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Healthcare Data Analytics: How It’s Improving Healthcare Delivery

Healthcare Data Analytics: How It’s Improving Healthcare Delivery

Healthcare Data Analytics

Data has never been more prevalent in our everyday lives. From what we search for on our computers and phones to what we purchase to how we move around our communities, data is being compiled about – and for – us. In healthcare, players across the industry must ask the question “how does data analytics help healthcare?” to deliver the high-quality, well-informed care patients need. 

This blog explores the opportunities of applied healthcare data analytics. We cover:

  • Best practices for data aggregation, cleanliness, and security
  • Leveraging social determinants of health (SDOH) data
  • Using data to benchmark organization performance
  • How data can inform individual patient encounters

How do Healthcare Data Analytics Improve Patient Care?

The need for healthcare data analytics at the point of care is not a new one. In today’s increasingly data-driven healthcare atmosphere, where organizations seek to improve both quality and economics, merely having the data is not enough to improve healthcare delivery. Even with analytics ran to inform and guide decision making, there is still more needed to drive impactful action. What truly sets top tier organizations apart from their peers is having in place a platform to ingest, normalize and continue to enrich the data they have – helping establish a genuinely data-driven healthcare organization.

The idea that rapid access to the right data will drive improvements in quality outcomes, economics and analytic capabilities has never been more true than right now. But too often, that data isn’t easily located, with different types of data housed in silos – sometimes in different departments or even in different buildings or geographic locations. We need systems that coordinate and collaborate to enable us to maximize the impact of the data we have. Without the power of analysis, we just have numbers. How much data we have matters much less than what kind of data we have. Where did the data come from? Is it accurate? What is the story that it tells, and do you have the right tools to interpret that story?

For the past decade, the economic and regulatory pressures on payers, hospitals and providers to demonstrate improved quality outcomes has driven a significant effort to combine provider clinical data with non-clinical or administrative payer data, as well as patient data from disparate sources. Organizations are making significant investments in underlying data technologies and partnerships with companies that can acquire and cleanly integrate that clinical data to meet this need and drive greater transparency, care continuity and clinical quality outcomes for their members.

The combination of this disparate data, including data originating from the patient, requires organizations to employ “smart” data management practices. The desire to extract important clinical insights that could be highly valuable for calculating quality and attaining desired outcomes is driving investments in natural language processing and machine learning.

Social Determinants of Health Data

The more data you have on your members, the better you understand them and the better you are at predicting how – and why – they respond to circumstances and information. As the healthcare industry continues to improve care quality, the focus on providing patient-centered care has significantly increased, and part of providing the most effective care is understanding the effect social determinants of health – where individuals live, work and play – have on overall well-being.

Developing a 360-degree view of the patient remains the gold standard in healthcare data analytics, and understanding more about patients’ social needs is a valuable piece of that equation. Social determinants of health are increasingly recognized as a key component to overall health and wellness of individual patients, as well as entire communities. Likewise, collecting and using social determinants of health data has become a fundamental aspect of improving care and lowering the skyrocketing costs of healthcare.

Individuals at greater risk of complications from COVID-19 are those with underlying conditions, including diabetes, heart disease and hypertension – conditions that are more common among socially disadvantaged populations. As more data on socioeconomic data on COVID-19 patients is collected, it’s becoming clear that social and health disparities rampant across the country before the pandemic are prevailing during the outbreak.

“While COVID-19 has not created the circumstances that have brought about health inequities, it has and will continue to severely exacerbate existing and alarming social inequities along racial and ethnic lines, e.g., in housing stability, in employment status, in healthcare access, and in food security.” – From a letter to Alex Azar, HHS Secretary, from the American Medical Association, American Academy of Pediatrics, American Academy of Family Physicians, National Medical Association, National Hispanic Medical Association, Association of American Indian Physicians, and National Council of Asian Pacific Islander Physicians.

Data on the social needs of patients has long been a valuable tool for healthcare providers; however, establishing a better understanding of how this information impacts each patient is key to providing the right care to the right patient. The social determinants of health are responsible for as much as 50 percent of total healthcare outcomes, according to the Robert Wood Johnson Foundation’s County Health Rankings and Roadmaps – that’s more than other frequently used determinants, such as a patient’s health history.

Advanced analytics facilitate deeper patient-level insights and inform effective, targeted outreach to the right patient, through the right venue and at the right time. When you layer in socioeconomic data on top of the electronic health records you already have, you create a more accurate account of your current and future patient population. Predictive analytics allow for more informed decision making about suspected conditions that may impact a specific patient’s care plan, producing better individual health outcomes while driving efficiencies and improving economic performance.

But, like so much of the healthcare data we collect, data on social determinants is often difficult to collect with the limited time and resources many organizations face, and the data that most organizations have is disparate and incomplete. However, failure to include social determinants of health data can keep providers from appropriately addressing those issues most significantly impacting their patient population.

To provide targeted interventions for patients, providers and health plans alike need information about the social determinants of health – data that is increasingly being identified through artificial intelligence and other technologies.

Predictive Analytics: Measuring Performance Ensures You Measure Up

Healthcare organizations are continually striving to effectively manage their performance and improve efficiencies – and doing so only their own data is like operating in the dark. If you are armed with broad and timely industry comparative data insight, then you know where and how you need to improve. This will be especially true in the aftermath of this pandemic. Right now, every organization is managing through these unprecedented times. And at the heart of this crisis, data and its analysis is playing an integral role in informing the development of effective strategies to navigate these unchartered waters and improve healthcare delivery.

Data-driven strategies that harness the power of advanced predictive and comparative analytics to benchmark performance against the market and inform strategy in real time will be a significant differentiator for health plans. And having actionable, healthcare data analytics that provide insight into current market performance trends is essential to informing future strategy and goals.

Healthcare payment models and quality incentive programs are based on relative performance – graded on a dynamic curve. Having data-driven insight into not only your own past, current and predicted future performance, but how you are performing compared with others in the market enables you to better target your performance goals and tailor the right strategies for success.

Encounter data is essential to gaining a better understanding of both the care we are providing and the care that should be provided. The ability to identify care gaps and more accurately monitor the disease status of patients is invaluable in today’s data-driven, value-based environment. This data can be used to effectively identify patients who are at risk or are overdue for an encounter – even virtual health visits in today’s environment where social distancing remains standard practice.

The data we collect is telling an important story about specific patients, entire populations and organizational performance. Those organizations that connect and communicate bidirectionally will realize the most significant improvements in their ability to respond. To truly elevate the quality of data and quality of care delivered because of that data, the healthcare industry must first understand what data they need and then develop the healthcare data analytics that put that data to work in the most effective, efficient manner.

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