Using Analytics to Reduce Healthcare Costs in Workers' Compensation
•
1 j'aime•811 vues
This document discusses using analytics to track, monitor, and reduce costs in the healthcare industry. It begins with learning objectives about identifying misuse and abuse, using analytics to identify trends, how pharmacy benefit managers can use analytics, and predictive analytics in workers' compensation. The document then discusses challenges claims managers face with long-acting opioid prescriptions costing much more. It identifies 5 key problems claims managers encounter and how Rx Intelligence analytics can help address these problems by flagging potentially problematic claims early. The impact of successful peer-to-peer conversations on reduced prescription fills is also demonstrated.
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (20)
Similaire à Using Analytics to Reduce Healthcare Costs in Workers' Compensation
Similaire à Using Analytics to Reduce Healthcare Costs in Workers' Compensation (20)
Plus de OPUNITE
Plus de OPUNITE (20)
Dernier
Dernier (20)
Using Analytics to Reduce Healthcare Costs in Workers' Compensation
- 1. Using Analy+cs to Track, Monitor, and Reduce Costs Anne Kirby Chief Compliance Officer and Vice President, Medical Review Services, Rising Medical Solu+ons James Masingill Vice President, Claims Opera+ons, Market First Comp Insurance Company Joe Anderson Director of Analy+cal Services, Progressive Medical Dr. Robert Hall Medical Director, Progressive Medical
- 2. Learning Objec<ves • Iden+fy warning signs of misuse and abuse and how claim managers can take ac+on. • Tell how payers can use effec+ve analy+cs to iden+fy relevant trends. • Explain how Pharmacy Benefit Managers can use analy+cs with strong clinical programs. • Describe the role and benefits of predic+ve analy+cs in the workers’ compensa+on industry.
- 3. Disclosure Statement • Anne Kirby has no financial rela+onships with proprietary en++es that produce health care goods and services. • James Masingill has no financial rela+onships with proprietary en++es that produce health care goods and services. • Joe Anderson has no financial rela+onships with proprietary en++es that produce health care goods and services. • Robert Hall has no financial rela+onships with proprietary en++es that produce health care goods and services. 3
- 4. Using Analy<cs to Track, Monitor, and Reduce Costs Anne Kirby, RN Chief Compliance Officer/VP of Medical Review Services Rising Medical Solu+ons
- 5. Accepted Learning Objec+ves 1. Iden+fy warning signs of misuse and abuse and how claim managers can take ac+on. 2. Tell how payers can use effec+ve analy+cs to iden+fy relevant trends. 3. Explain how Pharmacy Benefit Managers can use analy+cs with strong clinical programs. 4. Describe the role and benefits of predic+ve analy+cs in the workers’ compensa+on industry.
- 7. Challenge for Claims Claims with long-‐ac+ng opioid Rx cost 9.3 +mes more than claims without (Journal of Occupa+onal & Environmental Medicine) • Very manual process • Case selec+on not always on target • Trea+ng physicians and pain mgmt peer reviewers used drug names inconsistently • If a person was taking 1 or 2 opioids, it was likely they were taking upwards of 7 or 8 other drugs
- 8. 5 Key Problems 1. Difficult to iden+fy claims with ques+onable drug use before cases turn into large losses 2. Too +me consuming for adjuster to find at-‐risk cases 3. Not enough to have a pharmacist contact a trea+ng physician 4. Data not comprehensive enough – need integrated approach 5. Viewing opioids in a vacuum – need to look at other constella+on of drugs
- 9. Addressing the Problems Rx Intelligence Analy+cs 1. Expedites file iden+fica+on 2. Flags poten+ally problema+c claims early 3. Adds another level of interven+on 4. Looks beyond just opioids 5. Uses data to intervene
- 10. Rx Intelligence Analy+cs Sample Dashboard
- 11. Demonstrated Impact Effect of successful peer-‐to-‐peer conversa+on (between pain management physician and prescribing physician) Fills before interven<on Fills aFer interven<on
- 12. Demonstrated Impact • Decreased Rx Refills within 6-‐8 months of Peer-‐to-‐ Peer Review 65% Claims • Decreased Opioid Rx Refills 71% Claims • Decrease of All Injury Related Drugs • Opioids, Muscle Relaxants, Hypno<cs & 57% An<-‐Anxiety meds Claims
- 13. Connec+ng the Dots Where do we go from here? Treati ng Pain Mgmt Physi Peer Clai cian Reviewer ms UR Nurse Pers on PATI Pharmac TCM ENT y Benefit Nurse Mgr Clinical Pharmaci st
- 14. Using Analy<cs to Track, Monitor, and Reduce Costs Jamey Masingill Vice President of Claims Markel-‐FirstComp Insurance
- 15. Accepted Learning Objec+ves 1. Iden+fy warning signs of misuse and abuse and how claim managers can take ac+on. 2. Tell how payers can use effec+ve analy+cs to iden+fy relevant trends. 3. Explain how Pharmacy Benefit Managers can use analy+cs with strong clinical programs. 4. Describe the role and benefits of predic+ve analy+cs in the workers’ compensa+on industry.
- 17. WC Combined Ra+o: 1994-‐2012F Call To Ac+on…
- 18. Priming the Pump by Extrac+ng “Old School” Thinking from the Claims Environment • There is no right or wrong…only grey • Reduce ac+vity checks and surveillance • Targeted and directed case management • Own your data – Driven down to unit and individual levels • Adherence to established best prac+ces • Valida+on process
- 19. U+liza+on
- 20. LT Closing Ra+o Triangles Lost Time 2006 2007 2008 2009 2010 2011 2012 12 28.00% 22.90% 26.10% 26.00% 28.90% 26.20% 34.70% 24 64.80% 63.90% 69.90% 68.70% 70.20% 72.70% 36 82.80% 84.20% 86.00% 85.40% 88.20% 48 91.30% 92.30% 92.90% 93.30% 60 95.90% 95.20% 96.20% 72 97.60% 97.20% 84 98.30%
- 21. Impact of Reduced Claims Dura+ons
- 22. Notes Only Presenta+on Outline: • Preparing the claims environment before implemen+ng your program. Analy+cs and program will only be effec+ve if: – Extract “old school” thinking from claims processing – Reduce ac+vity checks and inves+ga+ons – Redeploy those resources into added medical exper+se / interven+on tools • Using claims triangles to track and improve performance • Importance of integrated approach from mul+ple angles to effec+vely tackle prescrip+on drug problem • Impact on overall costs
- 23. Using Analy<cs to Track, Monitor, and Reduce Costs Joe Anderson, Director of Analy<cs Robert Hall, MD, Medical Director Progressive Medical, Inc.
- 24. Learning Objec<ves • Iden+fy warning signs of misuse and abuse and how claim managers can take ac+on. • Tell how payers can use effec+ve analy+cs to iden+fy relevant trends. • Explain how Pharmacy Benefit Managers can use analy+cs with strong clinical programs. • Describe the role and benefits of predic+ve analy+cs in the workers’ compensa+on industry.
- 25. Disclosure Statement • Nothing to disclose
- 26. What Is Predic<ve Analy<cs? Predictive Analytics is making decisions with statistics and data. Company Goal of predic<ve analy<cs Result Target Iden+fy new mothers as quickly as Delivered coupons to young possible to get them in the habit of mothers before their family even shopping at Target. knew they were expec+ng. Nemlix Determine which movies customers Improved their predic+ons by 10%; will like based on what they have a $1 million prize was awarded. already rated. Oakland Choose the best baseball players 20 consecu+ve wins; the book and Athle+cs available for the next season, with a film Moneyball are based on this. limited budget. Sources: Duhigg, C., How Companies Learn Your Secrets, The New York Times Magazine. 2012 February 16 Lohr, S., A $1 Million Research Bargain for NeElix, and Maybe a Model for Others, The New York Times, 2009 September 21 Mahler, J., Smaller Markets and Smarter Thinking, The New York Times, 2011 October 14
- 27. How Can We Use It? • As a PBM, we see some of the data going through the system, but not all of it. • Each company in the industry can use analy+cs with their own data: – Imagine if Nemlix wants to know whether you’ll enjoy the movie Moneyball – Nemlix doesn’t know if you have read the book Moneyball, if you studied sta+s+cs or if you’re an Oakland Athle+cs fan – They do know if you like other baseball movies, other Brad Pir movies and other movies based on nonfic+on books Image source: http://www.managedcaremag.com/archives/1208/1208.pbm-functions.html
- 28. The Problem A solu<on is needed that reduces prescrip<ons most efficiently. Prescrip<on Drug Deaths and Time Constraints on Nurses, Increasing Costs Adjustors, Clinicians • More people are dying from • Cannot examine or intervene on prescrip+on drug use. every claim • Prescrip+on drug prices are rising. • Cannot determine which claims will • Workers’ compensa+on in par+cular have high long-‐term costs has seen increases in use of • Too many “false posi+ves” from prescrip+on pain killers. individual clinical triggers (i.e. only 10% of claims with morphine equivalence of 90mg result in high long-‐term costs)
- 29. The Solu<on: Mul<variate Sta<s<cal Model to Predict High-‐Cost Claims Our original model, since refined: Correlate early data … with resul<ng long-‐ about an injured term spend of that worker… injured worker. Workers injured in 2007 Resul+ng pharmacy costs in 2009-‐2010
- 30. Data Used in Sta<s<cal Models 100% 90% 80% 70% Pharmacy Behavior: Medica+ons, Percent of Number of Prescribers, Number of Significance 60% Pharmacies (Aggregated across mul<ple Injury: Body part, nature of injury variables) 50% Prescriber: Demographics of trea+ng 40% prescriber 30% Geographic and Other Demographics 20% 10% 0% 1 4 6 9 12 18 24 Months Since Date of Injury
- 31. The Risk Score Claim Risk Score Reason Allison 6.5 Mul+ple Neck Injury, High Total Medica+on Use (Including Narco+cs) Bob 5.4 Con+nued Medica+on Use, High Risk Prescriber: Allergy and Immunology Specialist Cindy 5.0 Mul+ple Prescribers in Early Months, High Days Supply of Various Medica+ons Dwayne 4.5 High Risk State and Moderate Injury Risk: Dislocated Disc Elaine 3.9 Prescriber Risk: Pain Management Specialist, High Narco+cs Use To-‐Date Frank 3.1 Moderate Injury Risk, Demographic Risk, and Prescriber Risk: Pain Management Specialist
- 32. Predic<ons Become Interven<ons • Types of clinical interven+ons: • Claims Professional Outreach • Physician Outreach • Drug U+liza+on Evalua+on • Peer-‐to-‐Peer Review • Interven+ons should be completed as soon as possible to avoid any developing complica+ons.
- 33. Measuring Effec<veness Statistical Confidence that Intervention Changes this Outcome 100% 90% 96% 80% 70% 70% 60% 50% 55% 40% 30% 20% 10% 0% Cost per Claim Morphine Equivalence per Claim Prescrip+ons per Claim
- 34. Analy<cs From a Provider’s Perspec<ve • Finding common ground with analy+cs and providers • Embracing challenges that can arise with analy+cs
- 35. Common Ground – Data Collec<on • Personal medical history • Family history • Social history • Physical examina+on • Diagnos+c studies
- 36. Common Ground – Risk Assessment Stroke Modifiable risk factors Non-‐modifiable risk factors • High blood pressure • Age • Atrial fibrilla+on • Gender • High cholesterol • Race • Diabetes • Family history • Atherosclerosis • Previous stroke • Circula+on problems • Fibromuscular dysplasia • Tobacco • Alcohol • Patent foramen ovale • Physical inac+vity • Obesity Source: National Stroke Association, Am I at Risk for a Stroke? Stroke Risk Factors. 2013 March 18
- 37. Common Ground – Outcome Predictors Stroke • Poor strength recovery predictors – Severe arm weakness at onset of stroke – No hand strength 4 weeks aLer stroke • 30-‐day mortality – EKG abnormali+es – Brainstem stroke – Elevated blood glucose in non-‐diabe+c pa+ents Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation Board Review. New York. Demos Medical Publishing. 2004
- 38. Common Ground – Outcome Predictors Stroke • Nega+ve predictors for return to work – Low Barthel Index score • Ac+vi+es of daily living – Prolonged length of stay in rehabilita+on – Aphasia (language/communica+on deficits) – Prior alcohol abuse Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation Board Review. New York. Demos Medical Publishing. 2004
- 39. Common Ground – Language • Data collec+on • Risk assessment • Risk factors • Outcome predictors • Interven+ons • Behavior • Effec+veness
- 40. Embracing Challenges Avoid Blame • Comprehensive claim evalua+on • Interven+ons may need to be mulNfaceted
- 41. Embracing Challenges Validate Success • Hill Physicians Medical Group – 2,200 physicians – 332,000 pa+ents – Predic+ve modeling • Management of chronic diseases – Prospec+ve Risk Score • Likelihood of pa+ent using physician resources in future • RNs are assigned to call pa+ents with high risk scores Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality and Reduce Costs, The Commonwealth Fund. 2009 March
- 42. Embracing Challenges Validate Success 0.5 x In-‐pa+ent days over last 365 days In-‐pa+ent days over last 90 days + 2 x ER days over last 365 days ER days over last 90 days 2 x (Prospec+ve Risk Score + adjustment factor) = Priority Score Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality and Reduce Costs, The Commonwealth Fund. 2009 March
- 43. Embracing Challenges Validate Success • Diabe+c pa+ents – High Priority Score – Contacted by nurse case managers – Reminders for screenings • Eyes • Kidneys • Cholesterol – Counseling with diabetes educator Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality and Reduce Costs, The Commonwealth Fund. 2009 March
- 44. Embracing Challenges Be Responsive • A provider’s ques+ons – Is my prac+ce style being ques+oned? – Will the care of my pa+ents be affected? – Where is the evidence? – Why now?
- 45. Embracing Challenges Reward Posi<ve Outcomes • Should providers be rewarded? – Pay for performance • Physician payments at the group level (not individual) • Mee+ng absolute benchmarks • Soon auer performance period – Preferred provider status • Recogni+on • Increased referrals Source: Gamble, M., GAO: 3 Ways CMS Can Incentivize Physicians Like Private Payors, Becker's Hospital Review, ASC COMMUNICATIONS. 2012 January 7; 2013 March 11
- 46. Takeaways • Common ground – Data collec+on – Risk assessment – Outcome predictors – Language • Embracing challenges – Avoid blame – Validate success – Be responsive – Reward posi+ve outcomes
- 47. Ques<ons?