Avoiding readmissions

Transcription

1 Avoiding readmissions

2 The power of predictive analytics to cut the cost of hospital stays For years, hospitals and health plans have been looking for ways to identify and remove inefficiencies in care delivery. Predictive analytics is becoming an indispensable tool to mine massive amounts of health data in an efficient way, especially important given the recent, unprecedented healthcare reform. The healthcare analytics market is predicted to grow nearly 24% annually to $10.8 billion in 2017 from $3.7 billion in 2012, according to marketresearch.com. For payers adjusting to the Affordable Care Act and other industry changes, understanding population trends and care outcome data is imperative. The rising cost of bad data It s no secret that the U.S. healthcare system, estimated at $2.5 trillion annually, is the most expensive in the world. Preventable hospital readmissions alone result in $25 billion every year, according to the non-profit National Quality Forum. As a result, many healthcare payers have sought sensible ways to reduce unnecessary admissions (and readmissions), but these attempts have not generated widespread returns. Health plans, providers and members all want better health and lower costs. Identifying the factors that lead to unnecessary hospitalizations and readmissions is crucial not only to patient care but also to payers financial health. Readmissions have been a concern for the past decade without significant progress, a health policy director at Dartmouth Institute told the Atlanta Journal Constitution. Only now are efforts to build effective predictive models to identify those most likely to be readmitted coming to the forefront. (See page 3 Competition Drives Analytical Solutions) With the Affordable Care Act, approximately 32 million more people will enter the healthcare system. To the degree traditional techniques for understanding and managing member risk worked in the past, they will no longer suffice. With health plans forced to take on risk, payers need more focus on the true drivers of escalating health care costs, such as the unchecked progression of chronic conditions, and the importance of helping members stay healthy in the first place. Collectively, payers and providers need to be more proactive, but when dealing with tens of millions of people, how do they target their activity? Health plans serve large populations of unique individuals with specific healthcare needs. They must look at members and how they can engage and empower them to take more proactive care of themselves, which can reduce hospital admission (as well as re-admission) costs. Decision analytics can help payers identify and stratify members to perform hospital case 1 ExlService Holdings, Inc. 2015

3 management at the right acuity level, maximizing outcomes not only for themselves but for providers and patients. Only when data is delivered as actionable information can stakeholders take steps to improve health and reduce readmission rates, thus reducing cost. Automating the identification, stratification and medical management for targeted, individualized intervention to improve health will, over time, improve spending trends as well as staff performance and productivity, reducing the incidence and the cost of hospital stays. The single source of truth Decision analytics provide a single source of truth across the industry, giving payers, providers, practitioners and members the same information. Not only do payers have real data to fuel actuarial analysis, decision analytics provide the transparency that care managers need to better target, communicate and engage members to help them make better healthcare decisions. This allows health plans to align more effectively with providers, practitioners and members to improve medical management operations, optimize costs and create better health decision making. Decision analytics in the form of algorithms analyze multiple factors and discern complex interconnections among them, including connections buried deep within data. Once identified, those causal relationships provide building blocks used to develop models that identify people who are most at risk for unnecessary hospitalizations. A type of artificial intelligence known as machine learning allows these algorithms to become increasingly effective at trend spotting over time, which allows care plan administrators to better target, communicate and promote ways plan members can make healthier decisions. The real world impacts could be huge. Hospital readmission models could provide an opportunity for individualized intervention to prevent diseases, postoperative infections and better manage conditions before they deteriorate to the point Application of Analytics in Care Management Examples Predicting Readmission Detecting Appendicitis Identifying Disease State in Breast Cancer hospital stays are needed. Models could aide in the biopsy decisions and identify the most effective treatment regimes, perhaps even assist in detecting appendicitis to avoid unnecessary surgery. Regardless of the specific outcome, analytics arm plan administrators with the knowledge they need to make decision such as discharge plans, home care plans, transitional care plans, medical home and mobile care alerts, all of which can help reduce hospital admissions. Benefits Opportunity for individualized intervention to prevent disease development, post operative infections, condition deterioration Avoid unnecessary surgery (appendectomy) Aid in biopsy decision and identifying most effective treatment regimens 2 ExlService Holdings, Inc. 2015

4 The healthcare industry is in a unique position today. Those most able to adapt to changing regulation and benefit from the ongoing IT advancements will be the most apt to succeed. Technology has made it possible for plans, providers and members to collaborate for better health. Modeling made possible by advanced algorithms and machine learning will underpin the movement toward evidence-based healthcare and a compensation system that values heath outcomes. That evolution is made possible, in part, by a massive government-led initiative to replace paper medical files with electronic health records (EHRs). The shift to EHRs is already underway. Going digital will change the way healthcare organizations capture, store, analyze and exchange health data. The availability of digitized data will allow predictive analysts to sharply curtail unnecessary hospital admissions while improving health outcomes for millions of patients. Competition drives analytical solutions To understand the value of predictive analytics in healthcare, look no further than the Heritage Health Prize Competition, a two-year, $3 million contest to develop a breakthrough algorithm that uses available patient data to predict and prevent unnecessary hospitalizations. More than 1,600 contestants are developing models based on limited, historic data about real patients: outpatient visits, prior hospitalizations, medication claims, outpatient laboratory visits and some test results. Contestants have included teams from companies such as Google and Bank of America; public sector groups such as The U.S. Food and Drug Administration, The Department of Energy, and Britain s National Health Service; universities Harvard, MIT, Stanford, Oxford, Ben Gurian University; and the Cleveland and Mayo Clinics. Teams are using data to predict hospital admissions producing results that can then be used for the purpose of taking pre-emptive action to avoid unnecessary hospitalizations. The contest s organizers score the models success at predicting the outcomes of actual patients, whose identities are unknown. Regular progress reports rank all contestants, who have refined their models to yield more accurate results since the competition began in April Relying on a machine-learning approach, the EXL team s predictive model has sat at or near the top of the leader board for most of the competition. The model allows EXL to identify people who are most likely to need interventions, and lowering the number of people who go to emergency rooms will lower costs. Imagine what analysts could do with richer data. 3 ExlService Holdings, Inc. 2015

5 EXL Service (NASDAQ: EXLS) is a leading provider of business process solutions that help companies drive business impact and adapt faster to change in the global marketplace. EXL s operations management, embedded analytics and technology platforms have created value for the most prominent multinational organizations in the healthcare, insurance, financial services and banking, utilities, transportation and logistics, and travel service categories. Today, with more than 22,800 employees around the world, we are a strategic partner for our clients in executing operations and identifying opportunities to gain a competitive edge and create new channels for growth and cost savings. Headquartered in New York and founded in 1999, EXL has locations in the U.S., U.K., India, the Philippines, Czech Republic and Bulgaria. EXLservice.com GLOBAL HEADQUARTERS 280 Park Avenue, 38th Floor, New York, NY T: F: UK SALES OFFICE 6 York Street, London, W1U 6PJ, United Kingdom T: F: INVESTOR RELATIONS Steven Barlow Vice President Investor Relations Phone: ir@exlservice.com United States United Kingdom Czech Republic Romania Bulgaria India Philippines