In recent years, the healthcare industry has seen a surge in the importance of data granularity. With vast amounts of data being generated by patients and healthcare providers, it has become increasingly necessary to understand and utilize data at the granular level.

The dire state of health data granularity

Old ways of anonymizing and sharing sensitive health data often involve aggregation and generalization. Both of these data anonymization techniques reduce data granularity by summarizing or grouping data. Combining multiple data points into a single summary results in a loss of detail and data granularity. As a result, important variations within the data can be overlooked, making it challenging to identify important patterns and trends.

Aggregating and generalizing data can smooth out variations and fluctuation within the original dataset, obscuring anomalies that may be valuable for research and analysis. Subtle changes are almost certainly missed, while a misrepresentation of minority groups is a very tangible risk. Aggregated data is a sure-fire way to arrive at incorrect conclusions. 

Summary statistics also strip away the contextual nuances associated with individual data points. Accuracy also suffers greatly and can lead modeling and data-driven processes astray. For example, predictive analytics in healthcare couldn’t thrive on aggregated or even heavily masked health data. Temporal distributions might be lost or greatly reduced as a result of legacy data anonymization techniques.

Privacy is also an issue. While old data anonymization tools like pseudonymization, generalization and randomization destroy important statistical information, a high risk of privacy violation remains. Especially in cases of time-series health data, like patient journeys, the task of de-identification is notoriously difficult. Healthcare data platforms offering synthetic data generation and assets provide the best alternative to old data sharing practices, as evidenced by the European Commission’s JRC Report on synthetic data and as practiced by Humana, one of the largest North American health insurance providers, who published a synthetic health data exchange.  

Understanding data granularity in healthcare

Data granularity is a mission-critical aspect of health data management practices. Choosing data anonymization tools and a health data platform that keeps patient data privacy-safe, yet granular, readable, accessible and shareable is the first step towards data-driven healthcare.

The richness and precision of data directly contribute to better healthcare outcomes, patient safety, and the overall quality of care provided. Moreover, healthcare organizations increasingly focus on population health management, which involves monitoring and improving the health outcomes of specific groups or communities.

Granular data helps in identifying population health trends, understanding risk factors, and designing targeted interventions. Fine-grained data allows for stratification of populations based on various characteristics, enabling proactive and tailored healthcare strategies.

In order to ensure data granularity, we must first understand what it means in the context of healthcare.

What is data granularity in a healthcare context? 

Data granularity in healthcare refers to the level of detail or specificity of clinical data. It is the measure of how fine or coarse the data points are that make up each patient's medical record. Greater granularity means more granular data points, which provides a more detailed picture of a patient's health status, medical history, treatment plans, and health outcomes. In contrast, less granularity implies fewer data points or less detailed data, which can limit the quality of insights that can be gained from the data.

Definition of data granularity

In healthcare, data granularity refers to the level of medical detail in a patient's electronic health record (EHR). Data granularity is a measure of the degree to which data in a system can be broken down into smaller data elements. This means that for data to be considered granular, the information must be detailed enough to reveal deeper insights and trends.

Importance of granular data in healthcare

Granular data is critical in healthcare because ultimately it improves the quality of care that patients receive. It also aids in the identification of health risks, the development of effective treatment plans, and facilitating critical decisions about care provision. More granular data can also enable healthcare providers to better track patient progress, improve outcomes, and develop more targeted interventions to mitigate specific health risks. 

The different levels of data granularity

Data granularity in healthcare commonly exists at three levels: administrative, clinical, and patient-specific data.

Administrative data includes patient data that identifies them within healthcare systems, like their name, address, social security number, and insurance information. This type of data is important for healthcare providers to have in order to properly identify and bill patients for services rendered. 

Clinical data pertains to data that healthcare providers collect during patient interactions. It includes information such as symptoms, diagnosis, medical history, laboratory values, medications, vital signs, and other clinical observations about an individual's health status. This type of data is crucial for healthcare providers to make informed decisions about patient care and treatment plans.

Patient-specific data includes any patient-generated data, such as health behavior, lifestyle habits, and social determinants of health. More granular patient-specific data is necessary for developing personalized treatment plans that facilitate optimal health outcomes. This type of data can be collected through patient surveys, wearable technology, and other patient-generated sources.

High data granularity is critical for healthcare providers and researchers to understand the intricacies of correlations and patterns. While the benefits of granular data are numerous, there are several challenges that stand in the way.

Challenges and limitations of data granularity

Although a high level of data granularity is always desired, some argue that too much data granularity is difficult to handle and too much details can make data useless. Although human-readability of highly granular data could indeed be an issue, when it comes to training AI and machine learning models or deriving insights on a population level, the more granular the data, the better for algorithms.

Storage requirements might also increase with granularity, however, the cost of data storage has been on a steady decline and is less of a concern today.

Data privacy and security concerns

The more granular the data, the easier to re-identify someone. This is especially true for behavioral data often encountered in healthcare settings. Until recently, striking the balance between data utility and privacy was attempted using subpar data anonymization tools, such as aggregation on a linear scale. The result: both data granularity and privacy suffered. Synthetic data generation offers a cost-effective and privacy-safe way of preserving maximum data granularity, unlocking a new dimension of data granularity for healthcare providers, researchers and policy makers.