Analytics in Healthcare: 5 Initiatives

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Quite a few of the projects I have done in the last three years have been around the practical use of analytics in healthcare. One could thank the Obama administration and the healthcare reforms for it. But these projects were interesting. They ranged from market expansion strategies for pharma clients to operational effectiveness for healthcare providers to process and risk assessment for payors. And while going through the drills of business development and engagement delivery, there are three things I noticed –

• Data assets– Healthcare, more often than not, has the best and the worst data. Best because of the nature of trail left behind for everything. Drug trials have data. Your medical history is captured in some shape or form. Medical transcription started some years back, adding more backbone to data. Claims data is analyzed significantly. Your test reports are getting digitized. At the same time, under the impression that most of it is “art”, its not organized data in some very significant parts. The movement from ICD9 to ICD10 – from analysis standpoint, is in progress. That being said, someone who can create a better, robust and standardized taxonomy stands to gain a lot here. Most of what I saw was trash. And I did see a bit.
• Application – The application of analytics has widened from the traditional clinical trials and drug discovery cycles to everyday business of healthcare. It is one of the better and more expansive adoptor right now. People may remember the Heritage Health competition with good bounty for analytics problem solvers. Which, by the way, followed the ubercool Netflix challenge.
• Simultaneous change across all entities. Payors, Providers and Pharma guys – All of them are adopting analytics in a wider way almost at the same time. Within banking, for instance, risk was one of the first, database marketing the second. I don’t think operations use analytics as much still. Within retail, companies are still struggling with POS analytics. A lot of the brand guys across industries still think of the art as the driver of sales, while discounting behavioral economics and analytics.

So, what are my top 5 picks for analytics investments in healthcare –

• Market Restructuring – Does this mean that there should upstream and downstream investments made by the players, yes. Like, Intermountain becoming an integrated payer and provider. Or, maybe, a Pfizer payer-provider-pharma integrated business. or Kaiser? At the least, the payor provider integration is imminent. And when that happens, how will you maximize your benefits, customer benefits, and social benefits, is where analytics should focus. This is where most strategy firms will find opportunities. If someone can productize the approach, awesome!
• Sales Force Redesign – As an effect of PPACA and Healthcare Act, there has been some consolidation in the industry, and the subsequent standardization of protocols and medical care. Hedging your risks seems to be one of the priorities that hospitals, individual practitioners have had to focus on. There has been an increase in network and institutional affiliation over the last couple of years. Hence, there is going to be a changing buyer design as well. Instead of the traditional sales rep model, newer sales strategies that will focus on horses for courses – such as account management model for network penetration, risk sharing model for adoption and share of wallet, and price war for competitive categories. Pharma companies will need to rethink their sales models, that’s a given. Analytics can do that. ZS traditionally has done that well. I believe Deloitte and PwC will be other companies to watch out for here.
• Plan design and administration – with the change in customer portfolio , and the commitment to medicare/Medicaid segments,  payors will need to revisit their plans, their pricing, and their claim analytics business. Organizations with strong risk and pricing analytics, underwriters, and actuaries, should customize their play to make some money on this one.
• From human to inhuman – A fun moment in an engagement is to convince a young analyst why, in certain cases, creating a disincentive for traveling to the hospital (which is a higher cost destination) and opting for low cost channels (telephonic care, medical vans on route, etc.), is not an inhuman way of looking at healthcare. But I guess, finding these more efficient, non-human interventions can help bring down healthcare costs. This will require working very closely with nurses, administrators, doctors. Non-human should become inhuman.
• From patient to household – Almost all healthcare analysis is conducted at a patient level. For payers, a part of the analysis is subscriber and/or plan level. However, for modular plan designs, better cross-sell, better service administration, the three groups need to evolve to the household level too.

 

And while on the subject, where is a good healthcare bureau data? There are some that are evolving rapidly and provide a good starting base (SKA, HIMSS, AHD, etc.), but lots of work is needed.