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Customer Experience and Design

Clinical Intelligence: A Key to Quality Care

The biggest challenge in today’s healthcare environment is the use of clinical data. Everybody knows that the healthcare industry is “behind the times” compared to other industries when it comes to using data to drive meaningful outcomes, largely due to the complexity of healthcare data. However, with rising costs, constant regulatory changes, and ever-increasing competition, healthcare organizations have no choice but to begin tapping into their enormous volume of clinical information to analyze gaps in their care quality, patient safety, cost-effectiveness and efficiency; develop integrated and coordinated care and contracting models; and prove their performance to purchasers and payers1. The use of clinical intelligence can no longer be ignored.

The What and the Why

What: Clinical intelligence (CI) is the knowledge acquisition resulting from collection, evaluation, analysis, and interpretation of clinical data, often times combined with financial, operational and research data, to drive informed medical and operational decisions across the enterprise organization.

Why: In the ever-changing and increasingly demanding field of medicine, providers must be able to make decisions based on clinical and evidence based medicine. In order to do this, proper assessment and utilization of data across the organization is needed2. The variations in the quality of care, after the fact performance improvement, government mandates increasing reporting requirements and the penalties and failure to obtain pay for performance quality incentives due to poor reporting are just some of the more common reasons why CI is so important3.

It is not that the data isn’t there (in most cases!), healthcare organization have been capturing massive amounts of data for years. However, the data is distributed among a number of siloed systems, locked in paper files, in stand-alone spreadsheets and desktop databases, and some still in three-ring binders4. Given the aforementioned reasons, it has become essential for health care organizations to use health information technologies that will allow them to collect, integrate and analyze their data to make better informed clinical decisions5. Up until recently, healthcare organization would rely on ad hoc reporting from multiple systems which would be labor intensive processes (e.g. manual chart abstractions) that would take days if not weeks to obtain. Once all the reports were gathered, they would piece together the information to help them make clinical and financial decisions. This can no longer be the case. Healthcare organizations need to start marrying their legacy data sources into a single enterprise view that will enable real-time analytics6 from a single repository that will drive clinical, financial and operational decisions.

Barriers to Adoption

Clinical intelligence may sound like a no brainer, but getting there can be a difficult. According to Drs. Scott Cullen and Wendy Wilson, there are a few things that can get in the way and need to be addressed in order for clinical intelligence implementation to be a success7:

Trusted Data:

You can have all the data in the world but if the data lacks quality it is a good as garbage. It is imperative that the right data elements are being captured, consistently, and being measured properly. Useful outcomes cannot be achieved when comparing apples against oranges. This brings up the issue of standardization and data integrity which invariably come into question when clinicians are not principally involved in managing the process of data collection.


Data needs to be consistent in order to build the infrastructure for clinical intelligence. Disparate systems across the provider enterprise need to move toward a standardized set of vocabularies, data elements, policies and procedures so that integration of information can be achieved in a meaningful way. This is a process of evolution, rather than revolution.


The most critical element to the success of implementing clinical intelligence is the creation of a strong data governance structure. Without clear enterprise-level guidance on how to facilitate the process of moving clinicians and IT professionals toward agreement on standards and procedures across the institution, very little will be accomplished at very great expense. In addition, an ongoing joint working group of IT and clinician leaders is necessary for the maintenance and integration of constantly evolving dictionaries, processes formation systems, such as alerts and design changes, as well as work process revisions and standards. Finally, a group of clinicians and administrators with the power and will to redesign workflow in response to their examination of the resulting data will involve changes to existing clinical information systems, such as alerts and design changes, as well as work process revision.

In today’s healthcare environment, organizations are swimming in data yet many lack the technology to use this data as valuable information. As regulations change and the amount of data increases, organizations are turning to clinical intelligence solutions to harness data for precise decision-making to help improve patient outcomes, reduce costs, and ensure their organization’s future8. In today’s healthcare world, you cannot achieve quality of care without knowing what data exists, having access to it and then being able to utilize it the right way at the right time2.

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Priyal Patel

Priyal Patel is a healthcare industry expert, strategist and senior solutions architect for Perficient. With more than 10 years of healthcare industry experience, Priyal is a trusted advisor to C-level executives, senior managers and team members across clinical, business, and technology functions. Priyal has a proven track record of helping providers and health plans execute enterprise-level transformation to drive business, clinical, financial and operational efficiencies and outcomes.

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