Supplementing Patient Records with Data to Optimize Health Outcomes
Data is empowering the healthcare industry in a variety of ways. Modern healthcare organizations are combining patient records with supplementary second- and third-party data from partners and data providers to provide higher-quality care with greater efficiency. In this article, we’ll look at the data sources that can supplement patient records and explain how they’re being used to improve health outcomes.
What Is Patient Records Data?
A patient’s health records are written accounts of their health history, most often stored in an electronic health record (EHR). Patient records are an extensive collection of information, including demographics, medical history, immunizations, vital signs, service history, and progress notes from providers. Patient records also include medications, radiology images, and lab and test results.
Going Beyond Patient Records Data
For data-driven healthcare organizations, patient records are just the beginning. Providers can now bring together health-relevant data from many sources to create a true Patient 360 and the factors impacting their health. Here are four types of data that healthcare organizations can mine for important insights.
Claims data provides valuable information on healthcare costs, quality, and outcomes. For example, organizations can use claims data to compare the prices of specific services across a geographic region or to compare services delivered by particular providers based on diagnoses. Claims data is one of the most common types of data used to supplement patient records for deeper insights.
Social determinants of health
Social determinants of health (SDOH) have an outsized impact on patient health outcomes. Factors such as a patient's level of education, income level, and proximity to healthy food sources are powerful predictors of how likely the patient is to experience certain types of chronic diseases. Healthcare organizations can enrich their patient data using anonymized third-party data such as financial information on patient debt levels based on geographic area, access to physical activity opportunities, public transportation availability, level of social isolation, and housing insecurity.
Lifestyle and dietary choices
Secure, anonymized data from patient grocery orders, online shopping activity, and food delivery services can provide important insights into dietary choices, helping healthcare providers better understand the connections between lifestyle choices and health outcomes. In addition, connected health devices such as fitness trackers, blood pressure monitors, and smart scales can provide a patient’s care team with a stream of actionable data that can be used to make more informed treatment decisions.
Measuring the influence of social media and influencers
Increasingly, patients are being influenced by social media sites and content on online platforms. Depending on the context, this content can significantly influence a patient’s attitude toward healthcare options such as receiving routine vaccinations. By analyzing social media posts and comment threads on online platforms, third-party data providers can identify patient types who are likely to be influenced by alternative sources of information, enabling physicians to understand and address potential sources of resistance to certain treatment options.
How Data Is Moving Healthcare Forward
When patient records are enriched with additional data sources, healthcare providers can access the information they need to improve their patients’ health outcomes, ensure the profitability of their organizations, and more. Here are just a few ways that data is moving healthcare forward.
Improving health outcomes
Analytics tools can be used to optimize staffing by identifying patterns in how and when patients seek care. For example, insights from emergency room data can help allocate staff resources to ensure patients receive timely care, even during periods of peak demand.
Input from patients is an essential part of the quality improvement process. Using patient surveys, healthcare organizations can listen and respond to patient feedback. Analyzing patient feedback can help identify opportunities for growth, such as providing training for physicians on shared decision-making.
Identifying risk factors for certain diseases is a key part of prevention. A patient’s individual biological, socioeconomic, and lifestyle risk factors can be analyzed and presented to their physician to identify early signs of chronic disease such as diabetes or hypertension. With more time to intervene, providers have a larger window of opportunity to slow or prevent disease progression.
Predicting disease outbreaks
Predicting outbreaks before they happen provides health policymakers and healthcare networks time to prepare. One example of how data is being used in new ways involves recent studies conducted by the Yale School of Public Health. Researchers used anonymous location information from mobile device users across Connecticut to assess current social distancing practices. By identifying the frequency of close personal contact, researchers accurately predicted COVID-19 outbreaks in specific municipalities.
Assisting physician decision-making
No data analytics tool will ever replace human decision-making in healthcare. But these tools can be effectively deployed to augment decision-making, saving physicians time and facilitating more accurate diagnoses. An example is flagging anomalies on MRI scans for additional investigation.
Reducing financial risk for health systems
Using predictive analytics, healthcare organizations can realize a range of cost savings. For instance, the use of supply chain analytics to optimize inventories of medical supplies ensures hospitals aren’t carrying excess inventory but have what they need when they need it.
Preventing insurance fraud and billing errors
Accurately billing patients for services rendered is a key part of maintaining trust. When that trust is broken, patients may seek care elsewhere or delay scheduling services they need. A pattern of billing errors can also put healthcare organizations at risk for audit. Analytics tools can easily spot trends that may indicate a pattern of billing mistakes requiring intervention.
Resolving Data Privacy Concerns Using Data Clean Rooms
Healthcare organizations, data partners, and third-party providers must exchange patient health data to create the rich data sets that healthcare insights depend on. Historically, data privacy and ethical concerns prevented healthcare organizations from collaborating in meaningful ways with other data providers. Modern data clean rooms resolve data privacy issues by providing a secure space for data sharing and collaboration. Using a data clean room allows providers to analyze distributed data from multiple providers and platforms without disclosing it to each other.
Enhancing the Quality of Healthcare with Snowflake
The Snowflake Healthcare and Life Sciences Data Cloud empowers organizations and care teams to securely collaborate, combining traditional information assets such as Rx, Dx, Hx, claims, and reimbursements data with a rich range of alternative assets. The near-limitless data storage capacity and elastic compute capabilities of the Data Cloud is an ideal platform for powering data-driven healthcare initiatives.
See Snowflake’s capabilities for healthcare and life sciences. To give it a test drive, sign up for a free trial.