The Future Is Now: Reducing Patient No-Shows with AI and ML
The Mark Twain quip — “Never put off till tomorrow, what you can do the day after tomorrow” — might be an irreverent, tongue-in-check approach to how some healthcare executives chose to purchase and adopt information technology. For many healthcare executives, the day after tomorrow has arrived.
A move from fee-for-service to value-based care, coupled with changes in the patient population (e.g., nearly half of all American adults have one or more chronic health conditions), has been a significant catalyst for the metamorphic changes that are occurring in healthcare. Shifting patient-provider demarcations are requiring healthcare executives to embrace best practices that drive patient loyalty, provider satisfaction and operational efficiencies. In that spirit, healthcare, more than any other industry, is embracing cloud technologies. What was seemingly unthinkable a few years ago has become the new norm. IDC estimated that worldwide healthcare provider spending on cloud solutions was close to $10.8 billion in 2018 and it is forecast to grow by 26.1% in 2019.
A Data-Driven Approach to Driving Better Outcomes
Innovative healthcare executives understand that flexible, nimble and patient-centric digital, cloud-based solutions are strategic differentiators that will be essential for healthcare organizations that want to remain competitive. With a rise in integrated, context-driven platforms that use artificial intelligence (AI) and machine learning (ML), healthcare organizations can deliver a seamless, data-driven approach to reduce the per-capita cost of healthcare, improve the patient experience and drive better outcomes.
What healthcare provider wouldn’t want the ability to utilize their existing data assets to:
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Identify at-risk patients for no shows/cancellation with predictive and prescriptive analytics
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Create miss/cancel probability of each patient with detailed, real-time reports to close scheduling gaps and forecast practice revenue
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Create data-driven recommendations to overbook patient appointments to minimize loss/maximize practice revenue
With nearly 95 percent of hospitals using EMRs, health organizations now have a vast array of data that can be used for strategic decision making. Yet at the same time, most healthcare executives are struggling to understand how to make better use of the data.
Taking Action on Your Data
To make data strategic and actionable, there are a couple of critical steps that healthcare executives need to consider before providers can efficiently use their existing data assets to improve their no-show rates. Providers should look for a flexible, agnostic HIPAA-compliant platform, but they should also:
- Determine organizational priorities and challenges. For many provider groups, improving the no-show/cancellation rate for patients is a critical priority.
- Evaluate their existing technology stack (e.g., data access, development environments, middleware services and infrastructure) to understand:
- What data sources are available?
- What data sources will be a value-add for creating trending models?
- How to separate backend concerns (i.e., security, authentication and scalability) from application development priorities?
- What additional technology capabilities are needed (e.g., chatbots, AI, and ML)?
Why let others decide what approach is best for your organization to reduce patient no-shows? With a flexible, digital, HIPAA-compliant solution, providers can:
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Utilize their existing scheduling data to create patient benchmarks
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Determine what patient strategies are best for reducing no-shows within their organization.
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Alison Nicole Haughton
With nearly 18 years of Healthcare IT product marketing experience, Alison Nicole Haughton has worked across the continuum of healthcare for companies such as IMS Health, Harvard Medical School, Parexel, and a variety of early stage healthcare companies implementing m-Health clinical. Ms. Haughton received her bachelor’s degree from American University and master’s from Tufts University School of Medicine/Emerson College.
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