Clinical Data Management: 3 Improvement Strategies

My Folder

There’s one in every office. A teammate with one of those cubicles full of papers stacked on his desk, shelves, floors, heat registers, and every other available horizontal surface. You look at that mess and wonder how he can ever find anything or if you’ll be able to in his absence.

The same issue with healthcare data also beleaguers our industry. Instead of an office with stacks of paper, however, it’s petabytes of data, spread across a number of disparate systems, with more being accumulated every second of every day (Figure 1). Clinicians would be wise to remember more data doesn’t always mean more insight. Sometimes more data is just that, more.

complex hc analytics

Figure 1 – Typical complex healthcare analytics environment

As the industry adapts to value-based care, clinical data management becomes even more crucial. Today, to assist organizations in answering its most pressing questions, a healthcare data analyst must navigate a complicated information infrastructure.

The first step is to identify the proper data sources. The analyst may work with an IT resource who can help determine which data would be most beneficial to start the analytic process. Collecting raw data from disparate systems is only the beginning. Distilling the information so it focuses on a specific question or strategy enables the analyst to discover more meaningful insights. Leadership, working together with one source of the truth, can identify areas for improvement and take quick action to address them.

Challenges for Traditional Clinical Data Management

The standard approach to clinical data management presents two formidable challenges.

Spreadsheet Mania

To tame the proliferation of data, many hospital departments create their own tools. Using multiple spreadsheets, often containing information that may not be accurate or conflicts with corporate data, can lead teams to focus on the wrong priorities or miss improvement opportunities altogether. Source data, once identified and captured, often become shadow systems with one spreadsheet linking to another and then another. This spreadsheet mania further complicates the process and introduces an unnecessary risk for error.

Data Analysts
The role of data analysts is changing in today’s value-based care environment. Equipping them with the right tools enables identification of performance gaps in the organization and to create actionable recommendations to drive improved outcomes. Providing the proper infrastructure mitigates the risk of compromising data during capture or transfer. Accurate data is crucial to gaining the trust and engagement of clinicians.

As Figure 2 shows, roughly 80 percent of an analyst’s time is spent hunting and gathering data as opposed to performing high-value work, such as interpreting data and making recommendations about how to improve the outcomes.

reduced waste time

Figure 2 – The current, wasteful state of clinical data management versus spending the majority of time on value-added activities

Most analysts would prefer to focus on conducting strategic analyses and contributing to organizational decision-making. Today, the approach to clinical data management focuses most analytic resources on the task of hunting and gathering. Creating a process that is efficient, effective, and reliable will enable analysts spend more time focused on gleaning insight from the data.

Most leaders also dislike the current approach. The multiple pressures of improving care, reducing cost, and planning for the future require data be made available in, almost, real-time. Submitting a request, being placed in a queue, and waiting weeks for a response is no longer an option.

3 Strategies to Improve Clinical Data Management

So, how do you change the way data is managed within a healthcare organization to ensure the right data is found quickly – and that all the analysts are using that data? Here are three things you can do to improve clinical data management.

  1. Locate the Analysts in Your Organization

To align the analysts, a good first step is to simply identify the current analyst pool sprinkled throughout your organization. Finding all of your analysts can sometimes be a challenge, but one way is by working with HR to get a list of positions with names similar to “analyst,” “specialist,” or “informaticist.”

  1. Assess Analytic Improvement Opportunities in the Organization

Once you’ve determined who’s in your analyst pool, you should elect a core analyst team who will be responsible for assessing the risk within your organization. Some of team’s new duties, along with the risks they are certain to uncover, include the tasks in the table below:


Risks and Challenges

Create a report inventory and find the logical “owners” of each report. It is most practical to start with recent reports, pulled during the last year. Reports that haven’t been run in over a year are candidates for archiving. Working with the owners, prioritize the work of combing through each report to document the report’s purpose, rules, tools used, frequency, data sources, formats used, and steps taken to produce it. This process will lead to better documentation AND a reduction in the number of reports that need to be touched upon system upgrade. Most organizations are surprised at how many reports have been created (and maintained) over the years. I’m guessing you probably have thousands of reports that are run out of the EMR. Folder security can sometimes force duplication (and probably modification) of the same reports with no audit trail for what is different. Additionally, the report filters and logic are often hidden from the report consumer, making it next to impossible to determine the rules and ultimately, the “source of truth.”
Bring your analysts together to develop a list of core competencies and a program to provide on-going training and mentoring. Analytic excellence is tough to measure, but without certain core competencies, there is no way to guarantee high quality, consistent results.
Assess the degree of silos and political will to improve alignment. Analytic silos breed duplication and potential waste. Although this seems fairly obvious, the political climate often complicates remedying this risk. However, it can help inform the type of alignment needed to reduce the risk.
Determine the current method for requesting reports and analytics. Without a formal intake, triage, prioritization, and assignment of work, the reporting and analytic environment becomes the “wild, Wild West.” Without a disciplined process, there is increased risk that staff are not focused on the highest priority work.
Identify current data governance processes and ownership within your organization. Current data governance might be performed through numerous, disconnected committees so you will need to dig around. Data governance focuses on managing data from initial capture in a transactional system, such as an EHR or laboratory system, through its aggregation into reporting structures and data stores and enterprise data warehouses. The intent is to make sure that each step of the health data management process is controlled and the effects of processes on the data are well documented and understood.

This initial assessment will clarify the current the level of risk and potential inefficiencies that exist in the analytic organization. This information can be shared with executive leadership to make the case for changing to a new organization model.

There are several analytic organizational models available including, Business Intelligence Competency Center or Analytics Center of Excellence. The most comprehensive model, however, is to align the analysts organizationally. All analysts are members of one analytics department, reporting to a single leader. They provide cross-organizational support, working for various departments on an as-needed basis.

It can be difficult to implement this model as it can be incredibly disruptive to the organization. The political capital needed to build the organization may also be lacking.

An alternative, less dramatic solution, is to create a central business intelligence group to serve as a consulting group. This group will offer specialized services to functional areas and produce findings to help you determine how much risk you have and your readiness for organizational change.

  1. Champion the Creation of an EDW as a Foundation for Clinical Data Management

Creating or purchasing an enterprise data warehouse (EDW) is a critical step in creating a robust analytic infrastructure. The EDW becomes a safe, central repository of data that is organized and optimized for measurement, analysis, and reporting. Sure, this is a large effort, but the payoff is huge–and lasting.

The true value of the data warehouse is to organize data, provide links across disparate data sources (so the analysts don’t have to), and provide access so analysts and clinicians can “fish for themselves.” Aligning the analysts and developing clear clinical data governance and management policies will strengthen the entire analytics environment.

For example, the patient identifier provides a valuable link between the EMR data, departmental sources, and patient satisfaction data. After the data is pre-linked, it can be organized in a way that allows for fast reporting and visualization. When used correctly, the EDW becomes the one place analysts rely on for information, creating a single source of truth for the entire enterprise.


Figure 3 – Late-Binding™ Enterprise Data Warehouse

Once you have an EDW, you need to ensure you are reaping the full benefits and getting a great return on your investment. Aligning the analysts and developing some important clinical data governance and management policies will strengthen the entire analytics environment.

Improving Care and Delivering Better Outcomes

Creating a new model that supports effective clinical data management requires an EDW to assemble and coordinate data from across the organization, providing the foundation improve care and deliver better outcomes.

This streamlined model greatly reduces lead time, enabling analysts to provide strategic insight from the disparate data collected across various systems. Healthcare organizations that embrace the value of data analytics, and harness it to create an effective clinical data management model, will be in the best position to survive and thrive in the new era of healthcare.

Which of the following common analytics problems represents the biggest roadblock in your organization?

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