Decision Support Systems & Health Care

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  • 01/18/12 21:04
  • Decision Support Systems & Health Care

    1. 1. MED INF 406 Decision Support to Reduce Readmission Rates 11/23/09 Larry Stofko, Steve Edwards, Aleksandar Zivkovic, and Cheri Krampert
    2. 2. Table of Contents <ul><li>Introduction (Steve Edwards) </li></ul><ul><ul><li>Institutional Background </li></ul></ul><ul><ul><li>Problem Statement, Objective, End Goal </li></ul></ul><ul><ul><li>Readmission Intervention Diagnosis Focus Areas </li></ul></ul><ul><ul><li>Readmission Facts </li></ul></ul><ul><ul><li>Benefit Areas/Stakeholders </li></ul></ul><ul><li>Model (Aleksandar Zivkovic) </li></ul><ul><li>System Description (Larry Stofko) </li></ul><ul><li>Evaluation (Cheri Krampert) </li></ul><ul><li>Concluding Remarks (Larry Stofko and Group) </li></ul>
    3. 3. Institutional Background St. Joseph Health System Profile <ul><li>$3.7 billion in annual revenues </li></ul><ul><li>14 hospitals in 3 states </li></ul><ul><li>Bed sizes ranging from <50 to >850 </li></ul><ul><li>Outpatient services, home health agencies, hospice care, skilled nursing facilities, and physician organizations </li></ul><ul><li>Employees: 24,000    (full-time equivalents) </li></ul><ul><li>Total discharges:     137,157 </li></ul><ul><li>Total outpatient visits:     2,061,026 </li></ul><ul><li>Total home health visits:     235,177 </li></ul>
    4. 4. Introduction <ul><li>Problem Description </li></ul><ul><li>CMS will be utilizing “30 day readmission rates” next year to a larger degree as an indicator of performance and quality in their reimbursement criteria. (Federal Register, 8/27/2009) </li></ul><ul><li>HIT funding efforts are in turn basing the “Meaningful Use” criteria on defined quality ratings . </li></ul><ul><li>To be proactive as these changes take place, the health system has decided to focus its efforts on improving 30 day readmission rates in key critical indicator areas. </li></ul><ul><li>Objective </li></ul><ul><li>A ssess the probability of 30 day readmissions related to key diagnoses or procedures, the timing of readmission, and potential follow up interventions. </li></ul><ul><li>A ssess Treatment effectiveness on selected key critical high 30 day readmission indicator areas. </li></ul><ul><li>I nterjection of Decision Support tools with improved intervention trees. </li></ul><ul><li>End Goal </li></ul><ul><li>Reduce readmission rates on key critical indicators thru improved decision support interventions. </li></ul>
    5. 5. Readmission Disease Type Diagnosis Focus Areas Diagnosis Background Heart Failure (HF) <ul><li>Leading cause of hospitalization in persons 65 years or older </li></ul><ul><li>20% of discharge patients are readmitted within 30 days. </li></ul><ul><li>2011 CMS Reporting Requirement (READ 30-HF: Heart failure readmission) </li></ul>Chronic Obstructive Pulmonary Disease (COPD) <ul><li>“ COPD is a major cause of disability, and it's the fourth leading cause of death in the United States. More than 12 million people are currently diagnosed with COPD.” (NIH) </li></ul><ul><li>60% of patients readmitted within 1 years </li></ul>Pneumonia (PN) <ul><li>6 th leading cause of death in the U.S. (CDC, 2006) </li></ul><ul><li>40,000 deaths in the U.S. annually and the overall case-fatality rate among the elderly is 30-40%.  </li></ul><ul><li>2011 CMS Reporting Requirement (READ 30-PN: Pneumonia 30-Day Risk Standardized Readmission Measure) </li></ul>Acute myocardial infarction (AMI) <ul><li>Leading cause of death worldwide </li></ul><ul><li>2011 CMS Reporting Requirement (READ 30-AMI: Acute Myocardial Infarction 30-Day Risk Standardized Readmission Measure) </li></ul>
    6. 6. Readmission Facts
    7. 7. Benefit Areas/Stakeholder Summary <ul><li>Expected Benefits </li></ul><ul><ul><li>Improve Quality of Care </li></ul></ul><ul><ul><li>Increase in Patient Safety </li></ul></ul><ul><ul><li>Improve Quality of Finances </li></ul></ul><ul><ul><li>Reduction in Costs </li></ul></ul><ul><li>Stakeholders </li></ul><ul><ul><li>Hospital Executives </li></ul></ul><ul><ul><li>Emergency Department Personnel </li></ul></ul><ul><ul><li>Patients and Families </li></ul></ul><ul><ul><li>Primary Care Physicians </li></ul></ul><ul><ul><li>Outpatient Physicians </li></ul></ul><ul><ul><li>Discharge Nurse/Case Management </li></ul></ul>Onward to the Model
    8. 8. The Model <ul><li>Potentially Preventable Readmission (PPR) </li></ul><ul><ul><li>Provision of quality of care in the initial hospitalization </li></ul></ul><ul><ul><li>Adequate discharge planning </li></ul></ul><ul><ul><li>Adequate post discharge follow-up, and </li></ul></ul><ul><ul><li>Improved coordination between inpatient and outpatient care teams. (1) </li></ul></ul><ul><ul><li>(1) “Identifying Potentially Preventable Readmissions” by Norbert I. Goldfield, Elizabeth C McCullough, John S. Hughes, Ana M. Tang, Beth Eastman, Lisa K. Rawlins, Richard F. Averill. Healthcare Financing Review/Fall 2008/Volume 30, Number 1 </li></ul></ul>
    9. 9. The Model <ul><li>Readmission: </li></ul><ul><ul><li>A medical readmission for a continuation or recurrence of the reason for the initial admission, or for a closely related condition (e.g. a readmission for diabetes following an initial admission for diabetes). </li></ul></ul><ul><ul><li>A medical readmission for an acute decompensation of a chronic problem that was not the reason for the initial admission, but was possibly related to care either during or immediately after the initial admission (e.g. a readmission for diabetes in a patient whose initial admission was for an acute myocardial infarction). </li></ul></ul><ul><ul><li>A medical readmission for an acute medical complication possibly related to care during the initial admission. </li></ul></ul><ul><ul><li>A readmission for a surgical procedure to address a continuation or a recurrence of the problem causing the initial admission. </li></ul></ul><ul><ul><li>A readmission for a surgical procedure to address a complication resulting from care during the initial admission. </li></ul></ul>
    10. 10. The Model <ul><li>Critical Areas: </li></ul><ul><ul><li>Acute myocardial infarction (AMI) </li></ul></ul><ul><ul><li>Heart failure (HF) </li></ul></ul><ul><ul><li>Pneumonia (PN) </li></ul></ul><ul><ul><li>Chronic obstructive pulmonary disease (COPD) </li></ul></ul>
    11. 11. The Model <ul><li>PN Exclusion Criteria: </li></ul>
    12. 12. The Model <ul><li>Analysis Steps: </li></ul>
    13. 13. The Model <ul><li>Findings: </li></ul><ul><ul><li>The model performs as expected given that the risk of readmission is likely much more dependent on the quality of care and system characteristics than on patient severity. </li></ul></ul><ul><ul><li>Results of studies that suggest optimal care for pneumonia during the index hospitalization may reduce the risk of subsequent readmission (2) underscore this potential for improving care and reducing readmissions. </li></ul></ul><ul><ul><li>(2) Dean NC, Bateman KA, Donnelly SM, et al. 2006. Improved clinical outcomes with utilization of community-acquired PN guide. Chest 130(3):794-799. </li></ul></ul>
    14. 14. The Model Decision Support Tree:
    15. 15. Model Summary Preventing Readmission <ul><li>CDS to address hospital quality of care: </li></ul><ul><ul><li>Identification of target population </li></ul></ul><ul><ul><li>Use of Evidence Based Order Sets for AMI, HF, PN and COPD </li></ul></ul><ul><li>CDS to address discharge planning and follow up </li></ul><ul><ul><li>Alert to Case Management </li></ul></ul><ul><ul><li>Case Management protocol for discharge planning and coordination </li></ul></ul><ul><ul><li>Discharge notification to primary care team </li></ul></ul><ul><ul><li>Alert for follow up phone monitoring post discharge </li></ul></ul>Onward to Systems
    16. 16. System Description <ul><li>Data Sources and Types </li></ul><ul><li>Interface Architecture </li></ul><ul><li>Amalga Architecture </li></ul><ul><li>User Interface </li></ul><ul><li>Output from the System </li></ul><ul><li>User Training </li></ul>
    17. 17. Summary of Data Sources <ul><li>Mission </li></ul><ul><li>St. Jude </li></ul><ul><li>St. Joseph Orange </li></ul><ul><li>St. Mary </li></ul><ul><li>Queen of The Valley </li></ul><ul><li>Santa Rosa </li></ul><ul><li>Petaluma </li></ul><ul><li>Eureka/Redwood </li></ul><ul><li>Covenant </li></ul>
    18. 18. Data Flow and Interfaces
    19. 19. Amalga UIS Architecture Data Parsers Data Store Tables & SQL Views Optimized by Use Data Acquisition Clinical Repository Data Parsers Client Devices
    20. 20. User Interface <ul><li>Log in with your network ID and password </li></ul><ul><ul><li>Also referred to as Active Directory (AD) ID </li></ul></ul><ul><ul><li>Format: AD ID @stjoe.org </li></ul></ul>
    21. 21. Data Viewer
    22. 22. Data Filtering <ul><li>Multiple Search Criteria </li></ul><ul><ul><li>Using “Contains” + the pipe-delimiter ( | ) </li></ul></ul>
    23. 23. Data Grid Section <ul><ul><li>Similar to a spreadsheet </li></ul></ul><ul><ul><li>Can move and re-order column headings </li></ul></ul><ul><ul><li>Can rename headings in cloned views </li></ul></ul>
    24. 24. Output to Excel <ul><ul><li>Print and Export to Excel </li></ul></ul>
    25. 25. Export to Microsoft Excel
    26. 26. Output to Dashboards
    27. 27. Output to Mapping
    28. 28. Output “Watch List” and Alerts
    29. 29. Last But Not Least… User Training <ul><li>Example Training Video Screenshot </li></ul>Onward to Evaluation
    30. 30. Evaluation Tool <ul><li>Amalga </li></ul><ul><ul><li>Data Aggregation tool </li></ul></ul><ul><ul><li>Clinical Data Warehouse </li></ul></ul><ul><ul><li>Business Intelligence </li></ul></ul><ul><ul><li>Provides advance analytics </li></ul></ul><ul><ul><li>Able to support research and quality improvement initiatives </li></ul></ul><ul><ul><li>Real time /Near real time </li></ul></ul><ul><ul><li>Able to create alerts </li></ul></ul><ul><ul><li>Retrospective analysis of target population for baseline comparison </li></ul></ul><ul><ul><li>Analysis of patient across continuum of care </li></ul></ul>
    31. 31. Intervention effectiveness <ul><li>Measure retrospective baseline readmission rate across multiple hospitals </li></ul><ul><li>Measure readmission at 30-60-90 day intervals </li></ul><ul><li>Initiate CDS interventions </li></ul><ul><ul><li>Amalga can consistently identify and alert for high risk patients utilizing admission data and clinical results </li></ul></ul><ul><ul><li>Some CDS interventions will vary across hospitals due to variance in implementation of EMR and CPOE </li></ul></ul><ul><ul><li>Intent is to identify best practice and measure outcomes of CDS interventions </li></ul></ul><ul><ul><li>CDS interventions include </li></ul></ul><ul><ul><ul><li>CPOE use of order sets </li></ul></ul></ul><ul><ul><ul><li>Care protocols that integrate EBM </li></ul></ul></ul><ul><ul><ul><li>Rules and Alerts </li></ul></ul></ul>
    32. 32. Business Intelligence <ul><li>Which CDS interventions demonstrate the most improved outcomes? </li></ul><ul><li>Identification of target populations </li></ul><ul><li>Alert for follow up care </li></ul><ul><li>Drill down on data </li></ul><ul><li>Identification of interventions by hospital </li></ul><ul><li>Understanding of variation in technology and CDS </li></ul><ul><li>Measure and Monitor outcomes </li></ul>
    33. 33. Interventions and Measures
    34. 34. Readmit Dashboard 30- 60-90 day readmission rates across 11 hospitals
    35. 35. <ul><li>Concluding Remarks </li></ul>

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