Data management is about more than simply storing information. Yes, it ensures healthcare providers and payers have up-to-date information about patients, services and costs, but when optimized, data management can do so much more. With the right tools, you can use your data to identify risks, predict clinical and financial issues, improve care, and reduce your organization’s costs. Here’s a closer look at why data management is so important in healthcare.
Gain valuable insights to guide your decision making
Would you rather make decisions based on guesses, or based on data? Data management allows you to see information about patients, care, costs and more so you can make informed decisions. By compiling data from a variety of sources – including your EHR, handwritten notes, claims data and more – in one convenient location, you get a clearer view of patients on an individual level as well as on a group level. Add the right tools, such as those offered by Lumiata, and you can also predict health risks, financial risks, make prediction about utilization, emergency visits and more. Armed with valuable insights, you can make informed decisions about patient care and your organization’s operations, leading to improved care and reduced costs.
Want to make the most of your data? Our artificial intelligence (AI) and machine learning (ML) solutions can help. After you provide us your data, we clean it and prepare it for machine learning. We then enhance it with our clinical IP and proprietary disease codes to help you gain improved insights and make predictions about health, care and costs. We create individual Person360 records, which provide detailed information regarding a patient’s coverage, claims, medication, labs, clinical encounters and more. This information helps you plan next steps that are beneficial for patients, providers and payers.
Identify health risks and predict problems
Data management helps ensure your data is organized and usable. Some of the ways you can use your data is to identify risks and make predictions. For example, does a patient’s current health factors put them at risk of developing a disease? Data management helps you identify high-risk patients and keep an eye on their key risk factors. You can also predict medical events, hospital admission and readmissions. When you’re aware of various health risks and financial risks, you can intervene sooner to provide better care. This helps you achieve better outcomes for patients as well as reduce claims costs.
With Lumiata, you’ll have access to our Lightning AI Model Builder and Spectrum AI Model Catalog, which help you identify risks and make predictions. The Lightning AI Model Builder allows your team to build high-performing data models from tens of thousands of machine learning features that are auto-generated and built specifically for healthcare. Our machine learning capabilities are enriched by our clinical IP, lab results, and disease codes. Our Spectrum AI Model Catalog provides access to more than 100 pre-trained models that can be customized or used as-is. These models include a financial model suite and a clinical suite that are significantly more accurate than other available options. Our pre-trained models can help you solve underwriting and pricing challenges, make predictions to help you design care management plans, and more.
Improve patient care for better outcomes
Providing patients with the care they need to stay as healthy as possible for as long as possible is crucial. By giving you clearer insights and the ability to identify risks, the predictions we can generate using clean data empowers you to improve patient care and make it more efficient. When you look at a patient’s data and see they’re at risk for developing a disease, for example, you can modify their care management plan and provide the care they need to help prevent the onset of that disease. The predictions we generate gives you the ability to intervene sooner and provide the right care at the right time, including preventive care, to achieve better outcomes for patients. The ability to address risks before they get worse and require more extensive care can also help reduce costs for providers and payers.
Reduce costs for providers and payers
The predictions you can get from clean, managed data ensures you have access to information that will help you identify high-risk, high-cost members. This allows you to provide care in a timely manner to help avoid costly scenarios, as well as prepare for costs ahead of time.
Lumiata’s prediction models can identify high-risk patients and their risk factors to help you provide the proper treatment, minimize clinic care, reduce risk, and increase margins. In addition to predicting high-cost members, we enable you to predict high group costs, which can help you overcome pricing challenges, budget accordingly for costs, and better manage your cash flow. Our payment integrity solutions enable providers to save money by making sure claims are paid correctly the first time. We also help payers identify suspicious claims before they’re paid and send vendors fewer and lower-cost requests.
How does Lumiata support your data management?
Lumiata takes data management to the next level so you can get the most value out of your data. We begin by shining a light on your incomplete and disparate data, then clean it and prepare it for machine learning. We enrich your data using our clinical IP derived from over 120 million patient records, 35,000 curated physician hours, and 50 million articles from PubMed, plus our proprietary disease codes. Using our Lightning AI Model Builder and Spectrum AI Model Catalog, you can build data models or use our pre-trained models to identify risks and make predictions.
By incorporating Lumiata’s machine learning and artificial intelligence capabilities, you gain valuable insights that help you identify issues faster, take action sooner, provide better care, improve patient outcomes, and reduce costs for your organization. You also free up your data scientists by reducing the amount of time spent cleaning data, building models from scratch and engineering data so they can spend more time on data science.
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