The definition of Healthcare Data Analysis is in its name. It is however not as simple as that. Healthcare Data Analysis goes deeper and encompasses so many areas of the healthcare system. Because data is integral to every section of the healthcare industry from the top level to the lowest level, having a proper data analysis in a healthcare organization is integral to its success. This is why the current state of healthcare data and healthcare data governance in the healthcare industry is currently trying to be improved on because unfortunately, there is a great need for improvement.
The State of Healthcare Data
The current administration is quite a mess and so is its handling of healthcare with a proposed healthcare bill that’s ludicrous, to say the least, hugely opportunistic of the low and middle class and blatantly cruel. This new act will make these already terrible statistics much worse. Yet despite the high cost of U.S. healthcare and proposed an increase, Americans are not any healthier than citizens of other industrialized nations, nor do they enjoy greater longevity. Consider these facts about the costs of healthcare and mortality:
The 2013 report U.S. Health in International Perspective compared the life expectancy of Americans to the citizens of 17 high-income peer countries from Western Europe, Australia, Japan, and Canada. The findings showed that life expectancy for American males ranks last, and life expectancy for American females ranks next to last.
Preterm-related causes of death accounted for 35 percent of infant deaths in 2009 as stated on the CDC website. Medicare could save at least $12 billion per year by reducing preventable readmission cases that are readmitted within 30 days according to the 2007 report to Congress Promoting Greater Efficiency in Medicare. At least 44,000 and perhaps as many as 98,000 Americans die in hospitals each year as a result of medical errors according to the book To Err Is Human: Building a Safer Health System.
One way to start tackling the shortcomings of the system is by creating a better flow of data between the producers and Consumers of Data and Information. The overwhelming majority of healthcare workers will never directly interact with an enterprise data warehouse. That is, most staff members won’t directly query databases, write reports, or analyze data looking for trends. Instead, they will rely on someone else to actually pull data from the EDW, analyze it, and then produce some kind of report. This report conveys meaningful information around a workflow over which the report requestor has some accountability. Let’s classify this group of people that receive the information in the form of reports as consumers. In contrast, the technical staff members tasked with data capture, analysis, and reporting are producers of information.
Consumers develop opinions of the EDW based on their ability to act on information found in reports generated by producers. It is not uncommon for a health system to invest millions of dollars and a handful of years into a data warehouse but still have consumers who are dissatisfied. How can such a significant investment result in dissatisfaction? It may be but is not always the case, that consumer dissatisfaction is a function of producers not having the right skills to really generate the analysis and information consumers need. In fact, producers may not even know what they don’t know.
If the team responsible for generating a return on investment from their EDW lacks the skills to manage and leverage the EDW, it can create real problems for a health system’s enterprise-wide analytics strategy. Clearly, this isn’t the result you want from your analytics efforts. By making sure the analytics team has the right skills, you can avoid these kinds of problems.