3 reasons for a hospital CFO to also be chief analytics officer July 23, 2014 Share Subscribe RFP Health care reform and the need to evolve financial and clinical data have caused hospitals to spend millions on IT. Yet, some serious challenges remain. Even the most sophisticated systems are struggling with disparate data and an overall lack of integrated analytics that can provide timely, meaningful answers. “When rightly applied, these analytics will change the patient and provider experience.” Erik Shannon, National Partner-in-Charge Health Care Advisory Services Abundance of data creates a need for informatics The rapid growth of data forms and quantities has resulted in a real need to provide informatic platforms to achieve the promises and aspirations of the health care industry. Erik Shannon, national partner-in-charge of Grant Thornton LLP’s Health Care Advisory Services, explains: “We are in a time right now where health systems are becoming ‘data rich.’ However, the common approach to collecting and using the information is rooted in traditional solutions that either ignore the new forms and types of data available or can’t incorporate the data effectively. Everything from data recorded by sophisticated imaging equipment to minutes spent in the operating room on a particular case to patient preferences for communication — for example, the best way to remind a patient to take his or her medicine — provides incredible opportunities to apply advanced analytics to improve health care services and population health. Health care CFOs use data and analytics as new currency CFOs will need to partner with CEOs, COOs, CMOs and CIOs to tie together clinical, operational and financial information. Despite the disruptive environment and ever-changing priorities, forward-thinking CFOs will leverage data as part of their overall financial management strategy. “We are in a time right now where health systems are becoming ‘data rich.’ However, the common approach to collecting and using the information is rooted in traditional solutions that either ignore the new forms and types of data available or can’t incorporate the data effectively.” Erik Shannon National Partner-in-Charge Health Care Advisory Services Three trends are the driving forces behind analytics in a post-reform era: 1. Clinical transformation — increasing quality and reducing costs — takes BI It is time to reap huge benefits from your EHR investment through actionable business intelligence. CFOs are critical partners in clinical transformation programs designed to improve quality and reduce costs. Ultimately, these programs should aim to have the right type and amount of care by the right provider at the right time and in the right setting. These programs require robust information about quality, outcomes, utilization/volume, revenue, cost, margin and experience. Detailed build-ups can be drilled into the information, cut across business units and made comparable along multiple dimensions. A fundamental starting point is the ability to compare physician practice patterns. Cost-reduction and quality-improvement stakes are high. Without meaningful quality and cost information that physicians and key stakeholders can accept and use, the chance of failure is high and the opportunity to optimize low. Improving cost accounting within hospitals and ambulatory sites is no longer a “nice to have” but rather a critical component in meeting increasing pressures on cost containment, changes in reimbursement, and new payment models. Most decision-support and costing systems can’t provide this detailed level of information because they rely on traditional cost accounting methods, e.g., relative value units (RVUs) and ratio of costs to charges (RCCs). Traditional methods spread costs across all departments or services. With large amounts of clinical data captured in today’s EHR and other systems, it’s possible to use more drivers than in the past to enable methodology that’s another step closer to matching costs in a cause-and-effect manner. In this way, a comparison of physician A and B — both performing knee replacement surgery — makes sense and is actionable. 2. The shift to population health redefines data requirements Managing the health of a defined population requires predictive modeling based on population/patient profiles. These profiles stratify the population via projected costs based on risks and historical health patterns, identifying those most likely to be high utilizers of medical services. Prescriptive analytics also play a role in matching patients and trigger points for care models and specific interventions. Models and interventions are tailored to the individual based on the modeling of structured and unstructured data, providing insight into the best approach for that patient. For example, they can demonstrate the best way to help ensure a particular patient complies with certain aspects of his or her care plan. On a wider level, these analytics identify key care gaps and the potential need to coordinate, manage or acquire a key aspect of the continuum of care. 3. New payment methods and smart growth plans are built on informed answers Building pressure to limit reimbursement for services and the growing number of alternative payment structures require modeling changes based on detailed margin and quality information. Health systems need to cross-reference market intelligence, referral patterns, margins and incremental capacity/margin information in order to develop successful growth strategies for the current and the future environment. A typical question from the board is about what the strategy will look like in three to five years. It’s a question that many CFOs feel they do not have adequate information for an effective answer. Consolidation and integration prompt calls for analytics While not necessarily a driving force on its own, the trend toward consolidation and integration does impact a number of decisions related to health care informatics. Every day, providers align as accountable care organizations, and consolidate hospital and health system, health system and health insurer, with all stakeholders having a goal to unify and ultimately strengthen their financial and operational footing. Hospitals are employing physicians, partnering with physician groups and investigating other models such as professional service agreements, co-management and alignment through clinically integrated networks. These transactions generate information that requires analyzing and putting into perspective. Analytics can help control risk at all stages.After the initial actions are taken, the next step — operationalizing — is where the most risk and the most failure happen. But in this and other areas, effective use of information can guide decisions. Performance analytics and enterprisewide solutions can help manage growth and change in health care’s dynamic environment.