For U.S. healthcare leaders, trusted data is more important than ever, as their organizations migrate from the fee-for-service model to value-based care. However, a recent survey of CIOs found that less than half of healthcare organizations show very strong levels of trust in their data.
The survey, by Burlington, Mass.-based Dimensional Insight, an analytics and data management solutions provider, is based on responses from 85 members of a professional organization of CIOs and other healthcare IT leaders about trust in data across their enterprises.
During this transition from fee-for-service to value-based care, healthcare organizations must weigh investments, risks, and trade-offs objectively with quantitative, trustworthy data. This kind of data driven decision-making will be critical in shaping the initiatives and high-stakes choices required by value-based care. The transition will require increased, high-level collaboration among different constituencies within a healthcare enterprise. It also will require decisions to be quantitatively assessed against reliable, trustworthy data, the survey report notes.
The survey sought to gauge the current state of data trust and access? How much trust do CIOs and stakeholders have in their clinical, financial, and operational data these days? How many have direct, self-service access to the information they need to make data-driven decisions? Are healthcare organizations ready to invest funds to improve trust in data and self-service capabilities?
Overall, few organizations have very strong trust in their data while levels of self-service vary across the enterprise, according to the survey. Most healthcare organizations plan to invest money toward improving both data trust and self-service, the survey found.
As part of the survey, CIOs were asked to rate the index of trust in data within their various user communities, on a 1-10 scale, with 10 being the highest. The index of trust was defined as how strongly “user populations believe that they can trust the data provided to make decisions.”
Forty-eight percent of respondents assessed financial data as an 8 or above. The percentage of “8-and-up” responses was 40 percent for clinical and 36 percent for operational.
Clinical users have the lowest levels of self-service in making data-driven decisions. More than half of CIOs report that 30 percent or less of their clinical population is self-serviced in data-driven decision making.
Approximately three-quarters of healthcare organizations plan to increase investments to improve trust in data and self-service capabilities. At least 70% responded “yes” to investments in trusted data in each of the three realms. In addition, most organizations (68 – 78 percent) plan to increase their investments towards improving users’ capacity for self-service data analytics.
The survey demonstrates that healthcare organizations have a long way to go in developing rock-solid trust in their data and self-service access to it. The survey results also indicate that executives are aware of these challenges and are ready to dedicate resources to improving both trust and access.
“Trusted data is more important than ever, as healthcare organizations migrate from the fee-for-service model to value-based care,” Fred Powers, president and CEO of Dimensional Insight, said in a statement. “During this transition, healthcare organizations must weigh investments, risks, and tradeoffs against quantitative, trustworthy data. This kind of data driven decision-making will be critical in shaping the initiatives and high-stakes choices required by value-based care.”
Dimensional Insight executives also provide a number of recommendations for improving trust in data and increasing self-service capabilities:
- Keep subject matter experts close to the data. Healthcare organizations will need programmers and data engineers to extract data from the source systems, but it is the subject matter experts who best understand the data and how it will be used.
- Automate business logic transformations. More automation is better when it comes to the often complex logic required to transform raw data into meaningful information.
- Promote transparency and visibility. The best way to make sure data is right is to let people — the frontline information consumers — at it.
