Important Metrics to show ROI and Program Success

Matt Phillips, MBADirector, Physician Advisor Program

Rochester Regional Health

Kalyana Kanaparthy, MD, FHMMedical Director, Physician Advisor Program

Rochester Regional Health

1

Goals for presenting metrics

As illustrated in the previous presentation you need strong metrics

However strong metrics/data are only part of the equation• Use them to tell a story• Guide your audience through data• Make your case based upon the strength of the metrics

How to monitor performance

Always track by using discrete case level data for:• Individual initiative performance metrics

CMI, Win/Loss rate, Obs rate etc. All correlate to ROI

• Overall program summary dashboards showing:ROI measured in multidimensional means• Supported vs. PAd owned• ROI type: Savings, Operational Risk Mitigation, or Improved Compensation

Keep organized files • Update regularly • Automate where possible

Discrete project tracking at Case Level

Hypothetical numbers for presentation purposes only

Finance vetted PAd Impact

Hypothetical numbers for presentation purposes only

Using data to tell a storyA Level of Care example

Reasons to take over Vendor management and education for LOC• Use compelling metrics to tell the tale• Walk the audience through the situation

Administration and Executive Leadership• Make your case!!!

Revisit the story later it could lead to more opportunities• This led to expediting the insourcing of reviews and denial

management

RGH Observation trends: Comparison with National Average

The Obs RateNational Average

vs. RGH’s current

Consistently >12% above national Avg.

Reasons for being a high outlier

2015 Average Observation rate 38.3%• Lack of awareness of changing regulations

We were efficient in using Observation initially, but were lacking in reassessing after 1 MN and changing status• Lack of physician education• Care Management not in sync with changing regulations

Resulted in:• Becoming an outlier with observation volumes worse than 90th

percentile in the nation

The Reasons to expedite focus on LOC

Observation Rate at discharge prior to PAd program (Using Vendor)

Jan

2012

Mar

201

2M

ay 2

012

Jul 2

012

Sep

2012

Nov

201

2Ja

n 20

13M

ar 2

013

May

201

3Ju

l 201

3Se

p 20

13N

ov 2

013

Jan

2014

Mar

201

4M

ay 2

014

Jul 2

014

Sep

2014

Nov

201

4Ja

n 20

15M

ar 2

015

May

201

5Ju

l 201

5Se

p 20

15N

ov 2

015

Jan

2016

10%

15%

20%

25%

30%

35%

40%

45%

ADT LOC Trends

Historical Trend Pre LOC Expected Trend

PAd educational intervention

Observation Rate after starting the PAd team through November 2015

(Before splitting Care Mgmt)Ja

n 20

12

Mar

201

2

May

201

2

Jul 2

012

Sep

2012

Nov

2012

Jan

2013

Mar

201

3

May

201

3

Jul 2

013

Sep

2013

Nov

2013

Jan

2014

Mar

201

4

May

201

4

Jul 2

014

Sep

2014

Nov

2014

Jan

2015

Mar

201

5

May

201

5

Jul 2

015

Sep

2015

Nov

2015

10%

15%

20%

25%

30%

35%

40%

45%f(x) = 0.00265655632918794 x + 0.310683180703098

ADT LOC Trends

Historical Trend Pre LOC Expected TrendADT LOC Trend Post LOC Trend

2015 RGH Audit

90% Accurate!

Vendor Contract Ownership/Renegotiation

Well below budget in 2015Jan

-13

Mar-13

May-13

Jul-13

Sep-13

Nov-13

Jan-14

Mar-14

May-14

Jul-14

Sep-14

Nov-14

Jan-15

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May-16

Vendor Monthly SpendVendor Charges Budget

Revisit the storyA Level of Care example

Unexpected obstacles present new opportunities• Used a logistical issue on Christmas and turned it into a pilot• Presented pilot data and findings to continue the data story

Illustrated compelling reasons to insource LOC and denial management process

Historic LOC Trends – Jan 2015 onwards

201501 201502201503 201504201505 201506201507 201508201509201510 201511201512 201601201602 201603201604 20160520%

22%

24%

26%

28%

30%

32%

34%

36%

38%

40%

42%

38% 37%39%

36%

39%

41%

39% 39% 38%37%

39%

37%

31% 31%32%

34%

31%

Observation Status at Discharge by MonthObs % 2015 Avg. Before December Split

LOC Trends – Since URS Creation

11/29/15 12/6/15 12/13/15 12/20/15 12/27/15 1/3/16 1/10/1620%

22%

24%

26%

28%

30%

32%

34%

36%

38%

40%38%

38%

36% 35%

29% 29%30%

Observation Status at Discharge% OBS at Discharge 2015 Average Before Dec Split

Christmas day Vendor Issues

Start of PAd weekend Pilot

PAd Program Launched along with education

PAd Concurrent LOC Review VolumeJa

nuar

y

Febr

uary

Mar

ch

April

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June July

Augu

st

Sept

embe

r

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ober

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mbe

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Dece

mbe

r

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Mar

ch

April

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2015 2016

0

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Referral Trend

OPObsIP

Vendor referral volume + PAd Concurrent LOC Review Volume

201501 201502 201503 201504 201505 201506 201507 201508 201509 201510 201511 201512 201601 201602 201603 201604 2016050

50

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Overall Referral TrendPAd Vendor

URS review: “step 2” reassessmentNet Obs to IP shift from initial to final LOC

Jan

2012

Feb

2012

Mar

201

2Ap

r 201

2M

ay 2

012

Jun

2012

Jul 2

012

Aug

2012

Sep

2012

Oct

201

2No

v 20

12De

c 201

2Ja

n 20

13Fe

b 20

13M

ar 2

013

Apr 2

013

May

201

3Ju

n 20

13Ju

l 201

3Au

g 20

13Se

p 20

13O

ct 2

013

Nov

2013

Dec 2

013

Jan

2014

Feb

2014

Mar

201

4Ap

r 201

4M

ay 2

014

Jun

2014

Jul 2

014

Aug

2014

Sep

2014

Oct

201

4No

v 20

14De

c 201

4Ja

n 20

15Fe

b 20

15M

ar 2

015

Apr 2

015

May

201

5Ju

n 20

15Ju

l 201

5Au

g 20

15Se

p 20

15O

ct 2

015

Nov

2015

Dec 2

015

Jan

2016

Feb

2016

Mar

201

6Ap

r 201

6M

ay 2

016

-10%

-5%

0%

5%

10%

15%

20%

Obs to IP Conversion RateConversion Rate Linear (Conversion Rate)

PAd Program Launched along with education

PAd Jan Pilot

Trends in LOC at discharge January Pilot

(PAd OBS list review)• patients that needed

step 2 review Significant difference from

baseline P-value =

0.0000397

Revisit the storyUnexpected Benefits

Finding other beneficial operational impacts to our surprise:• Throughput: length of stay example• Quality Metrics: CMI stable despite reduction in LOS

LOC Trends: Observation LOS > 2 MNSuccess in one Hospital illustrating need for a system wide initiative

2015 Q1 2015 Q2 2015 Q3 2015 Q4 2016 Q1 2016 Q2 Annualized0

50

100

150

200

250

300

350

400

450

388403

370

217

9672

RGH Observation Discharges with LOS >2 MDNRGH

Num

ber o

f Disc

harg

es

Impacting throughput

Jan Feb Mar Apr May0

1

2

3

4

5

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7

LOS Trends (2015 vs 2016)

2015 Operational LOS

2016 Operational LOS

2015 IP LOS

2016 IP LOS

2015 Obs LOS

2016 Obs LOS

Impacting throughput

- 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

Avg Operational LOS Decrease;

0.45

Avg IP LOS Decrease;

0.73

Avg Obs LOS Decrease; 0.43

LOS Change 2016 vs 2015

0% 5% 10% 15% 20% 25% 30%

Avg % Change Operational LOS;

10%

Avg % Change IP LOS; 12%

Avg % Change Obs LOS; 27%

LOS Percent Change

CMI: IP vs. Obs Trend 2016-1st QtrPatient Class Trends (note IP obs switch highlighted)

Discharge Percent

Discharge Count

Row Labels 2015 2016 2015 2016 Difference NWH NEWARK WAYNE HOSPITAL 14.88% 15.21% 7497 1993

Boarder Baby 0.04% 0.00% 3

-0.04% Hospice Inpatient 0.56% 0.60% 42 12 0.04% Inpatient 61.45% 60.36% 4607 1203 -1.09% Newborn 9.55% 8.98% 716 179 -0.57% Observation 22.02% 24.94% 1651 497 2.92% Psych Inpatient 6.36% 5.12% 477 102 -1.24% Surgery Admit 0.01% 0.00% 1

-0.01%

RGH ROCHESTER GENERAL HOSPITAL 85.12% 84.79% 42888 11110 Acute Rehab Inpatient 0.31% 0.00% 133

-0.31%

Hospice Inpatient 0.23% 0.32% 98 36 0.10% Inpatient 63.49% 68.82% 27229 7646 5.33% Newborn 5.40% 4.70% 2315 522 -0.70% Observation 28.35% 24.19% 12158 2688 -4.15% Psych Inpatient 2.15% 1.94% 922 216 -0.21% SNF Inpatient 0.00% 0.00% 1

0.00%

Surgery Admit 0.07% 0.02% 32 2 -0.06%

No significant change for NWCH

Large shift from Obs to IP at RGH

CMI Trends by Hospital

CMI Trends all Discharges excluding Obs PATIENT_CLASS (Multiple Items)

Column Labels Row Labels 2015 2016 NWH NEWARK WAYNE HOSPITAL Discharge Count 5846 1496 Average of

BILL_DRG_WEIGHT 1.0062 1.0745 106.8% RGH ROCHESTER GENERAL HOSPITAL

Discharge Count 30730 8422 Average of

BILL_DRG_WEIGHT 1.5290 1.5513 101.5%

Understanding if more IP has an impact

CMI Trends all Discharges including Obs proxy (.44 CMI) PATIENT_CLASS (Multiple Items)

Column Labels Row Labels 2015 2016 NWH NEWARK WAYNE HOSPITAL Discharge Count 7497 1993 Average of

BILL_DRG_WEIGHT 1.0042 1.0740 106.9% RGH ROCHESTER GENERAL HOSPITAL

Discharge Count 42888 11110 Average of

BILL_DRG_WEIGHT 1.5239 1.5493 101.7%

LOC optimization: Impact on LOS & CMI

IP discharges have increased at RGH through better UR processes and this has resulted in:• Shorter IP LOS• Shorter Obs LOS• No current impact to CMI

Revisit the story: DenialsA Level of Care example

Don’t forget to monitor downstream impacts of work For Concurrent Level of Care this means denials!• Compare performance and trend it!

Vendor vs PAd Concurrent Review Volume and Outcomes (including denials)

PAd Denials

Vendor Denials

0%10%

20%30%

40%50%

60%70%

80%90%

100%

26

23

3

40

3

5

Denials as a result of ReferralOverturned Pending Upheld

Denial Management Instituted Peer to Peer denial discussion with Insurance Medical Directors Created denial escalation process for cases with P2P timeframe for Physician Advisor review

Telling the CDI Data story

One of our first undertakings as new program Used PAd referrals as an escalation for:• Unanswered queries• Queries that were disagreed with that showed a compelling

reason to reconsider Used this data to show the initial value of the PAd

program • ROI: Income generator vs. a cost center

CDI Facilitation

How queries come to PAd and what happens to them.

Average change in CDI rates as a result of PAd. Statistically significant using a contingency test!

Sharing the Results!

Run the stats! Share the work with

the team!

Showing the value of CDI

Used CMI differences to calculate increase in compensation • Find CMI difference• Multiply times base payment rate• Keep track of all your discrete cases and final outcomes!• This all equals ROI!!!

Feedback and Questions?