Healthcare analytics is the process of turning data into insights that can improve patient care. Big data plays a major role in healthcare analytics, as it can provide a wealth of information used to identify trends and improve outcomes.

Healthcare is one of the most data-rich industries, but it has been slow to adopt big data analytics due to privacy concerns and the complexity of its data. However, as more healthcare organizations are recognizing the value of data-driven decision-making, they are beginning to invest in healthcare analytics solutions.

How Big Data in Healthcare Analytics Can Help Providers

Identifying trends and patterns in historical data

Healthcare providers are always looking for ways to improve patient care. Big data analytics can help them do this by identifying trends and patterns in historical data to help identify at-risk patients. By understanding trends and patterns in historical data, healthcare providers can develop more effective treatments and interventions. This can ultimately lead to better patient outcomes and lower costs for the healthcare system as a whole.

Better decisions about treatments and allocating resources

Healthcare analytics can help answer a number of important questions for healthcare providers, such as which treatments are most effective for certain conditions, what patient populations are most at risk for certain diseases, and how to best allocate resources. In addition, by analyzing patterns in patient behavior, healthcare analytics can help predict future health trends and identify potential problems early.

Understanding how patients interact with the Healthcare system

One way healthcare providers are using healthcare analytics is to track patient engagement with the healthcare system. This information can be used to identify patterns in how patients use healthcare services which can help healthcare providers improve the efficiency and quality of care delivery.

Another way healthcare providers are using healthcare analytics is to improve clinical outcomes. By analyzing data from large populations of patients, healthcare providers can identify trends in disease progression and treatment response. This information can be used to develop new treatments and improve existing ones.

Predict demand and plan for future needs

Healthcare providers are under constant pressure to provide high-quality services while containing costs. Analytics can help them do both by providing insights that predict demand for healthcare services and plan for future needs.

By analyzing data on patient demographics, diagnoses, and treatments, healthcare providers can develop models that forecast demand for specific services. This information can be used to improve capacity planning, staffing levels, and resource allocation. Additionally, healthcare analytics can help identify opportunities for cost savings and process improvement.

For example, a hospital could use analytics to identify patterns in patient admissions and discharge times. The hospital could then change the staffing model to match these patterns. This could reduce length of stay by half a day saving millions annually.

Analytics can also be used to predict future trends in healthcare demand. This information can be used to plan for new facilities, services, and staff. For example, if data shows that the number of patients with a certain condition is increasing, healthcare providers can plan to add capacity or staff accordingly.

Population Health and Predictive Analytics

Advances in healthcare data analytic technology combined with the large amount of data being amassed in recent years has fueled the analytical capabilities available to key stakeholders and decision makers in both government and the private sector. The healthcare system uses big data in medicine to better track resource management, understand individual practitioner performance, and trace the health of populations and identify people at risk for chronic diseases.

One of the largest costs to the healthcare industry involves the treatment of common chronic diseases like diabetes, COPD (chronic obstructive pulmonary disease) and CHF (congestive heart failure). On a population-wide level, improving healthcare using big data analytics can help greatly cut costs by predicting which patients are at higher risk for disease and arrange early intervention, before problems worsen. This involves the use of big data in healthcare by aggregating data related to a variety of factors such as medical history, lab values, pharmaceuticals, comorbidities and socio-economic profile.

Patient compliance and patient behaviors are important factors that impact health outcomes. Socio-economic factors like employment and education, genetic profile and physical environment are as important in improving outcomes as the medical evidence gathered by the patient’s doctor during the exam. Public health systems are expanding their perspectives to account for these ‘outside’ factors. Big data examples in healthcare analytics show these metrics being modeled to predict the risk of chronic disease with positive results. Examples of healthcare data using predictive analytics are widely used by big pharma in clinical trials and in the discovery of new molecules and compounds that can be used to produce new drugs.

Value-Based Compensation and Data Analytics

Value-based care is reaching its inflection point as more providers and payers agree to share risk and receive fixed global payments to manage patient care with more efficiency and improved outcomes.

For Medicare Advantage, payers and providers are compensated based upon a system that is composed of fixed, risk-adjusted payments (HCC) with added incentives for clinical quality performance benchmarks and patient satisfaction. Similar programs exist in Original Medicare, Medicaid Managed Care and federal exchange programs. Without powerful healthcare data analytic tools these sophisticated value-based compensation programs become increasingly difficult to manage. Analytics from big data in healthcare provide the essential information needed to optimize the financial risk or medical costs of the patient populations assigned to organizations like ACOs and IDNs. Clinical quality benchmarks are complex statistical measures that require strong healthcare data analytic tools to aid physicians in providing appropriate quality programs and services to their patient populations.

Public Health and Data Analytics

It's no secret that big data is playing an increasingly important role in healthcare. Hospitals and other healthcare providers are using data to improve patient care, identify trends, and make better decisions about everything from staffing to treatment options. But big data isn't just changing the way hospitals operate. It's also starting to have a major impact on public policy.

As more and more data becomes available, policymakers are beginning to use it to inform their decision-making. For example, data on hospital readmission rates is being used to develop policies aimed at reducing avoidable readmissions. And data on patient satisfaction is being used to shape policies on everything from provider reimbursement to hospital transparency.

Of course, not all data is created equal. And not all data is equally useful for policy making. But as big data continues to grow in importance, it's likely that we'll see more and more policymakers using it to inform their decisions.

Big data examples in healthcare show public health organizations, the CDC, NIH and all of the state, county and local governments are increasing their use of big data in healthcare. Data analysts (individuals and firms) are being hired, en masse, by these organizations as they seek to utilize and supply data that improve the achievement of their public health goals and efficacy of their public health programs. The CDC believes in the age of big data, more extensive information by place, person and time are becoming available to measure public health impact and implementation needs. In principle, big data in medicine could point to implementation gaps and disparities and then accelerate the evaluation or even modification of implementation strategies to successfully reach population groups most in need of interventions.

However, big data in healthcare articles typically indicate major challenges need to be overcome. For precision public health to progress, further advances in predictive analytics in healthcare are needed. Healthcare analytics is not a perfect science as data integrity is a major issue that needs to be addressed. In addition, there is a lot of important information “locked” up in documents (pdfs, text files) known as unstructured data.

During the coronavirus pandemic we see the benefits of big data analytics in healthcare. It has informed public policy by helping us better measure critical activities to manage the crisis. Healthcare analytics supported supply chain management, policy regarding mitigation strategies as well as measuring spread rates, death rates, health outcomes of populations by region, age and ethnicity. This is just one of the many examples of healthcare data collected to mitigate the spread of an epidemic. As time goes on, the use of big data in healthcare will continue to be used in a variety of ways to improve patient outcomes and resource management.

The Future of Healthcare Analytics

Healthcare analytics is still in its early stages, but it has the potential to transform healthcare decision-making and improve patient care. As more data becomes available and analytics tools become more sophisticated, we will likely see even greater use of healthcare analytics in the future.

Are you looking to improve your compliance, reduce work during sweep timeframes, and eliminate clerical bottlenecks using the latest healthcare analytics technology? See how ForeSee Medical can empower you with HCC risk adjustment coding support, and integrate it seamlessly with your EHR.

see how it works

by Dr. Seth Flam, CEO