Dirty data is a major problem running rampant across the US healthcare industry. It is already a prevalent concern for the US economy in general. IBM, for example, states that the American economy is shrinking by at least $3 trillion a year thanks to data issues.
But what is the true cost of dirty data in healthcare? This article will examine and explain the cost of dirty data in healthcare.
Big data in the healthcare industry is only likely to grow and grow. Even when people die, data does not disappear. As babies are born, new data emerges. Unless such data is organized efficiently, it can lead to a variety of concerns.
Let’s examine these concerns in closer detail and how we can help turn the tide for healthcare data quality.
Related: Top 5 Data Hygiene Tips
What Is The Cost Of Dirty Data In Healthcare?
According to recent statistics, dirty data costs the US healthcare industry around $300 billion each year. This is based on figures from the US Attorney, suggesting that around 14% of industry expense disappears through data mismanagement.
There are multiple pressure points for data problems to arise in the healthcare industry. Eularis estimates that there are more than 1,300 different medical databases in the US. That doesn’t include data.gov medical repositories, which are more likely to number around 85,000.
That is a lot of data and a lot of organization. However, as Eularis further states, big data collections don’t always guarantee smart data arrangement. Data laking, for example, could be an issue prevalent in more extensive databases in the healthcare industry. Data laking occurs where data duplicates and piles on top of each other with no apparent organization.
Furthermore, such occurrences cause confusion for operatives, which leads to potential mistakes. These potential mistakes can lead to delays to treatments and potential problems for US citizens in need of medicine.
Dirty data, as a term, refers to inconsistent, confusing records. It can refer to duplicates, outdated information, and even that which may be corrupted. Ultimately, it is data that is no longer relevant and which is, in the grand scheme, useless.
However, to better understand the legitimate effects dirty data has on US healthcare, we need to look at a few clear examples. Even if we cannot see the effects of unclean data on US citizens in the here and now, the potential effects further down the line could be devastating.
Dirty Data Problems In Healthcare
Here are examples of how unclean data affects the healthcare industry and its patients.
Duplicate Data In Healthcare
As Just Associates advises, duplicate data breeds more duplicate data, especially in a healthcare setting. When approached with multiple patient profiles, for example, staff may not know which records to update.
The confusion can lead to patients having multiple profiles in any one medical database. This occurrence implies that one file or record may include out-of-date information on hospital admissions, for example. The danger with duplicates in healthcare data is that the wrong information could cause serious harm.
Patient Care Itself is Compromised
Just Associates, again, puts it plain and clear. When data in a healthcare setting is dirty, it can harm the quality-of-care patients receive. As stated above, duplicate data could send some records out of date.
However, it could also mean that some data is unaccounted for. This could mean patients have to wait even longer to receive test results. It could delay treatments and increase expenses for both patients and businesses. Even worse, delaying treatments could cause serious harm.
Value Analysis Impacts
As Bruce Johnson, CEO for GHX, states, poor quality healthcare data can seriously impact the program and equipment value analysis. Value analysis is a crucial part of US healthcare administration. Many patients and hospital analyses rely on data that shows how effective specific treatments are.
If this data is missing, duplicated, or simply misleading, this could seriously harm research and development. Of course, this means medical facilities may not be able to diagnose and treat specific patients in the future confidently.
Why Is Data cleaning In Healthcare Important?
Data cleaning in the healthcare sphere is vital as there are potential lives at stake. Duplicate data or incomplete information do not purely add up to minor inconvenience. As the healthcare industry revolves around people’s lives, it is clear that data refinement is essential.
However, many sources also state that medical data cleanliness is essential in terms of cost. As examined earlier, the industry is reportedly losing more than $300 billion each year through inadequate quality information resources. The cost of dirty data in healthcare is only rising.
This may be seeping through compounding gaps due to unnecessary testing, record updates, re-issuing bills, and more. With a clear, simple data organization, as opposed to a lake model, the need to cover every gap is removed.
What’s more, as Innovu states, every healthcare organizational need will differ. It’s important to consider cleaning healthcare data purely for the need for customization. More importantly, as Innovu further states, data cleaning in healthcare is essential to improve services.
This echoes Bruce Johnson’s statement. Without clean, concise data management, value analysis is positively pointless. Clean data in healthcare offers straight answers and no need for additional queries.
What Is High Quality Data in Healthcare?
High-quality data in healthcare is that which is clearly organized and easy to retrieve. It is singular records and files which update regularly. Data lakes are incredibly problematic, but sadly, they are all too familiar.
WinPure works with healthcare providers to help ensure data is clean and easy to retrieve. With a simple cleaning solution and a single database, it is entirely possible to manage siloes of patient data. This, according to the information discussed in this article, can help to protect revenue and may even save lives!
The cost of dirty data in healthcare is astronomical. As a result, more bodies in the US need to consider new ways to clean and reorganize their siloes. Big data in healthcare will never stop growing!
