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Predictive Analytics Will Save Hospitals, Not IT Investment

Posted on October 27, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Most hospitals run on very slim operating margins. In fact, not-for-profit hospitals’ mean operating margins fell from 3.4% in fiscal year 2015 to 2.7% in fiscal year 2016, according to Moody’s Investors Service.

To turn this around, many seem to be pinning their hopes on better technology, spending between 25% and 35% of their capital budget on IT infrastructure investment. But that strategy might backfire, suggests an article appearing in the Harvard Business Review.

Author Sanjeev Agrawal, who serves as president of healthcare and chief marketing officer at healthcare predictive analytics company LeanTaaS, argues that throwing more money at IT won’t help hospitals become more profitable. “Healthcare providers can’t keep spending their way out of trouble by investing in more and more infrastructure,” he writes. “Instead, they must optimize the use of the assets currently in place.”

Instead, he suggests, hospitals need to go the way of retail, transportation and airlines, industries which also manage complex operations and work on narrow margins. Those industries have improved their performance by improving their data science capabilities.

“[Hospitals] need to create an operational ‘air traffic control’ for their hospitals — a centralized command-and-control capability that is predictive, learns continually, and uses optimization algorithms and artificial intelligence to deliver prescriptive recommendations throughout the system,” Agrawal says.

Agrawal predicts that hospitals will use predictive analytics to refine their key care-delivery processes, including resource utilization, staff schedules, and patient admits and discharges. If they get it right, they’ll meet many of their goals, including better patient throughput, lower costs and more efficient asset utilization.

For example, he notes, hospitals can optimize OR utilization, which brings in 65% of revenue at most hospitals. Rather than relying on current block-scheduling techniques, which have been proven to be inefficient, hospitals can use predictive analytics and mobile apps to give surgeons more control of OR scheduling.

Another area ripe for process improvements is the emergency department. As Agrawal notes, hospitals can avoid bottlenecks by using analytics to define the most efficient order for ED activities. Not only can this improve hospital finances, it can improve patient satisfaction, he says.

Of course, Agrawal works for a predictive analytics vendor, which makes him more than a little bit biased. But on the other hand, I doubt any of us would disagree that adopting predictive analytics strategies is the next frontier for hospitals.

After all, having spent many billions collectively to implement EMRs, hospitals have created enormous data stores, and few would argue that it’s high time to leverage them. For example, if they want to adopt population health management – and it’s a question of when, not if — they’ve got to use these tools to reduce outcome variations and improve quality of cost across populations. Also, while the deep-pocketed hospitals are doing it first, it seems likely that over time, virtually every hospital will use EMR data to streamline operations as well.

The question is, will vendors like LeanTaaS take a leading role in this transition, or will hospital IT leaders know what they want to do?  At this stage, it’s anyone’s guess.

Hospital CIOs Still Think Outcomes Improvement Is The Best Use Of EMR Data

Posted on August 4, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Sure, there might be a lot of ways to leverage data found within EMRs, but outcomes improvement is still king. This is one of the standout conclusions from a recently-released survey of CHIME CIOs, sponsored by the trade group and industry vendor LeanTaaS, in which the two asked hospital CIOs five questions about their perceptions about the impact of EMR data use in growing operating margins and revenue.

I don’t know about you, but I wasn’t surprised to read that 24% of respondents felt that improving clinical outcomes was the most effective use of their EMR data. Hey, why else would their organizations have spent so much money on EMRs in the first place?  (Ok, that’s probably a better question than I’ve made it out to be.)

Ten percent of respondents said that increasing operational efficiencies was the best use of EMR data, an idea which is worth exploring further, but the study didn’t offer a whole lot of additional detail on their thought process. Meanwhile, 6% said that lowering readmissions was the most effective use of EMR data, and 2% felt that its highest use was reducing unnecessary admissions. (FWIW, the press release covering the survey suggested that the growth in value-based payment should’ve pushed the “reducing  readmissions” number higher, but I think that’s oversimplifying things.)

In addition to looking at EMR data benefits, the study looked at other factors that had an impact on revenue and margins. For example, respondents said that reducing labor costs (35%) and boosting OR and ED efficiency (27%) would best improve operating margins, followed by 24% who favored optimizing inpatient revenue by increasing access. I think you’d see similar responses from others in the hospital C-suite. After all, it’s hard to argue that labor costs are a big deal.

Meanwhile, 52% of the CIOs said that optimizing equipment use was the best approach for building revenue, followed by optimizing OR use (40%). Forty-five percent of responding CIOs said that OR-related call strategies had the best chance of improving operating margins.

That being said, the CIOs don’t exactly feel free to effect changes on any of these fronts, though their reasons varied.

Fifty-four percent of respondents said that budget limitations the biggest constraint they faced in launching new initiatives, and 33% of respondents said the biggest obstacle was lack of support resources. This was followed by 17% who said that new initiatives were being eclipsed by higher priority projects, 17% said they lacked buy-in from management and 10% who said he lack the infrastructure to pursue new projects.

Are any of these constraints unfamiliar to you, readers? Probably not. Wouldn’t it be nice if we did at least solved these predictable problems and could move on to different stumbling blocks?

What About Data Beyond the EMR?

Posted on April 4, 2014 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I saw this tweet from the famous @HealthcareWen which asks a really good question:

While I enjoy the humor of the tweet as much as the next person (everyone who knows me knows I’m all about the humor), this conversation reminds me a lot of what was done with ICD-10. The “funny ICD-10 codes” got all the attention and made ICD-10 a joke in the minds of so many people. This was highlighted by this guest post on EMR and HIPAA called “Why Do People Find ICD-10 So Amusing?” Those who support the shift to ICD-10 did a poor job explaining why ICD-10 was valuable to the quality of care a patient gets. Talking about all the funny ICD-10 codes (and they are funny) goes against the goals of those who see value in the move to ICD-10.

I bring this up because the same thing could easily happen with big data in healthcare. While it’s funny to think about how a doctor might treat us if they know we had a donut for breakfast, there are really meaningful data sources beyond the EMR. If we focus too much on the periphery of the data, then we’re going to miss out on a lot of the value that comes from the not so funny parts of big data.

Right now our EMR systems can’t support most of the data that could come from outside the EMR. However, that shift is going to happen and it’s going to happen quickly. My gut tells me that it will start with the wave of consumer centric medical sensors. Then, I see genomic and social data getting integrated next (both really large projects). These three areas will set the baseline for how outside data is integrated with the EMR data.

Let me offer the key points to consider in these data integrations:
Automated: The data must pass seamlessly without the need for user interaction
Smart Data: The user of the system needs the system to be smart. The user should only be notified with what’s actionable, but with the ability to drill into the data as needed.
Bi Directional: The data needs to be seen and updated by both provider and patient. The system will need to have a great way to track who updated which data. However, we need both the patient and providers eyes on the data with the ability to update incorrect data.

These points should illustrate why integrating outside data is going to be such a challenge. However, it’s also why it holds such promise.