What you should know:
– InterSystems i Sage Growth Partners announced research that reveals how much healthcare organizations could save by investing in higher-quality data, including up to $ 42.1 million over three years.
Background to the report
According to respondents of 100 C-level leaders from major healthcare organizations, hospitals and healthcare systems are using inefficient practices that can impose a significant financial burden. The research found that 43% of computer staff’s time is spent extracting and harmonizing data, but reducing that time by only half could save an HCO of nearly $ 1.6 million in three years.
In addition, the report reveals that by investing in higher-quality data and solutions that promote interoperability, healthcare organizations could save more than $ 42.1 million over three years.
The report analyzes the cost of manual data entry, duplicate and inaccurate tests, and errors during the care transition as some of the impacts of poor data quality. It also takes into account the costs of shadow computer systems (hardware, software, or other programs not supported by a central IT department) and finds that they currently consume 40% of the total computer capital budget. Cutting them in half could save the typical HCO a total of $ 10 million over three years.
“The financial burden of the wrong data can’t be ignored,” said Stephanie Kovalick, director of strategy for Sage Growth Partners. “The annual cost of poor data quality in the United States across all industries is over $ 3.1 trillion. With the rising cost of healthcare in the U.S., healthcare systems need to start paying more attention here.Investing in smart data tissue is key to significantly reducing costs and saving physicians the valuable time they spend searching and cleaning up data to ensure the best possible results. “
Other key findings of the report include:
– Only 20% of healthcare organizations fully trust their data
– Half say that poor data quality has serious consequences, leading to ineffective or slow decisions and the inability to identify gaps in care.
– Data integration and interoperability issues impair its ability to achieve its strategic priorities related to data analysis
– A smart health data fabric that includes a robust data model and data harmonization could solve these persistent challenges