Sift Healthcare is honored to announce the addition of IKS Health as a strategic investor. IKS' footprint in the ambulatory care market and its dedication to improving the healthcare reimbursement journey make them an ideal partner in furthering Sift's work to bring data intelligence and Machine Learning optimizations to healthcare payments.
Health systems are taking action in the Patient Billing & Payment space with renewed momentum. Learn how Sift Healthcare transforms patient financial engagement in AVIA Connect’s Top Patient Billing & Payment Companies Report.
Sift's Blake Sollenberger and Affinity Strategies' Claire Vincent talk about the impact of AI on healthcare payments. They cover the challenges health systems face as they increasingly act as financial lenders, the importance of a positive patient experience in billing, the need for compassionate collections, and the challenges of moving to AI in a risk-averse environment. It’s an enlightening listen and will give you a new perspective on AI and the revenue cycle.
AI can drive payments and cut waste, but where do you start? Download our roadmap for healthcare executives, AI for the Healthcare Revenue Cycle, a free and unbiased implementation guide.
Integrating Machine Learned adds a decision engine to the revenue cycle, enabling you to determine the next best action that should be taken with a claim, denial, patient account, payer contract or vendor. Can't envision it? Here are five examples of what ML in the revenue cycle looks like, in action...
Wonder how Sift impacts healthcare? Watch Sift Healthcare's Founder and CEO, Justin Nicols talk with StartUp Health's Logan Plaster about the impact of Sift's AI and analytics on healthcare payments.
Rev/Track leverages Sift Healthcare's AI and machine learning to help healthcare providers and RCMs to optimize revenue cycle operations. Rev/Track delivers detailed intelligence around payments behavior, insurance denials, collection trends, patient segments and revenue cycle work efforts.
Introducing The Sift Quality Score. For revenue cycle managers, understanding account quality and its impact on patient collections is essential for forecasting revenue, optimizing rcm workflows and ensuring the best strategy is in place for managing resources. Sift’s Quality Score is derived from Sift’s predictive model scores — it tracks account quality over the early-out period, enabling RCMs to more intelligently forecast revenue throughout each billing cycle.
Providers of all sizes, as well as RCMs, will be challenged in 2020 to drive better results in patient collections. Here are six ways to maximize patient payments. They might require some data science, but Sift makes that accessible.
The term “automation” can refer to any number of automatic processes within the revenue cycle workflows. But, it doesn’t necessarily refer to the use of data science. Just because a process is “automated” doesn’t mean predictive analytics or any data science is applied. However, when you do truly use data science to automate your workflow (which you absolutely should do), you pick up a host of efficiencies and improvements. Our breakdown on automation vs rule-based segmentation vs true predictive analytics.
When healthcare providers leverage their patient payments data to drive their collections strategies, they create new and powerful opportunities to increase the revenue they collect, building patient relationships and using payments data to inform business operations. Here are four new and intelligent ways that healthcare providers are using their patient payments data to be more strategic and to drive better payment outcomes. Here are four innovative ways that healthcare providers are leveraging their patient payments data
Healthcare providers must understand federal regulations that apply to payments from self pay-patients, specifically the Fair Debt Collection Practices Act (FDCPA). Litigation around FDCPA is increasing and with surprise medical billing in the crosshairs, healthcare providers and revenue cycle managers need to minimize their risk.
Normalized and organized data is a gap in healthcare analytics, even in claim denials management. This is why Sift Healthcare is proud to introduce our Denials Dashboards. Sift scrubs and maps your 835 and 837 data to build a single, normalized data set. This data is organized in an intuitive interface that offers you a new level of oversight for the revenue cycle.
2019 stats demonstrating how vast and costly healthcare insurance claims denials are in America. These stats make a strong case for employing a denials management strategy that prevents denials and make working denied claims more efficient.