From Data to Insights

Jeff Fuller
2019 North Carolina Medical Journal  
Health care is ready for a transformation in how we leverage data, technology, and analytics to improve care. This requires starting off with a focus on establishing trusted data and setting clear paths within organizational cultures to accommodate and empower innovation in health care delivery with better insights from data and analytics. M any quotes attributed to W. Edwards Deming, a statistician and quality management pioneer, have motivated my work as a health analytics professional. My
more » ... orite is, "Without data, you're just another person with an opinion" [1]. In today's health care delivery environment, however, data is no longer sufficient. Thanks to the rapid onset of new technologies and electronic health records, we find ourselves drowning in an ocean of data, but have not yet seen the expected groundswell of innovation and change. So, what's the problem? As my boss, Jason Burke, chief analytics officer at UNC Health Care System, puts it, "We no longer have a shortage of data, we have a shortage of insights." The health care industry is on the verge of a new era in how we deliver care and manage health; the sooner we can resolve our "shortage of insights," the faster this transformation will occur. In order to gain the needed insights that will accelerate our move into this new era, we need to harness the power of analytics and data sciences. Within our health delivery organizations, two of the main reasons this hasn't happened are an over-reliance on descriptive reporting or business intelligence (BI) and a lack of an organizational structure and culture that is welcoming to adaptive challenges. See Figure 1 for a comprehensive view of the journey from a traditional strategy to an innovative analytics strategy. Whether you are just starting out or well on your way in your journey to adopt and sustain analytics as a strategy to transform health care delivery, consider addressing these two areas as an effective starting point. Data Governance There is much strategic value in reporting what happened, particularly when data is managed and governed centrally, but in an industry with over 9,000 standardized measures, how do we know which ones really matter? Furthermore, we should be leveraging this data to not only understand what happened yesterday, but to predict what will happen next. To make this shift, your organization needs an enterprise-level data governance strategy that aligns technical resources with prioritized business goals and an enterprise analytics infrastructure capable of deriving knowledge and insight from a complex data environment. An investment in good data management and data science is necessary to allow identification of the most critical measures and the most effective improvement strategies to elevate the patient's health outcomes and reduce costs of care. In assessing where to start in maturing your strategy to become more data driven, you should recognize that the skills and systems needed to transform transactional data into usable data assets must evolve in order to support analytical development. To better impact real-world situations, analysts should hypothesize from practice-based evidence. Rather than relying on the dataset most readily available for analysis, which can result in a misunderstood current state and misaligned strategies, datasets should be refined based on the strategic priority. The data strategy should also anticipate and support reusability of the data assets in order to promote institutional standards for a single source of truth. As we move to more predictive modeling, traditional data warehousing and data architecture-which are designed to support descriptive reporting and traditional BI-will also need to mature. Maturing an organization's analytics competency requires transitioning from being able to count and report on past performance to being able to model and synthesize future performance. Adapting Organizational Culture The second priority is to assess your organization's readiness and culture to take on the adaptive challenge of becoming more analytics driven. If an organization is truly to become data driven, analytics must be embedded strategically, programmatically, and culturally into the entire organization. Change is hard, especially in our busy industry in which we are operating with and reacting to the informa-
doi:10.18043/ncm.80.4.234 fatcat:oqymjhsmnbesveovc2uhwuyuhi