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Third-Party Payer Track, National Rx Drug Abuse Summit, April 2-4, 2013. Using Analytics to Track, Monitor and Reduce Costs presentation by Anne Kirby, James Masingill, Joe Anderson and Dr. Robert Hall
Citation preview
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
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.
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
Using Analy<cs to Track, Monitor, and Reduce Costs
Anne Kirby, RN Chief Compliance Officer/VP of Medical Review Services
Rising Medical Solu+ons
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.
Accepted Learning Objec+ves
Nothing to Disclose
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
Challenge for Claims
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
5 Key Problems
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
Rx Intelligence Analy+cs
Sample Dashboard
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
65% Claims
• Decreased Rx Refills within 6-‐8 months of Peer-‐to-‐ Peer Review
71%
Claims
• Decreased Opioid Rx Refills
57% Claims
• Decrease of All Injury Related Drugs • Opioids, Muscle Relaxants, Hypno<cs & An<-‐Anxiety meds
Demonstrated Impact
Connec+ng the Dots Where do we go from here?
Pain Mgmt Peer Reviewer
UR Nurse
Treating Physician Clai
ms Person
TCM Nurse
Pharmacy Benefit Mgr Clinical
Pharmacist
PATIENT
Using Analy<cs to Track, Monitor, and Reduce Costs
Jamey Masingill Vice President of Claims
Markel-‐FirstComp Insurance
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.
Accepted Learning Objec+ves
Nothing to Disclose
WC Combined Ra+o: 1994-‐2012F Call To Ac+on…
• 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
Priming the Pump by Extrac+ng “Old School” Thinking from the Claims Environment
U+liza+on
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%
LT Closing Ra+o Triangles
Impact of Reduced Claims Dura+ons
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
Joe Anderson, Director of Analy<cs Robert Hall, MD, Medical Director
Progressive Medical, Inc.
Using Analy<cs to Track, Monitor, and Reduce Costs
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.
Disclosure Statement
• Nothing to disclose
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 possible to get them in the habit of shopping at Target.
Delivered coupons to young mothers before their family even knew they were expec+ng.
Nemlix Determine which movies customers will like based on what they have already rated.
Improved their predic+ons by 10%; a $1 million prize was awarded.
Oakland Athle+cs
Choose the best baseball players available for the next season, with a limited budget.
20 consecu+ve wins; the book and film Moneyball are based on this.
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
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
The Problem
Prescrip<on Drug Deaths and Increasing Costs
Time Constraints on Nurses, Adjustors, Clinicians
A solu<on is needed that reduces prescrip<ons most efficiently.
• More people are dying from prescrip+on drug use.
• Prescrip+on drug prices are rising. • Workers’ compensa+on in par+cular has seen increases in use of prescrip+on pain killers.
• Cannot examine or intervene on every claim
• Cannot determine which claims will have high long-‐term costs
• Too many “false posi+ves” from individual clinical triggers (i.e. only 10% of claims with morphine equivalence of 90mg result in high long-‐term costs)
The Solu<on:
Mul<variate Sta<s<cal Model to Predict High-‐Cost Claims
Our original model, since refined:
Correlate early data about an injured
worker…
… with resul<ng long-‐term spend of that injured worker.
Workers injured in 2007 Resul+ng pharmacy costs in 2009-‐2010
Data Used in Sta<s<cal Models
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 4 6 9 12 18 24
Pharmacy Behavior: Medica+ons, Number of Prescribers, Number of Pharmacies Injury: Body part, nature of injury
Prescriber: Demographics of trea+ng prescriber
Geographic and Other Demographics
Percent of Significance (Aggregated
across mul<ple variables)
Months Since Date of Injury
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
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.
Measuring Effec<veness
96%
55%
70%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cost per Claim Morphine Equivalence per Claim Prescrip+ons per Claim
Statistical Confidence that Intervention Changes this Outcome
Analy<cs From a Provider’s Perspec<ve
• Finding common ground with analy+cs and providers
• Embracing challenges that can arise with analy+cs
Common Ground – Data Collec<on
• Personal medical history • Family history
• Social history • Physical examina+on
• Diagnos+c studies
Common Ground – Risk Assessment Stroke
Modifiable risk factors • High blood pressure • Atrial fibrilla+on • High cholesterol • Diabetes • Atherosclerosis • Circula+on problems • Tobacco • Alcohol • Physical inac+vity • Obesity
Non-‐modifiable risk factors
• Age • Gender • Race • Family history • Previous stroke • Fibromuscular dysplasia
• Patent foramen ovale
Source: National Stroke Association, Am I at Risk for a Stroke? Stroke Risk Factors. 2013 March 18
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
• 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
Common Ground – Outcome Predictors Stroke
Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation Board Review. New York. Demos Medical Publishing. 2004
Common Ground – Language
• Data collec+on • Risk assessment
• Risk factors • Outcome predictors
• Interven+ons • Behavior • Effec+veness
Embracing Challenges Avoid Blame
• Comprehensive claim evalua+on
• Interven+ons may need to be mulNfaceted
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
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
Embracing Challenges Validate Success
Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality and Reduce Costs, The Commonwealth Fund. 2009 March
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
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?
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
Takeaways
• Common ground – Data collec+on – Risk assessment – Outcome predictors – Language
• Embracing challenges – Avoid blame – Validate success – Be responsive – Reward posi+ve outcomes
Ques<ons?