# Presentation: 829727

## Linking Patients with Physicians within Primary Care Practice Networks:A Population-Based Approach to Quality Assessment

- Steven J. Atlas, MD, MPHJeffrey M. Ashburner, MPH
- Massachusetts General Primary Care Practice Based Research Network May 17, 2007

**Notes:**
Good afternoon. This work reflects efforts over the past several years to link patients with physicians within our PBRN for quality assessment and improvement initiatives.

## Major deficiencies in quality of care in U.S. persist (IOM)
Widespread agreement about importance of measuring and need for efforts to improve care
Primary care physicians (PCP) are a major source of acute, preventive, and chronic illness care
Efforts to measure and improve quality of care are often focused on these physicians

- Widespread agreement about importance of measuring and need for efforts to improve care
- Primary care physicians (PCP) are a major source of acute, preventive, and chronic illness care
- Efforts to measure and improve quality of care are often focused on these physicians

- Background

**Notes:**
Despite considerable resources devoted to health care, major deficiencies in quality of care in the U.S. persist.
There is widespread agreement about the importance of measuring quality of care and the need for efforts to improve care.
Primary care physicians are a major source of acute, preventive, and chronic illness care,
And thus efforts to measure and improve quality of care are often focused on these physicians.

## Assessing quality at PCP or practice level hampered by knowing eligible (or “denominator”) population
Many patients receive episodic care without establishing “continuity” with any one provider
Particularly for uninsured or underinsured
Patients with a “regular source of care” may be more likely to receive care consistent with guidelines
Physicians or practices that vary in patient continuity or linkage may differ in quality of care outcomes
We hypothesized that patients highly linked to a specific physician would have better quality of care

- Many patients receive episodic care without establishing “continuity” with any one provider
- Particularly for uninsured or underinsured
- Patients with a “regular source of care” may be more likely to receive care consistent with guidelines
- Physicians or practices that vary in patient continuity or linkage may differ in quality of care outcomes
- We hypothesized that patients highly linked to a specific physician would have better quality of care

- Background

**Notes:**
Assessing quality at PCP or practice level is hampered by not knowing the eligible or “denominator” population with a high degree of accuracy.
Many patients may receive episodic care without establishing “continuity” with any one provider, particularly for uninsured or underinsured individuals.
Prior work tell us that patients with a “regular source of care” may be more likely to receive care consistent with guidelines.
Physicians or practices that vary in the degree of patient continuity or as we term it here “linkage” may differ in quality of care outcomes.
We hypothesized that patients highly linked to a specific physician would have better quality of care.

## Method to link patients with a specific provider
“Gold” standard: physician review of panel
18 PCPs participated from 9 practices
~16,000 adult patients reviewed
Goal: PCP defined using local consensus approach
Highly accurate lists for PCPs (>90% specific)
Tradeoff: provider panel may not represent all of a provider’s “true” patients 
Since not all patients linked with a specific PCP, developed criteria to link to a specific practice

- “Gold” standard: physician review of panel
- 18 PCPs participated from 9 practices
- ~16,000 adult patients reviewed
- Goal: PCP defined using local consensus approach
- Highly accurate lists for PCPs (>90% specific)
- Tradeoff: provider panel may not represent all of a provider’s “true” patients
- Since not all patients linked with a specific PCP, developed criteria to link to a specific practice

- MGH Denominator Project: 2003-4

**Notes:**
In 2003, we began developing a method to link patients with a specific provider.
Since the goal was to use this data to measure PCP performance, we used as our “gold” standard the physician’s review of her/his panel.
18 PCPs participated from 9 practices and reviewed the records of approximately 16,000 adult patients.
Our PCP defined goal, developed using a local consensus approach, was to create highly accurate patient lists for PCPs that were at least 90% specific.
The tradeoff was that a PCP panel may not represent all of a provider’s “true” patients.
Since this would mean that not all patients would be linked with a specific PCP, we also developed criteria to link patients to a specific practice.

## Study Setting and Population
Massachusetts General Primary Care Practice-Based Research Network
13 practices with ~180 PCP’s in Eastern Massachusetts
Study period: January 1, 2003 – December 31, 2005
>160,000 adult patients seen in over 3-year period

- Massachusetts General Primary Care Practice-Based Research Network
- 13 practices with ~180 PCP’s in Eastern Massachusetts
- Study period: January 1, 2003 – December 31, 2005
- >160,000 adult patients seen in over 3-year period

- Methods

**Notes:**
Turning to the current study, the Massachusetts General Primary Care Practice-Based Research Network includes 13 practices with approximately 180 PCP’s in Eastern Massachusetts that are affiliated with MGH.
The study period examined included visits to the PBRN during the period January 1, 2003 through December 31, 2005.
Over 160,00 adult patients were seen over this 3 year period for more than 550,000 visits.

## Use of previously developed & validated model*
Specificity: overall 93.7%
Positive predictive value: 96.5% (range 90.1-100%)
Sensitivity: 80.4%
Algorithm uses the PCP designee field from hospital registration, combined with logistic regression model
Model variables: age, months since most recent visit, in-state resident
Model weights: PCP practice style (% of patient’s visits where provider is the registry listed PCP)

- Specificity: overall 93.7%
- Positive predictive value: 96.5% (range 90.1-100%)
- Sensitivity: 80.4%
- Algorithm uses the PCP designee field from hospital registration, combined with logistic regression model
- Model variables: age, months since most recent visit, in-state resident
- Model weights: PCP practice style (% of patient’s visits where provider is the registry listed PCP)

- Methods: Linking Patients with PCPs

- * Atlas et al, JGIM 2006

**Notes:**
We used the previously mentioned model that has been validated in development and test data sets. 
The overall specificity of the model was 93.7%. The positive predictive value was 96.5% and ranged from 90.1 to 100% for the 18 physicians. The overall sensitivity of the model was 80.4%.
The model algorithm uses the PCP designee field from hospital registration system, combined with a logistic regression model.
Model variables include age, months since most recent visit, and in-state residence.
Model weights are assigned to these variable based upon the PCP practice style which is defined as the percent of a patient’s visits where the provider is the registry listed PCP.

## Patients linked with a specific practice
Not be linked to specific PCP using model
Patients of resident physicians
Patients whose listed PCP was unknown or outside of practice
Utilization based criteria developed based upon visit frequency and patient age using local consensus methods
Patients not linked to either a physician or practice using these methods classified as “un-linked”

- Not be linked to specific PCP using model
- Patients of resident physicians
- Patients whose listed PCP was unknown or outside of practice
- Utilization based criteria developed based upon visit frequency and patient age using local consensus methods
- Patients not linked to either a physician or practice using these methods classified as “un-linked”

- Methods: Linking Patients with Practice

**Notes:**
For patients who could not be linked with a PCP, we developed a method to link them with a specific practice. This included:
Patients not be linked to a specific PCP using our model, patients of resident physicians, and patients whose listed PCP was unknown or outside of the practice.
Utilization based criteria were developed based upon visit frequency and patient age using local consensus methods with PCPs and practice leaders.
Patients who could not be linked to either a physician or practice using these methods were classified as “un-linked”.

## Process of Care Outcome Measures
Preventive cancer screening tests: breast, colorectal, and cervical cancer
Chronic disease management: Diabetes (HbA1C and LDL testing) and coronary artery disease (LDL testing) in prior year
Analyses
Impact of patient linkage to PCP or practice
Multivariate regression models adjusting for clustering at PCP and practice level
Subgroup analyses: private managed care insurance and ethnicity status

- Preventive cancer screening tests: breast, colorectal, and cervical cancer
- Chronic disease management: Diabetes (HbA1C and LDL testing) and coronary artery disease (LDL testing) in prior year
- Analyses
- Impact of patient linkage to PCP or practice
- Multivariate regression models adjusting for clustering at PCP and practice level
- Subgroup analyses: private managed care insurance and ethnicity status

- Methods

**Notes:**
To assess the impact of patient linkage status with PCPs or practices, we examined process of care outcome measures based upon HEDIS criteria.
These included preventive care screening tests for breast, colorectal and cervical cancer, and
Chronic disease measures for diabetes, HbA1c and LDL testing in the prior year, and coronary artery disease, LDL testing in the prior year.
Analyses assessed the impact of patient linkage to PCPs or practices using multivariate regression models adjusting for clustering at the PCP and practice level.
We also examined the impact of patient linkage status in 2 subgroups, patients with private managed care insurance who are required to designate a PCP, and by the patient’s ethnicity.

## Results: MGH Patient Linkage Status

- PCP Linked

- Practice Linked

- Un-Linked

**Notes:**
This figure show the results of our patient linkage analyses for the population of 155,590 patients seen between January 1, 2003 and December 31, 2005.
92,316 patients or 59.3% could be linked with a specific PCP,
53,668 patients or 34.5% could be linked with a practice, and 
9,606 patients or 6.2% could not be linked to either a PCP or practice. Random chart review analyses of this population demonstrate that most are either leaving or entering into the PBRN network.

## Linkage Status By Practice Site*

- * P-value <0.0001

**Notes:**
This figure shows patient linkage status by practice. As can be seen, the percentage of patients linked to a PCP, practice or un-linked varied significantly by practice.
The percentage of PCP-linked patients ranged from 45.6 to 71.2% across practices, 
The percentage of practice-linked patients ranged from 24.0 to 49.8%, and 
The percentage of un-linked patients ranged from 4.2 to 9.7%.

## Patient Characteristics by Linkage Status*

| Patient Characteristics,
 N (%) | PCP Linked
N = 92316 (59.3) | Practice Linked
N = 53668 (34.5) | Un-Linked
N = 9606 (6.2) |
| --- | --- | --- | --- |
| Age, mean (SD) | 52.0 (16.4) | 39.9 (15.0) | 51.6 (15.1) |
| Sex, female | 53612 (58.1) | 30398 (56.6) | 4745 (49.4) |
| Ethnicity
     Non-Hispanic white
     Hispanic
     Black
     Asian
     Other/Unknown | 
74917 (81.2)
6435 (7.0)
4358 (4.7)
3788 (4.1)
2818 (3.1) | 
37278 (69.5)
6013 (11.2)
3515 (6.5)
3014 (5.6)
3848 (7.2) | 
6468 (67.3)
1218 (12.7)
610 (6.4)
368 (3.8)
942 (9.8) |
| Primary language 
spoken, English | 
86434 (93.6) | 
48302 (90.0) | 
8331 (86.7) |

**Notes:**
This table compares patient characteristics by linkage status.
Patients linked to PCPs were significantly older and more likely to be women than those linked to a practice. The mean age of PCP-linked patients was 52 while that of practice-linked patients was 39.9 and un-linked patients was 51.6.
PCP-linked patients were significantly more likely to be non-Hispanic whites and less likely to be Hispanic or Black. For example, 81.2% of PCP-linked patients were white compared to 69.5% of practice-linked patients and 67.3% of un-linked patients.
Similarly, PCP-linked patients were more likely to speak English as their primary language.

## Patient Characteristics by Linkage Status*

| Patient Characteristics, N (%) | PCP Linked
N = 92316 (59.3) | Practice Linked
N = 53668 (34.5) | Un-Linked
N = 9606 (6.2) |
| --- | --- | --- | --- |
| Insurance Status
     Private 
         Managed care
     Government
         Medicare
         Medicaid
     No insurance         | 
63964 (69.3)
35373 (38.3)
23414 (25.4)
17922 (19.4)
5428 (5.9)
4938 (5.3) | 
36862 (68.7)
12885 (24.0)
9425 (17.6)
4585 (8.5)
4840 (9.0)
7381 (13.8) | 
5676 (59.1)
898 (9.3)
2137 (22.2)
1408 (14.7)
729 (7.6)
1793 (18.7) |
| # of visits last 3 years
     PCP, mean (SD)
         Median | 
6.7 (5.8)
5.0 | 
2.6 (2.7)
2.0 | 
1.6 (1.2)
1.0 |

- * All p-values <0.0001 comparing patient linkage status groups using general estimating equation methods.

**Notes:**
In terms of insurance status, 69.3% and 68.7% of PCP- and practice linked patients had private health insurance, but PCP linked patients were much more likely to have a private managed care plan.
Unlinked patients were least likely to have private health insurance.
Patients linked to PCPs were significantly more likely to have Medicare and less likely to have Medicaid or no insurance that  patients linked to a practice or un-linked. For example 5.3%, 13.8% and 18.7% of PCP, practice or un-linked patients had no insurance, respectively.
As expected given the utilization based nature of our algorithm, patients linked to a PCP had more visits than practice-linked patients, who had more visits than un-linked patients in the prior 3 years.

## Quality Measures By Linkage Status

- P<0.0001

- P<0.0001

- P<0.0001

**Notes:**
This figure shows quality measure completion by patient linkage status for preventive cancer screening tests among eligible individuals.
Unlinked patients are excluded from these analyses because most were in transition into or out of our PBRN. Performance measures for these patients were very low, but would not include tests performed outside of our network.
Cancer screening rates, especially for breast and colorectal cancer, were significantly higher for patient linked to a PCP than linked to a practice.
For example, colorectal cancer screening was performed for 68% of PCP-linked patients and 51% of practice-linked patients.

## Quality Measures By Linkage Status

- P<0.0001

- P<0.0001

- P<0.0001

- Diabetes n = 9,632; CAD n = 6,612

**Notes:**
This figure shows quality measure completion rates by patient linkage status for chronic disease testing.
For each process measure, rates of test completion were significantly higher for patients linked to a PCP than linked to a practice.
For example, among patients with diabetes, an LDL test had been performed in the past year for 68% of PCP-linked patients and 51% of practice-linked patients.

## Quality Measures By Linkage Status

- P<0.0001

- P = 0.005

- P<0.0001

- Patients with Private Managed Care Insurance*

- * n = 48,258

**Notes:**
Because capitated managed care patients are required to select a PCP, and this individual is listed in the registration system’s PCP designee field, we sought to determine whether quality assessment outcomes also differed by linkage status for this group of 48,258 patients.
This figure again shows quality measure completion by patient linkage status for preventive cancer screening tests in this sub-population among eligible individuals.
Though overall cancer screening rates were higher than for non-managed care patients, patients linked to a PCP had significant higher screening rates than for patients linked to a practice, especially for breast and colorectal cancer screening.

## Linkage Status by Ethnicity

**Notes:**
We also examined the impact of patient linkage status by the patient’s ethnicity. We hypothesized that differences in patient linkage status may partially account for disparities by patient ethnicity within our PBRN.
This figure shows the percentage of patients linked to a PCP, practice or neither by patient ethnicity.
Non-Hispanic whites were significantly more likely to be linked to a PCP and less likely to be practice linked or un-linked than other patient groups.
Linkage status among Black and Asian patients was similar, though Hispanics were even less likely to be linked to a PCP and more likely to be un-linked than other groups. 

## Mammography – By Race & Linkage

- 100

- 0

- 25

- 50

- 75

**Overall**

**PCP Linked**

**Practice Linked**

- White

- Black

- Asian

- Hispanic

- Other

- Aged 42-69 within 2 years*

- 78

- 71

- 76

- 60

- 81

- 63

- 66

- 73

- 55

- 62

- 75

- 76

- 76

- 67

- 81

- * n = 35,862

**Percentage Completed**

**Notes:**
This figure examines the impact of ethnicity on breast cancer screening test completion stratified by the patient’s linkage status.
Overall, White, Asian and Hispanic patients had very similar mammography completion rates while Black patients had lower rates.
As shown in the right 2 figures, stratifying by patient linkage status attenuated differences among different ethnic groups. Indeed, differences by patient linkage status were greater than for ethnicity.
For example, mammography screening rates among Black women were 76% and 60% for PCP and practice-linked patients, whereas among PCP-linked patients, 76% of Black women and 78% of White women had screening performed.

## CRC Screening – By Race & Linkage

- Aged 52-69*

- * n = 37,601;
-   CRC: 1) FOBT- 1 yr; 2) Sigmoidoscopy or DCBE - 5 yrs or  3)Colonoscopy – 10 yrs

**Percentage Completed**

**Notes:**
Similarly, this figure examines the impact of ethnicity on colorectal cancer screening tests stratified by the patient’s linkage status.
Greater differences in colorectal cancer screening rates are seen by ethnicity than for breast cancer screening.
Again, stratifying by patient linkage status attenuates differences among different ethnic groups, but does not completely eliminate them. 
For example, among Hispanic patients, linkage status has less of an impact than for other patients.  49% of PCP-linked patients have completed screening compared to 43% of practice-linked patients.

## Conclusions

- Many patients seen within our PBRN could not be linked to a PCP
- Linkage varied markedly by practice
- Linkage with a physician was strongly associated with better performance on quality measures
- Patient linkage status was also associated with quality performance measures among individuals with private managed care insurance
- Patient linkage status appeared to be a stronger predictor of performance than ethnicity

**Notes:**
In summary, we found that many patients seen within our PBRN could not be linked to a PCP, and linkage varied markedly among practices.
Linkage with a primary care physician was strongly associated with better performance on quality measures.
Patient linkage status was also associated with quality performance measures among individuals with private managed care insurance.
Finally, patient linkage status appeared to be a stronger predictor of quality performance than ethnicity.

## Limitations

- Utilization-based model of linkage developed and validated within one network
- Primary care networks with more homogeneous care delivery models may not need such models
- Linkage status is designed to be related to visit frequency and timing
- In a visit-based system, not surprising that patients without regular visits less likely to have quality measures performed
- Among patients seen in prior year, still see lower rates for practice-linked patients

**Notes:**
Several important limitations should be acknowledged.
Our utilization-based model of patient-provider linkage developed and validated within one network and may not apply elsewhere.
Primary care networks with more homogeneous care delivery models may not need such complex models.
Finally, linkage status is designed to be related to visit frequency and timing.
In a visit-based system, it is not surprising that patients without regular visits were less likely to have quality measures performed.
However, though not shown here, even among patients seen in the prior year, PCP linked patients still had higher completion rates than for practice-linked patients.

## Implications

- The strong relationship between patient linkage and demographic characteristics suggests disparities in care may be mediated in part through linkage
- Physicians and practices caring for patients less likely to establish provider linkage may appear to perform worse on quality measures
- Knowledge of a patient’s linkage status may be an efficient way to organize quality improvement efforts

**Notes:**
These results have several important implications.
First, the strong relationship between patient linkage and demographic characteristics suggests disparities in care may be mediated in part through provider linkage.
Second, physicians and practices caring for patients less likely to establish linkage with a PCP may appear to perform worse on quality measures.
Finally, knowledge of a patient’s linkage status may be an efficient way to organize efforts to improve the quality of care within a PBRN.
Thank you for your attention.

## Patient-Provider Linkage Methods

## Patient-Provider Linkage Model

- PCP designee field (computerized registry)
- Model variables
- Patient age
- Time since most recent visit encounter
- In-state resident
- Model weights
- PCP practice style (% of all visits where specific MD is designated as the PCP)
- Solo (70%)
- Collaborative (<70%)

## Criteria to Link Patient to a Practice

| Criteria | Response Category |
| --- | --- |
| Listed PCP in practice but not MD Loyal |  |
|       Age < 40 years | Total visits to practice within 3 years >1 |
|       Age ≥ 40 years | Total visits to practice within 2 years >1 |
| Listed PCP in practice and is a resident physician or preceptor |  |
|       Age < 40 years | Total visits to practice within 3 years >1 |
|       Age ≥ 40 years | Total visits to practice within 2 years >1 |
| Listed PCP is listed as “None or Unknown” |  |
|       Age < 40 years | Total visits to practice within 2 years >1 |
|       Age ≥ 40 years | Total visits to practice within 1 years >1 |

## Quality Measures By Linkage Status

- P<0.0001

- P = 0.41

- P<0.0001

- Patients with Commercial Managed Care Insurance

## Cancer Screening: How are we doing?

-  % Eligible MGH Patients Receiving Screening

**Notes:**
This is an example of a slide prepared about 5 years ago that was presented showing overall rates of preventive cancer screening tests among patients seen at one of our practices.
While we could compare how we were doing to national norms, our doctors and practice leaders were more interested in local comparisons and analyses that helped them see how they were doing.
So we did this.

## Cancer Screening By MGH Practice

-  % Eligible Women (52-69) Receiving Mammograms

- Practice

**Notes:**
This graph is an example of one we presented showing variations in mammography screening rates among our practices.
This got everyone’s attention but many practices with low scores were community health centers that took care of our most challenging patients.
The question about how individual doctors fared was raised, but with practice-level findings, doctors became concerned that they be rated on patients who they actually cared for, rather than patients who they may have had little or no contract with.