As another year ends, many healthcare magazines, websites and journals have an article or two on what the trends were over the past year. No surprise that business intelligence (BI) made it onto every list. There is no doubt that BI solutions and initiatives, which improve reporting and analytics, will empower healthcare organizations to provide quality of care, increase patient satisfaction, and function more efficiently and cost-effectively. Interestingly, nowhere in my reading did I find mention of the importance of having high quality data in order to maximize the BI solution or effort. It was assumed that the organization’s data was of high quality (better yet, actually there!) and that a BI solution will snap into place and magically collect, store, organize and transform the data to make better and actionable business decisions. Don’t they know that in healthcare assumptions can have deadly consequences?
Data Quality
Data quality is the foundation for effective BI and without it any BI solution you have is pretty much useless. Data quality involves ensuring the accuracy, timeliness, completeness, relevance, reliability and consistency of data used by an organization while also making sure that everyone who uses the data has accessibility and common understanding of what the data represents1. How can an organization or its clinicians make any sort of sound business or clinical decision if the data isn’t right, isn’t available, or if gingerbread men are being compared to snowmen (had to make a holiday reference!)? The answer is they can’t and they shouldn’t.
Causes of Poor Data Quality
Six hundred million dollars annually is what poor data quality costs American businesses each year according to the Data Warehousing Institute report2. Of these businesses, the impact of bad data to the healthcare industry is far greater than the consequences of accidently shipping an extra 25 boxes of Christmas ornaments to Macy’s. In healthcare, a decision made with bad data could literally mean life or death.
Up until recently, poor data quality had become the norm in healthcare. However, pressures from external regulatory bodies, hospital executives and even patients are forcing healthcare organization to take a closer look at their data and make necessary improvements. Many are upgrading their outdated technology and implementing new data capture systems (enter the EMR). However, it is important to note that technology alone will not fix this problem of poor data quality. Data quality is not a technology problem as much as it is a people and process problem.
Arts, DeKeizer and Schaffer3 divide potential causes of poor data quality into two areas, systematic and random. If you take a careful look, very few of the issues are technology related.
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Challenges to Improving Data Quality
Knowing the risks associated with poor data quality, you would think that healthcare organizations would address the data quality issue more aggressively and effectively. However, there are significant challenges that impede this daunting task:
- No business unit or department feels it is responsible for the problem.
- It requires cross-functional cooperation.
- It requires the organization to recognize that it has significant problems.
- It requires discipline.
- It requires an investment of financial and human resources.
- It is perceived to be extremely manpower-intensive.
- The return on investment is often difficult to quantify.
What’s the Answer
Data is an organization’s greatest asset and the importance of having quality data can no longer be underestimated. An increasing number of healthcare organizations are beginning to realize the consequences of not addressing the issue of poor data quality. Bailey-Woods et al states, “Quality information is essential to all aspects of today’s healthcare system. Many errors and adverse incidents in healthcare occur as a result of poor data and information. In addition to threatening patient safety, poor data quality increases healthcare costs and inhibits health information exchange, research, and performance measurement initiatives.4”
So what can an organization do to help remedy this unfortunate problem given the aforementioned causes and challenges?
Data Governance! Wikipedia describes data governance simply as5:
A set of processes that ensures important data assets are formally managed throughout the enterprise. It ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. Data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. It is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient.
Establishing data governance is fundamental to improving healthcare data quality for the long-term6. In my next blog post we will take a closer look at Data Governance and how it uses people, processes and information technology to manage an organization’s most important asset-its data!
Happiest of Holidays!
Resources cited in this blog:
- http://www.customerthink.com/article/importance_quality_control_how_good_data
- http://www2.sas.com/proceedings/sugi29/098-29.pdf
- http://www.docstoc.com/docs/112475288/Health-Care-Data-Quality
- http://www.healthcareitnews.com/news/data-integrity-essential-hies-ahima-says
- http://en.wikipedia.org/wiki/Data_governance
- http://www.cio.com.au/article/435050/improving_data_quality/
