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The Distributed Hospital On The Horizon

Posted on February 24, 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.

If you’re reading this blog, you already know that distributed, connected devices and networks are the future of healthcare.  Connected monitoring devices are growing more mature by the day, network architectures are becoming amazingly fluid, and with the growth of the IoT, we’re adding huge numbers of smart devices to an already-diverse array of endpoints.  While we may not know what all of this will look when it’s fully mature, we’ve already made amazing progress in connecting care.

But how will these trends play out? One nice look at where all this is headed comes from Jeroen Tas, chief innovation and strategy officer at Philips. In a recent article, Tas describes a world in which even major brick-and-mortar players like hospitals go almost completely virtual.  Certainly, there are other takes out there on this subject, but I really like how Tas explains things.

He starts with the assertion that the hospital of the future “is not a physical location with waiting rooms, beds and labs.” Instead, a hospital will become an abstract network overlay connecting nodes. It’s worth noting that this isn’t just a concept. For an example, Tas points to the Mercy Virtual Care Center, a $54 million “hospital without beds” dedicated to telehealth and connected care.  The Center, which has over 300 employees, cares for patients at home and in beds across 38 hospitals in seven states.

While the virtual hospital may not rely on a single, central campus, physical care locations will still matter – they’ll just be distributed differently. According to Tas, the connected health network will work best if care is provided as needed through retail-type outlets near where people live, specialist hubs, inpatient facilities and outpatient clinics. Yes, of course, we already have all of these things in place, but in the new connected world, they’ll all be on a single network.

Ultimately, even if brick-and-mortar hospitals never disappear, virtual care should make it possible to cut down dramatically on hospital admissions, he suggests.  For example, Tas notes that Philips partner Banner Health has slashed hospital admissions almost 50% by using telehealth and advanced analytics for patients with multiple chronic conditions. (We’ve also reported on a related pilot by Partners HealthCare Brigham and Women’s Hospital, the “Home Hospital,” which sends patients home with remote monitoring devices as an alternative to admissions.)

Of course, the broad connected care outline Tas offers can only take us so far. It’s all well and good to have a vision, but there are still some major problems we’ll have to solve before connected care becomes practical as a backbone for healthcare delivery.

After all, to cite one major challenge, community-wide connected health won’t be very practical until interoperable data sharing becomes easier – and we really don’t know when that will happen. Also, until big data analytics tools are widely accessible (rather than the province of the biggest, best-funded institutions) it will be hard for providers to manage the data generated by millions of virtual care endpoints.

Still, if Tas’s piece is any indication, consensus is building on what next-gen care networks can and should be, and there’s certainly plenty of ways to lay the groundwork for the future. Even small-scale, preliminary connected health efforts seem to be fostering meaningful changes in how care is delivered. And there’s little doubt that over time, connected health will turn many brick-and-mortar care models on their heads, becoming a large – or even dominant – part of care delivery.

Getting there may be tricky, but if providers keep working at connected care, it should offer an immense payoff.

Indiana Health System Takes On Infection Control With Predictive Analytics

Posted on February 22, 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.

At Indiana University Health, a 15-hospital non-profit health system, they’ve taken aim at reducing the rate of central-line associated bloodstream infections – better known to infection control specialists as CLABSIs.

According to the CDC, CLABSIs are preventable, but at present still result in thousands of deaths each year and add billions of dollars in costs to U.S. healthcare system spending. According to CDC data, patient mortality rates related to CLABSI range from 12% to 25%, and the infections cost $3,700 to $36,000 per episode.

Hospitals have been grappling with this problem for a long time, but now technology may offer preventive options. To cut its rate of CLABSIs, IU Health has decided to use predictive analytics in addition to traditional prevention strategies, according to an article in the AHA’s Hospitals & Health Systems magazine.

Reducing the level of hospital-acquired infections suffered by your patients always makes sense, but IU Health arguably has additional incentives to do it. The decision to attack CLABSIs comes as IU Health takes on a strategic initiative likely to demand a close watch on such metrics. At the beginning of January, Indiana University Health kicked off its participation in the CMS Next Generational Accountable Care Organization Model, putting its ACO in the national spotlight as a potential model for improving fee-for-service Medicare.

According to H&HN, IU Health has launched its predictive analytics pilot for CLABSI prevention at its University Hospital location, which includes a 600-bed Level I trauma center and 300-bed tertiary care center which also serves as one of the 10 largest transplant centers in the U.S.

Executives there told the magazine that the predictive analytics effort was an outgrowth of its long-term EMR development effort, which has pushed them to streamline data flow across platforms and locations over the past several years.

The hospital’s existing tech prior to the predictive analytics effort did include an e-surveillance program for hospital-acquired infections, but even using the full powers of the EMR and e-surveillance solution together, the hospitals could only monitor for CLABSI which had already been diagnosed.

This retrospective approach succeeded in cutting IU Health’s CLABSI rate from 1.7 CLABSIs over central-line days in 2015 to 1.2 last year. But IU Health hopes to improve the hospital’s results even further by getting ahead of the game.

Last year, the system implemented a data visualization platform designed to give providers a quick-and-easy look at data in real time. The platform lets managers keep track of many important variables easily, including whether hospital units have skipped any line maintenance activities or failed to follow-through on CLABSI bundles. It’s also saving time for nurse managers, who used to have to track data manually, and letting them check on patient trend line data at a glance.

The H&HN article doesn’t say whether the hospital has managed to cut its CLABSI rate any further, but it’s hard to imagine how predictive analytics could deliver zero results. Let’s wish IU Health further luck in cutting CLABSI rates down further.

Is Your Current Analytics Infrastructure Keeping You From Success in Healthcare Analytics?

Posted on February 17, 2017 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.

The following is a paid blog post sponsored by Intel.

Healthcare analytics is all the talk in healthcare right now.  It’s really no surprise since many have invested millions and even billions of dollars in digitizing their health data.  Now they want to extract value from that data.  No doubt, the promise of healthcare analytics is powerful.  I like to break this promise out into two categories: Patient Analysis and Patient Influence.

Patient Analysis

On the one side of healthcare analytics is analyzing your patient population to pull reports on patients who need extra attention.  In some cases, these patients are the most at risk portions of your population with easy to identify disease states.  In other cases, they’re the most expensive portion of your population.  Both of these are extremely powerful analytics as your healthcare organization works to improve patient care and lower costs.

An even higher level of patient analysis is using healthcare analytics to identify patients who don’t seem to be at risk, but whose health is in danger.  These predictive analytics are much more difficult to create because by their very nature they’re imperfect.  However, this is where the next generation of patient analysis is going very quickly.

Patient Influence

On the other side of healthcare analytics is using patient data to influence patients.  Patient influence analytics can tell you simple things like what type of communication modality is preferred by a patient.  This can be used on an individual level to understand whether you should send an email, text, or make a phone call or it can be used on the macro level to drive the type of technologies you buy and content you create.

Higher level patient influence analytics take it one step further as they analyze a patient’s unique preferences and what influences the patient’s healthcare decision making.  This often includes pulling in outside consumer data that helps you understand and build a relationship with the patient.  This analytic might tell you that the patient is a huge sports fan and which is their favorite team.  It might also tell you that this person has a type A personality.  Together these analytics can inform you on the most appropriate ways and methods to interact and influence the patient.

What’s Holding Healthcare Analytics Back?

Both of these healthcare analytics approaches have tremendous promise, but many of them are being held back by a healthcare organization’s current analytics infrastructure.

The first problem many organizations have is where they are storing their data.  I’d describe their data as being stored in virtual prisons.  We need to unlock this data and free it so that it can be used in healthcare analytics.  If you can’t get at the data within your own organization, how can we even start talking about all the health data being stored outside the four walls of your organization?  Plus, we need to invest in the right storage that can support the growth of this data.  If you don’t solve these data access and storage pieces, you’ll miss out on a lot of the benefits of healthcare analytics.

Second, do you trust your data?  Most hospital CIOs I talk to usually respond, “Mostly.”  If you can’t trust your data, you can’t trust your analytics.  A fundamental building block of successful analytics is building trust in your data.  This starts by implementing effective workflows that capture the data properly on the front end.

Next, do you have the processing power required to process all these analytics and data?  Healthcare analytics in many healthcare organizations reminds me of the old days when graphic designers and video producers would have to wait hours for graphics programs to load or videos to render.  Eventually we learned not to skimp on processing power for these tasks.  We need to learn this same lesson with healthcare analytics.  Certainly cloud makes this easier, but far too often we under fund the processing power needed for these projects.

Finally, all the processing power in the world won’t help if you don’t have your most important piece of analytics infrastructure: people.  No doubt, finding experienced people in healthcare data analytics is a challenge.  It is the hardest thing to do on this list since it is very competitive and very expensive.  The good news is that if you solve the other problems above, then you become an attractive place for these experts to work.

In your search for a healthcare analytics expert, you can likely find a data expert.  You can find a clinical expert.  You can find an EHR expert.  Finding someone who can work across all three is the Holy Grail and nearly impossible to find.  This is why in most organizations healthcare analytics is a team sport.  Make sure that as you build your infrastructure of healthcare analytics people, you make sure they are solid team players.

It’s time we start getting more value out of our EHR and health IT systems.  Analytics is one of those tools that will get us there.  Just be sure that your current infrastructure isn’t holding you back from achieving those goals.

If this topic interests you and you’ll be at HIMSS 2017, join us at the Intel Health Booth #2661 on Tuesday, 2/21 from 2:00-2:45 PM where we’ll be holding a special meetup to discuss Getting Ready for Precision Health.  This meetup will also be available virtually via Periscope on the @IntelHealth Twitter account.

UCSF Partners With Intel On Deep Learning Analytics For Health

Posted on January 30, 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.

UC San Francisco’s Center for Digital Health Innovation has agreed to work with Intel to deploy and validate a deep learning analytics platform. The new platform is designed to help clinicians make better treatment decisions, predict patient outcomes and respond quickly in acute situations.

The Center’s existing projects include CareWeb, a team-based collaborative care platform built on Salesforce.com social and mobile communications tech; Tidepool, which is building infrastructure for next-gen smart diabetes management apps; Health eHeart, a clinical trials platform using social media, mobile and realtime sensors to change heart disease treatment; and Trinity, which offers “precision team care” by integrating patient data with evidence and multi-disciplinary data.

These projects seem to be a good fit with Intel’s healthcare efforts, which are aimed at helping providers succeed at distributed care communication across desktop and mobile platforms.

As the two note in their joint press release, creating a deep learning platform for healthcare is extremely challenging, given that the relevant data is complex and stored in multiple incompatible systems. Intel and USCF say the next-generation platform will address these issues, allowing them to integrate not only data collected during clinical care but also inputs from genomic sequencing, monitors, sensors and wearables.

To support all of this activity obviously calls for a lot of computing power. The partners will run deep learning use cases in a distributed fashion based on a CPU-based cluster designed to crunch through very large datasets handily. Intel is rolling out the computing environment on its Xeon processor-based platform, which support data management and the algorithm development lifecycle.

As the deployment moves forward, Intel leaders plan to study how deep learning analytics and machine-driven workflows can optimize clinical care and patient outcomes, and leverage what they learn when they create new platforms for the healthcare industry. Both partners believe that this model will scale for future use case needs, such as larger convolutional neural network models, artificial networks patterned after living organizations and very large multidimensional datasets.

Once implemented, the platform will allow users to conduct advanced analytics on all of this disparate data, using machine learning and deep learning algorithms. And if all performs as expected, clinicians should be able to draw on these advanced capabilities on the fly.

This looks like a productive collaboration. If nothing else, it appears that in this case the technology platform UCSF and Intel are developing may be productized and made available to other providers, which could be very valuable. After all, while individual health systems (such as Geisinger) have the resources to kick off big data analytics projects on their own, it’s possible a standardized platform could make such technology available to smaller players. Let’s see how this goes.

Searching for Disruptive Healthcare Innovation in 2017

Posted on January 17, 2017 I Written By

Colin Hung is the co-founder of the #hcldr (healthcare leadership) tweetchat one of the most popular and active healthcare social media communities on Twitter. Colin is a true believer in #HealthIT, social media and empowered patients. Colin speaks, tweets and blogs regularly about healthcare, technology, marketing and leadership. He currently leads the marketing efforts for @PatientPrompt, a Stericycle product. Colin’s Twitter handle is: @Colin_Hung

Disruptive Innovation has been the brass ring for technology companies ever since Clayton Christensen popularized the term in his seminal book The Innovator’s Dilemma in 1997. According to Christensen, disruptive innovation is:

“A process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors.”

Disruption is more likely to occur, therefore, when you have a well established market with slow-moving large incumbents who are focused on incremental improvements rather than truly innovative offerings. Using this definition, healthcare has been ripe for innovation for a number of years. But where is the AirBNB/Uber/Google of healthcare?

On a recent #hcldr tweetchat we asked what disruptive healthcare technologies might emerge in 2017. By far the most popular response was Artificial Intelligence (AI) and Machine Learning.

Personally, I’m really excited about the potential of AI applied to diagnostics and decision support. There is just no way a single person can stay up to speed on all the latest clinical research while simultaneously remembering every symptom/diagnosis from the past. I believe that one day we will all be using AI assistance to guide our care – as common as we use a GPS today to help navigate unknown roads.

Some #hcldr participants, however, were skeptical of AI.

While I don’t think @IBMWatson is on the same trajectory as Theranos, there is merit to being wary of “over-hype” when it comes to new technologies. When a shining star like Theranos falls, it can set an entire industry back and stifle innovation in an area that may warrant investment. Can you imagine seeking funding for a technology that uses small amounts of blood to detect diseases right now? Too much hype can prematurely kill innovation.

Other potentially disruptive technologies that were raised during the chat included: #telehealth, #wearables, patient generated health data (#PDHD), combining #HealthIT with consumer services and #patientengagement.

The funniest and perhaps most thoughtful tweet came from @YinkaVidal, who warned us that innovations have a window of usefulness. What was once ground-breaking can be rendered junk by the next generation.

What do you believe will be the disruptive healthcare technology to emerge in 2017?

“Learning Health System” Pilot Cuts Care Costs While Improving Quality

Posted on January 11, 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.

As some of you will know, the ONC’s Shared Nationwide Interoperability Roadmap’s goal is to create a “nationwide learning health system.”  In this system, individuals, providers and organizations will freely share health information, but more importantly, will share that information in “closed loops” which allow for continuous learning and care improvement.

When I read about this model – which is backed by the Institute of Medicine — I thought it sounded interesting, but didn’t think it terribly practical. Recently, though, I stumbled upon an experiment which attempts to bring this approach to life. And it’s more than just unusual — it seems to be successful.

What I’m talking about is a pilot study, done by a team from Nationwide Children’s Hospital and The Ohio State University, which involved implementing a “local” learning health system. During the pilot, team members used EHR data to create personalized treatments for patients based on data from others with similar conditions and risk factors.

To date, building a learning health system has been very difficult indeed, largely because integrating EHRs between multiple hospital systems is very difficult. For that reason, researchers with the two organizations decided to implement a “local” learning health system, according to a press statement from Nationwide Children’s.

To build the local learning health system, the team from Nationwide Children’s and Ohio State optimized the EHR to support their efforts. They also relied on a “robust” care coordination system which sat at the core of the EHR. The pilot subjects were a group of 131 children treated through the hospital’s cerebral palsy program.

Children treated in the 12-month program, named “Learn From Every Patient,” experienced a 43% reduction in total inpatient days, a 27% reduction in inpatient admissions, a 30% reduction in emergency department visits and a 29% reduction in urgent care visits.

The two institutions spent $225,000 to implement the pilot during the first year. However, the return on this investment was dramatic.  Researchers concluded that the program cut healthcare costs by $1.36 million. This represented a savings of about $6 for each dollar invested.

An added benefit from the program was that the clinicians working in the CP clinic found that this approach to care simplified documentation, which saved time and made it possible for them to see more patients during each session, the team found.

Not surprisingly, the research team thinks this approach has a lot of potential. “This method has the potential to be an effective complementary or alternative strategy to the top-down approach of learning health systems,” the release said. In other words, maybe bottom-up, incremental efforts are worth a try.

Given these results, it’d be nice to think that we’ll have full interoperability someday, and that we’ll be able to scale up the learning health system approach to the whole US. In the mean time, it’s good to see at least a single health system make some headway with it.

Some Projections For 2017 Hospital IT Spending

Posted on January 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.

A couple of months ago, HIMSS released some statistics from its survey on US hospitals’ plans for IT investment over the next 12 months. The results contain a couple of data points that I found particularly interesting:

  • While I had expected the most common type of planned spending to be focused on population health or related solutions, HIMSS found that pharmacy was the most active category. In fact, 51% of hospitals were planning to invest in one pharmacy technology, largely to improve tracking of medication dispensing in additional patient care environments. Researchers also found that 6% of hospitals were planning to add carousels or packagers in their pharmacies.
  • Eight percent hospitals said that they plan to invest in EMR components, which I hadn’t anticipated (though it makes sense in retrospect). HIMSS reported that 14% of hospitals at Stage 1-4 of its Electronic Medical Record Adoption Model are investing in pharmacy tech for closed loop med administration, and 17% in auto ID tech. Four percent of Stage 6 hospitals plan to support or expand information exchange capabilities. Meanwhile, 60% of Stage 7 hospitals are investing in hardware infrastructure “for the post-EMR world.”

Other data from the HIMSS report included news of new analytics and telecom plans:

  • Researchers say that recent mergers and acquisitions are triggering new investments around telephony. They found that 12% of hospitals with inpatient revenues between $25 million and $125 million – and 6% of hospitals with more than $500 million in inpatient revenues — are investing in VOIP and telemedicine. FWIW, I’m not sure how mergers and acquisitions would trigger telemedicine rollouts, as they’re already well underway at many hospitals — maybe these deals foster new thinking and innovation?
  • As readers know, hospitals are increasingly spending on analytics solutions to improve care and make use of big data. However (and this surprised me) only 8% of hospitals reported plans to buy at least one analytics technology. My guess is that this number is small because a) hospitals may not have collected their big data assets in easily-analyzed form yet and b) that they’re still hoping to make better use of their legacy analytics tools.

Looking at these stats as a whole, I get the sense that the hospitals surveyed are expecting to play catch-up and shore up their infrastructure next year, rather than sink big dollars into future-looking solutions.

Without a doubt, hospital leaders are likely to invest in game-changing technologies soon such as cutting-edge patient engagement and population health platforms to prepare for the shift to value-based health. It’s inevitable.

But in the meantime it probably makes sense for them to focus on internal cost drivers like pharmacy departments, whose average annual inpatient drug spending shot up by more than 23% between 2013 and 2015. Without stanching that kind of bleeding, hospitals are unlikely to get as much value as they’d like from big-idea investments in the future.

A Look At Geisinger’s Big Data Efforts

Posted on December 28, 2016 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.

This week I got a look at a story appearing in a recent issue of Harvard Business Review which offers a description of Geisinger Health System’s recent big data initiatives. The ambitious project is designed not only to track and analyze patient outcomes, but also to visualize healthcare data across cohorts of patients and networks of providers and even correlate genomic sequences with clinical care. Particularly given that Geisinger has stayed on the cutting edge of HIT for many years, I think it’s worth a look.

As the article’s authors note, Geisinger rolled out a full-featured EMR in 1996, well ahead of most of its peers. Like many other health systems, Geisinger has struggled to aggregate and make use of data. That’s particularly the case because as with other systems, Geisinger’s legacy analytics systems still in place can’t accommodate the growing flood of new data types emerging today.

Last year, Geisinger decided to create a new infrastructure which could bring this data together. It implemented Unified Data Architecture allowing it to integrate big data into its existing data analytics and management.  According to the article, Geisinger’s UDA rollout is the largest practical application of point-of-care big data in the industry. Of particular note, Geisinger is crunching not only enterprise healthcare data (including HIE inputs, clinical departmental systems and patient satisfaction surveys) and consumer health tools (like smartphone apps) but even grocery store and loyalty program info.

Though all of its data hasn’t yet been moved to the UDA, Geisinger has already seen some big data successes, including:

* “Close the Loop” program:  Using natural language processing, the UDA analyzes clinical and diagnostic imaging reports, including free text. Sometimes it detects problems that may not be relevant to the initial issue (such as injuries from a car crash) which can themselves cause serious harm. The program has already saved patient lives.

* Early sepsis detection/treatment: Geisinger uses the UDA to bring all sepsis-patient information in one place as they travel through the hospital. The system alerts providers to real-time physiologic data in patients with life-threatening septic shock, as well as tracking when antibiotics are prescribed and administered. Ninety percent of providers who use this tool consistently adhere to sepsis treatment protocols, as opposed to 40% of those who don’t.

* Surgery costs/outcomes: The Geisinger UDA tracks and integrates surgical supply-chain data, plus clinical data by surgery type and provider, which offers a comprehensive view of performance by provider and surgery type.  In addition to offering performance insight, this approach has also helped generate insights about supply use patterns which allow the health system to negotiate better vendor deals.

To me, one of the most interesting things about this story is that while Geisinger is at a relatively early stage of its big data efforts, it has already managed to generate meaningful benefits from its efforts. My guess is that its early successes are more due to smart planning – which includes worthwhile goals from day one of the rollout — than the technology per se. Regardless, let’s hope other hospital big data projects fare so well. (Meanwhile, for a look at another interesting hospital big data project, check out this story.)

Paris Hospitals Use Big Data To Predict Admissions

Posted on December 19, 2016 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.

Here’s a fascinating story in from Paris (or par-ee, if you’re a Francophile), courtesy of Forbes. The article details how a group of top hospitals there are running a trial of big data and machine learning tech designed to predict admission rates. The hospitals’ predictive model, which is being tested at four of the hospitals which make up the Assistance Publiq-Hopitaux de Paris (AP-HP), is designed to predict admission rates as much as 15 days in advance.

The four hospitals participating in the project have pulled together a massive trove of data from both internal and external sources, including 10 years’ worth of hospital admission records. The goal is to forecast admissions by the day and even by the hour for the four facilities participating in the test.

According to Forbes contributor Bernard Marr, the project involves using time series analysis techniques which can detect patterns in the data useful for predicting admission rates at different times.  The hospitals are also using machine learning to determine which algorithms are likely to make good predictions from old hospital data.

The system the hospitals are using is built on the open source Trusted Analytics Platform. According to Marr, the partners felt that the platform offered a particularly strong capacity for ingesting and crunching large amounts of data. They also built on TAP because it was geared towards open, collaborative development environments.

The pilot system is accessible via a browser-based interface, designed to be simple enough that data science novices like doctors, nurses and hospital administration staff could use the tool to forecast visit and admission rates. Armed with this knowledge, hospital leaders can then pull in extra staffers when increased levels of traffic are expected.

Being able to work in a distributed environment will be key if AP-HP decides to roll the pilot out to all of its 44 hospitals, so developers built with that in mind. To be prepared for the future, which might call for adding a great deal of storage and processing power, they designed distributed, cloud-based system.

“There are many analytical solutions for these type of problems, [but] none of them have been implemented in a distributed fashion,” said Kyle Ambert, an Intel data scientist and TAP contributor who spoke with Marr. “Because we’re interested in scalability, we wanted to make sure we could implement these well-understood algorithms in such a way that they work over distributed systems.”

To make this happen, however, Ambert and the development team have had to build their own tools, an effort which resulted in the first contribution to an open-source framework of code designed to carry out analysis over scalable, distributed framework, one which is already being deployed in other healthcare environments, Marr reports.

My feeling is that there’s no reason American hospitals can’t experiment with this approach. In fact, maybe they already are. Readers, are you aware of any US facilities which are doing something similar? (Or are most still focused on “skinny” data?)

Easing The Transition To Big Data

Posted on December 16, 2016 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.

Tapping the capabilities of big data has become increasingly important for healthcare organizations in recent years. But as HIT expert Adheet Gogate notes, the transition is not an easy one, forcing these organizations to migrate from legacy data management systems to new systems designed specifically for use with new types of data.

Gogate, who serves as vice president of consulting at Citius Tech, rightly points out that even when hospitals and health systems spend big bucks on new technology, they may not see any concrete benefits. But if they move through the big data rollout process correctly, their efforts are more likely to bear fruit, he suggests. And he offers four steps organizations can take to ease this transition. They include:

  • Have the right mindset:  Historically, many healthcare leaders came up through the business in environments where retrieving patient data was difficult and prone to delays, so their expectations may be low. But if they hope to lead successful big data efforts, they need to embrace the new data-rich environment, understand big data’s potential and ask insightful questions. This will help to create a data-oriented culture in their organization, Gogate writes.
  • Learn from other industries: Bear in mind that other industries have already grappled with big data models, and that many have seen significant successes already. Healthcare leaders should learn from these industries, which include civil aviation, retail and logistics, and consider adopting their approaches. In some cases, they might want to consider bringing an executive from one of these industries on board at a leadership level, Gogate suggests.
  • Employ the skills of data scientists: To tame the floods of data coming into their organization, healthcare leaders should actively recruit data scientists, whose job it is to translate the requirements of the methods, approaches and processes for developing analytics which will answer their business questions.  Once they hire such scientists, leaders should be sure that they have the active support of frontline staffers and operations leaders to make sure the analyses they provide are useful to the team, Gogate recommends.
  • Think like a startup: It helps when leaders adopt an entrepreneurial mindset toward big data rollouts. These efforts should be led by senior leaders comfortable with this space, who let key players act as their own enterprise first and invest in building critical mass in data science. Then, assign a group of core team members and frontline managers to areas where analytics capabilities are most needed. Rotate these teams across the organization to wherever business problems reside, and let them generate valuable improvement insights. Over time, these insights will help the whole organization improve its big data capabilities, Gogash says.

Of course, taking an agile, entrepreneurial approach to big data will only work if it has widespread support, from the C-suite on down. Also, healthcare organizations will face some concrete barriers in building out big data capabilities, such as recruiting the right data scientists and identifying and paying for the right next-gen technology. Other issues include falling reimbursements and the need to personalize care, according to healthcare CIO David Chou.

But assuming these other challenges are met, embracing big data with a willing-to-learn attitude is more likely to work than treating it as just another development project. And the more you learn, the more successful you’ll be in the future.