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Gerri Birg, Pauline O'Dowd

Data is the foundation for the future of healthcare operations. This data must be highly accurate for medical advances including precision medicine, artificial intelligence and advanced analytics capabilities to fulfill their promise of improving health outcomes.

Ensuring accurate data for the future starts with highly effective clinical documentation improvement (CDI) programs today. Without effective, efficient data collection and recording, there is no guarantee that patient data is in fact accurate. This not only presents a roadblock to creating a more data driven future of medicine, but is also challenging for healthcare organizations today.

Your Brand Reputation Depends on Accurate Data

Just like consumers select restaurants based on online ratings, they’re turning to the Web to select their healthcare provider. With companies like HealthGrades using ICD-9 and ICD-10 codes to rate hospitals and The Leapfrog Group grading hospitals based on publicly reported data on 30 national performance measures of patient safety, hospital ratings are essentially a reflection of their data. If clinical documentation doesn’t reflect a full picture of the patients they treat, the data informing a rating may not accurately reflect a hospital. This can occur when appropriate documentation of a patient’s acuity, mortality, safety incidents and outcomes aren’t precisely reflected in clinical documentation. For instance, for those caring for the “sickest of the sick,” if the high acuity of their patient population isn’t reflected, a high mortality rate could make the hospital look subpar when they have good outcomes considering their patient population. In this case, consumers will be more likely to opt for care elsewhere.

At the same time, providers are increasing transparency by publishing quality metrics. While this makes it easier for consumers to access quality measures, problems will arise for those whose quality scores don’t measure up with the competition. Accurate clinical documentation can point out areas where quality needs to improve. On the flip slide, sharing inflated data (perhaps because acuity is consistently documented higher than it should be) will put you at risk for losing consumer trust.

To paint a more exact picture of an organization, improve metrics around payer mix and service line mix and more accurately reflect patients’ acuity, mortality and severity. This requires constantly evaluating, assessing and refining CDI program processes.

Future Decisions Need Data

The data of today must be accurate so that technologies and innovations on the horizon can deliver on their promises. Today, ICD-10 and ICD-11 codes are used by the World Health Organization (WHO) to monitor the spread of and track outbreaks of serious diseases. Without data that’s coded consistently across organizations worldwide, we won’t have a holistic picture of disease patterns across the world. This data is being leveraged by WHO and other organization to identify disease trends, understand correlations between health problems and develop new treatments. As a result, inconsistent documentation won’t give an accurate picture of health today nor will it give us the ability to improve health in the future.

Data is at the core of future care delivery models. Population health initiatives require accurate data to gain an understanding of entire populations so that providers have a better picture of who they’re caring for and how care will impact them. In addition, as healthcare costs continue to skyrocket accurate data will depict what the best treatment option is based on the condition, severity and mortality of a given case. This provides the opportunity to create high-value treatment plans rooted in data.

While big data’s value has yet to be unlocked within healthcare, there are other initiatives that rely on data that could make a big impact. For instance, the implementation of artificial intelligence (AI) to assist with clinical decision making is dependent on learning patterns in data. If the conditions for a given illness have been inconsistently documented, resulting in inaccurate patient data, AI capabilities may not be equipped to provide the best recommendations.” At the same time, many are thinking of ways to leverage AI to perform clerical processes like billing. While a machine could perform tasks by processing the data it’s been given, if the coding doesn’t accurately reflect the health condition and treatment plan, an insurance company will still deny the claim, essentially making the automated process ineffective.

Don't Let Technology Do All the Work

Electronic Health Records (EHRs) and clinical documentation software enable data collection and coding, but without a clinical documentation specialist’s critical thinking skill, the technology won’t generate an accurate picture of a patient’s condition. As a result, it’s imperative to create a culture focused on critical thinking that empowers both staff and clinicians to be strategic in their documentation.

There’s a saying of “data in is data out” and this is especially true in CDI. “Data in” starts with clinicians’ documents and it doesn’t require more documentation than they’re already doing. It’s about smarter documentation, understanding the end goals and providing the right information. With primary care physicians spending more time working with EHRs than with their patients, it’s clear that this technology isn’t the solution for more accurate documentation. However, education can enable physicians to be smarter in using EHRs to document more appropriately and efficiently.

CDI specialists must be equipped to ask the right questions when they need clarity around a given case so that physicians spend their time answering questions that add value to the case. Just like physicians can be overly reliant on an EHR for documentation, CDI specialists can become overly reliant on clinical documentation software to do their job. This can result in “data out” that fails to reflect a patient’s true medical history, leading to insurance denials and inaccurate information. While inaccurate reflections of a patient’s condition and/or treatment could lead to an insurance denial, continuous inaccuracies in clinical documentation can lead to skewed ratings for a healthcare organization and data that can’t be trusted for decision making.

As a result, the steps healthcare organizations take today to create strong, clinical documentation programs will benefit their organizations both today and for years to come.