In healthcare analytics, visualizing, interpreting, and disposing of data are critical phases. Data visualization aids in understanding complex patterns, with ethical considerations essential during interpretation. Proper data disposal adheres to HIPAA guidelines. Successfully navigating these phases ensures effective communication and responsible data management in healthcare analytics projects.
After conducting healthcare data analysis, it is essential for stakeholders to effectively visualize, interpret, and dispose of the information according to established best practices. The success of a healthcare analytics project hinges on how well project stakeholders navigate the data lifecycle, which encompasses data generation, collection, processing, storage, management, analysis, visualization, interpretation, and disposal.
These final stages of the data lifecycle are crucial for communicating the results of the analysis effectively and ensuring that data used in the project are handled appropriately.
This article is part of a series that delves into the healthcare data cycle. The first installment covered data generation, collection, and processing, while the second discussed data storage, management, and analysis. In this primer, HealthITAnalytics will explore healthcare data visualization, interpretation, and disposal.
Data Visualization
Data visualization, as described by the University of North Carolina (UNC) at Chapel Hill, is a method for uncovering complex patterns or large volumes of data that are not easily discernible by looking at tables of numbers or reading paragraphs of text. The goal of data visualization is to communicate information more effectively by leveraging our innate ability to recognize visual patterns.
Data visualization in healthcare is becoming increasingly important as the industry generates vast amounts of data daily. Healthcare organizations are increasingly relying on advanced technologies like artificial intelligence (AI) and machine learning (ML) to analyze this data and identify critical trends or patterns.
However, it’s essential to note that to make these tools truly useful, the insights they generate must be presented in the form of data visualizations to effectively communicate them to a broader audience. According to the American Health Information Management Association (AHIMA), data visualization is considered an “essential skill” for health information management professionals, as visualizations help people understand and identify key data trends more effectively than raw data presentations.
Data visualization employs various techniques such as infographics, tables, line charts, bar graphs, pie charts, scatter plots, histograms, heat maps, big data dashboards, and more. In the healthcare sector, major players often develop data visualization tools that researchers and policymakers can use to visualize healthcare trends. For example, the Agency for Healthcare Research and Quality (AHRQ) creates customizable data visualizations for health insurance coverage, emergency department visits, COVID-19 hospitalizations, and other trends across the United States, tailored to various demographic factors.
The Institute for Health Metrics and Evaluation (IHME) at the University of Washington also offers a range of data tools and interactive visualizations to capture global health trends. Recent research has demonstrated that interactive visualizations can be particularly valuable in healthcare.
Despite their potential, healthcare data visualization tools face challenges such as bias and trustworthiness. Organizations like AHIMA have developed best practices for data visualization, and researchers continue to refine these tools to address these issues.
Data Interpretation
Data interpretation involves making sense of the information analyzed and presented in the earlier phases of an analytics project. Interpretation is crucial because it helps uncover the meaning or insights generated within the context of the analysis, enabling stakeholders to use the information for decision-making.
It’s important to note that data visualization and interpretation often go hand in hand, as visualizations help convey the insights more effectively. However, at this stage in the data lifecycle, stakeholders must consider various ethical challenges. Ethical dilemmas can arise at different points in the project, from choosing the analytics method to interpreting the findings.
Awareness of potential ethical issues is the first step in addressing these challenges. Stakeholders should consider factors like the context in which the data will have an impact, who may benefit or be harmed by the data, principles of honesty and respect, and precedent based on similar analyses in the past. Ethical considerations should guide stakeholders in making culturally sensitive interpretations of the data and ensuring that the insights are presented in a way that benefits as many people as possible.
Data Disposal
Data disposal represents the final phase of a healthcare analytics project. Depending on the types of data used, stakeholders may need to adhere to state and federal guidelines for data retention and disposal. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) established national standards for protecting patient health information (PHI) from disclosure, and the HIPAA Privacy Rule outlines how PHI may be used and disclosed by covered entities.
Healthcare providers and payers are among the covered entities subject to the HIPAA Privacy Rule and are likely to undertake health-related analytics initiatives. To balance patient privacy with the potential for medical breakthroughs driven by data analysis, covered entities should establish appropriate data retention and destruction schedules.
These schedules must comply with HIPAA’s retention requirements, as outlined by AHIMA. Data retention policies specify how long data must be retained to meet regulatory or organizational needs and what should be done to the data after retention requirements have been met.
Proper disposal of physical data (e.g., paper medical records) involves safeguards to prevent unauthorized access to PHI, employee training on PHI disposal policies, and the use of secure disposal methods, such as shredding or incineration, often carried out by a vendor under a business associate agreement (BAA).
For electronic PHI, the National Institute of Standards and Technology (NIST) guidelines and recommendations from the US Department of Health and Human Services (HHS) should be followed. Proper disposal may involve exposing electronic PHI to a strong magnetic field, physical destruction, or overwriting with non-sensitive data.
Once data have been securely destroyed, the data lifecycle is complete, and stakeholders can consider initiating a new project or advancing work done in a previous one to support their organization’s goals in another area.
