# Presentation: 037634

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

**Randall D. Cebul, M.D.**
- Center for Health Care Research and Policy
- Case Western Reserve University at
- MetroHealth Medical Center
- _[rdc@case.edu](mailto:rdc@case.edu)_
- Supported by grant R01-HS015123
- Agency for Healthcare Research and Quality

- _[www.chrp.org](http://www.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 assignment 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 for 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 – Intervention 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-HS015123**
**Agency for Healthcare Research and Quality**

## Identifying/Locating Patients and Balancing Practices~8,000 Diabetics, by Race

_**Identifying/Locating Patients and Balancing Practices**_**~8,000 Diabetics, by Race**
**~100 PCPs, 10 Group Practices ****MetroHealth System**

## Random Assignment of Balanced Practices 

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

## 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 Support  (“DM2”)‏

- 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: Designed to Minimize FPs

**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 Linked to ACE/ARB Alert

- Patient name

- Patient name

## PI/PR - Practice Panel: Comparative Performance

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

## PM/PI: Registry and Related decision support

**Physician name**

**Patient name, hosp number and phone #**

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

_**Clinical Outcome: Change in Scores**_
**Ace/ARB*****	****	****1**
**Pnvx*****	****	****1**
**Eye Exam*****	****	****1**
**LDL<100*****	****	****1**
- A1C<7%		1
- BMI<30		1
- Non-Smker		1
- _SBP<130__	__	__1_
**0-8 points**

- “MD-Centric”
- Measures

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

**% of All ADA 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%

## 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 Lg**
**Integrated System:**
***Not missing much***

**Lg System but**
**No eRx Fncs:**
***Missing Filled Rxs,***
***Out of system use***

**Small practice:**
***Missing alot***

## HMO vs. Large System with Itinerant Patients- Who’s missing data?

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

## 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 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?