3 Data Analysis Strategies to Improve Patient Outcomes

Data analysis has transformed the healthcare industry, impacting health outcomes and operational efficiency. In fact, more than half of healthcare executives currently use data analytics, and of those, 42% reported improved patient satisfaction and 39% reported cost savings.

However, as one of the biggest contributors to the world’s data volume, the healthcare industry is overloaded with data. This complicates the industry’s ability to obtain and act on valuable insights. Healthcare organizations must approach data analysis strategically to sort through vast datasets and make data-driven decisions.

In this article, we’ll uncover three data analysis strategies healthcare professionals should implement to improve patient outcomes.

1. Establish an infrastructure for data collection and integration

Healthcare organizations collect data from various sources, such as clinical notes, pharmacy records, and self-reported patient data. As a result, data can be stored across numerous systems, resulting in fragmented and siloed datasets.

To build a robust database that supports successful analysis, providers must ensure their data is complete. The more comprehensive the data, the more accurate the analysis will be.

This requires an organized system for collecting data and combining it into one unified dataset. To have a comprehensive view of patient health, organizations must establish an infrastructure for these processes by:

• Leveraging advanced technologies. Investing in analytics tools is key to making quick yet informed decisions. Arcadia recommends leveraging healthcare dashboards because they “accelerate performance by empowering healthcare professionals to incorporate relevant, updated data insights into their decision-making processes.”
• Implementing data governance frameworks. Outlining clear procedures for handling data enables healthcare teams to analyze data safely and efficiently. This can include setting role-based data access controls or standardizing data-sharing processes.
• Ensuring interoperability between systems. Interoperability, or the ability to exchange data between different information systems, is necessary to integrate data from various systems for a robust database. Implementing interoperable analytics tools ensures that organizations can create a unified view of patient health.

Providers can rely on this infrastructure to gain a holistic view of patient data. As a result, they can make more accurate diagnoses, personalize treatment plans, and enhance care coordination to improve patient outcomes.

2. Apply data to population health management

With a strong data collection and integration system in place, providers can access insights into both individual patient health and population health. Population health management involves analyzing data to reduce disparities and improve outcomes across an entire community.

Providers must analyze population-wide data to build targeted interventions that prevent or address health issues in at-risk groups. For example, providers can target educational materials to populations at risk of medication non-adherence. Or, providers may offer preventive care services such as screenings and vaccinations to at-risk populations.

To identify health trends and at-risk groups, providers must analyze:

• Quality of care. Measuring the timeliness, equity, and efficiency of health services can indicate how much they increase the likelihood of a population’s desired health outcomes.
• Health indicators. Hospitalization rates, chronic disease prevalence, and other health indicators can provide valuable insights into population health and necessary interventions that should be implemented
• Social determinants of health (SDoH). Research shows that there’s more to predicting health outcomes than a patient’s genetic code. Many socioeconomic factors, such as income, education, and employment status, can affect a population’s health outcomes.

Additionally, risk adjustment analytics can power more comprehensive views of population health by predicting the utilization and cost of healthcare services. Incorporating this data into population health analysis offers greater insight into providers’ performance and any missed opportunities.

3. Implement predictive analytics

Data analysis isn’t limited to the current health status of patients or populations. By applying historical and current data to statistical modeling, artificial intelligence (AI), and machine learning techniques, providers can predict future health events.

This process is referred to as predictive analytics, and it can be broken down into the following stages:

1. Data collection. A provider collects, integrates, and stores data using its established infrastructure
2. Preprocessing. Data is cleaned to ensure accuracy and relevancy
3. Modeling. Predictions and recommendations are made for patient populations using data algorithms
4. Interpretation. Providers use predictions and recommendations to make data-backed decisions

Predictive analytics helps providers predict patient readmissions, potential complications, disease outbreaks, and other health issues that could be avoided by implementing preventive measures. For example, providers can identify patients at risk of readmission and offer personalized discharge instructions that proactively address their follow-up needs. Additionally, providers can automate personalized messaging to remind and engage patients in continuous care.

While predictive analytics is instrumental in improving patient outcomes, patients aren’t the only group that benefits. Care managers use predictive analytics to allocate time and resources, and point-of-care teams can access decision support to manage emergency situations. Even IT operators can use predictive analytics to maintain data quality and security.

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To implement successful data analysis strategies, providers must carefully plan their approach and remain open to continuous improvement. With the right tools and procedures, teams across the healthcare continuum can realize the benefits of data-driven processes. When healthcare providers and professionals are dedicated to making informed decisions, data analysis will improve outcomes for patient populations.