What Does A Health Data Analyst Do – Healthcare organizations are turning to enterprise data warehouses (EDWs) as the foundation of their analytics strategies. However, implementing an EDW alone does not guarantee organizational success.
One of the obstacles organizations face is that members of the analysis team do not have the appropriate skills to maximize the effectiveness of their EDW.
What Does A Health Data Analyst Do
The following six skills are essential for analytical team members. Structured Query Language (SQL). Ability to perform export, transform, and load (ETL) processes. data modeling. Data analysis; Business Intelligence (BI) reporting. and the ability to use data to tell stories.
What Is A Health Data Analyst?
Intelligent healthcare organizations are turning to enterprise data warehouses (EDWs) as the foundation of their analytics strategies to improve care delivery and cost. However, purchasing an advanced EDW system does not guarantee that an organization will be successful in reducing costs or improving care delivery. To get the most out of your EDW investment, you need the right people with the right skills: strong medical data analysis skills.
A common barrier organizations face is that their technical team may not have the appropriate skills to leverage their EDW. This gap can be overcome. Teach and learn skills to better support data-driven organizations.
The majority of healthcare workers do not interact directly with EDWs. This means that most staff members do not directly query the database, create reports, or analyze data trends. Instead, have someone else retrieve the data from her EDW, analyze it, and create a report. This report conveys important information about the workflow for which the report requester has some responsibility. We classify this group, which receives information in the form of reports, as follows:
Consumers form opinions about EDWs based on their ability to act on the information contained in reports produced by manufacturers. It’s not uncommon for health systems to invest millions of dollars and years in data warehouses and still have dissatisfied consumers. Why does such a large investment lead to dissatisfaction? Sometimes consumer dissatisfaction is due to producers not having the appropriate skills to generate the analysis and information that consumers need. there is. In fact, manufacturers may not even know what they don’t know.
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If the team responsible for generating a return on investment from an EDW does not have the expertise to manage and leverage his EDW, a health system’s enterprise-wide analytics strategy can be seriously challenged. Obviously, this is not the desired outcome of your analytical efforts. You can avoid these types of problems by ensuring your analytics team has the right skills.
For a healthcare organization to effectively utilize its EDW to support sustained improvement in outcomes, there are six competencies that must be operationalized by staff members responsible for analytical work (analysts or architects). I insist.
1. Structured query language. Members of the analytics team must be able to access and manipulate databases directly through Structured Query Language (SQL). We recognize that there are different dialects of SQL, and loosely refer to the ability to access and manipulate databases through code. You should be able to write SQL code without relying on intermediate guided interfaces (such as drag-and-drop tools). Many analysts rely on tools such as the Microsoft Access GUI interface or Crystal Reports to generate SQL for their reports. By doing so, you will achieve a basic understanding of queries. SQL gives users fine-grained control over extracted data. It also provides an effective way to explore data that is not filtered by predefined datasets or models, as is the case with business intelligence (BI) tools. Teams that cannot query data using SQL are locked into information coming from another source. A good place to start is to use a BI tool to generate SQL.
However, there are some potential drawbacks to using automatically generated queries from BI tools. First, these tools are poorly built (behind a GUI interface) and therefore typically have poor performance. The second, and much more common, is how these tools make incorrect assumptions about and manipulate data without the user being aware of the underlying logic. This could mean that you may not realize that your query is producing duplicate result sets (e.g. tables), or that you are excluding some patients that should be included in the result set, or other It’s dangerous because there can be many “I didn’t realize I was doing this” scenarios.
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If your queries feed into reports, and those reports provide information for people to act on, you need to make sure you understand the logic embedded in the underlying queries.
2. Export, Transform, Load (ETL). Healthcare data analysts must 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, users retrieve data from disparate systems that do not communicate with each other. For example, your EMR system, patient satisfaction system, and costing system may not be directly connected. Creating a copy of the data found in each of these systems and placing it in a warehouse allows you to integrate data from different systems. This data movement is performed through an ETL process.
3. Data modeling. Data modeling is a fancy way of saying that analysts can write code that models real-world processes and workflows. Consider a common medical scenario: a hospital stay. What information do I need to capture to model that workflow? In this example, I would need demographic information such as the patient’s name, birth details, gender, and full address. You’ll likely need to obtain insurance information, such as the name of the plan, copayments, and effective date of coverage. Clinically, you’ll want to know a little bit of history. Is this patient new to the system? Do we already have a patient record number (indicating we have seen her before)? What are the accepted diagnoses? Who are the providers participating in the admission? Did the patient visit the emergency room or elsewhere? A good data model captures all of these data elements and creates meaningful data that reflects the actual workflow. Relate them in a way.
4. Data analysis. Analytics team members need to be able to understand the data that goes into the EDW. The medical field generates a lot of information, but not all of it is relevant for analysis aimed at driving improvement. Great analysts have the ability to sift through data and extract relevant insights. This requires complex thinking about set theory and the ability to perform analysis using SQL, statistical reporting tools, or a combination of these. In the medical industry, the management of patients with diabetes has received significant attention. Diabetes is a chronic disease that affects a patient’s quality of life and can be fatal if not properly managed. Diabetes can be very costly economically if poorly managed.
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Analysts may be part of a clinical improvement team tasked with managing diabetes patients within a health system. But if diabetes is a medical condition, what value can an analyst bring to a team? Consider this:
Health systems can manage large numbers of diabetic patients who can be categorized into low-, intermediate-, and high-risk strata. Clinical markers identified by your doctor determine:
The percentage of patients who fall into one of these three categories. But what if the health system wants to know more, for example, the total number of patients with diabetes? How many of these patients fall into each risk category? Low to moderate risk in year-over-year trends What is the shift from medium risk to high risk?
Being able to highlight the underlying reasons that explain the “why” can add tremendous value. Why the move from low to medium? What statistical correlations can we draw and actually test? Healthcare data analysts fill this role on the team.
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5. Business Intelligence (BI) Reports. Analytics team members must be able to present data in a way that is intuitive to non-technical users. Visual representations must be easily interpretable to a general audience. It looks easy, but it’s a skill that’s difficult to master. It’s what separates the average analyst from the great one.
In a sense, this is like an interpreter. Interpreters listen to words spoken in one language and speak another language to the intended audience. Without good command of both languages, translation is difficult, if not impossible. In addition to word mechanics, language rules, and semantics, it incorporates metaphors, idioms, and other nuances that further enrich the communication experience. A good interpreter demonstrates the ability to convey perfectly
Similarly, data scientists must translate database languages (which mine data for meaning) into simple graphs that fully convey meaning, while avoiding potentially ambiguous conclusions.
6. Tell the story of your views. Analytics team members must be able to effectively tell the stories embedded in the data. Think of it like this:
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