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With lives on the line, data quality in healthcare is more than just a priority, it is a necessity. But healthcare organizations remain reliant on siloed data, hampered by talent limitations and overwhelmed by the sheer amount of available data.
Healthcare organizations need to continue to pursue data management best practices, improve communication and invest in talent to ensure data quality and patient care.
Healthcare industry and data management
The healthcare industry pulls in a significant amount of data but has been dominated by organizations gathering data in silos without much communication; different departments within healthcare organizations are gathering data separately. This expands to the wider industry with health organizations not communicating or sharing relevant data with each other.
For healthcare organizations, many diverse systems control different aspects of health services, from patient care to finances and billing.
"Each of these systems operates largely in different ways, capturing data independently without significant standardization around the way data is collected," said Paul Schulz, EVP of technology and innovation at Skedulo.
Without standardization across an organization, departments gather data in various ways. Silos demand tremendous communication and lead to poorer data quality without it.
Jason Macedonia, vice president of healthcare and patient experience at InMoment, said healthcare has not fully embraced the opportunity to use existing data to understand the larger story of a patient's experience. To him, the healthcare industry has traditionally had access to a significant amount of data but has had an acute lack of connectedness in available information.
The opportunity to use tools to tie together the data gathered from these different silos remains untapped as well. The healthcare industry remains slow to fully embrace digital transformation and more advanced data gathering and analyzing techniques.
Poor data quality leads to poor healthcare
When an organization doesn't put data quality as a priority, it and its patrons suffer. This is especially prevalent in the healthcare industry.
Hospitals and other healthcare organizations rely on data to make important decisions. When the information that gave rise to these decisions is inaccurate or misleading, it can cost time and money, and it can put a person's life in danger.
"To a large degree, all businesses rely on high-quality data to drive decision-making, but in the case of the healthcare industry, the quality of the data is particularly vital as it can lead, in some cases, to life-or-death decisions being made," Schulz said.
Unreliable information and data work against organizational efficiencies as well as consumer and patient trust. Poor results due to poor-quality data hamper the industry, and the resulting injury to trust continues to hold the healthcare industry back.
Ray D'Onofrio, principal data architect at SPR, said that often healthcare data is aggregated from a multitude of individual patients' clinical or biometric data. The risks are much higher when it comes to this industry. Poor data at its worst it can even lead to death.
Obstacles to quality data in healthcare
What prevents healthcare organizations from focusing on quality data is similar to what holds back other industries. Finding the right talent to gather, manage and maintain data is a difficult obstacle that some organizations can't surmount.
"The main challenge is finding the right talent for data engineering and creating a data and analytics capability in the organization," said Munzoor Shaikh, senior director of healthcare and life sciences at West Monroe, a business and technology consulting firm. "This is an area both payers and providers have underinvested in and have remained behind compared to other industries, while their consumers have grown accustomed to data-driven experiences from other industries."
There are heightened needs for the healthcare industry because data tends to come from numerous sources, and there hasn't been a widespread culture of data management for an extended period. Shaikh said the discipline of cleansing data has never quite been built by healthcare organizations.
The problems persist because the culture remains resistant to change. The sheer variety and volume of data healthcare organizations take in pose a significant obstacle to maintaining quality data.
"With more than 300 million individuals in the U.S., and billions globally, healthcare data of an almost endless variety is being provided by hundreds -- if not thousands -- of different healthcare systems storing data in different formats and often even with different coding," D'Onofrio said.
This requires dedicated time and expertise with which some organizations continue to struggle.
Active steps to improve
There is opportunity for improvement of data quality in healthcare.
Mike Hendrickson, vice president of technology and development products at Skillsoft, said that ensuring quality when it comes to data is tied to training staff. Keeping them up to date on the latest techniques for cleaning, structuring and normalizing data while building governance, security and access around the right data for the right use can drastically improve a healthcare organization's position.
Apart from employee training and upskilling, tackling the problem of poor data quality happens early in the data gathering process. Waiting too long to check that the data is worthy to gather can cost time and money and lead to more than organizational frustration.
"Make sure data quality is high before it gets to the data warehouse, not after, which is what we see a lot of companies do," said Roman Stanek, founder and CEO of GoodData. "It's easier, cheaper and makes for a better strategy to build this in at the very beginning."
Though the healthcare industry is known for having access to a large amount of data, it is crucial that the collection of this data and the questions being asked are relevant and useful.
"Ensure that at the patient level, the right things are being asked to solicit the relevant experience data in the first place," Macedonia said. "Organizations that are partnered with traditional collection providers generally do not take advantage of the industry advances in high-quality collection protocols."
This can be a mistake. To make sure that your healthcare organization is making the right decisions and using the right data requires an end-to-end approach. From the questions asked of patients to the communication between departments.