Data, data everywhere, but what does it mean?
The healthcare industry continues to push for recognition and dollars in the meaningful use race toward electronic medical records. While progress has been made and dollars spent, the reality of turning data into knowledge to transform the healthcare system is still a long way off.
The challenge now becomes how to extract meaningful insights from these vast data stores, and use those insights to make better decisions in near real-time in order to positively impact the quality and effectiveness of healthcare. To be meaningful, data must first be valid and its interpretation must be appropriate to the question asked. There are rules to be followed.
In healthcare, data management used to be viewed solely as a concern for researchers, financial analysts or IT computer geeks. The growing reality of accountable care requires more and more people throughout the organization to view and use data as a strategic and tactical business asset.
In a recent Network World article “Data Everywhere, But Not Enough Smart Management,” Thomas Wailgum described the “data, data everywhere” phenomenon as “an awe-inspiring and unprecedented push and pull of data and information needs.”
Just because data exists, it doesn’t guarantee its validity or quality. And data users are not equally qualified to do so. Knowing these differences is the first requirement to achieving meaningful use. In the book, Understanding Variation: the key to managing chaos, author Donald J. Wheeler describes this phenomenon of “numerical illiteracy” as pervasive throughout our preparation to become data-knowledge users. Data users and their audiences routinely misinterpret or misstate conclusions based on erroneous interpretations of data. This costs business and healthcare millions of dollars annually. Even worse, the purposeful use of data to confabulate support for a particular perception violates every ethical principle of meaningful use.
There is universal agreement to the strategic importance of data and information management. Hospitals are becoming acutely aware of the negative consequences. A hospital’s success is measured by the quality of its results. The results are dependent on the quality of care delivery decisions, which rely on the quality of the information. The information is based on the quality of the data. So why are we so far behind in the plan to move electronic knowledge for healthcare?
Data ownership is often cited as a limiting factor to achieving meaningful use. Hospital data users, administration, finance, clinical care and information technology all rely on data for information. The majority of IT managers identify data quality as their responsibility, while the majority of clinical managers say it is their responsibility. The hospital IT department acknowledges that while patient data is not theirs, data and information management systems are under IT’s control. This differing perspective puts IT, executives and clinical leadership in conflicting camps, particularly when it comes to data quality. It is becoming increasingly convenient to diffuse this responsibility across the organization, calling it data stewardship and somehow engendering corporate accountability for quality and meaningful use. However, when the most granular data is flawed or perceived as flawed, the knowledge derived is of little meaning regardless of the analytics performed. Accountability for data quality rests with those who generate and use it; that is where the meaning is. Smart management already knows this.