6 Essential Data Analyst Skills for Your Healthcare Organization


healthcare data analyst skillsSmart healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy to improve their care delivery and the cost of care. However, purchasing a sophisticated EDW doesn’t guarantee an organization’s success to lower cost or improve care delivery. To fully leverage the EDW investment, you need to have the right people with the right skills—strong healthcare data analyst skills.

Yet a common obstacle organizations run up against is that their technical team may not be equipped with the right skills to take advantage of the EDW. This deficiency can be overcome. Skills can be taught or acquired to better support a data-driven organization.

Producers and Consumers of Data and Information

The overwhelming majority of healthcare workers will never directly interact with an EDW. That is, most staff members won’t directly query databases, write reports, or analyze data looking for trends. Instead, they will rely on someone else to actually pull data from the EDW, analyze it, and then produce some kind of report. This report conveys meaningful information around a workflow over which the report requestor has some accountability. Let’s classify this group of people that receive the information in the form of reports as consumers. In contrast, the technical staff members tasked with data capture, analysis, and reporting are producers of information.

Consumers develop opinions of the EDW based on their ability to act on information found in reports generated by producers. It is not uncommon for a health system to invest millions of dollars and a handful of years into a data warehouse but still have consumers who are dissatisfied. How can such a significant investment result in dissatisfaction? It may be, but is not always the case, that consumer dissatisfaction is a function of producers not having the right skills to really generate the analysis and information consumers need. In fact, producers may not even know what they don’t know.

If the team responsible for generating a return on investment from their EDW lacks the skills to manage and leverage the EDW, it can create real problems for a health system’s enterprise-wide analytics strategy. Clearly, this isn’t the result you want from your analytics efforts. By making sure the analytics team has the right skills, you can avoid these kinds of problems.

6 Skills Healthcare Data Analysts Need

For a healthcare organization to effectively leverage an EDW to support sustained outcomes improvement, I submit there are six skills that need to be operational among staff members (either analysts or architects) tasked with analytics work.

Let’s explore each of the domains in greater detail.

  1. Structured query language. An analytics team member needs to be able to talk directly to and manipulate databases through structured query language (SQL). Recognizing there are various dialects of SQL, I refer generically to the ability to speak to and manipulate databases through code. He should be able to write SQL code without a dependency on an intermediary, guided interface (e.g., a drag and drop tool). Many workers rely on a tool like the Microsoft Access GUI interface or Crystal Reports GUI interface to generate SQL for their reports. In doing so, they attain a rudimentary understanding of querying.SQL offers users fine-grained control of the data being pulled. It also provide a powerful way to explore data that isn’t filtered through a predefined data set or model, as is the case with a business intelligence (BI) tool. Teams that can’t query the data with SQL are beholden to whatever information is being pushed to them from another source. Using a BI tool to generate SQL on your behalf is a good starting point.

There are a couple potential downsides to using auto-generated queries from BI tools, though. First, these tools usually underperform because they are poorly constructed (behind the GUI interface). Second, and far more prevalent, is the way these tools mistakenly make assumptions about the data and manipulate the data without the user being aware of the underlying logic. This is dangerous because he may not understand the query generates duplicate result sets (i.e., tables), or excludes some patients that should be included in the result set, or a host of other “I-didn’t-realize-it-was-doing-that” scenarios.

If your query feeds a report, and the report provides information people will act upon, you need to be sure you really understand the logic embedded in the underlying query.

  1. Export, transform, and load (ETL). The data expert needs to be able to perform export, transform, and load (ETL) processes. Simply put, you need to take data from one system and put it into another.In an EDW, a user pulls data from disparate systems (e.g., EHRS, finance, human resources) that don’t talk to one another. For example, you may have an EMR system, a patient satisfaction system, and a costing system that don’t interface directly. Making a copy of the data found in each of these systems and pulling the data into the warehouse will allow integration of data from the various systems. This movement of data is accomplished through the ETL process.
  1. Data modeling. Data modeling is a fancy way to say that you write code that models real-world processes and workflows. Let’s consider a common healthcare scenario: a hospital admission. What information do I need to capture to model that workflow? In this example, you’d need some demographic information, such as the patient’s name, data of birth, gender, and complete address. You’d likely want to pull insurance information, such as the plan name, copay amount, and effective coverage date. Clinically, you would want to know some history. Is this patient new to the system? Do we already have a medical record number for the patient (indicating we have seen her before)? What is the admitting diagnosis? Who is the attending provider for the admission? Did the patient come through the emergency department or some other venue? A good data model captures all of these data elements and relates them in a meaningful way to reflect the actual workflow.
  1. Data analysis. An analytics team member needs to be able to make sense of the data once it is in the EDW. There is so much information produced in healthcare, and not all of it is relevant for the analysis that needs to be done to drive improvements. A good analyst has the ability to sift through data to extract pertinent insights. This requires some complex thinking around set theory and the ability to do their analysis through SQL, a statistical reporting tool, or a combination thereof.Let’s give an example. In healthcare, there is a lot of attention around the management of diabetic patients. Diabetes is a chronic condition that affects the patient’s quality of life, and if not well managed, can be lethal. From a financial perspective, diabetes is extremely costly if mismanaged.

An analyst may be part of clinical improvement team tasked with managing the diabetics within the health system. But if diabetes is a clinical condition, what possible value could an analyst bring to the team? Consider this.

A health system…

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