Informed Decision-Making with Healthcare Analytics

In healthcare, the byproducts of past challenges—data assets and innovation—are helping us envision a future where clinicians are well equipped to predict health outcomes and make informed decisions. Imagine a future where organizations can save resources and policymakers can act fast during a public health crisis, such as a spike in influenza cases. We’re on the verge of a healthcare transformation where this future is becoming a reality. Thanks to advanced analytics, information standards, and holistic health records, data-driven decisions are becoming the norm, leading to efficient operations and optimized outcomes.

Data is interdisciplinary

Data helps providers, researchers, and patients bolster the overall health arc of our society. For example, data analytics helped healthcare stakeholders to navigate the COVID-19 pandemic and improve their response to the public health crisis. But what about the data from disparate applications, such as supply chain, electronic health records (EHRs) and other clinical information systems, or even human capital management (HCM)? How are these funneled into the clinical world for advanced analytics and decision-making?

Organizations have used varied sources of data for a long time, deployed by provider, payer, and pharmaceutical organizations, with SaaS and on-premises applications. These systems enabled transformation, but they also inadvertently created data silos. Health data that is trapped in many systems cannot easily move with the patients when they switch providers. Furthermore, the longitudinal health record that is valued for delivering better patient care is distributed and fragmented. Imagine if you had to re-enter all contacts, applications, and other personal data every time you bought a new phone? If not for the cloud, each new phone purchase would be challenging. When it comes to siloed patient health data, it can adversely impact patient lives.

Bringing all data into one centralized location helps, but it’s equally important for that data to be logical, understandable, accessible, and curated for use. Data must be normalized to be useful. Data from multiple sources—structured or unstructured, preprocessed, or raw —must be holistically accessible so it can be analyzed to drive meaningful insights.

Data Lakehouse: a path to interoperability

Understanding the context behind data is fundamental to informed decision-making. Data generated by enterprise applications is highly valuable yet seldom fully utilized, You can gain insights across disparate data sources such as electronic transactions, office visits, and phone transcriptions using services like Data Lakehouse on Oracle Cloud Infrastructure (OCI). A lakehouse simplifies access to data from multiple applications and data sources, and enables sophisticated data modeling. Interoperability is also supported not only because of the data structures or semantics but because of the context and prominence of data.

Historical data from inventory, research, Internet of Things (IoT), or EHRs has been living in “cold storage”—meaning, they are trapped in systems, faxes, or on paper. This data has not been optimized. Somewhere, there is device data from ten years ago or genomics data captured five years ago. Has that information been transformed into meaningful information in the current context of patient health? Using de-identified historical data in predictive modeling can help uncover insights into a patient’s health. Combining current data, real-time data, and historical data together makes predictive modeling stronger and more dependable.

Patient data belongs to the patient. Organizations worldwide must uphold patient data security to its highest standards. Through data sovereignty laws—the next natural progression of data security—countries are keeping their citizen’s data secure. This presents an opportunity to help patients build trust in the healthcare system, allowing them to see how their data is handled.

Anonymized data protects data privacy and it can also have a life-changing impact when shared with researchers for clinical trials. When aggregating data, solutions need to be flexible and mindful of data governance regulations.

Actions to prepare for advanced analytics solutions:

• Select an area where you can improve outcomes
• Identify the gaps in your processes and care delivery methods
• Use change management to address those gaps
• Implement change management practices across departments where you can add the most value
• Understand your data sources, data fidelities, and metadata, syntax, and semantics used
• Develop skills around data science and new AI/ML healthcare technology
• Harness analytics solutions that close the gaps and improve outcomes

Envisioning future solutions

Providers face myriad challenges when delivering care—from lack of resources to stress and clinician burnout. Adding to the pressure is the fact that care providers only see a broken picture of the patient because patients’ health histories are spread across different systems. Clinicians spend time putting the pieces together as they strive to provide holistic patient care. That’s all changing. Analytics are paving the way to life-saving possibilities in healthcare.

Predictive modeling and AI are interconnecting data across the entire ecosystem to drive meaningful health insights from global data, HCM, and clinical systems, among other sources. With the power of AI, hospitals can forecast and manage patient flow around flu seasons and accordingly adjust resources - fostering flexibility in schedules and reducing staff burnout. Through predictive diagnostics, they can better care for patients, optimize clinicians’ time, and provide greater insights at the point of care. Through AI-based precision medicine and interventional insights for patients with similar health criteria, providers can spend less time piecing broken information and more time treating patients.

Better care quality and better interventions produce improved patient outcomes, as well as reduce overhead. AI- and analytics-driven solutions have the potential to promote equitable access to the very best care, tailored for every patient, with heightened quality and reduced cost of care. With the convergence of technologies—AI and ML—we may be moving to a new realm that has the potential to elevate the human experience in healthcare.

Where the art of possible and reality meet

The future of healthcare lies in the data around us. Most healthcare organizations possess the data they need for transformation, embedded in applications across their enterprise, buried in data siloes, and behind hours of manual consolidation. Transforming the healthcare landscape at the data layer is where the art of the possible meets reality, data siloes are broken, and invaluable insights reach the right people, in the right place, at the right time.