Improving Office Care for Chest Pain

Thomas D. Sequist, MD MPH

Associate Professor of Medicine and Health Care PolicyBrigham and Women’s Hospital, Division of General Medicine

Harvard Medical School, Department of Health Care PolicyHarvard Vanguard Medical Associates

Why Chest Pain?

• Chest pain is a common symptom– Increasing burden in primary care

• Frequent missed diagnosis of acute MI

• Excess utilization of resources

Patient Care Model

Primary care visit

Home without further testing

Home with further testing

Emergency Department

Discharged

Chest Pain Unit

Inpatient

ICU

Patient Care Model

Primary care visit

Home without further testing

Home with further testing

Emergency Department

Discharged

Chest Pain Unit

Inpatient

ICU

Primary Care Challenges

• Low risk population– Limit excess resource utilization– Avoid missed diagnoses

• Time-limited care– Cannot usually observe over several hours

• No immediate cardiac stress testing

• No immediate cardiac enzymes

Can the Framingham Score Help?

• Main utility is to raise awareness

• FRS variables are generally available

• FRS compares favorably with exercise stress testing

Defining High Risk Patients

FRS Cutoff Sensitivity Specificity

≥ 5% ≥ 10% ≥ 20%

96

85

54

61

75

86

Sequist et al. Arch Intern Med 2006.

Study Questions

1.Can risk score alerts within an EHR improve risk-appropriate care for patients with chest pain?

2.What are the additional opportunities to improve the efficiency of chest pain care?

Harvard Vanguard Medical Associates

• Multi-specialty group practice

• Integrated electronic health record

• 15 ambulatory health centers

• 175 primary care physicians

• 300,000 adult patients

Randomization Scheme292 Primary Care Clinicians7,083 patients (≥ 30 years old)

Intervention Group149 clinicians3,634 patients

Control Group143 clinicians3,449 patients

High Risk717 patients

Low Risk2917 patients

High Risk610 patients

Low Risk2839 patients

Intervention Design

• Identification of patients with chest pain– Medical assistant training

• Automated calculation of Framingham Risk Score

• Delivery of risk-appropriate recommendations via electronic alerts

Risk Appropriate Recommendations

• High risk patients (FRS ≥ 10%)– Electrocardiogram performance– Aspirin therapy

• Low risk patients (FRS < 10%)– Avoidance of cardiac stress testing

Entry of Chest Pain Complaint

High Risk Patient Alert

Low Risk Patient Alert

SmartLink (.frsdetail)

Baseline Patient Characteristics

Intervention(n = 3,634)

Control(n = 3,449)

p value

Mean age, years

Female, %

Insurance Commercial Medicare Medicaid Uninsured

Framingham Risk Score < 10% ≥ 10%

49.7

63

761483

8020

48.6

65

771193

8218

0.001

0.03

0.01

0.03

Clinical Care and Outcomes

High Risk(n=1327)

Low Risk(n=5756)

p value

Evaluation and treatment Electrocardiogram Aspirin therapy Cardiac stress test

Follow up care Home Hospitalized

Diagnoses Acute myocardial infarction*

501917

917

1.1

437

10

963

0.2

<0.001<0.001<0.001

<0.001<0.001

0.01

* Among 26 cases of AMI, 10 (36%) represented missed diagnoses

Impact of Electronic Alerts

51

20

10

48

18

9

0

10

20

30

40

50

60

70

80

EKGPerformance

AspirinTherapy

Cardiac StressTesting

% R

ecei

ving

Intervention Control

High Risk Patients Low Risk Patients

Clinician Views on Intervention

5

4047

8

0

20

40

60

80

100

Always Often Sometimes Rarely orNever

% R

epor

tin

g

Is the Framingham Risk Score a valid tool for evaluating chest pain?

Clinician Views on Intervention

12

81

7

0

20

40

60

80

100

Too high About right Too low

% R

epor

tin

g

Is a Risk Score Cutoff of 10% to identify high risk patients….

Conclusions

• Acute MI is uncommon among primary care patients with chest pain

• Missed diagnosis of acute MI is common, while many low risk patients undergo cardiac stress testing

• Electronic risk alerts do not change care patterns

Implications

• Failure to change care patterns– Is it lack of belief in the risk assessment tool?– Is it failure to deliver information effectively?– Do we need more comprehensive efforts?

• Electronic health records represent one piece of a multi-component program

Improving Efficiency of Chest Pain Care

• Map flow of patients from primary care

• Evaluate cost implications for varied evaluation and management strategies

• Analyze variation in care patterns

Patient Care Model

Primary care visit

Home without further testing

Home with further testing

Emergency Department

Discharged

Chest Pain Unit

Inpatient

ICU

Estimated Average Costs Per Patient

Primary care visit

Home without further testing

$293

Home with further testing

$442

Emergency Department

Discharged

$1,087

Chest Pain Unit

$3,192

Inpatient

$17,562

ICU

$47,575

55%

40%

5%

37%

47%

13%

3%

Estimated Average Costs Per Patient

Primary care visit

Home without further testing

$293

Home with further testing

$442

Emergency Department

Discharged

$1,087

Chest Pain Unit

$3,192

Inpatient

$17,562

ICU

$47,575

55%

40%

5%

37%

47%

13%

3%

Physician Level Clinical Variation

Cardiac Stress Testing*

Emergency Department Triage*

% of patients referred for care within physician practices

3.8% 26.7%

1.3% 14.9%

* p<0.01 for random effects of physician level variation.

0% 50%

10.8%

4.7%

Legend95%

Lower CI95%

Upper CI

Average

How Can the EHR Improve Efficiency?

• Increasing awareness of pre-test probability– All variation is within low risk patients

• Focus on low value emergency department referrals

• Peer to peer education

Clinical Process Flow

Primary care visit

EKG

Stress ECHO

Stress Nuclear

ETT

Cardiology

Home

Triage Emergency Dept

ICU

Inpatient

Chest Pain Unit

Triage

Triage

Home