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1989Staff developed and supported innovative clinical, financial, and marketing applications for SysteMetrics/McGraw-Hill (New York, NY) 1989Staff developed.

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Presentation on theme: "1989Staff developed and supported innovative clinical, financial, and marketing applications for SysteMetrics/McGraw-Hill (New York, NY) 1989Staff developed."— Presentation transcript:

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2 1989Staff developed and supported innovative clinical, financial, and marketing applications for SysteMetrics/McGraw-Hill (New York, NY) 1989Staff developed and supported innovative clinical, financial, and marketing applications for SysteMetrics/McGraw-Hill (New York, NY) 1992HCIA, Inc. (Baltimore, MD): staff responsible for HCIA’s Provider Profiling Business Unit which led company’s IPO in February HCIA, Inc. (Baltimore, MD): staff responsible for HCIA’s Provider Profiling Business Unit which led company’s IPO in February The Delta Group founded by former HCIA staff to specialize in improving the clinical, financial, and market performance of healthcare organizations. 1995The Delta Group founded by former HCIA staff to specialize in improving the clinical, financial, and market performance of healthcare organizations. PresentThe Delta Group offers web-based provider profiling products to leading healthcare organizations across the country. COMPANY HISTORY

3 HOSPITAL ORGANIZATIONS  University HealthSystem Consortium (UHC)  Henry Ford Health System  Baycare Health System  Iowa Health System  Trinity Health System  SunLink Healthcare Corporation  Galaxy Health Alliance  Maine Health Alliance  Sagamore Health Network

4 TEACHING HOSPITALS & ACADEMIC INSTITUTIONS  Massachusetts General Hospital (Harvard)  Brigham & Women’s Hospital (Harvard)  Yale-New Haven Hospital  Mary Hitchcock Memorial Hospital (Dartmouth)  Vanderbilt University Hospital  Robert Wood Johnson University Hospital  University of Notre Dame  Medical College of Virginia Hospitals  University of Alabama-Birmingham Hospital  Erlanger Medical Center  Oregon Health Sciences University Hospitals

5 PHYSICIAN ORGANIZATIONS  Partners Community Healthcare (Harvard)  HealthCare Savings (North Carolina Medical Society)  Morton Plant Mease PHO (Physicians Health Alliance)  Presbyterian Medical Group  Preferred Health Services  Heritage Health System  Mid-Valley CareNet

6 OTHER ORGANIZATIONS  Tennessee Hospital Association  Oregon Association of Hospitals and Health Systems  CSC Healthcare Group  Cambio Health Solutions  Peat Marwick  Ernst & Young  Physcape (MGMA)  Prudential  BC/BS of Alabama  Ford, Chrysler & General Motors (MHA)

7 Provider Profiling System PARADOS ® Provider Profiling System  Physician Hospital Practice Analysis  ORYX Clinical Performance Analysis  Hospital Competitive Positioning Analysis  Hospital Quality Outcomes Analysis  Hospital Planning and Marketing Analysis (2004)  Physician Office Practice Analysis  Continuum of Care Analysis

8 APPLICATIONS OF PARADOS ®  Clinical Resource Management and Quality Improvement  Knowledge-Based Managed Care Contracting  Provider Network Evaluation and Monitoring  Strategic Planning, Marketing & Public Relations

9 $750,000 Increase in Financial Performance $415,000 Operating Room $185,000 Laboratory $150,000 Supplies “Using The Delta Group’s PARADOS Provider Profiling System, we were able to improve our financial performance by $750,000 by altering practice patterns and policies in laboratory, operating room, and medical/surgical supplies. Importantly, The Delta Group’s software and consulting services allowed us to achieve a favorable managed care position in the marketplace.” Jeff Judd, CEO The McDowell Hospital (65-bed general, acute care hospital) North Carolina

10 Severity & Risk Adjustment

11 Clinically Adjusted Data Accounts for Differences in Patient: Clinically Adjusted Data Accounts for Differences in Patient: Severity (stage of disease) Severity (stage of disease) Intensity (resource need) Intensity (resource need) Complexity (type of CCs) Complexity (type of CCs) Severity (stage of disease) Severity (stage of disease) Intensity (resource need) Intensity (resource need) Complexity (type of CCs) Complexity (type of CCs) Adjusted Data Provides an Accurate Unit of Measure for: Peer and Benchmark Comparisons Adjusted Data Provides an Accurate Unit of Measure for: Peer and Benchmark Comparisons

12 Accurate Outcome Comparisons Require Indicator-Specific Adjustment Methods Accurate Outcome Comparisons Require Indicator-Specific Adjustment Methods Charge/Cost: APS-DRG Charge/Cost: APS-DRG ™ Relative Charge/Cost Weights Length of Stay: APS-DRG Length of Stay: APS-DRG ™ Relative LOS Weights Mortality Rates: Risk-Adjusted Mortality Index Mortality Rates: Risk-Adjusted Mortality Index ™ Rates: Risk-Adjusted Complications Index Complication Rates: Risk-Adjusted Complications Index ™ Rates: Risk-Adjusted Readmissions Index Readmission Rates: Risk-Adjusted Readmissions Index ™ Charge/Cost: APS-DRG Charge/Cost: APS-DRG ™ Relative Charge/Cost Weights Length of Stay: APS-DRG Length of Stay: APS-DRG ™ Relative LOS Weights Mortality Rates: Risk-Adjusted Mortality Index Mortality Rates: Risk-Adjusted Mortality Index ™ Rates: Risk-Adjusted Complications Index Complication Rates: Risk-Adjusted Complications Index ™ Rates: Risk-Adjusted Readmissions Index Readmission Rates: Risk-Adjusted Readmissions Index ™ Indicator-Specific Severity & Risk-Adjustment Methods Are Required to Accurately Assess Variation in Clinical & Financial Outcomes Variation in Clinical & Financial Outcomes Indicator-Specific Severity & Risk-Adjustment Methods Are Required to Accurately Assess Variation in Clinical & Financial Outcomes Variation in Clinical & Financial Outcomes

13 Risk-Adjusted Readmissions Index (RARI)  Standard logistic regression was used to model risk of an unanticipated readmission to the same hospital within 30 days of discharge for specific diagnoses and procedures  Predictive variables used for risk of readmission were: oage osex opresence or absence of comorbidities “and” complications opresence of cancer (except skin cancer) oDRG cluster (risk associated with principal diagnosis/procedure) ototal number of comorbidities Source: “Risk-Adjusted Clinical Quality Indicators: Indices for Measuring and Monitoring Rates of Mortality, Complications, and Readmissions.” Quality Management in Health Care, Volume 9, No. 1, Fall 2000, pp

14 Risk-Adjusted Mortality Index (RAMI)  Standard logistic regression was used to model risk of death during a hospital stay for specific diagnoses and procedures  Predictive variables used for risk of death were: oage, sex, race opresence or absence of comorbidities (not complications) opresence of cancer (except skin cancer) oDRG cluster (risk associated with principal diagnosis/procedure) ototal number of comorbidities Source: “Risk-Adjusted Clinical Quality Indicators: Indices for Measuring and Monitoring Rates of Mortality, Complications, and Readmissions.” Quality Management in Health Care, Volume 9, No. 1, Fall 2000, pp

15 Risk-Adjusted Complications Index (RACI)  Standard logistic regression was used to model risk of post- surgical and post-obstetrical complications during a hospital stay for associated diagnoses and procedures  Predictive variables used for risk of complications were: oage osex opresence or absence of comorbidities (not complications) opresence of cancer (except skin cancer) oDRG cluster (risk associated with principal diagnosis/procedure) ototal number of comorbidities Source: “Risk-Adjusted Clinical Quality Indicators: Indices for Measuring and Monitoring Rates of Mortality, Complications, and Readmissions.” Quality Management in Health Care, Volume 9, No. 1, Fall 2000, pp

16 $5,897 /.654 $11,352 / CDI Norm: 1.000

17 All Payer Severity-adjusted DRGs (APS-DRGs ™ )  Developed by HSS, Inc. (participated in original Refined-DRG research with CMS—formerly HCFA)  Incorporates most recent CMS severity research (SDRGs)  Use principal and secondary diagnosis to indicate the severity of a patient’s illness to predict inpatient resource need  Use occurrence and degree of surgery as a discriminating variable; occasionally use patient’s age and discharge status  Applicable for all hospitalized patients, regardless of age, type of illness, or payer category  Comprised of 1,076 clinically homogeneous statistically stable groups  Reviewed by clinicians to ensure clinical integrity Source: “All Payer Severity-adjusted DRGs (APS-DRGs): A Uniform Method to Severity-adjust Discharge Data.” Topics in Health Information Management, Winter 1997.

18 APS-DRG ™ Patient Classification System Source: “All Payer Severity-adjusted DRGs (APS-DRGs): A Uniform Method to Severity-adjust Discharge Data.” Topics in Health Information Management, Winter 1997.

19 Differences in Patient Risk for Adverse Events within the Same Severity Class

20 PARADOS ® Clinical, Financial & Market Indicators: (Includes Severity & Risk-Adjustment where Appropriate) Charges Costs Gross Margins Lengths of Stay Mortality Rates Complication Rates Readmission Rates ORYX Core Measures Market Share Patient Origin Patient Outmigration Demographics Lifestyle Characteristics Acute Morbidity Projections Acute Use Rates Planning Indicators by MD, Employer, and Health Plan

21 PARADOS ® ORYX Core Measurement Sets*:  Acute Myocardial Infarction (AMI 1-9)  Heart Failure (HF 1-4)  Community Acquired Pneumonia (CAP 1-5)  Pregnancy & Related Conditions (PR 1-3)

22 FUNCTIONALITY OF PARADOS ®  Provides online access to evidence-based guidelines (EBGs) developed by the Institute for Clinical Systems Improvement (ICSI)  Easily sorts, finds, filters, graphs and trends data  Compares to national benchmarks of top performing providers  Compares to national, regional, or local peer groups  Customizes service lines and payer groupings  Drills-down to the diagnosis, procedure & patient level  Creates PDF and comma-delimited files for ease of distribution & customization

23 Consulting

24 CONSULTING SERVICES  Management consulting in executive summary format  Medical management consulting led by practicing physicians  Lean Six Sigma training in seminar format

25  Dwight Wooster, MD  James Kennedy, MD  Michael Langley, MD  William Hill, PharmD  Judy Homa-Lowry, RN, MS  Linda Easterly, MS, BSN  Cynthia Whitaker, BS, RRA  Henry Dove, PhD MEDICAL MANAGEMENT CONSULTING CONSULTING

26 LEAN SIX SIGMA TRAINING  Conducted by Master Black Belts certified by The George Group  Integrates Lean techniques for “minimizing complexity and eliminating waste” with Six Sigma methods for “improving quality and reducing variation”


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