This one-day workshop features speakers from Accountable Care Organizations and health IT companies who will discuss how they are using predictive analytics to improve patient care.
Predicting the Invisible Patient: Using Predictive Analytics to Reduce Suffering, Save Lives, and Optimize Cost of Care
Patient-centered care can be improved exponentially if the caregiver can forecast illness or complications so that preventative measures can be applied. Recognizing this, leadership from Baptist Health collaborated with a leading software developer and clinical experts to implement a predictive solution that analyzed patient phenotypes to predict disease and infection across the system. Their objectives were to reduce suffering and save lives while optimizing cost of care. The speaker will present predictive findings against one use case, CAUTI, including the business case for prevention; models used; and preliminary health and financial outcomes. Shantanu Nigam (Jvion)
Patient-centric Predictive Analytics
The Carolinas Center for Medical Excellence’s ConvergX applies predictive statistical modeling to proactively identify high-risk patients, so ACO care coordinators can deploy targeted resources that address patient needs and prevent unnecessary utilization. In this way, ConvergX enables ACOs to attain their goals of improved population health, reduced cost of care, and realized “shared savings” to providers. ConvergX enables ACOs to do more than just report data to CMS: it combines analytics with clinical consulting expertise to manage population health, navigate patient-level data, and improve both clinical and health outcomes for providers and patients. Angela Diaz (CCME) & Stephen Nuckolls (Coastal Carolina Health Care)
Real-Time Healthcare with BI and Analytics
Many healthcare organizations have turned to business intelligence (BI) as a primary method to bring information from disparate systems together. Implementing BI is a first step in the right direction; however, BI will not provide the answer to this question: How will Healthcare Service Organizations become more cost-effective in the future? Organizations not only need to know the present conditions and patient’s history, but also should be able to predict future outcomes. BI provides insight into the “now”: predictive analytics will provide insight into the future. Predictive analytics can help a healthcare service organization effectively identify patients who are highly-likely to return to a healthcare provider due to a failure to execute post-visit care instructions, and implement follow-up procedures with the patient. Predictive revenue analytics can help a healthcare organization predict revenue by merging complex patient accounting data, insurance claims revenue statistics, collection payments and historical patient visits. Kelly Crossley & J. C. Layton (InfraScience)
Improving Care for Medicare Patients through Analytics
As a 2015 starter in the Medicare Shared Savings Program, the Carolina Medical Home Network ACO seeks to better coordinate care for its safety net Medicare population by improving clinical, operational, and financial quality. Community Care of North Carolina is a key partner in this value-driven endeavor, as they provide the necessary analytics tools to create actionable data on clinical quality, cost, utilization, and patient risk, identifying those patients most likely to benefit from more intensive care management.” Annette DuBard and Marya Upchurch (CCNC)
Controlling Cost and Quality in Post-Acute Care
In the world of value-based reimbursement, it is essential for provider organizations to manage the quality of care for their patients across the full spectrum of care while also balancing the cost. One area where providers can impact what happens outside their offices is in managing the patient transition from inpatient hospital to post-acute care setting. By using information obtained from claims data (readmission rates, cost of care, etc.) together with publicly available quality data, CHESS has developed a post-acute care strategy based on a scorecard method to identify high quality / low cost post-acute care providers. Armed with information about both the patient and the post-acute facility, CHESS can utilize predictive analytics to assess the “risk of an adverse event for patient x at facility y.” This information helps CHESS develop and manage a narrow referral network of post-acute care providers for their clients in different geographic regions while also being able to predict the best choice of facility for a particular patient. Lisa Shock & Keith Thompson (Cornerstone Health Enablement Strategic Solutions)
We have a limited number of exhibit tables available. The fee is $500 NCHICA members/$750 non-members and includes an exhibit table, registration for two staff members, breakfast, lunch and breaks. Click here to reserve a table.
The registration fee is $199 NCHICA members/$299 non-members/$50 students and includes continental breakfast and lunch. To register, click here.
The Research Triangle Foundation is located at the RTP Headquarters on 12 Davis Drive, RTP.
For directions, please click here.