Category Archives: Electronic Health Records

Cancer Centers, Epic to Tap Power of IBM Watson Supercomputer

A supercomputer like IBM’s Watson is needed to be able to analyze the large number of detailed records needed to “develop patient treatment protocols, personalize patient management for chronic conditions, and intelligently assist doctors and nurses by providing relevant evidence from the worldwide body of medical knowledge, putting new insight into the hands of clinical staff.” The key to the success would be able to share and analyze patient-specific data in real time, a part of the standard workflow. This will allow Watson to “bring forth critical evidence from medical literature and case studies that are most relevant to the patient’s care.”

Cancer Centers, Epic to Tap Power of IBM Watson Supercomputer

Epic, Watson at work on interoperability

IBM’s Watson targets cancer and enlists prominent providers in the fight


Cleveland Clinic makes analytics available

This is an interesting development. It may be the only way some smaller hospital systems receive any assistance with healthcare analytics.

Cleveland Clinic makes analytics available

Process – The Neglected Continuum in Healthcare

Healthcare is undergoing a period of tremendous change, and while EHRs have gotten a great deal of attention, lately, the implementation of an EHR is only the first step in a long journey to becoming a data-enabled healthcare organization.

Process – The Neglected Continuum in Healthcare.

Ebola Lapse in Dallas Offers Few Lessons

Ebola Lapse in Dallas Offers Few Lessons, Except About Our Over-reliance on Technology

People are still trying to blame the Epic EHR for the Texas Ebola case missed diagnosis. From what I understand, the information was recorded in the EHR, but the staff didn’t connect the dots. Without the EHR the notes would have been recorded on paper charts and we know that nothing is ever missed in a paper chart.

Ebola Lapse in Dallas Offers Few Lessons, Except About Our Over-reliance on Technology


The Root Cause of Many EHR Failures is Poor Leadership

by J. Bryan Bennett

We recently learned of another C-level executive resigning over a failed or challenged EHR implementation (CEO of Georgia Hospital Resigns After Rocky EHR Implementation). These stories are beginning to come with increased frequency as most healthcare organizations are deep into their EHR implementation cycle. If you look closely, the reasons are almost always the same, i.e., lack of physician engagement, difficult implementation time frames or lack of the proper resources. When I read these stories, I usually come to one primary reason for the failure – bad leadership in two distinct areas.

The Root Cause of Many EHR Implementation Failures is Bad Leadership

I.T. blamed in Athens EHR debacle

I’m sure there’s a lot of blame to go around on why this implementation went wrong. I’ve found the bottom line comes down to leadership or lack thereof. It doesn’t matter which solution you’re implementing, if the plan has gaps, it’s going to fail. I said the very same thing several months ago in one of my HIMSS Future Care blogs: “Leadership Skills Needed for a Successful Data Enablement Transformation“.  @enabledhealth

I.T. blamed in Athens EHR debacle

Cardiac Patients Taking PHRs to Heart

Collection of good data and improved outcomes are the result of this program. Without good data, analytics has little benefits. @healthcarecoe

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Predictive Analytics Will Be A Game Changer – Eventually

HIMSS Future Care Blog

by J. Bryan Bennett

predictive analyticsHealthcare analytics has the potential to help identify potential health risks, promote better health and deliver more accurate diagnosis and treatment plans. There are several challenges that must be overcome before healthcare can deliver on that promise.

Let’s first agree on the kind of healthcare analytics we are discussing. It’s a broad term and can mean different things to different people. In fact, companies have been performing some kind of healthcare analytics for years, primarily around revenue cycle and claims data. For purposes of this discussion, we are looking at predictive analytics that is the basis for real-time or near real-time decision support. The use of this kind of analytics is still rare among healthcare organizations. In fact, Heather Fraser from IBM’s Institute for Business Value, states that although “two thirds of organizations consider analytics as a high priority and have an analytics strategy or road map in place, only one third are defining analytics based on new ways of using analytics such as predictive and beyond.”

There are 2 levels to healthcare predictive analytics. The first one, or what could be considered the ‘low hanging fruit’, is using risk factors to determine a patient’s propensity for certain health problems. These are the one-to-one or two or three risk factors that may lead to the problem based on lifestyle, ethnicity, family history or health condition. For instance, if a person is a smoker there is strong evidence that they may develop lung cancer or cardiovascular disease. This kind of analysis is fairly easy and can usually be performed in most of the major EHR software solutions.

The second level is much more challenging. This is the kind of analysis that involves hundreds or thousands of patients with similar profiles and health conditions which alerts the provider to the likeliness of a patient developing or having a particular health problem. The challenge here not only comes from incorporating all the other non-identifiable patient data, but also the volume of data that may be required for each patient. Additionally, at this point, no one really knows which data elements would be the most predictive. In other industries, you can start with a FICO score or lifestage or other segmentation and build upon that. In healthcare, we have several factors from demographics to healthcare condition, each with potentially hundreds of variables, which could be predictive. The computing power to manage this will be tremendous.

That leads to another challenge – the data warehouse. Most healthcare organizations are pretty weary from implementing and paying for their EHR solution. As detailed in previous blogs, that’s only the beginning of the technology transformation. The next step is getting the clinical data from the EHR, the claims data, the operations data and ambulatory data into one place where it can be analyzed. To some, this may sound easy, but the volume of structured and unstructured data, regularly extracted and loaded will be a huge burden for many. Dan Burton, CEO or Health Catalyst calls it “a level of data and an order or magnitude that most people can’t comprehend.” Fortunately, his company is helping to make the process a lot easier with their proprietary data loading process. The best part is that they have been able to scale their process to large and small healthcare organizations so that we don’t end up with the system of haves (large groups) and have nots (small groups).

This is not to say that predictive analytics for real-time decision support shouldn’t be pursued. Some companies like the ones I’ve previously mentioned as well as others are making some progress. George Dealy, V.P. from Dimensional Insight states that their customers are already seeing some progress predicting strokes, congestive heart failure and other health problems through their applications.

This is not an overnight transformation; it is something that will take years. Whenever it gets here though, it will be a gamechanger which will help us all live a longer, healthier life.

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Leadership Skills Needed for a Successful Transformation — HIMSS

By J. Bryan Bennett

Doctors Studying Data on ComputerExceptional leadership skills are necessary for a healthcare organization to navigate the transformation from a paper-based organization to one that is data-enabled. Without good leadership, the organization risks not realizing the benefits of the transformation or not being transformed at all.

Indeed, organizations are spending millions of dollars on just the EHR part of the data enablement transformation and could be putting their investment at risk without proper executive leadership.

In the leadership classes I teach for the School of Leadership and Business at Judson University, we study John Maxwell’s “21 Irrefutable Laws of Leadership.” Many of the laws apply, but I believe there are three that are most appropriate for healthcare stakeholders striving to be data-enabled organizations.

#4 – The Law of Navigation: Leaders who navigate control the direction in which they and their people travel. Leaders see the entire trip before leaving the dock and have a vision for how to get to their destination. They understand what it will take to get there, who they’ll need to take with them, and they recognize the obstacles long before they appear on the horizon. Good navigators draw on past experiences, listening to what others say and relying on fact and fiction (gut instinct) to make their decisions.

#14 – The Law of Buy-In: People buy into the leader before they buy into the vision. People don’t follow causes (vision), they buy into the person. To accomplish this, leaders must have credibility with their team members.  To establish credibility you have to develop good relationships with your team members to acquire their trust. This can be accomplished by setting a good example for your team members by holding yourself to high standards and providing them with the tools they need to succeed.

#15 – The Law of Victory: Leaders find ways to win despite the situation. The best leaders rise to the challenge and do everything in their power to lead their team to victory. To apply this law requires a unified vision among the team members, a diversity of skills and a leader dedicated to victory and raising team members to their potential. You can have the unified vision and a team with diverse skills, but without the proper leader to pull it all together, you just have a diverse team with a vision.

To be successful, executives are going to have to use all of their leadership skills to put their people in a winnable situation and to get them to willingly go over and beyond what they normally would contribute.

One of my favorite analogies is that of a baseball centerfielder running into the wall to make the catch for his team. He realizes that he might hurt himself (and many have), but he believes that helping his team win the game is more important than his personal safety. I will ‘go to the wall’ for my friends and as a leader, your people may have to do the same for you someday. Good leadership will help them feel good about making that choice.

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Big Data Needs the Big Three to Succeed — HIMSS Future Care Blog

By J. Bryan Bennett

Big Data is a topic that is widely discussed (even by me). Unfortunately, what most people don’t realize is that Big Data is only part of what is needed for a successful transition to become a data-enabled healthcare big threeorganization (DEHO). This is very similar to the early days of customer relationship management (CRM) when everyone was being told to “Buy my software and you’ll have CRM”. As we learned or should have learned from all the failed CRM implementations that littered the business landscape, a successful CRM implementation requires more than just installing new software. In the CRM implementation vision I co-developed back then, we identified three continuums that must equally be changed for a successful transition. This vision was selected by Gartner as one of the top three in the industry and has become the foundation for most successful CRM implementations still today.

We are facing a similar situation in healthcare. All the focus on Big Data may be clouding what is really necessary and organizations are neglecting key components that will significantly improve their chances for success and improved effectiveness. A successful transition to a DEHO requires more than Big Data; it requires the Big 3 and I’m not talking about Lebron James, Dwayne Wade and Chris Bosh of the Miami Heat basketball team I’m talking about data/technology, processes/workflows and organization/people.

First, let’s discuss the concept of a DEHO. When implementing a CRM solution, we wanted it to be data-driven, meaning that we want the data to drive how we treated and managed customers with as much automation as possible. In healthcare, we don’t want to rely solely on automated decisions except for some alerts and such. In a DEHO environment, physicians are provided real-time information to make the final decisions and diagnoses based on hundreds or thousands of patients with similar symptoms. A DEHO will also be able to better manage their “patient portfolio” to provide more consistency and an improved overall quality of care.

To become a DEHO, not only will an organization need to travel along all three continuums, but they will need to change all three at a similar pace. One continuum cannot get too far ahead of the others or your implementation will be like a 3-legged stool and fall over. Let’s examine each of these continuums a little closer.

Data / Technology
This is the continuum most might consider the easiest to implement, but it is fraught with roadblocks and potholes. The continuum includes the clinical data, claims data and any other sources the organization would like to integrate. It also includes the analysis and modeling of this data to provide real time decision support. Many organizations are implementing EHR solutions for the first time right now. Integrating new data sources is going to be another big challenge, not to mention the lack of user-friendly analytical tools currently available.

Processes / Workflows
The processes/workflow continuum involves how and when the data is captured, secured and integrated. It includes the workflow changes that need to take place to record the data at the point of encounter, whether it is at the individual entity or network-wide system level. After the data is captured, it has to be integrated and manipulated for use by various entities and people.

Organization / People
This is usually the forgotten continuum. Many organizations implement a new solution and can’t figure out why it was not successful. The difference usually comes down to whether or not personnel embrace the solution and are willing to make the change. Success requires not just changing processes, but also changing mindsets. This is accomplished through training and finding key people to champion the cause. The more the change is embraced throughout the organization, the better chance the organization has for success. As we’ve seen in many healthcare organizations, resistance to change has been strong by large pockets of the provider population.

We are undergoing an industry-wide transition, which is quite different from most technology transitions that usually only impact an individual organization. As each DEHO contributes to the data pool, the decision support becomes more effective than when the data is based on information from one or only a few organizations. An additional complication is the various stages different organizations are at in their DEHO transition. The sooner everyone gets to a similar point, the more beneficial the information will be.

If implemented properly, the DEHO won’t have to worry about one of the players not showing up for a quarter or even the entire game. In fact, if properly implemented, it can lead to many champion or quality patient encounters.

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