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Overcoming Roadblocks to Data Enablement — HIMSS Future Care

By J. Bryan Bennett

Many healthcare organizations are on the journey to becoming data enabled. roadblocksUnfortunately, a lot of themwill have a difficult time reaching it. Most organizations are just in the beginning stages, while some are still in the starting blocks. Additionally, there are many internal and external issues that may delay or stall the process. In this blog, we will discuss some of those ‘stumbling blocks’ and offer some suggestions on how to overcome them.

A previous blog discussed the three continuums an organization needs to address if it is going to become a data-enabled healthcare organization (DEHO). The three continuums are: data/technology, people/organization and process/workflow (see “Big Data Needs Big Three to Succeed”, August 8, 2013). If they do, they will have a better chance of success than those addressing this as purely a data or technology issue. In spite of that, there are still some major stumbling blocks that will impact the organization’s success. These will each be addressed by continuum.

Data / Technology

This is the continuum that gets the most attention. Most organizations have installed or are in the process of installing an electronic health record system. For many, the EHR alone has become a stumbling block. There have been delays on the part of the organization or the vendor that has cost millions of dollars in implementation costs and/or stimulus incentive money. Recently, articles have been published that discuss how CIOs have been terminated for buying a very popular EHR with a good reputation. Unfortunately, the real reason those CIOs were terminated was not because they choose this EHR solution but because they chose the wrong solution for their organization and/or didn’t have the right resources to implement the solution which led to extensive cost overruns.

To overcome this stumbling block, the organization has to first make sure they use a proper selection process. Not every solution will work for every situation. Second, they have to make sure they have the right resources to implement the solution. This will mean making a hard assessment of the staff’s capabilities to implement and customize the solution and/or determine if staff augmentation needs to be included in the budget. Third, they have to make sure they use proper project management methodologies. Without this, the implementation could spin out of control resulting in missed deadlines and exceeded budgets.

An area that is out of the organization’s control at this time is the lack of mainstream analytical tools. Most healthcare analytical tools are revenue cycle management focused, others are custom made or are being built into various EHR solutions. If your organization does not have one of the solutions with built-in analytics or can’t afford custom analytics, you could miss out on the real benefits of being data-enabled with the needed real-time decision support tools. A future blog will be dedicated to the current state of healthcare analysis tools. If you have any suggestions of products or companies that should be included, please contact me at

Process / Workflow

This continuum deals with the workflow of how the data is captured and managed. The biggest stumbling block on this continuum is gaining accurate data capture throughout the healthcare organization network. Some workflows may accurately capture data only some of the time when it needs to be accurately captured 100% of the time. Capture rates can vary by unit, hospital unit or hospital network. Some of this is addressed by the ‘People’ continuum stumbling block to follow.

This stumbling block is primarily overcome by utilizing the best workflow for your organization. When a software solution is purchased, some will follow your current workflow, some will force a workflow change to fit the solution, while the best will be flexible enough to incorporate your workflows into the industry best practices to maximize efficiencies.

People / Organization

There is still a lot of resistance to changing to an EHR by many in the provider community. From electronic charting to electronic order placement, providers have found many ways to keep from having to use the technology, including continuing to make verbal or telephone orders for the nurses to enter. This could result in the organization not meeting the stage 2 meaningful use requirements if it is done too often. To overcome this stumbling block, the organization has to help the providers embrace the change. This is difficult because we are making a change to an entire industry in a short amount of time. Telling them to embrace change is easy to say but difficult for most people to do.

To overcome this stumbling block, the organization has to manage the physician staff’s expectations. Physicians should be included in the EHR decision making and implementation planning process. The organization should also identify champions who naturally embrace the change and who will help drive the implementation amongst their peers. When an organization’s physician champion hears a colleague who is not completely on board complaining about the solution, they could help by providing positive and/or correct information. Lastly, make the physicians part of the ongoing process through recognition for usage and allowable incentives, for instance tracking and reporting on the top users of the solution.

Next Steps

We’ve discussed the three continuums in the previous blog and now the stumbling blocks in the way of becoming a DEHO. To implement these strategies is straightforward but requires a special skillset to manage. You need a good strategist, technologist, project manager, human behavior specialist and communicator all in one.

First, you will need to perform or have someone perform an assessment of where your organization is on each continuum. There is more to the continuums than has been covered in these blogs. Next, you will need to put a plan in place to advance your organization along the continuums recognizing the fact that there will be stumbling blocks that will need to be addressed. Lastly, follow your implementation plan looking forward to where you need to be while looking back at where you’ve been with an honest assessment of your capabilities and success.

Becoming a data-enabled healthcare organization is a journey. Treat is as such and you’ll have a successful and adaptable implementation.

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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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Healthcare Big Data is Déjà vu All Over Again – HIMSS Future Care

By J. Bryan Bennett

My colleagues and I chuckle when we hear about all the ‘new benefits’ from Big Data. We deju vuchuckle because we have been using large datasets (what today is called Big Data) for years to help companies glean insights and intelligence from customer data. The goal has been to get the “right offer, to the right person, at the right time”. This means that we sought to understand the customer and their needs enough to be able to make the right product offer to them when they were most likely to accept or respond to the offer.  To accomplish this, behavior analysis and predictive modeling was used to create more effective acquisition and retention programs and to build actionable customer segmentations. The segmentation schemes enabled the company to manage similar customers as a group with emphasis on keeping the top customers in their segments and motivating the other customers to ‘move up’ the segmentation schema.

What is old in most other industries is new in healthcare. Analysis and manipulation of large datasets has not been used in healthcare except by healthcare insurance providers, even then, the data has been used to manage insurance costs and not provide any real benefits to the customer. It has been used to analyze past transactions versus impacting current and future decisions.

The potential benefit of using Big Data in healthcare is very similar to how it has been used in other industries. Where it has been used to manage customer portfolios elsewhere, in healthcare, the data can be used to aid healthcare providers with their decision support. Instead of getting the ‘right offer, to the right person, at the right time’, healthcare can use Big Data to “get the right information/diagnosis, in the right (provider) hands, at the right time, to create the right treatment plan”.  In other words, it is to help healthcare providers make the right diagnosis or treatment plan using data from thousands of similar cases.

For instance, a 43-year-old Hispanic male presents himself at the provider’s office with chronic high blood pressure. The doctor has several patients with high blood pressure and believes this should be a fairly easy case to treat. The doctor prescribes one of his preferred medications and sends the patient along his way. Two weeks later, the patient returns feeling very sluggish. The doctor may or may not have seen this in other patients. What the doctor may not have known was that the medication he/she prescribed has an added side effect for Hispanic patients with sluggishness being one of them. With access to a large dataset of similar cases at the time of diagnosis, instead of prescribing another medication for the patient, thus wasting the money the patient spent on the first medication, the doctor could have originally prescribed the medication proven to work best in this situation.

This story actually happened to someone I know. The patient’s regular physician was on vacation and he had to see one of his colleagues. When the patient went back to see the regular physician, the patient didn’t have to list off the negative side effects, his regular physician knew them already. He said, “I’ll bet you’re feeling sluggish, etc.” Neither physician was of the same ethnicity as the patient. The bottom line is that not all patients react the same to medications. There are variances in ethnicities, age, sex, etc. Having access to data to make better treatment plans at the time of the visit is what it’s all about.

Let me emphasize that this is not to take the decision out of the physician’s hands. The physician standing in front of the patient knows best which treatment plan to follow. This is in place to potentially help the physician make those decisions that will improve the overall patient outcome.

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