Analyzing Healthcare Data for Better Population Health Outcomes

The ability to improve population health is a key initiative in the healthcare ecosystem. Achieving this objective requires many components, and one of the most critical is improving our ability to analyze healthcare data.
This data often comes from multiple, disparate sources and includes patient demographic and clinical data, typically from electronic health records (EHRs) and insurance claims data. Such real-world data (RWD) and the real-world evidence (RWE) derived from it are significant factors in facilitating healthcare analytics and the innovation it can drive.

While some data sources are broad, deep, and accurate, there are still challenges with providing a holistic picture of global populations. Addressing this gap is essential to improving population health outcomes worldwide.

In this article, we will explore the potential positive effects of analyzing global healthcare data on population health outcomes.

Why We Need Access to Global Healthcare Data

Access to diverse data is a problem in clinical data analysis. Most trusted sources of data available are European- and US-based, either underrepresenting or excluding many populations. Addressing this gap means identifying and accessing data sources that are currently not visible. Enriching current data sets with information from sources outside of the US and Europe is the key to developing better insights, enabling:
  • More comprehensive clinical research models
  • Shifting from reactive care to preventative care
  • Reducing patient outcome risks with evidence-based treatments
  • More personalized care
  • More reliable and consistent outcomes for populations

Common Methods of Clinical Data Analytics

Clinical data analytics is foundational for healthcare learning. From this valuable information, organizations can generate insights on trends, gaps in care, issues with current care provisions relating to interventions, and opportunities for cost reduction.

Clinical data analytics drives the improvement of processes and policies through various diverse data points. What are the methods of analytics of healthcare data? There are six to consider.

Modeling and Microsimulation

The modeling and microsimulation method estimates how demographic, behavioral, and policy changes affect outcomes. Researchers build models based on large-scale sets of data, often collected via public health surveys.

They then manipulate these microsimulation models by adjusting factors regarding program rules. Models go through several rounds of testing and validation before researchers can be confident in their accuracy.

Descriptive Statistics

The field of healthcare descriptive statistics aggregates data grouped into variables to assess values and their spread for each data set variable. These data sets include information about specific people and work best for small cohorts versus for large populations. 

This method uses various statistical measures, including central tendency, dispersion, and skewness to summarize data. Descriptive statistics are useful for organizing and meaningfully presenting the information collected. Stats are only created based on collected information and will not go beyond the available data.

Inferential Statistics

Inferential statistics uses tools like hypothesis tests, confidence intervals, and regression analytics. Researchers take samples of data from a population to make generalizations about it. This type of analysis enables a better understanding of the probability and circumstances under which an event could occur. 

Network Analysis

Network analysis studies the relationships between groups within a larger network to understand and manage healthcare. This process involves grouping patients who can benefit from being managed together

To illustrate the value of network analysis in driving better patient outcomes, researchers analyzed patients with diabetes and their physicians. The objective was to find hidden population structures to improve intervention targets and patient-centered care. Using network analysis, the study uncovered these links, which health plans could use to bolster quality improvement and disease management programs.

Geospatial Analysis

Geospatial analysis monitors disease outbreaks by collecting and analyzing healthcare data related to its location and how it’s spreading. This area of clinical data analysis is vital for public health, as it’s a way to surveil and investigate the spread of disease.

This type of analysis also has other uses, including understanding if a population has access to health services. A JAMA Network published study leveraged geospatial analysis to find a correlation between healthcare access and internet service availability. The results indicated this positive connection finding that areas without internet service were the most likely to be healthcare deserts.

Social Network Analysis

Social network analysis ties together the interaction and interconnection between people and social groups. It seeks to explain patterns relating to behaviors, thoughts, and feelings.

This growing area of analytics applies to disease transmission, health behavior, and social support and care systems. It hinges on the concept that people have a network of relationships and that those relationships affect health-related outcomes. Social network analysis concentrates on social support for an aging population and the prevalence of long-term chronic diseases.

Data is the thread that weaves all these analytical methods together. However, the effectiveness of these methods is limited by the completeness, accuracy, and the diversity of the RWD being used to drive them.

To understand why, let’s explore real-world examples of using healthcare data analysis.

Real-world Examples of Healthcare Data Analysis

Healthcare data analysis applies to many real-world scenarios. Here are five that top the list.

Population Health Management

With RWD data collection, the healthcare community can identify the risks of a population. There are multiple ways this component depends on clinical data. One example is addressing chronic health conditions. The American Hospital Association (AHA) identified its use in creating Age-Friendly Health Systems, battling the opioid epidemic, and providing palliative care.

Remote Patient Monitoring

The need to monitor patients outside traditional clinical settings grew significantly during the pandemic. This trend continues as technology improves and more medical devices are put into the field.

The data these tools collect is available in real time for immediate intervention. They also create a patient profile for providers to make treatment decisions and measure their effectiveness.

Telemedicine and Virtual Health

The other technology adoption accelerated by the pandemic is telemedicine and virtual health, as it became a priority to limit exposure.

It improves access, especially to mental health services, and can bridge the gaps in medical deserts. Patients and providers benefit, and the medical community supports its continued use and expansion.

The data these care practices generate involves patient data entered into the EHR. Other information it can deliver includes insights into the availability of care. It could help public health identify where care delivery is faltering to strengthen it through programs.

Drug Discovery and Development

Drug discovery and development are data-driven areas of the health ecosystem. These endeavors require much information about a disease, how it affects people, and how trials are evaluated. We can look to the accelerated development of the COVID-19 vaccine as the result of all this data coming together to deliver a safe and effective treatment to the population.

Automated Medical Imaging Analysis

Analyzing medical imaging through automation is prevalent and useful in the screening workflow. It speeds up medical imaging test reviews with artificial intelligence (AI) becoming the tool to detect problems earlier. This AI-powered approach analyzes data points from the medical report to discern if the patient does or does not meet the criteria of a disease.

With so many possibilities, what’s next for healthcare data analysis?

The Future of Healthcare Data Analysis

The horizon for healthcare data analysis is bright, with opportunities to improve population outcomes positively. Its future has three components that will influence it.

The Role of Big Data and Advanced Analytics

Big data consists of all the possible sources that fuel analytics—consumer, patient, physical, claim, and clinical. The vastness of data is only growing, but it’s not always clear, complete, or accurate.

It also doesn’t deliver insights without applying advanced analytics tools like machine learning algorithms. Otherwise, it’s an endless sea with no current. Healthcare needs structures to shape big data with advanced analytics so that it helps entire populations effectively.

Emerging Technologies and Approaches

Managing healthcare data analysis requires technology tools. With the massive amount of data available, the industry needs various resources to progress efficiently.

The first area we’ve already discussed is AI and machine learning. These tools identify, detect, and analyze data to deliver results for human interpretation and action. The healthcare community’s refinement of AI and machine learning applications is a significant objective because of the value it can bring and its ability to speed up workflows.

Another emerging trend is bioprinting, which leverages 3D printing to make external prostheses, implants, and stents. Large amounts of data are necessary to develop these, and continuing to monitor their performance will inform the next reiterations.

Collecting data is as important as analysis, and the Internet of Medical Things (IoMT) ushered in a new approach. These devices and wearables are data mines for patients and their outcomes. The data collected from these devices, on a larger scale, can also inform the research and treatment of common diseases like diabetes.

While there is much to celebrate in the future of healthcare data analytics, challenges and barriers remain.

Addressing Challenges and Barriers

We’ve discussed the problem with healthcare data—it’s fragmented, incomplete, not always diverse, and accessibility can be an issue. These challenges make the potential for healthcare data less achievable, but there are ways to address these.

Interoperability is key for aggregation, and standardization would reduce the need to format and clean it. The diversity situation is less talked about but just as crucial.

Syndesis is solving this problem by partnering with hospitals, healthcare systems, and insurers outside the US. As a result, we can curate millions of de-identified patient journeys, including demographics, treatments, procedures, medications, pathology, interventions, and more. Our goal is to create better patient outcomes and improve research and data analysis excellence by centralizing access to this global RWD.

Better Patient Outcomes Depend on the Collection and Analysis of Healthcare Data Generating RWE 

Obtaining and classifying data isn’t enough to move the needle on outcomes. Analyzing healthcare data to generate RWE using many methods and data sources, and putting those insights into action is where global change happens.

That directive matters most in the future of well-being and health delivery for everyone. With this in mind, it’s time to expand your data capabilities. Often that’s been challenging for healthcare, as data isn’t always as accurate or complete. Syndesis Health is working to close these gaps by providing unified access to global real-world clinical data, sourced primarily from underrepresented regions. This data has the power to drive better population health outcomes.

Contact our team today to explore our data offerings and capabilities.

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