Overcoming big data bottlenecks in healthcare : a Predictive Modeling case study
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1 Overcoming big data bottlenecks in healthcare : a Predictive Modeling case study Predictive Analytics World, San Francisco April 5, 2016 Paddy Padmanabhan, CEO Damo Consulting Josh Liberman, Ph.D, Executive Director RD & D, Sutter Health
2 About Damo Consulting, Inc. Founded in 2012 : Management consulting, focused on healthcare sector Healthcare Market Advisory : Technology, Analytics, Digital Leadership team from big 5 consulting firms and global technology leaders Thought leadership and deep market knowledge: Published extensively in industry journals, speak regularly at leading industry conferences. 2
3 Healthcare analytics : key drivers and data sources High cost, inefficient system $ 3 Trillion annual spending, highest in the world $ 750 Bna year in waste, fraud and abuse Govt push towards a value-based system of reimbursement Population health management (PHM) and personalized care Improving patient experience and managing health outcomes at population level Data and Analytics plays important role 30-day readmissions: key measure of clinical outcomes Sources of data Over 30 BN spent on EMR systems has set up patient medical record backbone Other data sources to harness: notes, images, demographic data Medical claim information from insurers Emerging sources such as wearables, IoT 3
4 Sutter Health 4
5 Transitions in Care The movement of a patient from one setting of care to another Hospital to Ambulatory primary care (home) Ambulatory specialty care Long-term care Home health Rehabilitation facility 5
6 Why do we care about Transitions in Care? Hospital re-admissions are a real problem Hospitals are paying the price Patients and providers are overwhelmed Hospitals and doctors offices need to talk to each other For patients, knowledge about their health = power Patients need to continue care outside the hospital Discharge plans should come standard Medications are a major issue Caregivers are a crucial part of the equation Hospitals and other providers are making improvements 6
7 Predicting 30-day readmissions Why? Hospitals have limited resources so efficiency is important CMS penalties for exceeding thresholds 7
8 Figurative Current State Discharge Process 8
9 Literal Current State Discharge Process And this process is based on national best practice standards! 9
10 Factors that Can Lead to a Hospital Readmission Illness severity and complexity Inadequate communication with patients and families; Reconciliation of medications; Poor coordination with community clinicians and nonacute care facilities; Care (post-discharge) that can recognize problems early and work towards their resolution. High risk patients can and should receive more support 10
11 Project RED ( ) Project Re-Engineered Discharge (Project RED) recommends 12 mutually reinforcing tasks that hospital care teams undertake during and after a patient s hospital stay to ensure a smooth, efficient and effective care transition at discharge. 1. Ascertain need for and obtain language assistance 7. Teach a written discharge plan the patient can understand. 2. Make appointments for follow-up medical appointments and post discharge tests/labs 8. Educate the patient about his or her diagnosis. 3. Plan for the follow-up of results from lab tests or studies that are pending at discharge. 9. Assess the degree of the patient s understanding of the discharge plan. 4. Organize post-discharge outpatient services and medical equipment. 5. Identify the correct medicines and a plan for the patient to obtain and take them. 6. Reconcile the discharge plan with national guidelines. 10. Review with the patient what to do if a problem arises 11. Expedite transmission of the discharge summary to clinicians accepting care of the patient. 12. Provide telephone reinforcement of the Discharge Plan. 11
12 A model for predicting readmissions: LACE (the Epic standard) L Length of stay of the index admission. A Acuity of the admission (admitted through E.D. vs. an elective admission) C Co-morbidities (Charlson Co-morbidity Index) E Count of E.D. visits within the last 6 months. LACE score ranges from = Low risk; 5 9 = Moderate risk; 10 = High risk of readmission. 12
13 LACE issues - Sutter Health Hospitals > 18 years of age 65+years of age Modest AUC (better than most) Lower in higher risk population Calculable only at/near end of admission (L) Model accuracy a moving target 13
14 Don t let the perfect be the enemy of the good Even modest incremental knowledge of risk can improve the cost-effectiveness of interventions. and can trigger collection of additional data Housing status Access to care Health literacy Substance abuse Lacks social determinants 14
15 Now you have a predictive model : now what? Using a Model Issues to Consider Can you operationalize the model at scale? Can you deliver it to the person when they need it? Will they use it? If they use it, do they know what to do with it? 15
16 Now you have a predictive model : now what? Can you operationalize the model at scale? Can you deliver it to the right person when they need it? Will they use it? If they use it, do they know what to do with it? 16
17 Data bottlenecks: the major challenge to implementing advanced analytics in healthcare Complex workflows and lack of interoperability between systems: More reactive than proactive to patient and provider needs Data management challenges and data silos: Lack of co-ordination, willingness to share data Suitability and reliability of data Just because there is some data out there, it doesn t mean it is usable Operationalization of analytics: Most analytics solutions are offline, not integrated into day to day clinical workflows Privacy & Security: HIPAA, data breaches and liabilities 17
18 Now you have a predictive model : now what? Can you operationalize the model at scale? Can you deliver it to the right person when they need it? Will they use it? If they use it, do they know what to do with it? At admission? Prior to discharge? Case manager Discharge coordinator Nurse Patient Doctor Caregiver Pharmacist Scheduling services 18
19 Now you have a predictive model : now what? Can you operationalize the model at scale? Can you deliver it to the right person when they need it? Will they use it? If they use it, do they know what to do with it? 19
20 Now you have a predictive model : now what? Can you operationalize the model at scale? Can you deliver it to the right person when they need it? Will they use it? If they use it, do they know what to do with it? 20
21 Our Solution? A Discharge Planning Application Browser-based solution. Manages inpatient discharge process. Full workflow visibility (Project RED) on patient's care transition plan. Admissions worklistthat provides real-time discharge status information of each patient. Note manager streamlines communication between care team. 21
22 Project RED UX Integration 22
23 Discharge Planner - Patient Detail View E A Launched from EPIC Patient Banner. A User Authentication B C B Real-Time EPIC Patient Admissions Data. C Single view task Management for all User Roles. D D E Non clinical notes management to Streamline communications. Key Metrics visibility. 23
24 Discharge Planner - Patient WorklistView A A D A Launched from EPIC Worklist or App side tab B B C B B C At-A-Glance view of admitted patients and its corresponding data Full visibility into patient discharge status D Real-time Key Metrics visualization 24
25 Maestro Our Engine for Developing Solutions 25
26 How can we make the best and most affordable care the easiest care to deliver and receive? Make analytics invisible Understand workflows Eliminate manual tasks Eliminate need for remembering Simplify, simplify, simplify 26
27 One Lincoln Center 18W140 Butterfield Road Oakbrook Terrace, Suite 1500 Oak Brook, Illinois,
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