The Wrap: Healthcare’s Look Back at 2014

Looking back on 2014As we look in the rear view mirror back on 2014, the year that was supposed to change our coding and billing systems, we see that ICD-10 dominated the news with it’s postponement in April and CMS’ subsequent new date of October 1st, 2015. Over the last few weeks though, I’ve heard rumors of yet another delay, this time due to the new political climate in Washington, D.C. These rumors are unsubstantiated, so I would keep them as such. I would record it as a risk to the program for those seeking to restart their ICD-10 initiatives.

Healthcare provider and payer organizations were in “shock and awe” (shocked and many people saying “awww”) at the same time, and high number of provider organizations deciding to postpone their programs indefinitely until the new date was announced and many just restarting the programs either late in the year or planning on doing so in the beginning of the new year, 2015.

The mission of provider IT organizations changed. Just as it was in the pre and post-Y2k days, organizations now wanted to get actual intelligence or analytics from the large systems that they had implemented at such great cost. We came full circle and Analytics started coming to the forefront during the year and it matured after all of the interest, talk and presentations  of Big Data, Business Intelligence and Analytics over the last few years. Leveraging actual data for case studies that I know of this year on Population Health Management and better response times in the ED.

In a recent article in Clinical Innovation and Technology, it was reported that ” as of November 2014, 11,478 eligible professionals and 840 hospitals have attested to Meaningful Use Stage 2. In total, 15,481 new EPs and 221 new hospitals have attested in 2014″. The healthcare provider ecosystem was able to move forward despite some inertia at the beginning of the year.

Many organizations also realized that their infrastructure needed to be updated with projects such as XP to Windows 7 migration; something that needed to occur due to the end support in April by Microsoft of their well known Microsoft XP Operating System.

Consumer health devices starting to get mainstream traction with products like Fitbit and Google Glass starting to look at possible mHealth applications for providers in the future.

The vision and ‘utopia’ of an Interoperable Healthcare ecosystem received a major boost with The Office of the National Coordinator for Health Information Technology releasing it’s “10-Year Vision to Achieve An Interoperable Health IT Infrastructure” by 2024. This would be a baseline for future infrastructure development across the United States and possibly even a starting point for world leadership in healthcare systems and infrastructure interoperability.

What a year it has been and we have so much yet to come in 2015 and beyond.

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On the Doorstep of a Healthcare Analytics Utopia

What is Data Good for?In understanding the vast amounts of data that healthcare organizations have at their fingertips, is to understand the meaning of life! Vast amounts of unused and unfiltered data that is being stored and has been stored since the advent of information technology in healthcare is phenomenal.  Using this data is where the opportunity lies for the future.

Operations and IS&T departments will be asking themselves questions such as “What kind of quality of care is the organization delivering and what is the cost of that quality? What kind of engagement is the patient experiencing and is does that call for a different kind of skill sets that we need to bring in (e.g. a Chief Experience Officer)? What kind of safety protocols do we have and have any of them been compromised in our delivery of care to a patient? How many physicians do we need to enroll in our tap badge enrollment so that we can comply with board of pharmacy regulations?”

These days, we see many organizations outside of healthcare leverage such things as predictive analytics. That and ‘Big Data” seem to be the buzz words of the day for healthcare. To me, making sure that we build the appropriately followed process that is not difficult to follow is the key to any clean and efficient system. Nobody goes into any organization thinking “How complicated can I make this process?”

Healthcare’s “Paradigm Shift” continues to play a large part in why we are now able to analyze and predict patterns than we were in the past. While leveraging the vast amounts of time, money and human resources experience spent on implementation of EMRs, that investment is now expected to pay dividends in understanding the ways that patient populations need to be treated.

During our upcoming Central & Southern Ohio HIMSS event on October 24th, 2014 at The Drake Center in Cincinnati (csohio.himsschapter.org), we will focus on analytics as the core of why all of the implementation and upgrade activities that have taken place over the last few years due to MU1 & 2.

Ultimately, we want to be able to predict, utilizing our billions of dollars of investments, what will be the outcomes of certain procedures and understand what course of action a provider wants to take based on those predictions. These are some of the applications that IBM’s “Watson” was going to focus on and my understanding is that they have been partnering with certain healthcare payer organizations for just this type of thing. Time will tell us whether all of the data that has been accumulated for these predictions pay off. Clean data is required as the predictions are only as good as the underlying data that powers it. The continued stride towards following process and procedure, collaboration and interoperability will be the key in making this analytics utopia a reality.

 

 

Analytics and Operational Intelligence Projects in Healthcare! Oxymoron or possibility?

For many industries, the terms Business Intelligence”  has been around for more than a decade. “BI” has been much sought after over the last decade and it is finally healthcare’s turn to take advantage of analytics or intelligence initiatives to better the care of the population.

Being able to provide a predictive view of your operations, analytical tools have the ability to offer unlimited possibilities for healthcare organizations as a well-structured analytics application can give the organization a great return on investment for the organization and for the population the organization serves.

Analytics projects cut across the organization and managing an analytics project is much more involved than managing a traditional IT project as different techniques may be involved for a successful outcome. Depending on the organization, different operational users will be responsible for prioritizing and gathering their requirements for the project. They will need to document them as formal requirements.

The earlier phase will give us the ability to be able to understand the gap existing in the operations and the knowledge required to properly develop a roadmap for the project and understand the duration, cost and other project resources that would be required to successfully complete the objectives set out in a Project Charter and being able to understand your analytics and operational intelligence requirements is foremost. Being able to translate operational needs into technical requirements documents is necessary. This will help the technical resources into understand what their deliverables need to be and keeps them in sync with their operational counterparts.

In addition, I’ve seen that many recommendations is to not just talk to “Operational Power Users”. Every operational and clinical group has it’s Champion or go to individual when someone needs data for analysis. Those are your Power Users and they typically know where the data is and how to get analyze it and get at it. These people are the “C” in the RACI chart you put together, but make sure that you don’t assume that they represent the typical usage of the rest of the group, but only themselves.

Once the objectives are defined, the project architect will identify and select (if not already known) the right tools and technology for the initiative. They will create the data models and also map the workflow required from the source to the target system and work more often than not, with the ETL individuals from the database team and be able to define the operational and data requirements. As the team moves on to the development phase of the initiative and create and develop operational analytics content such as models, reports and queries.

Testing of the analytics content and system load is critical before any go live and operational user testers will be required to provide feedback on overall on any defects or functionalities, features, and data accuracy. Technical team testing will also be required to ensure the integration and performance of entire system is defect/bug free.

Being able to have a great deployment of your analytics solution is now is where the rubber hits the road. Making sure that your resources, infrastructure or otherwise, set up and configuration and completed dry runs ahead of time will require a great deal of collaboration and all of the resources working in concert to ensure success.

ICD-10s future and Population Health

US Congress-NightWidely publicized and taking the industry quite by surprise was how someone could slip in the delay of ICD-10 regulatory go live to 2015 through voting on repealing the Sustainable Growth Rate reduction of 24% (aka Doc Fix”).

Now that both the Senate and the House have voted to delay ICD-10 (with no mention of it during the 5 hour debate), the question of gaining better data by capturing, storing and analyzing medical information electronically that could have facilitated better quality of care, improved population-based knowledge and the development of new tools for medicine is up in the air. Project sponsors and stakeholders seem divided about next steps, though being able to complete their planned work effort, now with more flexibility, seems wise as it will be one less “priority #1” that they have to deal with in the future and the costs of transitioning would be less hectic and possibly lower cost if done now.

A few examples are patient medical records, radiological images, clinical trial data, FDA submissions, human genetics and population data. ICD-10 – CM will be instrumental in supporting the healthcare data that is growing exponentially from digitizing existing data and generating new forms of data. HHS and CMS e-health initiatives are key drivers providing pathways for the appropriate people to get access to the data – the problem lists, medication lists, lab orders that were performed and the computer assisted codes (CAC) generated out of all the narrative generated output.

ICD‐10‐based segmentation opportunities assess the health needs of each segment of the patient population with increased accuracy, enhancing each member’s experience by providing additional touch points and addressing gaps in care. ICD‐10 allows advanced engagement, compliance and care management efforts to result in healthier members. Informed allocation of resources for clinical intervention enables significant cost reduction.

ICD-10 will also feed scientific healthcare data for research and population health management. Over time, ICD-10 data would provide more information on disease progression and treatment efficacy. From a high-level perspective, ICD-10 will generate more detailed healthcare data and a greater flow of specific and viable data that improve medical communication, which could contribute to advanced disease protocols and clinical pathways. Predictive modeling is now gaining ground more than ever and while the retail industry has been able to understand their customer’s buying patterns and behavior, so too will healthcare organizations with their patients and payers with their members. ICD-10 data has the potential to yield more information about the quality of care and, as a result, this improved data will support better a understanding of complications, better design of clinically robust algorithms and better accuracy of being able to track of patient outcomes as the codes better describe the gravity of a patient’s illness.

A healthcare leader mentioned reminded me today about a conversation that we had just before the end of last year which was that the delay in ICD-10 was one of the reasons why many healthcare IT executives wait until the last minute to get things accomplished as many times, it does not pay to be a leader in completing your initiatives, and, due to the instability of the political and regulatory climate, being an early adopter of either technology or regulatory mandates doesn’t always pan out. ICD-10 is more reflective of the scientific advances that have occurred in medicine in the last 30 years. ICD-10 has been out since 1994 and we need to adopt it sooner rather than later. Hundreds of millions of dollars have been spent around the country to get ready by October 1st, 2014, which has not seemed to have been understood by the political community in Washington, D.C. While I don’t offer any political viewpoints or advice, I would, on this topic, ask if any of the Congressional leaders understand the immensity of their vote and the sunk costs by healthcare organizations around the country to be ready by October 1st, 2014.  Healthcare advocacy has now taken on a different dimension. Ask your elected officials to Congress why they voted for this and I would like to see what they say. It would be good to find out who added the ICD-10 delay into the final document.

Healthcare’s sign of the times – Big Data, Analytics and Patient Profiling.

To profile or not to profile.Analytics and Big Data are in everything now. They are used for online couponing to analyze your buying patterns, in your (sic) email  and what your likes and dislikes are, in your browser with pop ups and in your social media. It has been in healthcare by the industry leaders, but was going to get more penetration as soon as the industry realized that they would have to get to know their patients through Patient Engagement initiatives as part of Meaningful Use Stage 2 where it is mandated that 5% of patients view, download and transmit their own health data, healthcare provider organizations who are concerned about that percentage of their patient population, can leverage analytics to help drive that engagement. Now that predictive modeling is the hot button topic of our healthcare IT times, I have given a lot of thought towards patient profiling and how that will progress over the years through better ways to collect, transform and present patient engagement data.

Building an enterprise data warehouse within a healthcare delivery organization brings together the many disparate systems that hold data become integrated into a single source of truth for operations, clinicians and the consumers of the data or analytics. The ability and focus now by many in the healthcare ecosystem that the way to progress is through the process of integration of disparate data, much also from legacy systems where the data was never was never clean and easy, but organizations now think that having this data will give them an edge in a newer, more cost conscious care delivery ecosystem

How much of a risk are you really and how effectively can a care dlivery organiation manage their costs and quality of care when dealing with a patient that may have the likelihood of hospitalization and possibly be re-admitted in the near future and a risk to the organization, especially for an ACO?

Physicians have the opportunity to prevent these patient readmissions utilizing profiling techniques that currently, may be exactly what large CPG or retail organizations already do well. Making sure that the ED (Emergency Department) as one of the most expensive locations for care that an organization has the ability to be increasingly efficient without losing the high quality of care that it requires to be for the community. Being able to keep patients away from using the ED’s facilities and be able to have regular ambulatory visits by identifying their conditions or health characterists early on and leverage newer technologies such as tele-health (Ohio HB 123 was recently passed here in Ohio covering just that topic and effective 5.20.2014) can lower the costs for an ED and make the delivery of care more efficient and target care for specific, previously identified patients more pro-actively.

Profiling can allow a physician to help lower the cost of medications that a patient is prescribed by reviewing and substituting equivalent, lower costs medications for the patient based on the information at hand. Medications account for one of the highest areas of healthcare costs today.

I sum up today’s blog post by reminding everyone that whether you work for or are a healthcare provider, a vendor, a professional services firm or a consumer of healthcare services, you have your work cut out for you. My encouragement goes out to everyone as I know and have seen how busy your day to day lives are and I have also seen how EMR teams, reporting and analytics teams and functional managers are tasked with many times doing the seemingly improbable tasks of getting all of the work effort completed in the short time frames that they have and somehow, it all comes together. For those of you in a state (clue, NC) the South East coast, United States who I know have gone Big Bang at all locations with everything live this past Friday morning with your EMR, my hats off to you, for you are one of those teams that have worked countless hours to make the seemingly impossible, seem doable. Collaboration and good team dynamics is the key. Don’t forget that!

Oh, and if you are viewing this from an XP machine after April 8th, well, you better unplug your computer from the internet because Microsoft has stopped supporting XP. Talk to your IS&T team if you are in an organization and think about options for a different platform.

A Data Scientist walked up to me at a party and…

Ok, well,it wasn’t exactly a party, but I did meet a Data Scientist, a new profession that didn’t exist even a couple of years ago, who worked for one of my favorite companies; Linkedin. I asked him what he did and he said he basically “made complicated data seem simple to understand”. The concept of Big Data has been used a lot in healthcare to make sense of the huge volumes of information that comes in and out of EMR and other clinical systems these days.

A data scientist represents a natural paradigm shift in thinking from the traditional role of a data or business analyst in that both their backgrounds are similar and need a good foundation typically in math and computer science, data modeling, statistics and analytics. A true data scientist has strong business analysis skills and the ability to communicate findings to both operational and information technology teams in a way that can direct healthcare organizations on ways the most pressing business and clinical challenges can be solved. Understanding what the underlying data really means and how healthcare provider organizations can leverage that data for lower cost and higher quality outcomes gives an opportunity for a Data Scientist in our industry to achieve great things.

New jobs and new opportunities do exist in healthcare. Analytics to understand the impact of ICD 10 codes and terms, reviewing why patients get re-admitted and reducing those rates, understanding the relationship between certain seemingly variables. Labor Day gives us something to think about indeed.