Using analytics to_track_monitor_and_reduce_costs_final

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


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.


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




•  Diabe+c pa+ents – High Priority Score – Contacted by nurse case managers – Reminders for screenings

•  Eyes •  Kidneys •  Cholesterol

– Counseling with diabetes educator


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?

Economy & Finance

Using analytics to_track_monitor_and_reduce_costs_final