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The value of big data in health care is realized when raw data are transformed into insights that drive practice change and decision making.
Data in Health Care
In recent decades, the amount of data we routinely generate and collect, and our ability to analyze it, have exponentially increased.1 Commonly called big data, this massive amount of data being generated on a day-to-day basis is being leveraged to drive industries to become more efficient and productive. The value of big data in health care is realized when raw data are transformed into insights that drive practice change and decision making.2
Data in health care are being used to predict and improve outcomes, enhance quality of life, and avoid adverse events.1 It has been estimated that the health care industry generates 30% of the world’s stored data.2
Every piece of technology, automation, and software in a health system produces large quantities of data in different formats (text, numeric, digital, video, multimedia, etc), which come from each department across the organization. New sources of data will continue to appear, such as glucometers, fitness trackers, and smart watches. Aggregating these various streams of information into a single, usable system, such as a data warehouse, makes these data accessible and actionable.3
Although electronic health records (EHRs) have made progress in standardizing the data capture process, barriers still exist. For instance, free text documentation of notes poses challenges because it results in unstructured data. Data captured in this manner are difficult to aggregate and analyze in a consistent manner. As EHRs continue to evolve, health care professionals are shifting the paradigm of traditional practice as they adjust to standard workflows and become more familiar leveraging discrete fields. This evolution will continue to provide access to higher-quality data that will be more usable for reporting and analytics.3
Data Aggregation in Specialty Pharmacy
Similar to other facets of health care, pharmacies and pharmacy services produce and manage significant quantities of data. Pharmacy data include prescriptions, prescribers, insurance companies, patients, and much more.4
Many pharmacies were early adopters of computerized systems, allowing for better inventory management, as well as enhanced prescription and insurance coordination. Pharmacies’ focus on data has traditionally involved capturing and using data relevant to specific transactions, such as information about refills, insurance, and pickups. However, there is typically much more data generated within a specialty pharmacy, often in separate systems.4
Data aggregation strategies allow pharmacies to leverage data on issues as large as adherence across a population or as small as a single insurer’s practices. With this information, they can significantly enhance their service and patient outcomes.4
Specialty pharmacies have evolved over the past decade with the exponential growth of new high-cost medications being approved and added to the market. The disease states these medications target include, but are not limited to, hepatitis C, cystic fibrosis, multiple sclerosis, and oncology. These disease states represent areas with often poor outcomes on previously available therapies, and the new treatments drastically change outcomes.5
Pharmacies must possess a number of services in order to be considered a specialty pharmacy and to receive accreditation. These services include a commitment for resources to do benefit investigation, copay assistance, clinical support, and patient counseling, and the ability to aggregate and transmit data to all stakeholders, including manufacturers and payers. The ability to collect and aggregate data is essential for success in specialty pharmacy. The level and complexity of the data that must be collected for the specialty pharmaceutical and patient goes well beyond the traditional transaction-based data required for typical chronic medications. For instance, specialty pharmacies must maintain information on International Classification of Diseases, Tenth Revision, codes; reasons for discontinuation of medications; and adverse effect profiles.5
The major barrier to aggregating specialty pharmacy data lies in the fact that outpatient pharmacies, even within a health care system, often do not use dispensing software that is integrated with their health system’s EHR. Even in systems that have adopted a unified EHR such as Epic, the EHR may not have an outpatient pharmacy platform (or the system has not adopted it) and there are still many pharmacy-facing systems that are still not integrated (eg, call tracking, purchasing, inventory, etc).
There are many specialty drugs that are only available through limited-distribution channels. Some manufacturers desire more information about the dispensation than just the claim can provide and may require specialty pharmacies to be able to report information about call attempts, diagnosis code(s), and patient- level factors not routinely collected by an outpatient pharmacy. This is also occasionally seen at the pharmacy benefit manager level. Although these data may be available to the pharmacy, they are typically not aggregated in an easy-to-disseminate way.
BEST PRACTICES
1. Engage frontline staff early and often when developing processes and tools to simplify data aggregation:
2. Partner with your reporting/analytics team early and often when evaluating new systems, changing workflows, and partnering to provide data to outside vendors:
3. Whenever possible, use a shared unique patient identifier (preferably not a social security number):
4. Use standard work, especially as it pertains to uncommon situations:
5. Validate your data:
References