Decoding health: The transformative power of data analytics
Gaining insights from patient data is crucial for organizations to better follow the evolving pathway of healthcare through insight-driven decisions.
Data analytics are crucial to providing valuable insights to health insurance companies and healthcare providers regarding patient care. They help professionals assess the effectiveness of interventions for specific conditions by looking at patient outcomes, treatment protocols, medical histories, genetics, lifestyle factors and socio-economic data.
Predictive models can identify individuals at high risk of developing certain conditions. This enables healthcare providers and insurers to offer proactive interventions such as preventive screenings, lifestyle interventions or early disease management strategies.
By analyzing vast amounts of data, including billing records, claims data and provider information, anomalies and patterns indicative of fraud or abuse can be detected. As a result, insurance companies are more likely to prevent fraudulent claims, ultimately reducing costs and ensuring that resources are allocated appropriately.
Additionally, healthcare and insurance providers gain insight into population health trends by analyzing aggregated patient data. Data analytics identify high-risk patient populations, prevalent diseases or conditions and healthcare utilization patterns. This information facilitates proactive interventions, targeted preventative measures, and the development of tailored care management programs to improve health outcomes and reduce costs.
Nowhere was this more sharply brought into focus than during the height of the COVID-19 pandemic. During that time when thousands of people were being hospitalized, the Louisiana Department of Health and Hospitals used Palisade’s @RISK, their own predictive analytics tool to track — among other things — fatalities in the hospital and monitor the availability of ICU beds.
Improved outcomes and quality of care
Healthcare company Kaiser Permanente began integrating data analytics in 2015. Five years later, it reported reduced patient wait times and time spent preparing manual data. This would not have been possible without data analytics and its myriad tools, including data warehousing, natural language processing, business intelligence, clinical decision support, machine learning and artificial intelligence tools.
Data analytics supports better patient care by analyzing data that can identify differences in care provision based on factors including gender, ethnicity or geographic location. These insights highlight potential disparities across populations and provide opportunities to address them to ensure good quality healthcare for all patients, including improved wait times, and discovering and eliminating medication errors.
Researchers at Stanford University and Intermountain LDS Hospital effectively used data analytics to install depth sensors with machine learning algorithms in the intensive care unit at the hospital. The sensors were designed to track the mobility of critical care patients to aid in their recovery, and managed to identify, among other things, patient movement 87 percent of the time.
Best practices
While there are numerous benefits to using data analytics in the healthcare industry, it’s imperative for providers and insurance carriers to create and implement the appropriate steps for a robust and efficient data analytics program. Clear objectives must be defined for each company’s program by first determining the problem that needs to be addressed by asking: Is the goal to improve patient outcomes, optimize resource allocation or detect fraud?
The next step is to implement strong data governance practices, including establishing standards, policies and procedures for data collection, storage, sharing and access. This is achieved by creating a data governance committee with stakeholders from different departments, including IT, legal and compliance.
It’s important that companies do not skimp on investing in a robust data infrastructure — one that is capable of handling large volumes of data and supporting advanced analytics capabilities. Of equal importance is to develop a highly skilled team of data analysts, data scientists and domain experts who understand the healthcare industry and have strong analytics capabilities. To guarantee that workers are continually honing their skills and keeping up with the latest trends and techniques, companies need to invest in providing appropriate training and professional development opportunities.
For data analytics to be leveraged to their maximum potential, companies cannot remain insular. Instead, they need to promote collaboration and communication between different stakeholders, such as healthcare providers, insurance carriers, IT and analytics teams.
At the same time, it’s crucial to consistently monitor the analytics to evaluate their performance so that adjustments can be made to ensure ongoing success. This can be done by tracking key performance indicators and metrics aligned with the company objectives.
Avoiding pitfalls
While it’s easy to get excited about the potential benefits data analytics can bring to the healthcare industry, there are several thorny issues to address.
First, there are concerns with respect to highly sensitive healthcare data. That’s why it’s essential that any implemented data analytics program be compliant with the relevant privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA). Companies need to establish robust security measures, including data encryption, access controls and audit trails, to safeguard patient data and build trust among stakeholders.
While implementing data analytics may seem daunting, embracing the future is essential if companies want to remain relevant. That requires fostering a culture that values data-driven decision-making and promotes analytics to propel insights. Companies are often resistant to change. That’s why it’s important to communicate the benefits and value of data analytics to stakeholders, engage key leaders to champion the program, and provide training and support to help employees embrace analytics.
That alone is not enough for a company to fully integrate data analytics into its practices. Data analytics requires skilled professionals with domain knowledge, data analysis expertise, and the ability to leverage advanced analytics techniques. A collaboration with external partners or consultants will help bridge the skills gap during the initial stages of implementation. To make sure they don’t have an inadequate or outdated IT infrastructure, companies can invest the time and funds in obtaining the necessary hardware, software and network capabilities to support data analytics, including cloud-based solutions, data warehouses and data integration platforms.
Embracing new models
The application of data analytics in healthcare is diverse and continually evolving, which enables data-driven decision-making and enhances the overall healthcare ecosystem. Healthcare generates vast amounts of data.
Without analytics, organizations cannot fully leverage data’s potential to drive innovation, improve processes and enhance patient care. It’s also important to remember that data analytics is not a one-time effort but an ongoing continuous improvement process.
Organizations should prioritize the regular monitoring of performance metrics, evaluate outcomes and refine analytics initiatives based on insights gained. Embracing an agile and iterative approach supports adapting to evolving needs and optimizing the impact of data analytics over time.
Once healthcare companies and insurers adopt these measures and strive to maintain appropriate guardrails, data analytics can revolutionize healthcare for the betterment of all.
Mohan Krishna Mangamuri is a senior application architect with more than 14 years of experience in design and development in a variety of enterprise applications, including data lake, data analysis, ETL development and programming. He also has more than 10 years of experience in the healthcare field.