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:
  • The best process or system in the world will not be successful if it does not fit within the standard workflow of your pharmacy. Engaging pharmacists, technicians, and any other frontline staff early to candidly share the feasibility of the ideas generated can help save headaches down the road.
  • If your specialty pharmacy shares systems with other outpatient and retail pharmacies, make sure to engage their staff as well to make sure you don’t make changes that negatively affect them.
2. Partner with your reporting/analytics team early and often when evaluating new systems, changing workflows, and partnering to provide data to outside vendors:
  • Depending on the structure of your organization, you may not be able to leverage a member of the reporting team to participate in these meetings, but likely they can provide you a list of discerning questions that you may have missed (eg, what database system does this software use? Will we be able to report against it directly or do we have to use your software to pull reports?).
  • It is easy to forget this step, but it can cost valuable time because the process that was changed had a net zero effect on the ability to aggregate data.
3. Whenever possible, use a shared unique patient identifier (preferably not a social security number):
  • Leverage your existing systems when possible to share a unique patient identifier (such as a medical record number [MRN]) to facilitate merging data down the line).
  • Although most systems do not let you edit their MRN/unique identifier, there are often extra fields that can be leveraged to store another system’s MRN. Make sure this field is reportable and not necessary for other uses.
  • When possible, use the health system’s standard patient record number (ie, the EHR MRN) because it likely will be the most useful, both now and in the future.
4. Use standard work, especially as it pertains to uncommon situations:
  • Although this seems counterintuitive, there are situations that arise within the operations of an outpatient pharmacy that can affect data aggregation that must be accounted for.
  • One example is when the PBM has the incorrect date of birth (DOB) for the patient. Often, the DOB is simply changed within the pharmacy system to allow for the claims to adjudicate successfully, but this becomes a problem when trying to validate patient matching. Depending on your system, this can be managed similarly by identifying a reportable, unused field within your system to document when this occurs and exactly what will be put in this field when the situation occurs.
  • Fields that have been identified as vital for data aggregation should not be able to be edited by all users or should go through some sort of a validation process. Manual errors in free text fields can be major barriers to successful data aggregation.
5. Validate your data:
  • Perhaps the most important best practice, it is vital to validate your data not only at the beginning of data aggregation, but on a regular cadence and with extra validation after system upgrades. This should be done both for successful matches and those who have no match and multiple matches.
  • A survey conducted by the American Health Information Management Association found that only 57% of respondents work with duplicate patient records regularly. Forty-three percent of respondents indicated that they measured data quality as it relates to patient matching.6
References
  1. Marr B. How big data is changing healthcare. Forbes. 2015. https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/#1c6e7d6f2873
  2. Huesh MD, Mosher TJ. Using it or losing it? The case for data scientists inside health care. NEJM Catalyst. 2017. https://catalyst.nejm.org/case-data-scientists-inside-health-care/
  3. Leseur D. 5 Reasons Healthcare Data Is Unique and Difficult to Measure. Health Catalyst. 2014. https://www.healthcatalyst.com/insights/5-reasons-healthcare-data-is-difficult-to-measure
  4. Chilcott M. Understanding data aggregation in the pharmacy industry. Forbes. 2018. https://www.forbes.com/sites/forbestechcouncil/2018/07/13/understanding-data-aggregation-in-the-pharmacy-industry/#2e9b41c97fee
  5. Calla N. Data Aggregation: A Key Component to Success. Specialty Pharmacy Times. 2012. https://www.specialtypharmacytimes.com/publications/specialty-pharmacy-times/2012/october-2012/data-aggregation-a-key-component-to-success
  6. Survey: Patient Matching Problems Routine in Healthcare. Journal of AHIMA. 2016. http://journal.ahima.org/2016/01/06/survey-patient-matching-problems-routine-in-healthcare