of life. It’s possible, in radiology for example, to manage the workflow in such a way that people feel better about their job, they don’t burn out, they stay longer and do a better job. It’s really hard not to see good everywhere that data science is used well by people who want to use it.

Last but not least, the data scientists themselves learn a lot, and become intrinsically motivated and become very valuable partners in the hospital and can become seen as a value proposition within the hospital.