Population Health

Machine learning (ML) can deliver critical insight to clinicians at the point of decision making and replace manual processes, such as reviewing a patient’s lab history. However, many clinicians don’t reap these ML benefits due to a lack of understanding and data infrastructure. To maximize the many advantages ML can bring to the bedside, organizations need to educate team members about ML and then invest in data infrastructure that supports ML capabilities. A transparent explanation of benefits can garner support and understanding that ML augments—not replaces—clinicians. With this increased understanding, leaders see the value of data integration infrastructure. A robust data platform allows organizations to aggregate data from multiple sources, ensuring ML algorithms deliver accurate insight based on comprehensive patient data.

With the healthcare industry move towards value-based payment (VBP), financial executives must navigate a shift away from volume and embrace quality care as a key driver of financial health—particularly as accountable care, quality measures, shared savings, and bundled payments gain traction. To meet this ongoing quality-cost challenge, health systems must understand their progress in clinical quality measures and costs of delivering care, as clinical quality is an increasingly significant predictor of financial outcomes. While the traditional fee-for-service environment emphasized volume, today’s VBP paradigm puts quality ahead of older metrics.

Former U.S. Surgeon General C. Everett Coop, MD, once said, “Drugs don’t work in patients who don’t take them.” Yet, patient adherence to prescribed medication tends to fall short of optimal. For example, according to the Centers of Disease Control & Prevention, patients don’t fill approximately 20 to 30 percent of prescriptions. The consequences for medication non-adherence can include poor quality of life, physical limitations, hospitalization, or worse, making patient compliance an integral part of quality healthcare delivery. Providers aiming to keep patients on track with their medications can look to patient engagement technology to proactively communicate critical education about therapies and the risks of non-compliance as well avoid common barriers to adherence.

Even though medication-associated errors affect over 7 million patients and cost more than $40 billion each year, healthcare often falls short when it comes to prioritizing patient safety. For example, in October 2021, a draft of the Department of Health and Human Services Strategic Plan FY 2022–2026 didn’t include reducing preventable harm as part of its mission to improve the quality of care. Meanwhile, other complex and adaptive industries, such as aviation and nuclear, give top precedence to safety oversight and compliance. To catch up to other sectors and actively pursue patient safety improvement, healthcare needs a straightforward framework for integrating patient safety across the continuum of care—an approach involving culture, clinical analytics, and frontline adoption of best practices.

Advanced data and analytics are a good foundation for developing highly effective products for healthcare, but they’re not enough. Anne Marie Bickmore, Chief Product Officer at Health Catalyst, explains that building a product portfolio is more than a list of offerings—it starts with an immovable foundation of high-quality data and analytics. Bickmore describes four specific guidelines organizations can follow to create better products that drive sustainable improvement:1. Build products on a strong data foundation.2. Mind the changing healthcare landscape with a strong data foundation.3. Take a patient-centric approach.4. Consider a clinical perspective.

The unforeseen pandemic changed many industries, but according to Sadiqa Mahmood, General Manager and Senior Vice President of Life Sciences Business at Health Catalyst, COVID-19 had a particularly notable effect on the life sciences industry. With the surge in digital solutions, pharmaceutical process changes, and accelerated innovation, the life sciences are experiencing never-before-seen changes. Mahmood suggests that these pandemic-fueled transformations ignited seven positive trends within life sciences that will impact the healthcare industry, pharmaceutical companies, medical technology providers, and health systems:1. Building partnerships.2. Accelerating digitization.3. Shortening vaccine development timeline.4. Expanding the use of real-world data.5. Scaling cloud platforms and securing data.6. Improving supply chain.7. Focusing on health equity.

As healthcare increasingly digitizes, organizations must prioritize the patient experience to create a seamless digital health journey. Drivers of patient experience range from the use of empathy in outreach to the technology that delivers information to a patient. Engagement software and strategies optimize patient activation when they follow the following best practices:1. Simplify communication channels.2. Consider the timing of communication.3. Optimize content.4. Determine the cadence.5. Consider the tone.6. Leverage data to make improvements.

Healthcare organizations across the globe have one thing in common—the desire to leverage data to improve outcomes. However, achieving this goal isn’t as easy as it sounds, according to, Jeff Selander, Senior Vice President and General Manager of Global Expansion Business at Health Catalyst. Selander says to effectively leverage data to drive systemwide improvements, organizations must democratize—or free—their data. Data democratization starts with a digital infrastructure that can support robust digital needs, such as electronic patient records and efficient patient data distribution. Once the infrastructure is in place, systems must connect digital patient records to other source systems (e.g., patient safety ancillary systems and pharmacy) for a complete picture of a patient’s health. Keeping these steps in mind, health systems are now ready to drive the greatest results for the greatest amount of people.

As data becomes more widely available, healthcare organizations are turning to self-service analytics to empower team members to make data-informed decisions. Rather than waiting for manual reports from analytics teams, self-service analytics allows individual team members to perform queries, run reports, and dive into the data details on their own. However, this broader distribution of data also presents concerns, such as deciding who will oversee the data, who can access which data, and how to best deliver the data to end users. Leaders can address these concerns and reap the benefits of self-service analytics by focusing on answers to five common hesitations:1. Invest in advance data platforms to standardize data.2. Teach analytics best practices and data literacy.3. Leverage tools to take data beyond historical analytics.4. Shift the analytics teams’ mindset.5. Follow data security procedures and policies.

A growing interest in data, analytics, and digitizing healthcare among organizations in the Asia-Pacific (APAC) region also brings challenges. According to Farhana Nakhooda, Senior Vice President at Health Catalyst and longstanding healthcare expert in APAC, health systems in this part of the world face many obstacles that complicate the transition to digital healthcare, including strained financial assets, sufficient clinicians, and disparities based on rural and urban areas. However challenging these barriers might be, Nakhooda provides three strategies that systems in APAC and beyond can leverage to accelerate their digital healthcare journey:1. Promote data and analytics literacy.2. Implement a data governance structure.3. Invest in data protection and security.