Harnessing the Power of Data Collection and Coordination in Oncology Practice
Business intelligence is the process of collecting data from disparate systems—internal and external—and turning it into information that is meaningful and actionable toward achieving strategic goals. Business intelligence encompasses reporting, analysis, data mining, data quality/interpretation, and predictive/prescriptive analytics.
Collaboration and Data Generation
According to Jeffrey Reichard, PharmD, BCOP, BCPS, Director of Pharmacy–Oncology, Specialty Pharmacy, and Hospital-Based Infusion Centers, Novant Health, North Carolina, pharmacy provides a tremendous amount of data to their organizations; therefore, establishing the organizational drive to create, maintain, and develop data management and business intelligence is necessary and provides a tremendous opportunity for data consumers. Dr Reichard discussed this topic at the 2018 Practice Management conference of the Hematology/Oncology Pharmacy Association.
“How are specialty pharmacists being alerted at the right time, for the right patient, to make their intervention?” Dr Reichard asked attendees. “It doesn’t just happen. To get it right for the right patient, there really needs to be some technology to help them.”
According to Dr Reichard, pharmacy leaders often assume they can undertake data analysis without external help. “But this is where collaboration is helpful, especially in the case of large organizations, where you get a much better long-term product and better long-term buy-in if operational finance, business intelligence, and data architects are involved, also taking into account the intended outcomes and impact,” he said.
“So knowing the larger targets is really helpful. Many of us assume we can use our experience in all aspects of situations, but in hindsight, we may not have had clear visibility in regard to those aspects in which analytics can really help us; we may not have seen the decision-making opportunity on our own.”
Accessing Data by Oncology Practices
Data value and data availability are 2 problems often faced by oncology practices, even as the demand for data and reports is increasing over time. Although this is a good “problem” to have, the demand outweighs the supply, Dr Reichard said. “There’s a lot of noise in our data, so it’s really important that we think about what we are trying to extract, and then use.”
Novant Health set out to modify its business intelligence program goals. It accelerated its strategic approach to analytics to leapfrog competitors, who invested earlier in business intelligence and information technology infrastructure.
Novant Health integrated its fragmented systems and built a single repository of clinical, nonclinical, and external data, and transitioned to a proactive, action-oriented approach to data analytics that now drives their clinical and business decision-making. Through the use of these strategies, they continue to maximize the benefits from their past and ongoing investment in their own technology.
A coordinated deployment approach to this strategy involves participation from the business intelligence team and the business unit, or pharmacist. “If they don’t all work together, we don’t solve the problems,” he said.
The business intelligence team is made up of the consumer and the data owner: the individual who creates and curates the information and works in collaboration with other members of the business intelligence team to get data incorporated into the business intelligence warehouse, with the eventual goal of obtaining feedback on metrics for other departments.
The data owners then employ and train “business intelligence wizards,” or stakeholders in the department with a higher level of understanding and training, who then create further content. Finally, the data scientist is involved in the architecture creation and management of the infrastructure, as well as incorporating and removing pieces of systems around the data.
External data go into the data warehouse and then move into units that process and maintain the data. These data are then organized into analytics tools before reaching the scientists, wizards, analysts, and finally, the consumer. Dr Reichard noted that this type of coordination also involves a financial commitment.
Getting started begins with a conversation with the team, Dr Reichard said. “You have to have the right people as part of the conversation, and often they’re not in pharmacy.” They could be subject matter experts, analytics leaders, or data scientists.
“Pharmacy should develop data capability alongside other system partners and seek opportunities to collaborate,” he said. “Pharmacy really can—and should—be a key partner, alongside other health systems parties.”