Data is critical to the success of any enterprise. Without data, anything we do is based on inference and conjecture with little evidence to support decisions. Most industries know that the quality, accuracy and completeness of data for every transaction is essential to survivability. They cannot tolerate incomplete, inaccurate and inconsistent data. Collection of high value data is a critical operational mandate.
For physicians, medical school taught us that understanding all of the data parameters about the patient’s health state is critical to making wise decisions to improve or maintain their health status. What we didn’t learn, was the value of capturing those parameters in a complete, consistent and standard way. Without a consistent commitment to capturing data that is complete, accurate and standard, we cannot expect to get information about healthcare that can be used to understand:
- Patterns of illness and changes in those patterns
- The risk and severity of disease in a population
- The value of health care in terms of outcome and experience of care
- Causes of diseases and injuries that could be mitigated
- The effectiveness of policies to improve healthcare value
The potential for the use of transactional data to understand healthcare across all healthcare enterprises is immense; if we could just trust it. Ironically, healthcare seems to be the one industry where data collected on healthcare transactions is often considered an administrative burden. Unlike other industries, data quality is not considered a key focus of healthcare transactions. As long as payment occurs, there has been little focus on the level of accuracy and completeness of that data. Unfortunately we often use this data to make assumptions and decisions that simply can’t be supported considering the quality of the data.
The Good News
While there are significant challenges historically with data, changing incentives may drive towards the business relevance of accurate transactional data.
- The trend towards “population health” is adding a new focus to large transactional data sets.
- Bundled payment and episode based models require better data definition.
- Quality and outcome measures require a better definition of the risk, severity and complexity of the patient’s health status.
- There is a greater focus on disease surveillance about the safety and health of the population.
- Payment models that adjust for risk severity, complexity, case mix and other parameters make the definition of the level of illness and co-morbid conditions critical.
- There is no doubt that high quality healthcare data is critical to the evolution of a value-based healthcare model.
- Historically cross-enterprise transactional data has not demonstrated the level of quality needed to support wise healthcare policy decisions.
- Evolving models are changing incentives and drivers that hopefully will put greater focus on a more accurate definition of the patient’s health state.
About the author: Dr. Nichols is a board certified orthopedic surgeon. After 16 years in active practice, he has been involved in healthcare IT for the past 18 years. On behalf of CMS, payers, providers and other healthcare entities, Joe presents on healthcare data, ICD-10 and clinical documentation improvement. He is also an AHIMA approved ICD-10 coding trainer.