Though not commonly acknowledged, data quality and data integration are critically important to the health care sector. Every medium and large-sized health care organization has an enormous number of systems that are often not integrated with each other. Among other things, these systems contain patient, test, trial and billing information. The ability to collect and rationalize all of this data across all of these systems is extremely difficult, to say the least. Ultimately, health care begins to suffer because doctors and health care providers are unable to access and use this information to improve patient care.

The health care industry is a multitrillion-dollar industry. Some of the stats from the FBI and the Center for Disease Control estimate that 10 percent of U.S. medical expense is a result of fraud. Another sobering statistic from the Physicians for a National Health Program is that 31 percent of the national health care budget goes to claims processing and administration overhead. This is a huge amount of spend, especially considering that overhead has nothing to do with patient care. If we look across health care providers and the things that they’re trying to do, there are many instances where data can improve the health care systems.

While it might seem inexplicable as to why health care providers don’t readily invest in data management, the situation is significantly more complicated. The primary goal of health care is patient care, and health care spending is expected to reflect that. Health care providers are in the business of providing the best service to patients, and realistically, there is only so much money they can spend. When presented with the option of spending money on a new MRI machine or spending money on improving data, just about every single time, the organization will opt for the MRI machine. It provides them with better patient care and additional revenue.

Nevertheless, there are several ways in which patient care can be improved with better data management. Three things immediately jump to mind. The first is something very basic, but important: being able to identify a patient. Think of how many different ways a patient’s name might appear in a physician’s database. From misspellings to inconsistent middle name initial usage, multiple combinations of a name can lead to confusion. If health care providers don’t know who their patients are, how can they provide them with quality service?

The second way better managed data can provide improved patient care is through a holistic view of a patient’s medical history, including medications, procedures and tests. This is very closely related to knowing who the patient is.

Finally, creating a diagnostic database for analysis of drug effectiveness and common diagnoses will allow health care providers to better understand individual patients and help treat other patients with similar symptoms and medical histories. This is all driven by data.

The driver for better data shouldn’t be new technology nor back office improvements. While those are great, the primary reason to improve data is to provide better patient care. Until that is more clearly communicated to the health care industry, it will be very difficult to get the required emphasis on data quality and data management.

Perhaps even more problematic is the current structure of health care spending on administrative overhead. Effective data management can easily reduce this cost. Like every other company, health care companies are primarily concerned with three business activities: compliance, cost containment and revenue optimization.

Compliance involves taking information from many different systems and lines of business, pulling that information together, rationalizing it and providing the combined data to the industry and government compliance boards, which is a big data issue.

Cost containment also is an important area where data can help organizations. As an example, the same test is often performed multiple times because patients will move from a general practitioner to a hospital or to a specialist. The results of tests that were done at the general practitioner are not migrated to the next provider. This means additional tests and costs.

Revenue optimization presents a bit of a yin and yang scenario. More patients and clients require more capacity and a higher cost of doing business. However, if those patients feel the additional procedures are repetitive or unnecessary due to previous tests and diagnoses, they will take their business somewhere else. Finding the sweet spot between these two scenarios is one challenge where data management can help.

It would be unfair, however, to insinuate that data management is a get-up-and-go process. It isn’t. It requires extensive planning, dedication from practitioners and administrative staff as well as the endorsement of management. The barriers to adoption of data management aren’t easily dismissible, which is yet another reason that health care data quality isn’t prioritized often enough.

Data management at an enterprise level is overwhelming for health care providers and hospitals. The culture is not set to operate in a way that is conducive to strong data management practices. It’s also common knowledge that many hospitals don’t have the people, skill set, resources or hardware to devote to a governance program. It’s absolutely essential that when we start talking about data management in health care we’re not just talking about how to get back office systems to talk to other back office systems.

The real challenge is how to provide the people on the front line – the people who are directly interfacing with patients – with technology that can be used to better address patient needs. Providing doctors with an appropriate interface that allows them to move what they’re currently doing on a piece of paper into an electronic format is just as big of a barrier as the back office issue. Data entry will need to be easy and seamless if we’re to succeed in perpetuating adoption.

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