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Big Data Could Generate $450 Billion For Healthcare System

Here’s some information that should give all of us something to chew on this week, courtesy of the always-interesting Jane Sarasohn-Kahn at the HealthPopuli blog. In a recent piece, Sarasohn-Kahn pulls data from McKinsey & Company suggesting that if big data is properly harnessed, it can produce nearly — wait for it — $450 billion in value for the healthcare system.

As always, however, there’s a catch. This value explosion can’t happen, McKinsey says, unless big data is leveraged across five dimensions of care. These dimensions, which McKinsey calls “new value pathways,” offer opportunities for better efficiencies and economies of scale for the health system, HealthPopuli notes.

The five dimensions include:

* Right living, in which big data is used to help patients take an active role in staying healthy, by such mechanisms as daily health reminders and getting patients to seek care early when problems do arise.

* Right care, in which big data tools, particularly coordination of data across providers and settings, make sure that patients get the right care at the right time.

* Right provider, in which data analytics matches patients with the ideal provider for their situation, sometimes to lower-cost providers that can provide appropriate care.

* Right value, which uses big data analysis to reward providers who produce the best outcomes.

* Right innovation, a pathway in which big data is mined to promote continuous improvement and productivity in healthcare processes as well as R&D.

Sounds great, doesn’t it? Well, maybe not so much given what has to change. To travel down these pathways, McKinsey notes, it will take re-aligning several key forces in the healthcare system, including privacy and data security, a shift to value-based reimbursement, partnerships across industry segments currently found in deep silos (such as payors and providers), and data analysis capabilities current lacking in the health IT workforce. Sigh. And I was feeling hopeful there for a bit.

As Sarasohn-Kahn notes, one way McKinsey sees to meet some of these goals more quickly would be to promote transparency as a cultural norm. But honestly, the silos we see today exist for important institutional and competitive reasons. If we want key partners in the big data effort to cooperate, it’ll probably take a governmental club to that head. Hey, ONC, are you ready to get rough with those who don’t want to play in the same data sandbox?

Technologies Hospital Leaders Should Watch

Courtesy of non-profit research house the ECRI Institute, here’s some of technologies that they believe hospital C-suite execs should be watching this year. This list was generated by ECRI’s in-house analysts, reports HealthLeaders. Not all of these are directly related to EMR/EHR technology, but we’ve included a few that might be of interest on the broader HIT level.

* Electronic Health Records: This is so obvious it hardly bears mentioning, but yes, EHRs are number one on the list. ECRI notes that execs should beware of possible patient harm in the effort to achieve Meaningful Use, as some HIT-related errors are emerging that can lead to serious care issues.

* mHealth: Mobile applications are becoming an increasingly commonplace part of health IT infrastructure, but managing them effectively isn’t as simple as download-install-use. This is likely to be the year hospitals need to get it right.

* Alarm Integration Technology: Alarm fatigue has been and continues to be a major issue for clinicians, with some critical care docs experiencing 350 alarms per patient per day. Increasingly, alarm integration systems are being implemented which send alerts to phones or pages, leading to more controllable alerts and quieter environments.

* Imaging and Surgery: ORs are increasingly hosting full-scale angiography systems to help guide high-risk minimally invasive surgery, as well as guiding combined open and minimally invasive surgery and verifying successful surgical completion. These hybrid ORs are expensive but have arguably improved results.

* PET/MR: The PET/MR scanner is beginning to emerge as a new mainstay in oncology, improving on the results delivered for years by the hybrid PET/CR. The PET/MR offers greater detail, helping physicians detect cancers and tumors.

I would have expected to see something on the data analytics technology front to appear this year, but it was absent from the list. I might also have expected to see cloud solutions turn up, but again, not this year. What technologies would you add to this list?

Healthcare Big Data Trends Leading To Analytics Spending

Ready to exploit big data? So are your competitors, and they’re preparing to spend big bucks in areas where they’ve historically been weak, such as predictive analytics and data discovery, reports HealthcareITNews.

Technology vendor Lavastorm Analytics recently surveyed more than 600 technology professionals in healtlhcare and other industries about their IT investment plans for this ear.

Right now, researchers found, three-quarters of respondents still routinely use Excel for self-service analytics processes, and 35 percent use the R programming language. Of the remaining 24 self-service analytics tools listed by the survey, 17 of them were used by less than 10 percent of the audience. In other words, once you get past R and Excel for analytics, there’s little agreement as to what works best.

But the coming months should bring some big changes in this landscape, Lavastorm’s research suggests. As the desire to exploit big data grows, providers are planning investments that will allow them to exploit it. Nearly 60 percent of respondents plan to increase their investments in areas where their capacity is limited.

Those areas include gleaning insights from data (25 percent), accessing data (22 percent) and having the ability to integrate and manipulate data (19 percent), HealthcareITNews says.

To meet those goals, providers intend to invest in predictive analytics (51 percent), big data (35 percent), dashboards (32 percent), reporting (31 percent) and data exploration and discovery (30 percent). At the same time, 27 percent said that they’d invest in advanced visualization tools and 24 percent self-service analytics tools for business users.

All this being said, my hunch that providers probably aren’t particularly sure where they’re headed with this technology yet. I’d like to have seen Lavastorm ask which clinical or business goals, specifically, they hoped to meet by making these investments, wouldn’t you?

UPMC Sinks $100MM Into Big Data

The University of Pittsburgh Medical Center has announced plans to spend $100 million over five years to create a massive data warehouse, a move which puts it well at the forefront of hospital “big data” efforts.

According to Information Week, UPMC’s data warehouse will bring together clinical, financial, administrative, genomic and other information. The health system has targeted more than 200 data sources across the Medical Center, UPMC Health Plan and other affiliates.

I’ll let Information Week describe the technical set-up:

To collect, store, manage, and analyze the information maintained in the data warehouse, UPMC will use the Oracle Exadata Database Machine, a high-performance database platform; IBM’s Cognos software for business intelligence and financial management; Informatica’s data integration platform; and dbMotion’s SOA-based interoperability platform that integrates patient records from healthcare organizations and health information exchanges. These tools will manage the 3.2 petabytes of data that flows across UPMC’s business divisions.

As to how UPMC plans to use these tools, they’re hoping to do all of the things you might imagine, including genomically-tailored prescribing, population analytics and sophisticated tracking of individual patient data to make predictions about possible risks.

As I see it, UPMC’s efforts highlight both the importance of big data efforts and the downside in making the investment.

On the one hand, you’ve got the benefits. For example, patients will clearly see better outcomes if doctors can use top-drawer analytical tools to predict how treatments will work or know well in advance if a patient’s condition is about to go south. And hospitals will clearly run better if execs get insights into issues that cross clinical and administrative boundaries, such as ED or OR utilization.

On the other, you’ve got the reality that big data projects are prohibitively expensive for all but the best-funded of healthcare organizations, and probably won’t produce returns on investment for several years at best. Average community hospitals won’t be consolidating and analyzing their data this way anytime soon.

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Big Data Could Generate $450 Billion For Healthcare System

Technologies Hospital Leaders Should Watch

Healthcare Big Data Trends Leading To Analytics Spending

UPMC Sinks $100MM Into Big Data

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