Healthcare organizations seek cure for data analysis paralysis
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The COVID-19 pandemic has proven that hospitals and healthcare systems can be much more agile than many executives realized. This may be key to better using healthcare data to drive decision making. Which could help many organizations recover from analysis paralysis due to outdated systems and untrustworthy data.
These are among the findings of a new study, “2022 CFO Outlook for Healthcare: Finance Leaders’ Priorities for Addressing Workforce, Data, and Costing Challenges,” from Syntellis Performance Solutions. Syntellis surveyed 420 healthcare finance leaders on how they view the financial outlook for their organizations, and their plans for adopting analytics tools and advanced technologies.
The bad news: a vast majority (82%) of respondents said their organizations should do more to better leverage financial and operational data. Worse still, most organizations are using inadequate systems for decision-making. Nearly two-thirds (61%) are still relying on outdated tools such as spreadsheets.
“Procuring accurate data — and using that data to drive decision-making — is the key to healthcare organizations bouncing back after a tumultuous few years,” notes Flint Brenton, CEO of Syntellis. “Despite having greater access to an abundance of data than ever before, many healthcare finance executives still struggle to utilize this data.”
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Data is plentiful, but insights not so much
As evidence, most respondents said their decision support systems gather data from across the enterprise, inside and outside of hospital walls. But less than half (44%) have visibility across the entire enterprise. Another 34% capture data from hospitals and outpatient facilities, but this data isn’t optimized for planning and forecasting.
“Lack of data visibility inhibits healthcare leaders from understanding what departments may require in terms of more support and resources, and where to invest in the future — ultimately trickling down to the quality of care,” Brenton explains.
The healthcare sector has long been viewed as slow to adopt new technology, except for clinical use. But Brenton said Syntellis researchers were surprised to see just how high the numbers were around those hospitals and healthcare providers who are still relying on outmoded processes and insufficient technologies to address financial and operational challenges.
“Yes, many health organizations have been burdened over the past year between juggling widespread supply chain shortages, staffing issues and skyrocketing expenses. But without taking a hard look internally to identify the gaps caused by outdated systems, these healthcare finance teams will continue to struggle until they implement optimal tools and robust solutions to boost the financial health of their organization,” Brenton stresses.
Analytics and advanced technologies are just what the doctor ordered
To overcome this challenge, hospitals and healthcare organizations need to smartly invest in advanced technologies. This includes data analytics, artificial intelligence and machine learning tools.
“Simply put, advanced technologies that we have at our disposal are the solution to better predict the future, reduce costs and improve efficiencies,” Brenton says. “For instance, scenario modeling and rolling forecasting capabilities give healthcare leaders a better understanding about how to manage similar crises going forward and to maintain financial stability in the long term. Additionally, taking advantage of available benchmarking data can help healthcare executives to pinpoint areas where they are thriving and there are opportunities for improvement.”
Looking ahead, Brenton says more healthcare finance executives want to use predictive analytics to better determine the best path forward for patients and providers. Nearly half (46%) of respondents want to tap into this functionality, Brenton says. This indicates a strong need for insights to guide decision-making based on previous outcomes.
“Using predictive analytics accompanied by technologies such as artificial intelligence and machine learning can help forecast clinical, operational, and financial needs from historical data. With this solution, healthcare executives cannot only improve cost efficiency but also ensure patients are getting the best quality care,” Brenton says.
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