Using Data to Improve Population Health Management

The healthcare industry is finding ways to improve health outcomes and lower costs, and it’s using data to do so. Insights gathered through the collection and analysis of patient and health data can result in significant improvements to population health management, enabling more proactive and personalized care. In this article, we look at how data analytics can help healthcare providers improve population health management. We discuss potential challenges to implementing a successful analytics initiative and ways to overcome them.

Data Sources for Population Health Management

Modern healthcare organizations have access to an unprecedented amount of patient and health data. But this data is often siloed across the organization. Effective population health management practices depend on identifying available sources of health information and understanding how this data can be used to improve outcomes. 

Claims

One of the easiest data sources to access is claims data since it is highly structured, easy to access, and contains information useful for a variety of population health inquiries. Claims include patient demographic data, diagnostic codes for the services provided, when and how often the patient received care, and the billable amount for each visit. However, since claims data is intended for billing and reimbursement, important clinical details and information regarding the provider’s process of providing care are often absent. Also, since insurance claims only involve past events, their ability to inform proactive population health management initiatives is limited.

EHR

Electronic health records (EHRs) help fill in some of the gaps in claims data. EHRs hold a wealth of information regarding the process of providing patient care, health concerns shared with providers during a visit, and provider perspectives. EHR data also include vital statistics, current medications, and results from imaging or lab reports. 

Socioeconomic data

Average income, access to healthy food options close to home, employment status, education level, and other socioeconomic indicators (also known as social determinants of health) are powerful predictors of population health. Since this data isn’t immediately relevant to providing patient care, it’s rarely collected directly from patients. However, healthcare organizations have begun to realize the importance of socioeconomic factors for population health management. As a result, some insurance payers, providers, and research institutions have formed partnerships to actively collect and analyze these new sources of data in an effort to more accurately predict how specific social challenges faced by individual communities impact individual patients. For example, Snowflake’s Marketplace is providing many healthcare organizations with the data they need to improve population health.

Medication adherence data

Failure to take prescribed medication has significant implications for managing population health. Complex patients taking numerous medications present challenges because they are typically seeing multiple providers in different organizations. This separation of care elevates the chance of dangerous contraindications. Data gathered from claims, EHRs, and e-prescribing databases can be combined with socioeconomic indicators to help healthcare organizations more accurately predict which patients are more likely to stop taking medication due to financial hardship. Analyzing data from these sources can also help avoid contraindications.

Patient-generated health data

Valuable patient-generated health data comes from a variety of sources, including patient satisfaction surveys, messages sent to providers through patient portals, and stats streamed from IoT sensors such as fitness trackers and home monitoring devices. Data gathered from these devices can include information related to sleep patterns, exercise, and heart rate. This information can be used to proactively alert providers to patients who are at elevated risk for various lifestyle diseases. 

Challenges Involved in Analytics for Population Health Management

Because healthcare organizations face significant obstacles when seeking to access insights from health data, many aren’t benefiting from data to the extent they could be. Here are the top challenges and how to overcome them to improve population health management.

Data integration and governance

Because data is generated from many different sources and stored in various locations, unifying data can be difficult. A healthcare organization’s data platform must be capable of processing, storing, and analyzing data in a variety of formats, generated at highly variable rates and volumes. In addition, sensitive individual patient data must be stored in accordance with HIPAA regulations in a fully governed manner. 

Variables outside healthcare

Effective population health management programs depend on accurately understanding the health needs of individual patients. But this information is not always readily accessible. As mentioned, socioeconomic factors such as income level, education, and employment status are predictors of patient health, yet this data is seldom collected during interactions with patients. In addition, the steady stream of health-related data from wearables such as fitness trackers and other IoT devices remains inaccessible to most healthcare organizations. However, leading organizations are seeking ways to collect this ancillary data that is so valuable for population health management, including using third-party health data and encouraging patients to track and share wearable data.

Deriving actionable insights from data

The sheer volume of data stored across multiple platforms can make it difficult to know where to begin when it comes to mining the data for actionable insights. Healthcare organizations often struggle to translate available health data into workable solutions that enhance patient care and lower costs. But the growing emphasis on value-based healthcare and pressures to reduce costs require data-driven insights to provide higher levels of patient care without additional spending. Implementing a scalable data platform that unifies data sources and supports discovering and securely sharing live governed data is a crucial starting point.  

How Snowflake Supports Population Health Management

Snowflake’s modern, advanced data analytics tools unlock insights healthcare organizations need to effectively manage population health, reduce costs, and improve individual patient outcomes. The Snowflake Data Cloud offers an ideal solution for storing data from various sources within a single, fully governed, HIPAA-compliant cloud data platform. Unlock the insights your healthcare organization needs to enhance care delivery and increase operational efficiencies at every level of your operations. 

To see Snowflake’s healthcare data capabilities firsthand, sign up for a free trial.