Reducing 30-Day Readmissions with Smooth Transitions of Care

Transcription

1 Reducing 30-Day Readmissions with Smooth Transitions of Care A Complimentary Webinar From healthsystemcio.com Sponsored by McKesson Your Line Will Be Silent Until Our Event Begins at 12:00 ET Thank You!

2 Housekeeping Moderator Anthony Guerra, editor-in-chief, healthsystemcio.com Ask A Question We will be holding a Q&A session after the formal presentations. You may submit your questions at any time by clicking on the QA panel located in the lower right corner of your screen, type in your questions in the text field and hit send. Please keep the send to default as All Panelists. Download the Deck Go to Shortened URL at bottom of all slides View the Archive You will receive an when our archive recording is ready. Separate registration is required.

3 Agenda Approximately 45 Minutes minutes: Dwayne McNeil, Assistant VP, IS, Carolinas HealthCare System 5 minutes: A Word From Our Sponsor: Deborah Bulger, VP, Product Marketing, McKesson minutes: Q&A w/dwayne McNeil

4 Reducing 30-Day Readmissions with Smooth Transitions of Care

5 Reducing 30-Day Readmissions Today s Topics: Carolinas HealthCare System Introduction Readmissions Background Applying Analytics to Readmissions Results PCPCS Next Steps Current Focus Q&A 5

6 Carolinas HealthCare System Introduction 39 hospitals and 900+ care locations in North Carolina, South Carolina and Georgia More than 7,400 licensed beds 11 million patient encounters per year 2,500+ system-employed physicians, 15,000+ nurses and 60,000 teammates $1.5 billion in community benefit in 2013 More than $8 billion in annual revenue PCPCS More than 50 disease-specific certifications from The Joint Commission one of the highest totals in the country among comparable systems The region s only Level I trauma center One of five academic medical centers in North Carolina One of the largest HIT and EMR systems in the country 6

7 CHS Information Services Overall Information Services responsibility for Acute and Ambulatory Care, Continuing Care, Corporate Operations and our physician practice Medical Group 2 Main Data Center Locations: 801 S. McDowell Data Center and CMC-NorthEast campus, plus disaster recovery location on the CMC Campus (LCH Data Center) Information Services By-the-Numbers (yearly totals): 490,000 customer support calls through the 24/7 Support Center PCPCS 60,000 IS customer requests (OSRs), and 9,800 non-is related requests 2,500 training sessions for staff and physicians (35,000+ attendees) 3,700 system, file and print servers, with 3,500+ terabytes of storage 42,000 accounts and 67,000+ devices (PC s, Laptops, Tablets, Printers and Mobile Phones) 12.7 million sq. ft. of wireless network coverage across our care locations 800+ applications supported 700+ IS teammates 7

8 Readmission Imperative 18 percent of Medicare patients discharged from the hospital have a readmission within 30 days of discharge, accounting for $15 billion in spending. Pay for Performance programs Value Based Purchasing Insurance contracting Mitigate/eliminate CMS financial readmission penalties Benchmarked & public quality metrics PCPCS One of most significant drivers of higher cost (payer perspective) Medicare Payment Advisory Commission Report to the Congress: Promoting Greater Efficiency in Medicare. The Right Thing to Do. Better Care for Our Patients. 8

9 Readmission Imperative CMS Hospital Readmission Reduction Program Objectives: Improve Quality and Reduce Cost Initial conditions (2013): Acute Myocardial Infarction Heart Failure Pneumonia 2015 expansion: COPD Elective Hip and Knee 9

10 Readmissions as a CHS Goal 2009: Readmission baseline established 2010: Number of 30-day all-cause readmissions for AMI, HF and PN; Medicare only 2011: Rate of 30 day all cause readmissions following an index admission for AMI, HF and PN; patients age 18 years; all payers 2012: Rate of 30-day all cause readmissions following an index admission for AMI, HF and PN; patients age 18 years; all payers (definition modified to match CMS) 2013: Observed-to-expected (O/E), defined as the number of unplanned readmissions within 30 days of index admission, divided by the expected number of readmissions; patients age 18 years; all payers 2014: Same as 2013 with minor definition change to match CMS 10

11 Reducing 30-Day Readmissions Recent Areas for Readmission Action: Awareness & Education Identification of high risk patients (analytics) Standardize interventions for high risk patients Communication of high risk patients to providers Evaluation of each readmission cause 11

12 Patient-Centered, Point of Care Clinical Decision Support Learning Collaborative Clinical Practice PCPCS Analytics 12

13 Analytically-driven, Personalized Care Value Value Point-of-Care Touch points Unique Individuals Information Infrastructure EHR Social Media Consumer Data Learning Pharmacy Collaborative Patient Experience of Care: Quality and Satisfaction Clinical Expertise Transactions Vendors Claims Lab Population Health Patient Profile Cost of Healthcare Analytics Interventions Rules Applications 13

14 Project Vision We will analyze health and consumer data for insights into individuals clinical risks and through the CHS Learning Collaborative enable the best intervention and treatment decisions at the point-of-care that optimize quality and cost-effective health services. 14

15 Historical Done After EMR and Patient Review Care managers need to review the patient s chart and examine the patient prior to assessing risk Limited Capability Care managers assign risk based on a a few simple criteria that group patients into two buckets: low risk and high risk Patient Risk Assessment Future State Done Prior to Seeing Patient Allows care managers to work more effectively by prioritizing their workflow and more efficiently through automating the risk assessment. Risk Assessed from Predictive Model Patient risk for readmission is predicted, automatically, from over 40 key variables pulled from Cerner Case Manager Variation Care manager ability to find and assess risk factors varies Done at Admission Care managers only have capacity to assess patient risk at admission Automation Decreases Variation Patient risk is automatically calculated for the care managers Updated Hourly A patient s condition and likelihood for readmission can change throughout a hospital stay; our tool captures these changes hourly as clinical data change 15

16 Historical Care Interventions Future State Difficult to Hardwire Care managers required to recognize a certain patient type and remember what interventions are to be assigned to the patient Recommendations Assigned Automatically Patients automatically assigned interventions based on their personal characteristics Difficult to Measure Interventions Current care management tools do not allow for evaluation of intervention efficacy; limits our ability to leverage our System Measure Efficacy of Interventions Capture of interventions and data around outcomes will allow us to measure the efficacy of interventions and determine patients who optimally benefit 16

17 True Positive Rate Analytics: Risk Model Accuracy CHS risk model accuracy of 79% 100% Our model is better than most other predictive models in published literature Based on 2 years of readmission modeling by our Dickson Advanced Analytics (DA 2 ) team 0% 0% False Positive Rate 100% 17

18 Analytics: Segmentation Model Purpose of Segmentation Model Example Segments Population Segments Low Risk Medium Risk High Risk Very High Risk Total Insured Healthy Adult 14.37% 10.91% 6.04% 4.18% 35.50% Medicaid Pediatric 4.13% 2.54% 1.18% 0.36% 8.21% Medicare Independent 5.13% 6.56% 6.10% 5.12% 22.91% Medicare w/ Frequent Visits and 9X9 0.78% 2.65% 5.61% 5.19% 14.24% Middle Age w/ Frequent Visits and Comorbidity 0.55% 2.31% 6.03% 10.25% 19.14% Total 25.00% 25.00% 25.00% 25.00% % 18

19 Analytics: Risk Model Design Risk model predicts a patient s individual risk for a 30-day, unplanned readmission Our Canopy EMR and Enterprise Data Warehouse are primary data sources Pulling over 40 predictive fields hourly to risk score patients Using over 15 operational fields hourly to support decision making 19

20 Predicted Risk Validation of our Readmission Risk Model Very High High Medium Low 0% 10% 20% Actual Readmission Rate 20

21 Readmission Analytics Current Status Solution fully-deployed during Q at our Metro hospitals 65,000+ Patients have had their risk for readmission automatically calculated on an hourly basis 97,000+ Interventions have been assigned by our Case Managers to patients based on their risk for readmission and clinical segment 208 Case Managers actively use the tool 21

22 Readmission Analytics Key Points Our analytics model is accurately identifying patients with low/high risk of readmission Case management teams are able to focus their resources on the right patients and we are improving our interventions to best fit patients Impacting readmission requires interventions, collaboration and a team approach 22

23 Readmissions O/E 1.00 Our Readmission Trends Carolinas HealthCare System Hospital-Wide Readmissions System and SA Benchmark Performance 0.90 Carolinas HealthCare System SA Average 0.80 SA Top Quartile 0.70 SA Top Decile Baseline 23

24 Highlight: Readmissions and Heart Failure CHS developed the Heart Success program in 2013 Key components: Risk assessment Transition clinic Co-management of the patient with PCP Telehealth interaction Remote patient monitoring when needed Results: Reduction in all-cause CHF readmissions by 3.5% to

25 Readmissions and Heart Failure 25

26 Other Resources We Use to Tackle Readmissions Customer Relationship Management: evariant Discharge call management Transitional Care Management team Interfaced with our analytics warehouse Interactive Patient Education: GetWell Patient education readmission intervention options Interfaced with our EMR for tracking & analytics Fully leverage our clinical call center: TeleHealth Solutions 24x7 access Integrated with acute, specialty, primary care and continuing care (post acute) Online patient portal (web & mobile): MyCarolinas 26

27 Next Steps Continue to refine our interventions & personalized solutions, and spread across our enterprise Enhance our view of patients through care transitions transitions across all care locations Improve our population health care management solutions broaden the team of support using clinical decision support and automated workflow Leverage virtual visit and online patient engagement initiatives Innovate with new strategies and technologies 27

28 Reducing 30-Day Readmissions with Smooth Transitions of Care Deborah Bulger, VP, Product Marketing, McKesson

29 Readmissions is a $25B issue Part of a larger $700B cost reduction opportunity Connected Care & Analytics Source: NEHI health policy institute 29

30 Readmission Management Continuum Connected Care & Analytics 1 Risk identification Acute Post acute 30

31 Readmission Management Continuum Connected Care & Analytics 1 Risk identification Acute 2 Care transition Post acute 31

32 Readmission Management Continuum Connected Care & Analytics 1 Risk identification Acute 2 Care transition Post acute 3 Post discharge 32

33 Readmission Management Continuum Connected Care & Analytics 1 Risk identification 4 Measurement Acute 2 Care transition Post acute 3 Post discharge 33

34 Q&A Click on the Q&A panel located in the lower right corner of your screen, type in your questions in the text field and hit send. Please keep the send to default as All Panelists. Slide Deck:

35 Thank You! You will receive an when our archive recording is ready. (Separate registration is required) Thanks to our sponsor: McKesson! CHIME CHCIO Credits Attending our Webinars = 1 CEU Questions/Comments Anthony Guerra aguerra@healthsystemcio.com Go to to view our upcoming schedule and see the last 12 months of archived events. Slide Deck: