The Healthcare Mandate: How to Leverage Disruptive Innovation to Heal America’s Biggest Industry by Nicholas Webb

Author:Nicholas Webb [Nicholas Webb]
Language: eng
Format: epub
Publisher: McGraw-Hill
Published: 2020-09-07T16:00:00+00:00

FIGURE 12.1 Growth of Health Data

(Source: International Data Corporation)

2. Cleanse Data

Just because you can collect huge amounts of data doesn’t mean that all of it will be reliable or in the correct form to be processed with other data from other sources.

Also referred to as data scrubbing, data cleansing is the process of detecting dirty data (data that is incorrect, out of date, redundant, incomplete, or formatted incorrectly) and then removing and/or correcting the data.

In the context of healthcare, where the very lives of constituents may be at stake, it’s especially important to verify the accuracy of data and the conclusions that are made from multiple data sources. Data cleansing is necessary to bring consistency to different sets of data that have been merged from separate sources; the cleansing may involve consolidating data within a database by removing inconsistent data and duplicates, and reindexing existing data in order to achieve the most accurate and concise database.

To verify the accuracy and completeness of the data, the CHOS module will leverage artificial intelligence and other standardized methods.

It is important to recognize that any resulting insights will have the same quality—good or bad—as the raw data initially provided. As the old computer saying goes, “Garbage in, garbage out.” It’s analogous to teaching people about a new topic, such as the history of heart pacemakers, and then asking them to write an essay about the subject. If you provide them with outdated or inaccurate source materials, the report they write will perpetuate those same errors. The same goes for artificially intelligent systems; the analytics and conclusions are only as good as the quality of the information provided.

Because the data for a constituent will be gathered from multiple sources, including internal, immediately external, and global, the various originating systems must be interoperable. Among its other benefits, interoperability helps to ensure that systems can “talk” and “understand” one another, meaning that data can be shared between different software platforms while retaining its original meaning.

As KHN.org reported, a lack of interoperability has hampered our current healthcare system. Years ago, healthcare experts welcomed the advent of electronic health records (EHRs), and expected the records to inhabit a seamlessly networked system that could instantly share the patients’ computerized medical histories with doctors and hospitals anywhere in the country. Unfortunately, the EHR universe is far from ideal, largely because officials allowed hundreds of competing firms to sell medical records software unable to exchange information.7

According to GHX, a healthcare business and data automation company, leaders of healthcare organizations often blame inaccurate data for their inability to deliver accountable and informed care. The massive amount of data generated by healthcare IT systems only adds to the substantial challenges for ensuring accuracy.

GHX has found:8

• Each month, bad data contributes to over 2 million transactional errors.

• Each month, 192,000 edits or corrections must be made to hospital databases.

• On average, each year manufacturers make changes to one-third of the over 30 million medical-surgical products on the market in the United States.

Compliance is also an issue.

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