Healthcare orgs face significant data modernization challenges

Investments in data initiatives are a consistent return on investment (ROI) for healthcare organizations, but a majority need to modernize their data stack in 2024, according to Hakkoda’s Healthcare State of Data 2024 report.

The report, which surveyed 145 director to CEO level leaders from large healthcare organizations, highlights some of the significant challenges and opportunities that health systems face in terms of their data strategy.

As technologies like artificial intelligence (AI) continue to generate hype in the healthcare industry, leaders are faced with major decisions around how to leverage the wealth of data held by their organizations and how to invest in those efforts.

The report found that 11.3 percent of healthcare organizations surpassed their financial goals in 2023, leading some to struggle to fund data initiatives among other pressing concerns like staffing shortages. However, the survey data suggest that these efforts yield an average 124 percent ROI, making data initiatives consistently profitable for many organizations.

Outside of funding, other challenges also hamper efforts to utilize healthcare data. Roughly 94 percent of respondents indicated that their organization must modernize its data stack this year. Just over half of the organizations surveyed reported that they need to modernize “a great deal.”

Just under 80 percent of respondents who stated they need to modernize reported that they will need “moderate” or “large” amounts of external support to do so. But even with outside resources, internal hurdles will need to be addressed.

Leaders indicated that they face many data management and operations challenges within their organization: 46 percent noted that creating a data-driven culture was a significant barrier, followed by ensuring data quality and governance at 44 percent and integrating data across silos at 42 percent.

Data literacy also remains low at healthcare organizations, with only 28 percent of respondents indicating that they believe their organizations have a high rate of data literacy.

Despite these challenges, the report also identified key opportunities that healthcare organizations may be able to take advantage of moving forward.

Approximately a quarter of healthcare leaders stated that their organizations are currently monetizing their data, while 40 and 28 percent plan to do so in 2024 and beyond, respectively. Only 8 percent reported having no plans to monetize their data.

The survey further underscored that healthcare leaders have already identified multiple use cases for machine learning and generative AI. Healthcare outpaced several other industries in the report in terms of leaders’ confidence around use cases for these technologies, with 34 percent of leaders reporting that they strongly agree that their organization has defined use cases for generative AI that they are ready to implement.

Some respondents indicated that their organizations are already using generative AI, and 59 percent of organizations indicated that they expect the technology to be “very important” to their success by 2027. Currently, 43 percent of healthcare organizations use generative AI for data cleaning, while 49 percent use the technology for automation tasks.

The report emphasizes that healthcare organizations will need to find the right external partners to help bolster their data initiatives and help them centralize on cloud platforms in order to succeed in data utilization in the coming years.

The report comes as healthcare and life sciences stakeholders rush to keep up with the rapid advancement of tools like generative AI.

Yesterday, Google shared updates on its generative AI, health equity and other initiatives at its annual health event, The Check Up.

Of note, the company detailed plans to build a personalized health large language model to provide wellness coaching to Fitbit users and to assess the assistive capabilities of the Articulate Medical Intelligence Explorer (AMIE) model.

The company also released its Health Equity Assessment of Machine Learning performance (HEAL) framework to prevent inequitable AI models from being deployed in healthcare settings.