Although health systems have adopted various technology platforms designed to collect a wide array of patient data, including, but not limited to, pharmacy dispensing, case management, and EHR, very few are using the data to change care treatments based on comparative analytics and best practice identification. This diminishes the opportunity for improved processes, better patient outcomes and a stronger health care system
Fortunately for the health care industry, trends are moving in the right direction for better utilization of data, as well as more defined metrics for success and failure of treatment.
Collect Everything for Patient Outcomes
Clinical patient data is vastly important to the growth of the health care industry. The digital revolution gives practitioners access to real-time patient data with just a few clicks. The mobile revolution means access to that data wherever and whenever it’s wanted, yet this access brings an important question: How much data should be collected?
Collect everything.
Data collection should not stop when the regulation or value-based reimbursement requirements are met. Keep collecting. While analyzing and searching for insights, each question answered leads to a new question. Health care needs a large amount of data to answer questions as they arise. While any data is better than no data, all data can lead to unforeseen insights, innovations that, when leveraged, lead to improved patient care.
Once the data is in, it needs to be categorized to become useful for analysis. This includes the vast amount of qualitative data the health care system generates, such as MRI and X-ray scans. Both structured and unstructured data need to be quantified as structured data and stored in the right place for access.
Once the vast array of data is structured properly, it is ready for analysis. For example, in evaluating multiple sclerosis, data analysts can look at relapse rates by region and start analyzing the impact climate or genealogy might be having on the effectiveness of a drug or treatment regimen. When examining data, geo-trends may emerge, such as a large cohort living in cold climates may have higher relapse rates for MS. Is this from the cold weather or could it be that Minnesota has a high percentage of people from a particular ancestry and there is a genetic link? Unless there is data on both variables, it’s impossible to draw a significant conclusion. Collect all the data possible now to build the database, as there is no way to anticipate how the data will be useful for future studies.
Using Data to Improve Patient Outcomes
As an industry, health care has been creating a vast repository of data through the use of EHRs, yet this data isn’t being harnessed to truly impact patient outcomes. Doctors are getting frustrated because they aren’t getting the value back from their data and are not getting the information they need to improve patient care.
Data from EHRs already follow patients from specialist to primary care and back again. Robust data analysis will greatly improve processes and patient outcomes. Big data has been an industry buzzword for nearly a decade. An enormous amount of data has been collected, and now it's time to activate it. Utilizing this data can change the treatment modalities on a global scale. Patient data can help identify which treatment modality has the best outcomes for the lowest cost. This is the crux of value-based care and is rapidly replacing fee-for-service as the major health care reimbursement methodology.
The Future of Data Use
Expect to see more data used to change patient care treatments as well as identify new challenges for our health care system. Health systems are uniquely positioned to integrate the rich clinical data available to them from multiple systems to establish best practices yielding to superior patient outcomes.
The future will provide better reporting, which will help shape care across the globe. The data health systems collect will enable better tracking of impact metrics like adherence and recovery rates, as well as manage for genetic differences and other factors that can affect outcomes.
Health system specialty pharmacies have access to unique data. The analysis of specialty pharmacy data will provide a comparative benchmark between health systems that have access to dispense a specialty drug versus those that do not and the differential outcomes from each. The analysis of this data can help create better results with specialty and limited-distribution drugs, and change the landscape of drug access based on real world results.
As data is utilized on a greater scale, the accuracy, responsiveness and growth of the health care industry will change. Acentrus provides additional resources to health systems to drive better results by using repositories of retrospective and prospective data to identify and implement better ways to treat complex diseases and conditions.
Defining the Metrics of Success
There’s still a lot of work ahead before the data can be fully utilized to its maximum potential. Data can be analyzed, but without defined and standard success metrics, data comparison is subjective. This creates fragmentation of conclusions around data.
Industry standard metrics are coming. One such example includes the opportunity to evaluate disease-specific ICD10s for success and failure metrics. This standardization will set the bar for optimal patient treatments and patient care.
Conclusion
Data is a powerful tool in modern medicine, and the health care industry is just beginning to operationalize the vast data collected in our EHRs. Acentrus is excited to partner with its health system clients to be on the cutting edge of this novel initiative that will improve patient care in an objective, standardized and metric-defined manner.
