"We'll cross that bridge when we come to it" has been standard operating procedure for most healthcare organizations, especially when it comes to analytics. Traditional productivity and financial metric reporting, typically performed by department analysts such as database administrators and special report writers, has long been the backbone of
healthcare analytics. Given that today's healthcare data deluge offers potential to not only interpret the past but to also shape current operations and forecast the future, new competencies are now in order. A fundamental healthcare analytics shift is needed to transition from counting to preventing, from reacting to historic events to dynamically targeting desired outcomes.
Determining which business and service models to grow and develop or minimize and curtail requires much more than checking off mile markers on your analytics journey. It's now critical for healthcare leaders to continually define the destination, plot coordinates, and note progress toward the future. Should you dedicate resources to bridge to new ventures, or should you tunnel through, go around or simply avoid some opportunities?
Forbes recently declared the data scientist as the "sexiest job of the century" based on scientists' ability to transform data into business value that generates results.1 Data science leaders must be able to work in teams, be comfortable and experienced with "big data", and be skilled communicators in order to transition the healthcare culture from data acquisition and IT management into one associated with information-driven behaviors.2 What is readily apparent is that these skills are not traditionally associated with data analysts. Where analysts have historically tended toward introversion and work isolation, today's healthcare data scientists must be collaborative and team-oriented. They need to speak languages like Hadoop, Python, and JAVA while understanding predictive modeling and computing algorithms.3 These are not skills that can be easily attained through weekend continuing education courses—even if you have internal prospects to fill the role. Given that an estimated 100,000 person talent shortage of data scientists is predicted to occur over the next decade, and that a recent Google search on data scientist jobs yielded 122 million results, heavy competition is predicted.4 This skill gap will affect most healthcare organizations, which should prioritize data scientist recruitment and retention, and deploy them wisely once they're on staff.
To ensure you're building a bridge to prosperity, rather than a doomed effort to span the River Kwai, consider these three best practices:
- Recognize that your organization's data—and the competencies needed to support it—are strategic assets that must be developed.
- Assess your organizational talent, and standardize your analytics roles and competencies to differentiate between roles such as report writing and true analysis.
- Inventory your current reporting standards, and eliminate recurring reports that don't support targets that are clearly aligned with your organization's goals. Instead, develop a consumable reporting set that aligns accountability with enterprise focus and priorities.
Even the most skilled healthcare data scientists need tools to convert healthcare data into actionable insights. Learn how
McKesson Performance Analytics helps turn knowledge into action.