Your Data Can Find the Answers

As the healthcare industry battles workforce burnout, financial pressures, the transition from fee-for-service, and harnessing the mountain of data now at providers’ fingertips, high-value data, insights, analytics, and augmented intelligence (AI) are key tools that can help drive efficiencies, cost savings, and provider and patient satisfaction. 

How Digital Care Technology Can Alleviate Provider Burnout 

A lack of workforce support within the industry was an already mounting pressure that was further exacerbated by the pandemic–when responding to COVID-19, some organizations were able to successfully reduce this pressure by using digital care technology. 

Healthcare provider burnout is negatively impacting the recruitment and retention of key professionals, resulting in long-tail consequences for organizations. The industry is still seeing elevated turnover with prolonged pressure as healthcare employment is still below pre-COVID levels. 

According to a 2022 Physician Burnout & Depression Report from Medscape, the two specialties that report the highest burnout are Critical Care and Emergency Medicine, with 56 percent and 60 percent of surveyed physicians reporting burnout, respectively.

AI support and automated patient engagement technology solutions can help stave off caregiver burnout, as the top contributor reported by physicians was too many bureaucratic tasks (paperwork, charting, etc.). 

One example of how digital care technology can alleviate mounting healthcare needs is seen in a March 2022 case study. A large healthcare system identified the need for robust telehealth-based efforts in response to the COVID-19 pandemic. The organization deployed a new patient engagement platform and was able to rapidly expand care capacity to meet increasing demand, ensuring that more than 38,000 patients were supported through COVID-19 screening, testing, treatment, and monitoring.

The technology enabled the organization to quickly centralize support for COVID-19 screening, testing, and monitoring for a large population.

Your data is not only an asset when managing new and rapidly changing situations but also ongoing pressures of staffing shortages and charting. As burnout continues to mount, turning to data and digital care technology can help provide insights and solutions that can reduce that stress.

How Data Can Reduce Financial Pressures by Predicting Labor Spending

Another healthcare trend that data can help remedy is the growing financial pressures accelerated by COVID-19 and inflation. During COVID, the financial margins between cost-of-care and incoming revenue shrunk. Financial pressures remain, suggesting that we have a long way to go to see a reset to the pre-COVID revenue baseline, in addition to rising costs. 

While changes in acuity, increasing outpatient volume, and easing costs are helping, margin recovery has been slow, driving financial discomfort for C-suite and investors in the first and second quarters this year. One of the major drivers of healthcare operating expenses is labor management, as an August Kaufman Hall National Hospital Flash Report shows labor spend has increased over 9 percent from 2021, after a 16.4 percent increase year-over-year from 2020.

Technology solutions can help by predicting labor spending. According to a June 2022 case study, Hawai‘i Pacific Health (HPH) used a data platform to forecast its workforce needs and effectively manage staff schedules—two changes that led to $2.2 million in savings in just 16 months, while maintaining high-quality outcomes.

As health systems face immense pressure to improve efficiency and reduce costs, leveraging technology solutions and high-value data can help drive financial improvements.

High-Value Data Clears the Path to Value-Based Care 

The necessary transition away from fee-for-service (FFS) to value-based care (VBC) is another industry challenge that data can help healthcare organizations rise to meet.

While medical economic forces are driving the transition to VBC, the migration has been slower than expected or predicted; in 2016, 65 percent of health systems leaders said VBC would be the standard payment model by 2021, according to a report from the Medical Group Management Association (MGMA). While small, nimble market disruptors are migrating to VBC faster, 75 percent of total revenue for large organizations still comes from FFS, according to an updated report.

High-value data can support predictive technology models that elucidate the risk/reward ratio, ultimately presenting a glide path for faster adoption. A willingness in organizations to make large shifts in financial infrastructure to support population-based payments will also accelerate the shift. 

This winning combination of high-value data and a willingness to innovate can be seen in the successful transformation made by UC San Diego Health to value-based care. UC San Diego Health sought to transform its organization, expanding beyond fee-for-service, transitioning to value-based care, and improving the health of its patient population by forming its Medicare Shared Savings Program (MSSP) ACO. The organization’s leaders realized they needed a better understanding of their organizational strengths, opportunities for improvement, and actionable, timely data that would enable them to improve outcomes, reduce waste, and succeed in value-based care. The organization leveraged an analytics platform to give insight into performance and improvement opportunities, educating and engaging ACO providers. In addition to supporting VBC efforts, UC San Diego Health’s data-informed improvements have lowered waste and decreased costs, including $883K in cost avoidance, the result of a reduction in per member per month.

While questions remain about what will drive a faster, greater shift to value-based payment models and factors behind the slow migration from FFS to value-based payment models, diving into the data will give us better insights into what is working and what is getting in the way. 

Integrating Data, Analytics, and AI into Provider Workflows and Executive Decisions

Finally, many healthcare providers feel they’re drowning in a tsunami of data, but an incomplete understanding and adoption of data, analytics, and applied AI technology are preventing their optimal use of that data for point-of-care solutions. 

Executive use of AI and high-value data insights in decision-making needs improvement and multiplying these problems is a mismatch between providers’ and executives’ perceptions and the reality of their data intelligence. While we are still a long way from optimizing the use of data intelligence to solve our industry problems, enabling strategies using analytics and technology is critical to reducing costs and optimizing care outcomes. 

To accomplish this, we need to increase providers' trust in the quality and validity of the data and educate them on the use of high-value data and analytics applications. If we can shift organizational perspectives on the value of data, analytics, and AI-engineered solutions, we can leverage the use of advanced tools and AI in decision making to reframe AI as a tool to help providers work better–not replace–human judgment. Informed decision-making, combined with AI adoption, will enable streamlined, scalable care decisions that can effectively address many of the trends impacting the industry today.