Healthcare Data Management Best Practices

Modern healthcare benefits in myriad ways from data. Patient data, including medical records and test results, and public health data are being used to predict illness, prevent chronic disease, dial in treatments, and more. Ultimately, data is driving improvements in health outcomes. Healthcare data management is crucial to healthcare professionals’ ability to make use of this data. It involves preparing, storing, securing, and analyzing various types of healthcare data from a variety of sources. To balance data quality and access while staying compliant, healthcare organizations must implement several healthcare data management best practices. 

Examples of How Data Is Improving Modern Healthcare

Modern healthcare makes use of many different types of data, including the following:

• Patient medical records

• Behavioral data

• Biometric data

• Patient population data

• Insurance claim data

• Medical referrals

• Public health data

• Resource utilization data

With this data, healthcare providers can improve the patient experience, achieve better health outcomes, and optimize operations to reduce costs. Let’s take a look at four of the most significant ways data is advancing healthcare.

Prevent and treat disease more effectively

Physicians can use family history, biometric data, lab tests, and patient population data to identify patients with risk factors for developing certain chronic conditions early in the disease’s progression. When physicians know that a disease is likely to manifest, they can begin providing preventative care to help these patients avoid complications. Additionally, physicians can analyze patient data to determine which treatments are likely to be most effective for each patient. They can also avoid prescribing medications or providing therapies that are contraindicated. 

Improve the patient experience

Healthcare organizations can use data to develop a comprehensive picture of the patient experience. Patient surveys, online review pages, resource utilization data, and staff allocation data can indicate where problems lie and where the patient experience can be improved. Additionally, providers can track the outcomes of their patient outreach efforts to determine which communication strategies are most effective.

Boost patient engagement

Patient health engagement models can help predict the risk of a patient becoming disengaged and alert physicians to the need for proactive intervention. Additionally, connected health device data can be used by patients as well as physicians to track progress toward health goals, keeping patients engaged in their healthcare.

Identify underserved populations

Funding and resource allocation has been a significant challenge for decades. Now, anonymized medical record data and Google map data are being combined to identify geographic areas with underserved populations. With this data, providers are armed with the numbers they need to gain additional funding and deploy resources to those that need them most.

Best Practices for Healthcare Data Management

Before data can be used, it must be managed effectively. Healthcare data management ensures that data is available, accurate, secure, and actionable. Here are four key best practices for healthcare data management.

Data integration for a 360-degree view 

Many healthcare organizations are faced with the challenge of siloed data (data stored in various systems, databases, applications, and so on). As a result, they can’t effectively cross-reference data or build multidimensional dashboards. A modern data management system that integrates this data and serves as a centralized repository is a must for a 360-degree view of patients and healthcare organization operations. 

Data governance for access and integrity

Allowing healthcare professionals to access data without strategic governance in place creates significant risk and can result in inaccurate insights. For this reason, organizations must have a reliable framework for managing data, ensuring quality, minimizing risk, and meeting data-focused regulatory compliance mandates such as HIPAA. 

Data security for protecting sensitive data

Beyond governance frameworks, healthcare organizations must ensure their data platform and data management tools are built with sufficient data security features such as dynamic data masking and end-to-end encryption for data in transit and at rest. Additionally, organizations should seek vendors with certifications such as HITRUST, SOC 1 and 2 Type II, PCI DSS, and FedRAMP. 

Analytics capabilities for making data actionable

When data is integrated into a single platform, analysis becomes much simpler. But because healthcare providers need the ability to analyze massive amounts of information for many use cases, storage capacity is not the only requirement. Sufficient compute power and efficient analytics tools are also necessary. 

Snowflake for Healthcare Data Management

Healthcare data comes from myriad sources. The Snowflake Healthcare and Life Sciences Data Cloud enables data from multiple sources to be integrated on a single platform, making it easy to run advanced analytics tasks designed to uncover actionable insights. Additionally, Snowflake allows near-unlimited storage and computing capacity that enables faster analytics queries. Sensitive data is stored securely with built-in security and governance that supports HIPAA, HITRUST, SOC 1 and 2 Type II, PCI DSS, and FedRAMP (medium) requirements. 

Today’s healthcare organizations now have the depth of insight, analytical power, and collaborative tools needed to operate efficiently and deliver the best results for patients. In addition to its built-in analytics features, Snowflake offers seamless integrations with analytics tools. And because Snowflake quickly scales up and down, on the fly, or automatically, with per-second pricing, you can efficiently process and analyze massive data sets as the need arises. 

Explore Snowflake’s capabilities for healthcare data management and see what it can do.