1. Establish a tolerance for change.
In the past, strategy propelled the request for data. But now, in this new world of health analytics, data should propel strategy. To make the transition to a data-driven strategy model, organizations need an executive champion. Historically, this executive has been a CIO or chief data officer, but today, they are more likely to be a CMIO, CMIO of Population Health or chief analytics officer. They are the cheerleaders and motivators behind the evolution from a data production team to an insight creation team. When choosing an executive champion, consider these factors:
- Foremost, does the potential executive champion have the willingness and availability to partner with the analytics team consistently and with some time intensity?
- Do they sit on pertinent committees? Will they be heard? Can they inspire change?
- Do they have the clinical know-how to gain buy-in from critical clinical leaders?
- Is the organization ready to culturally embrace analytics and recognize analytics as a change agent?
If the answer is no to any of these questions, it may take longer to see robust movement in your analytics initiatives. A consistent commitment from an executive champion who is valued by the organization and cross-leadership buy-in is required for any strategy to be successful. To build a foundation for success, a focus on data-oriented decision-making must be a priority.
2. Assess the data and analytics team’s skills and tools.
Moving from data reporting to insight analysis calls for a unique skillset. Gone are the financial analysts of the past. New age analysts, consultants and visualization experts are emerging with focused degrees in business analytics. They offer programming language knowledge and visualization expertise that demonstrate their skills. If these new-age roles don’t exist in your organization today, fear not, qualified candidates are graduating from programs now. While they may need to be taught the intricacies of health care, hiring them right after school will increase the momentum and effectiveness of an analytics team. Remember, if you’re lucky enough to hire one of these bright individuals, make sure your organization has the proper IT infrastructure and software to empower them to execute to their fullest potential.
3. Promote communication and retention.
Understanding personalities and the desire for career growth is part of fundamental human resources training and is even more important in this competitive space of analytics. Young and ambitious analysts tend to be introverted. Creating a deep understanding of individual and team dynamics and paving a clear growth path will increase team unity and talent retention. Assessment tools, such as Meyers-Briggs, are helpful in developing a common understanding among team members as they share and get excited about learning who they are and how they can better communicate with each other. As a leader, knowing the strengths and weaknesses of your team members will assist in building a career ladder within the team. One such job progression example is (from entry level up):
- Application Analyst: Visualization Expert
- Population Health Analyst
- Senior Population Health Analyst
- Population Health Consultant
- Senior Population Health Consultant
- Manger of Population Health Services and Strategic Analytics
- Director of Population Health Services and Strategic Analytics
4. Shift from specialized to system-level thinking.
To heighten learning potential and increase momentum across the organization, analytics and the insights they produce can’t function in a vacuum. The analytics of tomorrow demand interdisciplinary interaction. Both data and teams, spanning beyond analytics, need to rise above the silos and collaborate to maximize outcomes and stabilize performance. The analytics team is just one stakeholder in the change management initiative to become more data-driven. The union of multiple cross-specialty teams can encourage performance at a higher level from both a clinical and business perspective.
5. Consider outside expertise.
The willingness to change is key in transitioning to a data-driven strategy model. While having the personnel and technological resources to execute is an added benefit, there is another way. Consulting groups can help bridge gaps in personnel and technology to fast track an establishment’s journey to insight creation. When considering consulting groups, be cautious that they aren’t operating in a black box that is lacking transparency or the ability to drill down to patient-level details. Changing the way an organization thinks, driving change with clinicians and diving into the data where analytics outcomes are concerning, takes a level of grass roots visibility not all consulting groups can provide. Ensure the consulting group is willing to tailor its expertise to the organization’s unique characteristics and everchanging environment. Does the consulting group have a dedicated analyst that can become part of the team? With what other similar businesses have they worked, and is there the ability to speak to those parties before signing an agreement?
As we continue the push for smarter care, analytics will continue to grow in importance across the health care continuum. Organizations that invest in data, and learn to use it strategically, will be positioned for greater efficiency, lower costs and improved patient outcomes.