I started by observing that healthcare IT finds ourselves in a rather remarkable place as we look to Health IT Week as a focus for reflection. The adoption of EHR’s is widespread and these applications are facilitating a rapidly-growing trove of data with virtually unimaginable potential. As an industry, we’re poised to take the lessons-learn from other industries that have gone before us and realize the full potential of our data much more rapidly and without the failures we would otherwise experience. The promise of big data looms large in terms of the potential to drive a shift from the treatment of disease to the promotion and management of health. And lastly, the beginnings of a shift to paying for value rather than activity are starting to align financial motivations with clinical quality to drive unprecedented quality, efficiency, safety and value from what can – and should be—the best healthcare available anywhere in the world.
So after four days I think it’s fair to put in a plug for Informatica and the role we have to play in helping transform healthcare. When I speak on our vision for data driven healthcare, I highlight three core capabilities that are required for healthcare organizations to realize the full potential of their data:
- Data integration must be a core competency. Organizations must be able to rapidly connect to new sources of data; profile the quality of the data and apply data quality rules to eliminate incorrect and erroneous data from being used to make decisions; map and transform this high-quality data into new formats; and load the cleansed data into a wide variety of target systems that may include an enterprise data warehouse. All of this work must be accomplished in an environment that provides end-to-end transparency from source to target of everything that has been done to the data along the way, since it is only with this transparency that business and clinical end users can build the faith in the data required to move from questioning it’s accuracy to using data as the basis for rapid and effective decision-making.
- All healthcare participants and relationships must be clearly understood and documented. Delivering efficient, high-value care requires that we know with confidence who all the participants in the healthcare process are, and to measure performance and be proactive also requires that we know the relationships between the participants. For instance, knowing that John Smith and Jonnie Smyth are the same patient is key achieving the 360 degree view of the patient required for making the right healthcare decisions for John and managing his health. Similarly, knowing that Geoff Johnsen MD and Jeff Johnson MD are in-fact the same provider is equally important if we are going to capture his practice patterns and profile the care he provides. But equally, if not more important, to becoming data driven is knowing with confidence that Dr. Johnsen is Mr. Symth’s primary care physician. With this fact, we can now quickly attribute patients to providers for the purposes of quality and outcome reporting; deliver proactive alerts to Dr. Johnsen whenever one of his patients goes to the emergency room, etc. And this concept of trustworthy data about participants and relationships cannot be limited simply to patients and providers – it must include employees, members, locations, implantable medical devices or legal entities –basically anything that we want to develop a 360 degree view around must be managed as trustworthy master data so that when the data is needed, it can just be consumed without fear of errors.
- Organizations must be able to take action on what they know. Healthcare delivery is fragmented across organizations, facilities, hospitals, doctors, specialists and departments, to name just a few. Our information systems are similarly fragmented having grown up supporting the silos of care delivery, and these facts make it very difficult for these same information systems to deliver the decision support required to promote coordinated care that spans care settings. To be successful, healthcare organizations need a solution that can delivery alerts in near-real-time (during the moment of interaction with the patient) that provide health maintenance reminders; deliver key information such as a warning that a patient should not be discharged with a particular set of unresulted studies; or push discharge history and care plans to the point of care as a means of reducing avoidable readmissions. This sort of “decision support in the white space” is not a replacement for more traditional EHR-based features, but rather a complementary capability to bridge the gap between systems that exist both between, and within, organizations.
With this perspective, I will conclude my series of thoughts on National Healthcare IT Week with the final observation that all-in-all I think as healthcare IT professionals we’ve has a pretty good week.
And I really can’t wait for what the next few years have in store for us!
Here it is day four of National Health IT Week and there have been lots of interesting things to talk about and be excited for the prospects of Health IT up to this point: EHRs capturing lots of great data; analytics and predictive modeling to discover insights we’ve not been able to have previously; and where the hype of big data meets reality. But having access to data and being able to do cool things with it to deliver higher value care is only half the story – the other half is how our reimbursement system is also changing to align financial incentives with quality and value.
We’re seeing the early beginnings of value-based reimbursement changing behavior, with one of the more intriguing being the Center for Medicare and Medicaid Services (CMS) Hospital Readmission Reduction Program (HRRP). Under this program, CMS calculates a risk-adjusted ‘expected readmission rate’ for each hospital in the country for congestive heart failure (CHF), acute myocardial infarction (AMI) and pneumonia. If a hospital’s actual readmission rate exceeds the expected rate, their Medicare reimbursements are penalized for the following year by up to 1% in 2013, 2% in 2014, and 3% in 2015. Given that hospitals typically receive 50% or more of their revenue from CMS, these penalties rapidly add up to real money and are prompting meaningful action on the part of hospitals to understand the causes of readmissions and address them proactively. But what’s intriguing is to get beyond thinking of the HRRP as a penalty-imposing program, and instead think of it as an outcomes-based reimbursement program.
What CMS has really done is effectively say the payment for a CHF admission, for example, is not just for the individual admission, but rather a payment to keep the patient out of the hospital for the next 30 days. And not just keep them out of the hospital for CHF, but for any reason since ‘readmission’ is all-inclusive of most any reason for the readmission and not limited to just the original reason for discharge. This requires some radical rethinking of how hospitals and health systems measure quality and value, since it brings to bear things they have traditionally had little control over such as patient compliance with discharge instructions and adherence to best-practices by physicians in the community. Even though it may not be directly under the hospital’s control, hospitals for the first time have a meaningful financial incentive to encourage patients and others to ensure appropriate follow-up care and activities are accomplished.
Tomorrow we will be a quick wrap-up and a pat on the back for all the tireless effort of an entire industry in the midst of unprecedented change.
So it’s ‘hump day’ of National Health IT Week and we’ve already talked about how EHR’s are capturing a treasure-trove of rich data, and the burgeoning enthusiasm for analytics and predictive modeling that is nature consequence of having this data.
But what about the whole “big data” thing? I’ve been on the fence as less than enthusiastic about all the hand waving and bell ringing surrounding the big data movement in healthcare, simply based upon our inability as an industry to really do anything particularly useful with the “little data” we already have. We need look no further than all of the angst and heartfelt bickering over the very modest data requirements of Meaningful Use Stage I requirements to validate this assessment of our industry readiness for “big data”. This isn’t saying that there have not been pockets of incredibly talented individuals, applying extraordinary effort, to do cool things with data, and yielding some glimmers of hope for “big data”. But as an industry, we have not really done much with the data we already have to understand what works, and doesn’t work, and use that insight to change our behavior. And when it comes to data, I don’t think you can run before you can walk, and in my mind big data is Olympic-caliber running. It’s also important to consider my opinion only in the context of strongly structured discrete data like lab results and coded clinical data entered into EHRs. This distinction is relevant since when it comes to things like digital imaging, PACS, and advanced visualization algorithms, the broader healthcare market has arguably been dealing with “big data” for more than a decade.
But I’m beginning to change my mind on big data in healthcare. And rather than walking before you run, big data may present on opportunity to leap ahead in deriving value from data in very focused areas, even before full competence in ‘small data’ analytics has been achieved. The reason for my change in thinking is really related to (a) an evolving understanding of how big data technologies can be applied to existing data problems, and (b) compelling new sources of data, and potential solutions, that have never before been possible.
Big data isn’t just about doing analysis of twitter feeds and facebook posts (which encompass all three V’s of big data – volume, variety and velocity) it can also be about having the cost-effective processing horsepower to do much more sophisticated analytics on the clinical or billing data we already have. Or the data that we’re going to have from our EHRs. Rather than testing clinical or financial models against a month’s worth of data, or a quarter’s worth of data, now we can test those same models against a year, or five, or a decade’s worth of data. This same processing horsepower means analysis that might have taken days or weeks can now be done in minutes or hours, which makes the results that much more valuable in impacting clinical care and changing frontline staff behaviors. And to the extent these big data technologies can be adopted and applied to today’s data analytics needs by mere mortals, then all the better. For example, Hadoop has been a centerpiece in the whole big data hype circus, and historically has been a complex beast that can only be mastered by the most advanced and sophisticated IT shops. And who in their right mind with all the other challenges facing healthcare IT (ICD-10 conversion, EHR implementation, HIPPA privacy audits, flat budgets, health information exchanges, etc.) wants to try something like that? But what we’re seeing is a maturing of the Hadoop technology stack and a vendor ecosystem developing around the platform with solutions that make it much more practical that healthcare organizations will be able to try out some of these big data solutions. For example, Informatica has recently announced support for Hadoop such that any transformations or data quality rules created in Informatica can be run on Hadoop unchanged – taking advantage of the lower cost and higher performance of the Hadoop platform while avoiding much of the complexity and specialized skills that have traditionally been a huge barrier to adoption. One potential area this sort of approach could be applied is crowdsourcing medical decisions – a topic I have previously written about that I think has very real implications for the providers aspiring to become “learning healthcare organizations” which you can read here.
There was also a good report from InformationWeek titled 2013 Healthcare IT Priorities that observed a growing proliferation of data coming from mobile devices and personal medical monitors, which in my mind will inevitably hit all three V’s (volume, velocity and variety) that traditionally define big data. They further state that we are rapidly heading towards a future where acquiring data ceases to be the problem, but figuring out what to do with it becomes the real challenge.
In this same vein, I also believe that some of some of the more consumer-oriented “big data things” have potential promise in healthcare. The creation of an individual insurance market is going to drive a tectonic shift in the perspective of payors as they reorient their sales and marketing from selling coverage in large chunks to employers, and instead need to understand and target the far more finicky individual consumer with a very different perspective on value and customer service than their traditional employer buyer. In this situation, all those social media feeds and the sentiment analysis they can reveal become very compelling with a demonstrable ROI, as does old-fashioned analytics on big data such as web click-stream analytics to optimize the consumer experience on their websites.
There is also clear potential in combining consumer data with clinical data to provide a more complete 360 degree view of the patient for providers – bringing key information such as what over-the-counter drugs a patient may have bought over the last 30-60-90 days that may have a marked adverse reaction with a prescription they are taking, or just be clinically undesirable (such as someone with hypertension taking an OTC antihistamine for example). Providing this insight to their provider at the time of the patient’s next visit – or better still, apply rules in near-real-time as soon as the data becomes available and alerting the patient’s care team even when no visit is scheduled – can really change the role of physicians in orchestrating a patient’s health and wellness rather than simply treating symptoms and disease during an office visit or hospital admission.
Tomorrow we will move on to what is motivating healthcare providers to finally take a genuine interest in analytics and business intelligence, with that thing being align financial incentives.
Yesterday I wrote about the proliferation of rich clinical data being captured by electronic health records and how unlocking the potential in this data will be the fuel of healthcare innovation. We’re seeing a true blossoming of the market for analytics and predictive modeling in healthcare, and no amount of delay or potentially backtracking on The Accountable Care Act is going to un-ring this bell. This focus on analytics is a natural consequence of simply having the data available to gain insight into business and clinical processes and outcomes, much as analytics was a natural follow-on in the years after the implementation of SAP and other new ERP applications motivated by the Y2K programming flaw. And the groundswell is only just beginning as organizations stick their collective toes in the water of analytics and find that sharks, and alligators, and other predators are not nearly so common as they thought. I spend a significant amount of my time meeting with senior leaders in large healthcare organizations looking to get started in a big way with business intelligence, or analytics, or enterprise data management. By any name, there’s a common perception dating back a decade or more that an enterprise data warehouse was something that cost millions of dollars, took years to implement, ultimately failed to meet business expectations, and often resulted in the CIO having the opportunity to apply their skills in a new organization. The good news is that the patterns and behaviors that can lead to failure in an enterprise data warehousing initiative are pretty well understood having been learned through hard knocks in industries that have figured out how to do enterprise data warehouses – retail, manufacturing, and financial services to name a few.
Equally inspiring that healthcare will be successful with enterprise data warehousing efforts is that our historical reluctance as an industry to looking outside healthcare to other industries for best practices seems to be taking a backseat to the practical reality that there’s a lot to be learned from folks who have already been successful in data warehousing – even if it’s not specifically in healthcare. This only makes sense, since the architecture and discipline of what it takes to make data useful at enterprise scale is a real talent, and an art form, and folks who have done it successfully are a rare breed. Would it be nice if they had specific healthcare expertise? Sure. But the reality is that a modern, successful enterprise information management strategy hinges on the ability of the business, clinical experts, and IT to collaborate in a highly dynamic, iterative fashion to really derive value from data. So with this team approach, the fact that the IT folks may be just data and process experts is very much mitigated by the other team members who are business and clinical experts.
Tomorrow we will jump on the hype wagon of “Big Data” and see what that has to offer the healthcare industry.
Another national Health IT Week is upon us, and with no immediately apparent need to send flowers, chocolates or baked goods to some worthy recipient, I am left to simply reflect on the rather remarkable state we find ourselves in as an industry and as healthcare IT professionals. Starting today and for the next four days I’ll take a moment to opine on the state of the industry and why I think there’s never been a more promising time for health IT to transform our healthcare system, or a more exciting time to be a health IT professional.
Despite lots of grousing and some inevitable bumps along the way, HITECH is largely a success in serving as a catalyst for the widespread adoption of electronic health records among providers. Health and Human Services recently announced that more than 50% of physicians and 80% of hospitals will be using an EHR by the end of 2013 — up from just 17% and 9%, respectively, in 2008. So after decades of being relegated to making due with claims data as the principle fodder for analytics, we are beginning to experience the dawning of a new age of healthcare analytics and predictive modeling with rich clinically relevant data as the raw material to feed our analytics appetite.
Meaningful Use Stage I has been a masterful first increment in beginning the culture shift necessary for healthcare providers to begin to appreciate both the value of reliable, trustworthy data, as well as understand that useful, high-quality data is the result of an end-to-end process that begins with data entry in the application and ends with useful analytics, reports, and other data products. This is not to say that technical missteps along the way don’t have a role in adversely impacting data quality, but by far the most pervasive challenges are in how the data is captured at the point of care or data entry. This shift in awareness is a critical step in moving beyond simple compliance with Meaningful Use data requirements, and instead looking at data as an enterprise asset where the business and clinical sides of the business are held accountable for the quality of the asset, rather than IT.
Tomorrow we’ll look at how analytics and predictive modeling is poised to unlock the potential of all this data to transform healthcare.
All the talk about whether or not healthcare organizations will adopt cloud solutions is much ado about nothing – the simple fact is that they already have adopted cloud solutions and the trend will only accelerate.
The typical hospital IT department is buried under the burden of supporting hundreds of legacy and departmental systems, the multi-year implementation of at least one if not more enterprise electronic health record applications to meet the requirements of meaningful use, all the while contending with a conversion to ICD10 and a litany of other never-ending regulatory and compliance mandates. And this is happening in an economic climate of decreasing reimbursements and flat or declining IT budgets. (more…)
I had the privilege to be invited to testify to the Health I.T. Policy Committee workgroup on the topic of data quality back in November. I’ve been an advocate for the work of the committee for years and am constantly impressed with the considerable insight and genuine passion they bring to their work. The opportunity to testify, however, was my first opportunity to actually participate in the policy-making process and it certainly was both a learning opportunity for me, as well as a chance to share my thoughts on the important topic of data quality. (more…)
The widespread adoption of electronic health records (EHRs) is a key objective of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009. With the pervasive use of EHRs, an enormous volume of clinical data will be readily accessible that has previously been locked away in paper charts. The potential value of this data to yield insights into what works in healthcare, and what doesn’t work, dwarfs the benefits of simply replacing a paper chart with an electronic system. There’s appropriate enthusiasm that this data is going to be a veritable goldmine for enterprise data warehousing, business intelligence, and comparative effectiveness research. However, there are other, equally valuable, uses for this data to enhance clinical decision-making and improve the value of healthcare spending. Simply having instant access to large volumes of data that span thousands or tens-of-thousands of physicians, hundreds-of-thousands of patients and millions of encounters, offers an unparalleled opportunity to increase the quality and lower the cost of healthcare. (more…)
I’ve been advocating for years that replacing the paper chart with an electronic system is not the value of the EHR, but rather collecting data that can be used to understand and improve care. So I was very pleased to see Dr. John Showalter’s blog address this very issue – making a compelling case with real-world examples where wisdom derived from data has made demonstrable improvements in healthcare quality and corresponding reductions in cost. (more…)
As a routine matter of delivering care, billing for services and operating their hospitals and physician practices, healthcare providers deal with patient’s protected health information all day, every day. Dealing with the data becomes routine and it’s easy for sometimes onerous security and privacy policies and procedures to be overlooked. While we’d all like that not to be the case, delivering healthcare (and getting paid for it) is a hugely complex undertaking and focusing exclusively on human processes and calling for constant vigilance and attention to detail can only go so far. (more…)