Using EMRs for PM, PI, PR, and P4P: Using EMRs for PM, PI, PR, and P4P: Opportunities and Lessons We’re LearningOpportunities and Lessons We’re Learning

Randall D. Cebul, M.D.Center for Health Care Research and Policy

Case Western Reserve University atMetroHealth Medical Center

rdc@case.edu

Supported by grant R01-HS015123Agency for Healthcare Research and Quality

Center for

Health Care

Research &

Policywww.chrp.org

What We Thought We Knew

• Claims-based performance measurement (PM) is inadequate, lacking:– Accurate patient assignment for performance

reporting (PR) and P4P– Timeliness for clinical decision support and

performance improvement (PI)– Completeness and efficiency (sampling patients’

charts)– Granularity of measurement – few results– Ability to measure uninsured patients

• EMRs can solve all of these problems

We had friends who thought the same:Views on Accuracy of Performance Measures

Health Aff. 2007; 26: 492-97

30%

88%

85%

82%

38%

35%

There is evidence Re: unstable and untimely There is evidence Re: unstable and untimely assignment of patients using claimsassignment of patients using claims

• ~50% of patients change “assignments” over a 2-year period*.

* Pham etal. Care patterns in Medicare and their implications for Pay for Performance.

NEnglJMed. 2007;356:1130-9.

Some Limitations of Claims Some Limitations of Claims for PM, PR, and PIfor PM, PR, and PI

• PM/PR: Lack reliability of assignment of patient-physician links/related attribution

• Efficiency: Patient exclusions for specific measures/need for costly chart audits

• PI: Long intervals between audit & feedback

• No claims no performance measurement – 47,000,000 uninsured in the U.S.

“EMRs can solve all of these problems” Diabetes Improvement Group – Diabetes Improvement Group –

Intervention Trial (DIG-IT*): OverviewIntervention Trial (DIG-IT*): Overview

• Commercial EMR-facilitated: – study design– patient assignment/PCP attribution– PM: performance measurement– PI: real-time clinical decision support – PI: Nurse Case Management

*Funded, in part, by grant R01-HS015123Agency for Healthcare Research and Quality

White BlackHispanicOther

Identifying/Locating Patients and Balancing Practices

~8,000 Diabetics, by Race~100 PCPs, 10 Group Practices

MetroHealth System

Random Assignment of Balanced Practices Random Assignment of Balanced Practices

5Groups

5GroupsDM2

Epic

Only

10 Practices, ~100 PCPs, ~8000 Patients

Primary Aims: Quality & Utilization (including sub-groups by insurance etc)

Secondary Aims: Adoption/Correlates; Unintended Consequences

““Table 1”. Pre-trial Characteristics of Table 1”. Pre-trial Characteristics of Practices after BalancingPractices after Balancing

0.228<0.001-0.57-0.66Slope A1c

0.392<0.00119.618.5% on Insulin

0.1380.00116.918.7% A1c>9

0.859<0.001136.2136.1Ave Syst BP

0.0490.00122.625.2% Smoker

0.830<0.00149.148.7% A-A

20252281# of Pts

55# of Practices

P-ValueICCGroup B Group AVariable

EMR-Based Patient Assignment EMR-Based Patient Assignment

• Patient Eligibility:– Diagnosis: ICD-9 codes or antidiabetic meds

• PCP Links/Attribution:– Two or more eligible patient visits with PCP– Initialization of Lists: PCP can report:

• “Not my patient” or “Not Diabetic”• Conflicts adjudicated (<.1%)

– Weekly Updating: • New patients• Transfers within or across practices• No complaints

PM/PI/PR - Clinical Decision PM/PI/PR - Clinical Decision Support (“DMSupport (“DM2”2”))

• Alerts and Linked Order Sets

• Patient and Physician Education

• Patient Lists/Registry, Current Status

• Practice panel performance feedback

Practice Panel Tools

PM/PI - encounter-based Alerts: PM/PI - encounter-based Alerts: Designed to Minimize FPsDesigned to Minimize FPs

What do we know about this patient?What do we know about this patient?• She has diabetes and is visiting her PCP• Her kidneys are leaking protein.• She is not on an ACE inhibitor or ARB

and has no documented allergies to them.

• She has no other contraindications (K, Cr)• There are several alternative drugs/doses

{Links to Automated Order Set}

PI - SmartSet PI - SmartSet Linked to ACE/ARB AlertLinked to ACE/ARB Alert

Patient name

Patient name

Re-cap of indications

Choice of Rxs/doses

Follow-up testing

PI/PR - Practice Panel: Comparative PerformancePI/PR - Practice Panel: Comparative Performance

“My panel” vs. Comparator

PM/PI: Registry and PM/PI: Registry and Related decision supportRelated decision support

Physician name

Patient name, hosp number and phone #

Click on tab to sort

Downloadable educational matls

Trends, linked to meds

Illustrative PM:Illustrative PM:“ADA Scores” Measured Every Week“ADA Scores” Measured Every Week

Clinical Outcome: Change in ScoresAce/ARB* 1Pnvx* 1Eye Exam* 1LDL<100* 1

A1C<7% 1BMI<30 1Non-Smker 1SBP<130 1

0-8 points

“MD-Centric”Measures

Changes in Scores, Exptl Pts (n=5288)Some Measures are Easier To Change than Others

Percent ADA Score by Patient Week

Overall ADA MD-Centric ADA

40

45

50

55

60

65

70

75

80

Patient Week0 10 20 30 40 50 60 70 80 90 100

% of All ADA Measures Met% of All ADA Measures Met

% of 4 MD-Centric Measures Met% of 4 MD-Centric Measures Met

EMRs can Measure Performance in All Patients

• Baseline Trial Data by:– RACE– Insurance

PM/PR/P4P: Poor Glucose Control by Race/ethnicity in One System

• Poor glucose control is strongly associated with diabetic complications– Eyes, kidneys,

amputations, admissions • P4P programs reward

practices with lower than 20% “poor values”

• More than half of our diabetic patients are Black or Hispanic

15.7%

21.9%23.3%

White Black Hispanic 1 '0

5

10

15

20

25

Perce

nt wi

th Po

or Gl

ycem

ic Co

ntrol

15.7%

21.9%23.3%

Poor Glucose Control by Insurance

At baseline:• 25% of our Medicaid

pts were in poor control.

• Almost 30% of our uninsured pts were in poor control.

• About 40% of our patients are uninsured or covered by Medicaid

25.0%

20.2%

11.2%

29.2%

11.2

20.2

25.0

29.2

EMRs for PM, PR, PI, P4P:Some Summary Thoughts

• Using EMR-centered data, we can:– Identify and link patients satisfactorily– Identify patients pretty well for whom measures are

appropriate (eg, no c/I to ACE/ARBs)– Measure performance at a granular level (eg, BMI, A1c)– Measure performance on all eligible patients, regardless

of race or insurance status– Monitor and provide meaningful feedback in a timely way– Measure improvement in process and outcomes

• Some measures are harder to improve than others• P4P systems should consider stratifying by

insurance, at least.

Additional Questions About PM/PR/P4P

• Are Our Data Complete?– NO

• Are There Biases in Our Data?– Probably, yes– Mostly Under-ascertainment (of good performance as

well as undesirable outcomes)

• Absent full regional HIE, are EMR-based system-level data Fair for P4P?– For within system PI and P4P, probably– For cross-system comparisons, it depends

Incomplete PM with EMRs alone: Depends on size, financing, functions

“HMO” or Very LgIntegrated System:Not missing much

Lg System butNo eRx Fncs:

Missing Filled Rxs,Out of system use

Small practice:Missing alot

HMO vs. Large System with Itinerant Patients

- Who’s missing data?

36.437.697.9Eye Exam, Last Yr

74.279.786.5Non-Smokers

92.096.969.7ACE/ARB if alb’inuria

84.886.156.4Pneumovax, ever

49.451.248.5LDL < 100 mg/dl

45.645.336.1Systolic BP < 130

56.1%59.9%44.2%A1c < 7%

8,0905,00211,849# of Patients

MHS Total

MHS Comm’cial + MedicareHMOStandard

All outside eye exams are billed

HMO vs. Large System without eRx Functions- Who’s missing data?

36.437.697.9Eye Exam, Last Yr

74.279.786.5Non-Smokers

92.096.969.7ACE/ARB if alb’inuria

84.886.156.4Pneumovax, ever

49.451.248.5LDL < 100 mg/dl

45.645.336.1Systolic BP < 130

56.1%59.9%44.2%A1c < 7%

8,0905,00211,849# of Patients

MHS Total

MHS Comm’cial + MedicareHMOStandard

Filled Prescribed

Where are the needed performance data?

• Outside the System (benefit from claims)– Eye Exams, Labs, Pharmacies, ED Visits/admissions

• In the System, but relatively inaccessible– Text portions of record

• Performance completed outside, noted• Patient exclusion for measure, but not in “check box”

– Unlinked computerized systems; e.g.• Echo lab – HF• ETTs – CAD• PFTs – asthma

• Or, they don’t exist…

Closing Thoughts: Measurement Challenges Closing Thoughts: Measurement Challenges Using EMRs for P4P (before HIE…)Using EMRs for P4P (before HIE…)

• We need to better capture exclusions: eg terminal patient, refusal, relative C/I, etc

• We need to better capture necessary ancillary data: e.g., echo’s, PFTs, etc

• We need simpler means to identify [necessary measures] obtained out-of-system– Insured patients’ claims? – What about Uninsured patients?