The consumerization of technology has led us to expect accurate, targeted information at the touch of a button. We can quickly run a Google search for obscure facts and trust AI-based chatbots to provide customer service. Over the years, we have increasingly relied on Netflix recommendations to present the type of bingeable TV content that’s perfect for our mood at any moment.
Call it the “Netflix Effect” – the uncanny ability of brands to know exactly what we want, when we want it. It’s so effective that we willingly share our data to get even more accurate recommendations. Other industries are mastering the ability to leverage data and predictive analytics to provide a superior service based on the customer’s specific needs and interests. And it’s only a matter of time before healthcare works like this. To achieve the Netflix Effect in healthcare requires the following four best practices.
1. A deeper understanding of healthcare data. For more than a decade, healthcare has inched toward fully automating its business processes to improve efficiency and eliminate waste. As we scrambled to shore up critical PPE and direct products to the areas of greatest need, the pandemic accentuated the importance of data’s role in healthcare. Technology has put us on a path to knowing precisely what patients need, when they need it, and the best medical product or prescription based on an individual’s unique makeup. However, the march toward personalized healthcare will be challenging given the continuous and explosive growth of healthcare data. Making the vision a reality starts with a deeper understanding of how healthcare data is managed and ensuring that data are known and trusted.
2. Increased use of predictive analytics. Predictive analytics is one of the most significant change agents in healthcare. Not only will it enable more personalized patient experiences, but it will also force the reinvention of the healthcare supply chain.
From a personalized healthcare perspective, predictive analytics can help identify the best course of action for patients based on their individual needs and aggregate data of similar symptoms, treatments, and products across more expansive population data sets.
The reinvention of the healthcare supply chain was already underway before COVID-19, but its progress was accelerated throughout the pandemic. One of the most prominent examples of the surge in predictive analytics was its use in anticipating and matching PPE supply and demand. For suppliers, predictive analytics helps get ahead of shortages, better identify and address leakage, and maintain the integrity of the supply network.
The increased use of predictive analytics in healthcare will lead to a more resilient supply chain. Achieving this comes down to leveraging data points from the past, converting them into actionable information, and using that data to ask the right questions to forecast what could happen next accurately. The more data and insight you have, the better the recommendations and results, leading to faster and more accurate decision-making, reducing waste, cutting costs, and improving the standard of care.
3. Aligning the right data sets and identifying variables. The strategic use of data and predictive analytics will improve patient care.It will also build a more clinically integrated supply chain. To do this requires aligning the right data sets and identifying variables within those data sets to make real-time recommendations. For example, a hospital can use data and predictive analytics to anticipate its operating room’s short- and long-term needs. Variables such as population health, diagnoses, and treatments can provide this insight. In preparation for a spike or decrease in activity, the hospital can use predictive analytics for decisions around purchasing medical supplies based on the anticipated needs of the operating room.
Over time, as more data and variables become available, the predictions will become increasingly more accurate. Let’s revisit the operating room example. A data set might include every patient who had an appendectomy over the past three years and the outcomes of those procedures. Applying predictive analytics to the data set informs future patient care and medical device design and manufacturing. Through cloud-based collaboration, more data sets can be included, providing even more insight.
As a result, the combination of data and predictive analytics helps healthcare understand the true cost of delivering care, including the cost of supplies and their role in driving anticipated patient outcomes. It will help suppliers better understand product utilization and uncover opportunities for those products that will yield the best results for patient outcomes.
4. Collaboration among healthcare providers and supply chain stakeholders. The massive and continuous growth of data creates both challenges and opportunities for healthcare providers and suppliers. The challenge is in managing the data and using it to effect positive change, specifically, aligning the right sets of data and using predictive analytics to make sound recommendations in real-time.
To move the ideal scenario into everyday reality requires greater alignment among providers, payers, suppliers, and patients. Collaboration and cloud-based platforms that ensure accurate and consistent data, enhanced through predictive analytics, are the keys to achieving this reality.
With the help of known and clinically aligned data, healthcare providers and suppliers can collaborate on delivering the best results for patient outcomes – providing that same personalized Netflix Effect but with far more profound implications.
About the Author
Chris Luoma is senior vice president of Global Product Management at Global Healthcare Exchange (GHX). In his current role, Luoma leads the procure-to-pay, credentialing, and business intelligence product portfolio teams and has overall responsibility for the Vendormate subsidiary. With more than 16 years of experience in the health information technology industry, Luoma’s responsibilities have spanned customer service, consulting, sales, strategy, product marketing and product management.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1
- Home page
- Content Marketing
- Digital Marketing Strategy
- Digital Marketing Strategy
- Digital Marketing Strategy
- Social maketing
- WordPress web development
- Data science