1 / 34

Controlling Cost $ through Data-Based Medical Management

Controlling Cost $ through Data-Based Medical Management. Patrick Venditti , MHA Director - BJC HealthCare. Jo Daviess. Jo Daviess. Stephenson. Stephenson. Winnebago. Winnebago. Boone. Boone. Mchenry. Mchenry. Lake. Lake. Carroll. Carroll. Ogle. Ogle. Kane. Kane. De Kalb.

menora
Download Presentation

Controlling Cost $ through Data-Based Medical Management

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Controlling Cost$ through Data-Based Medical Management Patrick Venditti, MHA Director - BJC HealthCare

  2. Jo Daviess Jo Daviess Stephenson Stephenson Winnebago Winnebago Boone Boone Mchenry Mchenry Lake Lake Carroll Carroll Ogle Ogle Kane Kane De Kalb De Kalb Du Page Du Page Cook Cook Whiteside Whiteside Lee Lee Kendall Kendall Rock Island Rock Island Will Will Henry Henry Bureau Bureau Grundy Grundy La Salle La Salle Putnam Putnam Mercer Mercer Kankakee Kankakee Stark Stark Marshall Marshall Knox Knox Livingston Livingston Henderson Henderson Warren Warren Woodford Woodford Iroquois Iroquois Peoria Peoria Ford Ford Tazewell Tazewell Mclean Mclean Worth Worth Schuyler Schuyler Putnam Putnam Mcdonough Mcdonough Scotland Scotland Fulton Fulton Clark Clark Atchison Atchison Mercer Mercer Hancock Hancock Harrison Harrison Nodaway Nodaway Mason Mason Gentry Gentry Sullivan Sullivan Adair Adair Vermilion Vermilion De Witt De Witt Champaign Champaign Schuyler Schuyler Knox Knox Logan Logan Grundy Grundy Lewis Lewis Holt Holt Piatt Piatt Menard Menard Cass Cass Adams Adams Andrew Andrew Brown Brown Daviess Daviess De Kalb De Kalb Linn Linn Macon Macon Macon Macon Marion Marion Livingston Livingston Shelby Shelby Douglas Douglas Sangamon Sangamon Morgan Morgan Edgar Edgar Buchanan Buchanan Caldwell Caldwell Scott Scott Pike Pike Moultrie Moultrie Clinton Clinton Christian Christian Coles Coles Ralls Ralls Monroe Monroe Chariton Chariton Shelby Shelby Randolph Randolph Carroll Carroll Pike Pike Platte Ray Ray Clark Clark Greene Greene Clay Clay Cumberland Cumberland Macoupin Macoupin Montgomery Montgomery Audrain Audrain Saline Saline Howard Howard Calhoun Calhoun Lafayette Lafayette Jersey Jersey Effingham Effingham Lincoln Lincoln Jackson Jackson Crawford Crawford Jasper Jasper Fayette Fayette Boone Boone Montgomery Montgomery Bond Bond Cooper Cooper Madison Madison Callaway Callaway Warren Warren St. Charles St. Charles Clay Clay Johnson Johnson Pettis Pettis Richland Richland Lawrence Lawrence Moniteau Moniteau Marion Marion Cass Cass St. Louis St. Louis Clinton Clinton Cole Cole Osage Osage Franklin Franklin Morgan Morgan St. Clair St. Clair Gasconade Gasconade Wayne Wayne Edwards Edwards Wabash Wabash Henry Henry Washington Washington Monroe Monroe Jefferson Jefferson Benton Benton Bates Bates Jefferson Jefferson Miller Miller Maries Maries Perry Perry Hamilton Hamilton White White Camden Camden St. Clair St. Clair Randolph Randolph Franklin Franklin Washington Washington Crawford Crawford Hickory Hickory Ste. Genevieve Ste. Genevieve Phelps Phelps St. Francois St. Francois Vernon Vernon Pulaski Pulaski Jackson Jackson Saline Saline Gallatin Gallatin Cedar Cedar Williamson Williamson Perry Perry Laclede Laclede Dallas Dallas Polk Polk Dent Dent Iron Iron Barton Barton Hardin Hardin Madison Madison Union Union Johnson Johnson Dade Dade Cape Girardeau Cape Girardeau Pope Pope Texas Texas Reynolds Reynolds Bollinger Bollinger Webster Webster Wright Wright Greene Greene Jasper Jasper Pulaski Pulaski Massac Massac Alexander Alexander Shannon Shannon Wayne Wayne Lawrence Lawrence Scott Scott Carter Carter Christian Christian Douglas Douglas Newton Newton Stoddard Stoddard Mississippi Mississippi Howell Howell Stone Stone Barry Barry Butler Butler Oregon Oregon Ripley Ripley Taney Taney Ozark Ozark Mcdonald Mcdonald New Madrid New Madrid Dunklin Dunklin Pemiscot Pemiscot Consists of 13 Hospitals and multiple community health locations within 150 mile radius of metropolitan St. Louis BJC has 27,105 employees, and is the largest private employer in Missouri. (31,000 + 3,800 Volunteers) • BJC retains 4 of the Top Ten occupations with the most musculoskeletal disorders with days away from work: * • #2 Nursing aides, orderlies, and attendants • #5 Registered Nurses • #7 Janitors and cleaners • #10 Maintenance and repair workers * U.S. Department of Labor, Bureau of Labor Statistics. Nonfatal Occupational Injuries and Illnesses Requiring Days away From Work. 2006”. http://www.bls.gov/iif]

  3. Self-Insured in Missouri since 1995; in Illinois since 2002 SIR for Injuries - $1,000,000 per event; Illnesses - $1,000,000 per individual 1995-2004 – Administered by various TPAs 2005 - Self-Administered • 3 Senior Case Coordinators (Adjusters) • 3 Technical SME’s • Claims System & Compliance • Medical Claims Auditor – Coding • System Programmer Analyst

  4. 3 Soft-Tissue Backclaimsfrom Lifting Brian Pete Sam • Male • 32 years old • File Clerk • 1 prior claim • Employed 6 years • Male • 38 years old • Welder • 3 prior claims • Employed 2 years • Male • 48 years old • Mechanic • No prior claims • Employed 15 years Pete Brian Sam

  5. 3 Soft-Tissue Backclaimsfrom Lifting Brian Pete Sam • Female • 32 years old • File Clerk • 1 prior claim • Employed 6 years • Male • 38 years old • Welder • 3 prior claims • Employed 2 years • Male • 48 years old • Mechanic • No prior claims • Employed 15 years Which claim is likely to: - Have delayed recovery? - Develop narcotic dependency? - Lose most time from work? - Become difficult to manage medically? - Cost the most?

  6. Who is at the Highest Risk?

  7. Get hooked on opiates See 5 or more doctors Haveseveral “unsuccessful” surgeries ?????? >$300K Chronic Pain Syndrome

  8. Step One: Case Risk Assessment • Data Gathering Tool • Collect Information on factors that could detect high probability for delayed recovery… from supervisor, occupational health nurse, employee, health care providers

  9. Four Categories Of Risk Medical Severity Pain Perception Psychosocial Behavioral Factors Personal Risk Factors

  10. Minimal Medical Severity • Moderate Based on expected Lost-time and/or Surgery or Hospitalization • High

  11. Pain Perception Medical Severity Pain Perception

  12. Measuring Pain Perception Pain Drawing 0 – “Normal” 1 – “Abnormal” Visual Analog Scale 0 – 10 Scale

  13. Psychosocial Behavioral Factors Medical Severity Pain Perception Behavioral Factors

  14. Psychosocial Behavioral Factors • Prior Claims • Causation Issues • Delayed Reporting • Contrary Witnesses or No Witnesses • Upset, disgruntled • Absenteeism, disciplinary issues • Layoff, termination issues • Lack of Objective Findings at time of report • Expectations (recovery, pain, RTW) • Catastrophizing • Litigation • Other Psychosocial issues (Financial, Domestic, etc.)

  15. Personal Risk Factors (CoMorbidities) • Pre-existing Medical Conditions • Biological, Lifestyle Factors, e.g. • Age • BMI • Smoking • Diet • Exercise • Sleep, etc. • Under Physician’s Care • Prior Injuries, Accidents, Surgeries • Current Medications

  16. Adding non-traditional data elements from multiple sources adds insight and perspective when evaluating the potential exposure of a claim. Brian Pete Sam • Male • 32 years old • File Clerk • 1 prior claim • Employed 6 years • Married • Male • 38 years old • Welder • 3 prior claims • Employed 2 years • Single • Male • 48 years old • Mechanic • No prior claims • Employed 15years • Married • Medical Severity – Benign – no expected Lost-time • Pain Perception – Normal • Calm, cooperative • Objective Findings at time of report • No Psychosocial Issues • (+) Lifestyle Indicators • Medical Severity – Benign – no expected Lost-time • Pain Perception – Abnormal • Upset, disgruntled • Lack of Objective Findings at time of report • No Psychosocial Issues • (-) Lifestyle Indicators • Medical Severity – Benign – no expected Lost-time • Pain Perception – Abnormal • Upset, disgruntled • Lack of Objective Findings at time of report • Other Psychosocial issues (Financial, Domestic, etc.) • (-) Lifestyle Indicators

  17. Brian Sam Claim Outcomes Pete EXPOSURE LOW HIGH

  18. Case Coding for Risk Level Medical Severity Pain Perception Behavioral Factors Minimal Personal Risk Factors

  19. Case Coding for Risk Level Medical Severity Pain Perception Behavioral Factors High Personal Risk Factors

  20. Step Two: Select Appropriate Medical Provider

  21. Good Clinical Outcomes Med-Legal Management Skills

  22. Vetting Process Education • WC 101 • Our Expectations • Our Processes >150 Providers Analytics Communication • Internal Data • Key Performance Indicators Guidelines: ODG, MDA-ACOEM

  23. HIGH RISK HIGH RISK Skills

  24. Low RISK Skills

  25. See 5 or more doctors Get hooked on opiates Haveseveral “unsuccessful” surgeries $300K+ Chronic Pain Syndrome

  26. Workers Compensation Administration Surgical History 2005-2010

  27. Average 180 58% Average 76

  28. Average Lost-Time Cases a week for 27,000 + employee - 13 Hospital Organization 2005-2010 Average = 6 week Average Range = 4-8

  29. $533,000 74% $140,326

More Related