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Leadership in Quality Improvement

Data to support patient care comes from a variety of sources that contain differing data types. Key activities to use clinical data include identifying the sources of data, understanding the data types and associated methods to work with the data, and identifying the necessary resources to complete your IT project.
The scope of your IT project will determine the level of data access required and the associated data storage needs. Data used in multisite projects will require IRB oversight and often require the execution of a DUA if transferring data outside of the institution or receiving data from another institution.
Identifying and assembling an adequate project team is based on the needs of the project. At a minimum, you will need to include frontline staff that will use the product, a data analyst capable of completing the ETL process on the data, and potentially statisticians to conduct appropriate model building and outcomes analyses.
There are multiple approaches to analyzing data. AI is the latest advance in machine learning approaches that include supervised, in which data is labeled and the algorithm is guided with statistical considerations, and unsupervised, in which unlabeled data is used to infer meaning. While robust, machine learning approaches require interdisciplinary teams and large resource dedication to complete.
All projects require review and potential revision over time. Follow-up and review of implemented programs should be included in the initial planning stages and resource allocation decisions at project inception.

Full Answer Section

Project Scope and Data Needs:

The scope of your project will dictate the level of data access required. Larger, multisite projects involving data transfer between institutions will necessitate oversight by an Institutional Review Board (IRB) and potentially a Data Use Agreement (DUA). Additionally, consider the data storage needs based on the volume and type of data collected.

Assembling the Project Team:

Building a successful team is critical for project success. Here are some key team members to consider:

Frontline Staff: Their input ensures the developed solution aligns with real-world clinical workflows.
Data Analyst: This individual possesses the expertise to perform the Extract, Transform, Load (ETL) process to prepare the data for analysis.
Statisticians (Optional): For projects involving complex statistical modeling and outcome analysis, including statisticians is recommended.

Choosing Your Analysis Approach:

There are various approaches to analyzing clinical data. Here's a brief overview of two popular methods:

Machine Learning (ML): This advanced technique leverages algorithms to learn patterns from data. ML can be supervised, where labeled data guides the algorithm, or unsupervised, where unlabeled data is used for pattern discovery. While powerful, ML projects often require significant resources and interdisciplinary teams.
Traditional Statistical Analysis: This established approach utilizes statistical methods to draw conclusions from data. It can be a robust and interpretable option for many projects.

Project Evaluation and Follow-up:

No project is complete without a plan for evaluation and follow-up. Include mechanisms to review and revise the implemented program after launch. Allocate resources for these activities during the initial planning stages. By following these steps and considering the different data sources, team compositions, and analysis approaches, you can leverage clinical data effectively to improve patient care through well-designed IT projects.

Sample Answer

This guide outlines the key steps involved in utilizing clinical data to enhance patient care through an IT project. It emphasizes the importance of data identification, team composition, and appropriate analysis methods.

1. Identifying Data Sources and Types:

The first step is to identify the various sources of clinical data relevant to your project. This could include electronic health records (EHRs), lab results, imaging reports, and patient registries. Each source will likely contain different data types, such as structured data (e.g., dates, numbers), semi-structured data (e.g., coded diagnoses), and unstructured data (e.g., physician notes). Understanding these data types is crucial for choosing appropriate tools and methods for analysis.