Healthcare lags behind many other industries when it comes to analytics adoption. For example, in a report published in February 2014, the International Institute for Analytics and HIMSS Analytics said just three of 22 healthcare providers that participated in a detailed assessment of their analytics maturity ranked at a level where they were deemed to be "good at analytics," with data-oriented operations and widespread use of data analysis software.
The assessment, which was based on a survey of 1,800 workers at the participating organizations, found that 14 of the providers were still at a stage where they could see the value of data analytics but were struggling to become more analytical. The other five organizations primarily had limited and siloed reporting applications in place, according to the report. None scored at the highest level: "analytics nirvana," characterized by broad use of analytics as a competitive differentiator.
In this edition of the Talking Data podcast, we take a look at some of the reasons why healthcare has been slow to adopt data analysis tools. The most obvious and frequently discussed answer for the slow adoption of data analytics software is that data privacy regulations make it hard to move data throughout an organization and to use cloud-based analytics services. But there's more to it than that. Some of the main challenges are cultural. Healthcare providers tend to be risk-averse, making newer big data management and analytics technologies like Hadoop and Apache Spark tough sells in many cases. There's also hesitancy among clinicians to embrace new ways of doing things given the stakes of providing care to patients.
But despite these challenges, things are changing. A number of regulatory and economic forces are combining to push providers toward a greater reliance on data analysis software and advanced analytics techniques. A transition to more data-driven practices is already underway at many organizations, and, soon, it likely will become harder for hospitals to operate effectively without some level of sophisticated analytics capabilities.
Listen to the podcast to learn more about why healthcare has been so slow to implement data analytics tools and why industry observers expect that to change in the near future.
Data stewardship is key to getting the most out of analytics in healthcare
How physicians benefit from greater reliance on data and analytics
A little creativity goes a long way in healthcare analytics implementations